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  • Published: 29 October 2019

A systems approach to cultural evolution

  • Andrew Buskell   ORCID: orcid.org/0000-0001-6939-2848 1 , 2 ,
  • Magnus Enquist 1 &
  • Fredrik Jansson   ORCID: orcid.org/0000-0001-8357-0276 1 , 3  

Palgrave Communications volume  5 , Article number:  131 ( 2019 ) Cite this article

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  • Anthropology
  • Complex networks

A widely accepted view in the cultural evolutionary literature is that culture forms a dynamic system of elements (or ‘traits’) linked together by a variety of relationships. Despite this, large families of models within the cultural evolutionary literature tend to represent only a small number of traits, or traits without interrelationships. As such, these models may be unable to capture complex dynamics resulting from multiple interrelated traits. Here we put forward a systems approach to cultural evolutionary research—one that explicitly represents numerous cultural traits and their relationships to one another. Basing our discussion on simple graph-based models, we examine the implications of the systems approach in four domains: (i) the cultural evolution of decision rules (‘filters’) and their influence on the distribution of cultural traits in a population; (ii) the contingency and stochasticity of system trajectories through a structured state space; (iii) how trait interrelationships can modulate rates of cultural change; and (iv) how trait interrelationships can contribute to understandings of inter-group differences in realised traits. We suggest that the preliminary results presented here should inspire greater attention to the role of multiple interrelated traits on cultural evolution, and should motivate attempts to formalise the rich body of analyses and hypotheses within the humanities and social science literatures.

Introduction

Research in cultural evolution aims at understanding and explaining cultural change at multiple causal levels (e.g., Mesoudi, 2011 ; Colleran and Mace, 2015 ; Gjesfjeld et al., 2016 ). Culture, like many targets in science, is complex, with multiple processes interacting at a variety of spatial and temporal scales. This is evident both in the multiple definitions of culture, many of which selectively highlight features and processes of culture and cultural change (Kroeber and Kluckhohn, 1952 ; Weiss, 1973 ; Keesing, 1974 ; Mesoudi, 2011 ), and in the variety of methods used to decompose and analyse the constituent causal processes of culture. Footnote 1 Despite variation among these attempts at describing and understanding the complexity of human culture, there has long been consensus on its key features: that culture is composed of a number of distinct elements (or traits), that these traits bear varying relationships to one another, and that these traits are realised in overlapping yet heterogeneous ways by different populations in the world.

In calling this a consensus, we draw attention to the long history of viewing culture as a complex dynamic system, composed of multiple traits and their relationships, which can change over time. This is a view arguably as old as the discipline of anthropology itself: clear precursors of such thinking can be found in the writings of British sociocultural evolutionists (Stocking, 1987 ) and the various schools of nineteenth century German anthropology (Smith, 1991 ). This consensus view persisted in the works of twentieth century American evolutionary anthropologists (Carneiro, 2003 ), as well as in anthropology’s interpretivist, structuralist, and post-structuralist traditions (Kuper, 1999 ). More relevant for current considerations, this consensus view is also evident in the qualitative descriptions accompanying early cultural evolutionary models (e.g., Cavalli-Sforza and Feldman, 1981 ; Boyd and Richerson, 1985 ) and in banner claims about the scope and power of cultural evolutionary theory (e.g., Sperber, 1996 ; Henrich, 2016 ).

Nonetheless, formal modelling within the contemporary cultural evolution literature has tended to idealise away key features of this consensus picture. Large families of models represent culture via a small number of traits, and, further, represent such traits as ‘atomic’ elements with no substantial interaction between them (e.g., Durham, 1991 ; Henrich, 2001 ; Kitcher, 2001 ; Henrich and Boyd, 2002 ; Rogers, 2010 )—with a few notable exceptions (e.g., Enquist et al., 2011 ; Kolodny et al., 2015 ). Typically, when multiple traits are represented, they are taken to vary along a single dimension (e.g., Cavalli-Sforza and Feldman, 1981 ; Boyd and Richerson, 1985 ; Henrich, 2004 ), or function as an index of some other feature of interest (e.g., Fogarty and Creanza, 2017 ; Fogarty, 2018 ). While these models are all significant achievements, by idealising away multiple traits and trait interrelationships, they may be unable to represent a range of phenomena; notably those where the clustering of traits influences the downstream origination, distribution, and change in the trait pool over time.

Consider, as an illustration of the complex relationships among traits, communities of the Tyva Republic. The Tyva are pastoralists who engage in seasonal migrations. As they migrate from pasture to pasture, the Tyva engage in costly rituals around cairns that mark out pasture boundaries, regional borders, and salient geographical landmarks. These costly rituals involve offerings of food, tobacco, money, and the performance of ritualised behaviour. As experimental and ethnographic evidence shows, a plausible explanation for the origin and persistence of these costly rituals appeals to the Tyva’s pastoral subsistence strategy. The rituals demonstrate to nearby populations the acknowledgement of local norms, and in so doing, may diffuse potential tensions about the use of common resources—such as pasture lands—by unfamiliar and potentially untrustworthy economic free riders. The costly rituals, then, signal trustworthiness and cooperation to the groups whose land may be being crossed and grazed (Sosis, 2005 ; Purzycki, 2010 , 2011 , 2016 ; Purzycki and Arakchaa, 2013 ).

This example shows how a rich system of interlocking religious practices, moral judgments, and patterns of subsistence can jointly explain the origin, organisation and persistence of costly rituals as a solution to intergroup relationships and the management of resources. Such a complex explanation, however, requires explicit consideration of multiple cultural traits, specific ecological circumstances, and salient interrelationships between the two.

The case of the Tyvan pastoralists is illustrative of the need for a broad theoretical and empirical endeavour aimed at capturing the dynamics of multiple cultural traits and their interrelationships. Here we motivate a systems approach as such an endeavour. We do so by examining implications of such an approach for key features of social transmission and the acquisition of traits, and how these generate macroevolutionary patterns and features. We illustrate these with simple models, and draw on a range of empirical and theoretical literatures to suggest how such models might be expanded into a broader research program. Though we here adopt a graph representation of trait interdependencies for modelling culture and cultural change, we nonetheless think there may be multiple ways of modelling cultural systems that better represent the complexity and heterogeneity of its constituent parts. Given this, the current paper may best be understood as offering one avenue through which a more fully-fleshed systems approach—that is, a distinctive approach encompassing novel models, concepts, and research questions—may be realised. The major contribution of this paper is to lay the conceptual foundation for such a research endeavour.

Despite the limited aspirations of the current piece, the conceptual ground-clearing we undertake here does suggest some immediate methodological and epistemological benefits that come with adopting a systems approach. Importantly, the explicit representation of traits and their interrelationships highlights how traits themselves function as a novel medium through which causes of cultural change can intersect at multiple levels. As we suggest below, the traits agents acquire can change how they learn, modulating the overall behaviour of the population in which they are a part. At the same time, the aggregate behaviour of the population can influence the availability and valence of such traits. The systems approach thus highlights how individual (micro) and population (macro) levels can influence one another through effects on trait relationships and availability. Here we predominantly focus on the first of these levels, looking at the effects of multiple traits and their interrelationships at the individual level. Yet we expect these models to complement the growing body of macro-level models (Kandler et al., 2012 ), and we return to consider multilevel causation and macro-level phenomena more fully in the discussion section.

A second important upshot is that a systems approach allows for the modelling of processes of path dependence and self-organisation (Enquist et al., 2011 ). Already well-recognised within evolutionary and systems biology (e.g., Kauffman, 1993 , Carroll, 2005 , Sansom, 2011 ), network interactions can impose structural and situational constraints that influence the synchronic behaviour and diachronic constitution of such networks. The graph-based models we adopt here provide some of the first links between this literature and cultural evolutionary theory—links that we also consider in more detail in the discussion.

The plan for the paper is as follows. After a brief introduction to the approach in the next section (§2 ‘What is a cultural system’), we highlight four domains of phenomena for which the systems approach has implications at both the microlevel and macrolevel: the cultural evolution of decision rules (‘filters’) and their influence on the distribution of cultural traits in a population (§3 ‘Cultural filters’); the contingency and stochasticity of system trajectories through a structured state space (§4 ‘Evolutionary trajectories and historical dependencies’), where trait interrelationships modulate rates of cultural change (§5 ‘Stability versus change’); and, where trait interrelationships contribute to inter-group differences in realised traits (§6 ‘Group phenomena’). We conclude by highlighting a number of possible avenues for future research, noting that a systems approach is poised to formalise and make explicit theories and hypotheses concerning culture that have been made in the humanities and social sciences.

What is a cultural system?

Researchers identify a wide variety of entities as candidate cultural traits. Typical lists include such diverse things as beliefs, myths, stories, and material artefacts, and often include larger societal structures like practices, norms, and institutions, like kinship systems or subsistence strategies (see e.g., Cavalli-Sforza and Feldman, 1981 ; Boyd and Richerson, 1985 ; Mesoudi, 2011 ; Henrich, 2016 ). Many of these elements bear connections, or relational properties, to one another that impact the acquisition, maintenance, and transmission of other traits. Beliefs, for instance, bear evidential and entailment relationships to other beliefs. If I believe that the dice are loaded, then I should change how often I expect to roll a seven. Material artefacts bear relationships to one another, often in ways that affect their functioning. Tin and copper, for instance, combine to make an alloy suitable for weapons and cookware, while tin and mercury make an amalgam suited for silvering mirrors. Speaking generally, models adopting a systems approach aim at capturing three key features: an explicit representation of multiple traits (perhaps of multiple trait types); trait relationships of different valence and character; and how traits and their relationships generate dynamic interactions over time. To put the motivation for a systems approach briefly, in human cultures, traits bear a wide range of relationships to one another, and these can have a variety of important consequences.

In the illustrations of this paper, we represent traits and their relationships as weighted graphs, where the nodes are the cultural traits, and there is a weighted edge with a positive value between two nodes if the traits are compatible, and with a negative value if they are incompatible. Relationships can also be asymmetric and represented with directed edges. In our simulations, there is a well-mixed population of agents, who are gradually replaced through a birth-death process. Agents can acquire traits either by inventing, through sampling from the universe of available traits, or by copying other agents. Agents copy traits with a probability proportional to how compatible the observed trait is to all other traits in the agent’s current repertoire. The ideas in this paper are most clearly illustrated using small cultural systems and trait universes, so we will typically include only a few traits in the models, but our approach is general and could easily be scaled up to include many traits, with a range of asymmetric compatibilities on a continuous scale. For a specification of the simulation model and the parameter values used in the different examples, along with Python code implementing it, see the Supplementary information.

One important kind of consequence of a systems approach bears upon how traits may be distributed in a population. To see this, consider a simple model with four trait types: A, B, C, and D. Assume that these trait types begin with equal starting frequencies in a generational model with random copying. On the assumption that traits are acquired independently of one another, one would expect the frequency of trait types to be autocorrelated over time, varying only with the vagaries of random copying. Yet when pairwise relations are introduced—for instance, where traits pairs (A, B) and (C, D) (or AB and CD for short) facilitate the acquisition of their partners and inhibit the acquisition of other traits (e.g., C and D inhibit the acquisition both of A and of B, and vice versa) (Fig. 1 )—this simple arrangement generates very different dynamics, ones that eventually settle into an equilibrium state where most agents have either AB or CD trait pairs (Fig. 2 ).

figure 1

Cultural system with simple attraction and repulsion. The left panel shows which pairs of traits attract and repel, and the right panel shows an example with individual repertoires and relationships between individuals

figure 2

Number of individuals with 0 to 4 traits, over time. Two traits can either be compatible or incompatible

Of course, the nature and effects of trait interrelations themselves may change over time. This too is an important consequence of approaching culture as a system constituted by linked elements. Note that a preference (for a cultural trait) can also be considered a cultural trait. Shifts in preferences and beliefs are particularly noteworthy, as these both govern behaviour and change constantly in the face of exposure to new evidence and ideas (Fig. 3 ). In our modelling framework, preferences could be modelled with a positively weighted edge from the preference trait to the preferred trait.

figure 3

Cultural system with preference traits. The left panel shows examples of traits relationships, where + indicates a preference for a trait, and – a preference against it. The right panel shows examples of three individual repertoires, where II acts as a cultural model. Individual I is more likely to copy II, including the preference for B, since I prefers A, while individual III has an aversion to II due to trait A

The complex tangle of changing traits and relationships can be illustrated by looking to the work of Heidi Colleran and colleagues ( 2015 ; Colleran, 2016 ; Colleran and Snopkowski, 2018 ) on the demographic transition—the decline in fertility that has been observed in multiple human populations over the previous two centuries. The demographic transition is a striking trend, with families around the world increasingly limiting themselves to two or fewer children. It is also an unusual trend, evolutionarily speaking, since standard evolutionary reasoning would hold that organisms should produce as many viable offspring as their resources allow.

As Colleran articulates it, the demographic transition is a complex phenomenon, with tangled and imbricated causal processes interacting at multiple levels. Decisions on childrearing are influenced by the makeup of social networks, the prevailing social norms, ties among kin groups, socioeconomic classes, and more encompassing structures such as the regulations and institutions of the local polity and state. Nonetheless, distinct causal pathways and their effects can be discerned. For instance, combining ethnographic work with sophisticated network and statistical analyses, Colleran ( 2016 ) and Colleran and Mace ( 2015 ) were able to chart the distribution of contraceptive strategies used (if any) among a group of communities in Poland—separating out general contraceptive strategies (any decision or strategy for controlling fertility) from ‘artificial’ contraceptives (encompassing a range of modern contraceptive technologies).

Colleran’s explanation highlights both individual-level and population-level causes. At the individual level, agents exerted variable influence: knowledge and use of contraception strategies by close kin and friends were key causal factors in determining not only whether any particular individual would use contraception, but also the particular strategy adopted. Yet community level indicators such as religiosity and education played an important role modulating and changing both the rate at which contraceptive strategies diffused through populations, and the particular strategies adopted. Highly educated populations accelerated the adoption of contraceptive strategies in general, but had limited effects on the spread of artificial contraceptives. Highly religious populations, on the other hand, tended to slow down the adoption of artificial contraceptive use, but not the diffusion of contraceptive strategies and decision-making more generally. Thus while individual networks and the transmission of knowledge and preferences are important, average population-level characteristics also influence the diffusion of contraceptive strategies by changing the background conditions against which individual transmission occurs (Colleran and Mace, 2015 ).

This example illustrates both the aspirations and the difficulties of a systems approach to culture: there is an enormous range of possible traits and trait relationships that are affected by wide-ranging causes. We cannot hope to offer an exhaustive taxonomy of such entities and effects in this paper. Nor do we suggest that the models we develop here provide more than thumbnail sketches as to how multiple interrelated traits might influence the composition and structure of culture over time. Nonetheless, by combining illustrations using graphs and simulation models with existing empirical research, we hope to articulate a number of implications of such models, sketch a number of compelling research objectives, and provide the conceptual tools for developing a distinctive systems approach to culture.

Importantly, we see the humanities and social sciences as playing an important role in the development of a systems approach. From Marxist approaches to postmodernism, researchers in philosophy, anthropology, literary studies, sociology, and many more besides have developed a range of theories and hypotheses about how best to describe cultural traits, their interrelationships, and the structures that they produce. It would be an overwhelming task to summarise the riches of the many fields in the humanities and social sciences, but we suggest that these resources have mostly not been integrated into the datasets or everyday theorising of cultural evolutionary research.

The reasons for this lack of integration may be a number of disciplinary and methodological features. One might be reticence on the parts of humanities and social scientific scholars regarding the past history of unilinear theory, which promulgated racist and Eurocentric accounts of cultural development and change (Steward, 1955 ). Another might be the failure of current work in cultural evolution to speak to the phenomena that interest researchers within the humanities and social sciences, perhaps because of mutual ignorance of the rich literatures within the humanities and social sciences (Ingold, 2007 ) and of cultural evolution (Lewens, 2015 ). Or, perhaps, the lack of integration may reflect methodological differences, with many of the theories and results of the humanities being resistant to formulation in formal, quantified models (Mesoudi, 2011 ).

These are all legitimate explanations for the lack of integration and conversation between the cultural evolutionary literature and other scholars within the humanities, social sciences, and natural sciences. Yet we think one roadblock not sufficiently addressed concerns the family of models used by many cultural evolutionary researchers. While humanities and social science scholars are interested in complex phenomena—often involving the interaction between behaviour rich in semantic information, networks of social interactions, material artefacts and persisting institutions—many prominent cultural evolutionary models focus on the evolution of a few select cultural traits, or traits that vary along a single dimension (Cavalli-Sforza and Feldman, 1981 ; Boyd and Richerson, 1985 ; Durham, 1991 ; Mesoudi et al., 2006 ; Rogers, 2010 ). Moreover, when such models do build in more traits, these typically are taken to evolve independently of one another (Hahn and Bentley, 2003 ; Henrich, 2004 ; Bentley and Shennan, 2005 ; Enquist and Ghirlanda, 2007 ; Enquist et al., 2008 ; Strimling et al., 2009 ; Eriksson et al., 2010 ; Aoki et al., 2011 ). Though these families of models are impressive, and have generated a rich body of research, they represent a substantial epistemic gambit, one akin to that undertaken by mid-twentieth century work in population genetics (Provine, 1971 ). Within cultural evolutionary theory, this strategy holds that the dynamics and structure of cultural evolutionary phenomena can be extrapolated from models that represent a small number of cultural traits interacting in independent (or non-epistatic) processes. This kind of strategy licences the modelling of simple trait systems, either with an eye to describing the kinematics of those simple systems, or to illuminate the evolution and operation of mechanisms underpinning their transmission (e.g., Boyd and Richerson, 1985 ; Henrich, 2004 ).

To be clear, many, but by no means all, modelling families in contemporary cultural evolution are based on equations and results drawn from populations genetics. Yet, even those that do not tend to adopt the epistemic gambit of extrapolating from simple trait systems that model only a few, independent entities. These models have produced an exceptional range of compelling theoretical and empirical results. Yet what we are stressing here is that these models need to be complemented by those that explicitly represent how multiple traits and their interrelationships together affect the downstream distribution and structure of the cultural trait pool. In these circumstances, a systems approach that explicitly represents these elements and their relationships is needed. We turn to highlight these scenarios in the next four sections.

Cultural filters

A common view among many cultural evolutionary researchers is that the cognitive architecture implicated in cultural evolution is composed of special-purpose evolved cognitive mechanisms (Sperber, 1996 ; Sperber and Hirschfeld 2004 , 2006 ; Boyd and Richerson, 2005 ; Richerson and Boyd, 2005 ; Mesoudi, 2011 ; Sperber and Mercier, 2017 ). As a case in point, early cultural evolutionary models (e.g., Boyd and Richerson, 1985 ) explicitly assumed that mechanisms for social learning and selective social learning strategies were under genetic control. Subsequent modelling and empirical work continued to assume the innate nature of these strategies—like prestige bias (Henrich and Gil-White, 2001 ) and conformity bias (Henrich, 2001 )—usually on the basis of their perceived ubiquity in human populations (cf. Henrich, 2016 ).

Yet recent empirical research challenges many of these assumptions. Consider the recent work on selective social learning—the capacities involved in adopting particular strategies for learning from others. Work in both experimental and developmental psychology plausibly suggests that selective social learning strategies emerge from simple associative learning, where learners acquire links between certain individuals or cues and the value of information (reviewed by Heyes, 2018 ). This dovetails with developmental results that suggest that children preferentially attend to models on the basis of a number of cues, including competency, reliability, status, and certainty, as well as features including relative age, resemblance, and sex (Wood et al., 2013 ). Other evidence suggests that the nature of the cues, and their weighting in particular circumstances, is also controlled by associative mechanisms (Behrens et al., 2008 ; Heyes, 2018 ). Selective social learning may thus result from simple mechanisms of learning conjoined with exposure to the local structure of the informational landscape. Such exposure leads to the association between simple cues and the identification of agents bearing useful information across a range of situations.

More generally, there is growing empirical research supporting the claim that even central capacities of human social learning may be culturally evolved. Philosophers and psychologists have recently argued that the plasticity of human psychology provides opportunity for the acquisition not only of strategies for learning (as above) but also of novel cognitive functions. Kim Sterelny ( 2003 ), for instance, has argued that mindreading capabilities—the capacity to attribute and explain behaviour using mental state attributions—are assembled in development in an environment “soaked not just by behaviourally complex agents, but with agents interpreting one another” (p. 222). Such an assertion is backed up by a range of empirical results that suggest that the acquisition of key capacities differs in sequence and rate across different developmental and cultural circumstances (Siegal and Peterson, 2008 ; Wellman and Peterson, 2013 ; Shahaeian et al., 2013 ; Peterson et al., 2017 ). More recently, Cecilia Heyes ( 2018 ) has argued that not only mindreading, but also imitation, selective social learning strategies, and language may be the result of simple domain-general learning capacities occurring within culturally enriched, and perhaps designed, learning environments.

These accounts suggest that cultural evolution may be critically involved in the evolution of what we call filters : ‘decision rules’ that modulate the flow of traits in a cultural system. Footnote 2 These filters not only include those involved in acquiring traits—such as is the case with selective social learning, which sifts and sorts different sources of information—but also those involved in innovating (deciding whether to introduce a trait or set of traits to a system) and diffusing traits (deciding, out of many traits, which to express). We call these capacities ‘filters’ because they do just that: they filter out some traits while letting others through.

At this point, it is helpful to distinguish between origin explanations and distribution explanations (Godfrey-Smith, 2012 ). The accounts emphasised above provide origin explanations, which aim at explaining how a particular trait came about, often by pointing to studies in palaeoanthropology, developmental and experimental psychology, and cognitive neuroscience that lay out the evolutionary and developmental circumstances required for certain capacities to come about. Sterelny ( 2003 ) and Heyes ( 2018 ) are exemplary in this regard in bringing together a wealth of such data in their synthetic cultural evolutionary accounts of the origin of critical cognitive capacities of human beings.

Distribution explanations, by contrast, explain the distribution of traits in a population, or across populations. Food preferences represent one domain where filters may contribute to a distribution explanation. There is great between-culture variation in patterns of acceptance and rejection of food, and individuals are often strongly influenced by their cultural backgrounds in what foods they come to like or find distasteful (Rozin, 1988 ). Though only supported anecdotally, acceptance of fermented foods—for example, the slimy Japanese soybean ferment called natto or the strongly ammonia-scented fermented shark kæstur hákarl from Iceland—is often highly regionalised and culture specific (Katz, 2012 ). This may be because food acceptance or rejection is often tightly linked to culture-specific norms around what is considered disgusting (Rozin et al., 2016 ). Fermented foods are, after all, foods in a controlled process of decomposition. In this example, culture-specific norms influence individuals in filtering out possible traits ( natto, kæstur hákarl ) as incompatible with those they already possess.

In the discussion, we offer some speculations as to how a systems approach may contribute to origin explanations. But by and large, the graph operationalisation of cultural systems adopted here is apt for providing distribution explanations that demonstrate how cultural filters might modulate downstream distributions of traits.

We close this section by considering two ways in which such modulation might occur. The first is through direct , or trait, filtering , where the relationships between traits influence the distribution of other traits. The case of food preferences is case in point. Here, the history of trait sampling by a population means that only some traits are available for individuals to acquire. These realised traits then influence decision making: some foods are desirable, while others are filtered out in virtue of being disgusting.

Yet traits might also be modulated through indirect filtering , for instance, where such filters determine with whom one associates. One such indirect filter is the example of selective social learning (or model-based filtering) given above, where individuals selectively choose from whom to learn on the basis of informational cues. With such a filter, the traits one acquires will be skewed by the model one is oriented towards. Another indirect filter is a similarity filter , where individuals associate with others who bear similar traits (sometimes called homophily ), either through deliberate choice of association, or by pruning their social networks of individuals with dissimilar traits (Axelrod, 1997 ; Centola et al., 2007 ). Unsurprisingly, similarity filters decrease the within-group heterogeneity while increasing the across-group heterogeneity of realised traits.

Evolutionary trajectories and historical dependencies

Interdependencies among traits reduce the number of sets of cultural combinations that are likely or even possible, and as a consequence, the number of likely or possible evolutionary trajectories that lead to those assemblages. For clarity of illustration, we will here consider strict dependencies, such that traits are not only facilitated by, but also contingent on, the existence of other traits. This can be illustrated by a simple unidirectional example. Consider ten traits, labelled by the first letters of the alphabet. Were the traits to be unilinearly dependent, as in Fig. 4a , subject to stepwise acquisition—such that B was contingent on the existence of A, C on B, and so on—then there are only ten possible cultural combinations: one for each trait, including all the preceding traits it is contingent on. There is also only one trajectory for each combination: the one that passes through each trait in alphabetical order, up to the last possible addition.

figure 4

Example dependencies between traits. Here, traits are ( a ) unilinearly dependent, ( b ) arranged in a tree structure, ( c ) combined in different ways. Traits can also be acquired in different sequences ( d , e ) and inhibit other traits

Compare this to the case where traits are independent, with no limitations on the order of acquisition. In this case, the state space of possible combinations explodes. Any combination of traits is possible, so the state space equals the power set of the ten cultural traits, meaning that the number of potential combinations is 2 10  = 1024 (minus one if we exclude the case of having no culture), and doubles for every trait added. The number of evolutionary trajectories that lead to such states is almost ten million ( \(\mathop {\sum}\nolimits_{i = 1}^{10} {\mathop {\prod}\nolimits_{j = 1}^{10} {j = 9,864,100}}\) ). Interdependencies thus provide a path dependence that can significantly facilitate the emergence of a particular cultural system on several occasions.

Relationships between traits can also lead to more complex and diverse cumulative culture, beyond the trivial accumulation of making culture larger by adding independent elements to a collection of traits, and beyond the predetermined stepwise acquisition of the previous example.

Cumulative culture is likely to be a significant contributor to path dependence in cultural evolution. When traits are preserved and build upon past innovations, culture generates traditions —historical chains of cultural variants linked through patterns of cultural transmission. Cultural evolutionary researchers often use the metaphor of a ‘ratchet’ to describe this historical process, since like a ratchet, things move steadily in a single direction—changes are kept, ‘ratcheted’, into the future rather than ‘slipping back’ over multiple transmission events (Tomasello, 1999 ; Dean et al., 2014 ). This ratcheting metaphor is meant to capture the way that cumulative culture differs from a range of possible (cultural) evolutionary scenarios—for instance, where evolution occurs stochastically, moves cyclically through a range of variants, or merely tracks environmental features in ways that do not involve building upon priorly held cultural variation. In so doing, cumulative culture can explain the production of climate appropriate clothing (Boyd and Richerson, 2005 ), counter-intuitive food extraction and processing techniques (Henrich, 2016 ), social organisation and institutions (Bowles and Gintis, 2013 ; Richerson et al., 2014 ), the differentiation and specialisation of tools (Basalla, 1988 ), and culturally evolved cognitive novelties (Heyes, 2018 ). Because cumulative culture produces traditions where future states of the tradition depend on the past states of that tradition, it is the kind of process that generates path dependence.

There have been only a few attempts within the cultural evolution literature that describe or model path dependence, partly since most previous models cannot describe historical processes of ‘ratcheting’ in ways that account for dependencies between traits. One recent exception is a model of the cumulative evolution of technology (Kolodny et al., 2015 ). Central to the model is a highly structured description of a cultural state space, which delimits three kinds of cultural innovations. The structure involves a central ‘main-axis’ with stepwise modification as in the unidirectional example above, but each trait on the main-axis can also be modified in a separate direction, to create ‘toolkit innovations’, and traits on the main-axis can be combined. While this sequential and combinatorial structure may be apt for understanding the evolution of (some aspects of) technological evolution, it seems less apt for characterising the opportunistic and creative processes involved in myth and storytelling (Morin, 2016 ; Acerbi et al., 2017 ), ritual and religions (Whitehouse, 2000 ), or social norms and institutions (Sperber, 1996 ; Bowles, 2004 ).

The combinatorial combination of traits in Kolodny and colleagues’ model draws attention to the various relationships between traits. As illustrated by Enquist et al. ( 2011 ), two important kinds of interdependencies that can structure the cultural state space includes the combination and differentiation of elements. A sweater consists of a combination of cloth and thread, items which can be used also for other purposes. Even though a needle is not part of a sweater, it vastly facilitates the creation of one. With the further introduction of cultural traits, for example, dyes or pigments, we can have a differentiation of sweaters, such as different colours. Simple graphs exemplifying such relationships are given in Fig. 4b, c . Representing relationships between traits in graphs like these enable us to easily describe facilitative and inhibitory relations, characterise the possible and likely trajectories of cultural evolution, and to consider how such relationships among traits themselves might produce new kinds of path dependent phenomena.

The existence of an organised—that is, structured—cumulative culture means that culture can carry traces of its historical trajectory, and, thus, has deep history (Sterelny, 2014 ; Sterelny and Hiscock, 2014 ). To illustrate this, consider the graph in Fig. 4d , with four traits, A, B, C and D, of which the latter three depend on the existence of another trait, and which can all be inhibited by another trait. For clarity of illustration, let us assume that the inhibition is strong enough to completely suppress the inhibited trait, such that the carrier loses it. Footnote 3 Were these traits to be independent, there would exist 2 4  = 16 possible cultural states (including the possibility of having no culture). The traits of such independent assemblages may have occurred in any order (and if traits can disappear and reappear, then potential evolutionary trajectories are boundless), and as a result, the state of a particular system contains no information on its history, except that its constituent elements must all have occurred (at least once) at some point. The relationships between traits, posited in Fig. 4d , halves the number of possible states, and there is one unique trajectory leading to each of these states. The possible states that include at least one trait (and the corresponding trajectories) are: {A} (A), {B} (A → C → B), {C} (A → C), {D} (A → C → B → D), {A, B} (A → B), {A, D} (A → B → D), and {C, D} (A → C → D). Even if the present state of a cultural system does not include all traits that had (at some point) been acquired over their evolutionary trajectory, the scheme of relations makes it possible to recreate their evolutionary history. The fact that culture, due to these structural constraints, often carries so much of its history also enables cultural evolution to have complex path dependence while having the Markov property in terms of predictability: while historical events dictate where we are now, the future cultural states depend only on the present state.

It is a straightforward conclusion from the fact that cultural traits can have downstream effects arising from their interrelations, or compatibility, that acquiring certain traits can have vast effects on which traits can be acquired later on, and thus potentially lead to cultural systems that differ in most of the traits they include. For an extreme example, consider the tree-like structure in Fig. 4c . Each new acquired trait prevents the acquisition of the traits on the other branch, by making them unreachable.

Yet it is not only which traits are acquired that determines which cultural states are accessible, but also the sequences of events can determine which traits can coexist. Let the traits B and C be dependent on A, and B be compatible with C but inhibit A, as in Fig. 4e . The two traits B and C can then be maintained simultaneously, in the same system, provided that B is acquired first. If, on the contrary, C is acquired first, then there are no traits allowing for the acquisition of B. As an example, A may be a generic or non-explanatory answer to a politically charged issue, B a populist answer, and C a complex answer providing a real explanation. B could then be attractive enough not to be lost in a population even in face of a real answer, and even if it would not appear if there already existed such an answer, while C could easily replace the unsatisfactory answer A. The importance of the sequence of acquisition is further amplified if B and C enable different clusters of traits down the line.

For a more concrete example based on Fig. 4e , consider the Lancet MMR autism fraud. In 1998, former physician Andrew Wakefield (A) submitted a paper linking the MMR vaccine to colitis and autism spectrum disorders. (B) The paper was accepted and led to a drop in vaccination rates and a loss of confidence in their safety, with a concomitant increase in anti-vaccination propaganda (e.g., Gross, 2009 ). However, the paper was filled with flaws, the results had been misinterpreted, it had been conducted unethically, and its main findings were later refuted, which led to (C) a late rejection (a retraction) of the paper by the Lancet twelve years later (Dyer, 2010 ). Even so, the strengthening of the anti-vaccination movement that B sparked, and the spread of anti-vaccination ideas it caused, was not cancelled out by C. Had the paper been rejected, C, directly, without publication, then that would have inhibited B and its consequences.

Stability versus change

Empirical observations of cultural phenomena reveal extensive variation in the rate at which culture changes. These rates can range from traits and systems that remain more or less the same over many generations, to traits and systems that change rapidly within a single generation. For instance, there are many examples of religious beliefs and social norms that have remained similar over long periods of time (Geertz, 1973 ; Glenn, 2010 ). At the same time, however, clothing styles may be subject to fast changes (Shepard, 1972 ; Belleau, 1987 , Herzog et al., 2004 ). Not only are there diverse rates of change, but these rates themselves may also change over time. To give one example, Gjesfjeld and colleagues ( 2016 ) show how changing rates of origination and extinction rates have changed the landscape of car models, with competition between manufacturers being a substantial driver of a decreased diversification in automobile models. And, of course, different elements within culture may vary in their rates of change. Comparative and phylogenetic studies of language evolution, for instance, demonstrate both fast and slow changes in different lexical and grammatical elements (e.g., Greenhill et al., 2017 ).

A number of explanations have been suggested for the variation in the rate of change in cultural evolution, including external factors such as the physical and ecological environment (Vegvari and Foley, 2014 ), demographic factors (Powell et al., 2009 ), and cultural complexity (Querbes et al., 2014 ). Here, we explore how trait relationships and a systems view of culture could potentially explain variation in the pace of cultural change. Two factors seem important to consider. One is the intrinsic properties of traits that determine their relationships with other traits, and the other is filtering processes that may favour collections of traits that either promote stability or drive change. We first consider trait relationships that can promote stability and then relationships that can drive changes. We end with describing systems with fashions or fad-like dynamics, in which traits may change more quickly than when they are modelled as independent traits.

It is a plausible extension of the idea that traits are more or less compatible with one another that traits which mutually support each other’s transmission could form stable cultural clusters that are maintained over many generations. We have investigated this idea in a series of simulations similar to those in the other sections. Here we generated a situation with 20 traits with predominantly negative relationships, and explored how groups of two, three or four mutually supporting traits could influence each other’s existence in such a trait environment. Examples of these simulations are illustrated in Fig. 5 (see the Supplementary information for more details). It shows, in the situation explored, that two mutually supporting traits promote each other only ephemerally, with three traits the effect was stronger, and finally with four traits a stable cluster was formed. Note that two traits have only one relationship; three traits have three relationships; and four traits have six relationships that can support stability (in general, n ( n  − 1)/2, where n is the number of supporting traits).

figure 5

Mutual support may maintain system configurations over a long time (where a time step is one round of interactions). The number of traits supporting each other is two in the top panel, three in the middle panel and four in the bottom panel, from a total of twenty traits. The figure shows the frequencies of the supporting traits and the average frequency of the other traits included in the simulations

Though the model emphasises the stability brought about through compatibilities between traits, incompatibilities or negative relationships could also contribute to a stable cluster if these inhibit traits outside the present cluster. This would decrease the likelihood of new traits invading the cluster in question.

This supports previous work showing that such conservative tendencies can easily evolve (Ghirlanda et al., 2006 ; Acerbi et al., 2009 ; Acerbi et al., 2014 ). In these models, individuals are born open and acquire traits in interactions with other individuals. Whether copying occurs depends on how compatible the observed trait is with the other traits already acquired by the individual (this is what we call trait filtering above). The reason why conservatism evolves in these models is that open individuals are more likely to acquire traits that make them more conservative while conservative individuals are less likely to acquire traits that make them more open. Over generations, increasingly conservative systems become established.

As one can see, the stability of cultural systems—or as is more likely to be the case, specific trait assemblages—is plausibly promoted both by mutual relationships among its parts, and potentially by incompatible relationships with traits not part of the system. This describes one kind of evolutionary history; here, trait assemblages successively increase internal compatibility and decrease external compatibility. If such features were to characterise most traits of a cultural system, then one would expect such a system to eventually enter a basin of attraction where little subsequent change could occur. However, as we will see, there are also circumstances and arrangements of trait relationships that promote change.

While mutual support can give rise to stable cultural systems, there are other relationship distributions that will promote change rather than stability. Some arrangements may even give rise to rates of change that are higher than for independently evolving traits. One type of trait relationship that would promote change rather than stability is an asymmetric relationship between two traits: for instance, trait A may facilitate the acquisition of a trait B while B has the opposite effect on A (inhibiting its acquisition). Such an asymmetric relationship could lead to a succession of trait replacement events. If A appears first, then it will promote B, but when B becomes common, it will cause A to disappear. The processes are directly dependent on properties of the current cultural system and can lead to an accelerating generation of new cultural traits (Lehman, 1947 ; Ogburn, 1950 ; Enquist et al., 2011 ; Kolodny et al., 2015 ).

Theoretical work also shows how fluctuations in the rate of change may arise, with periods of rapid change interspersed with periods of slow change (Aoki, 2015 ). Within the humanities, a classical model for such fluctuations in the rate of change is the ideas of dialectic processes (Cohen, 1978 ), which recognises that cultural systems may give rise to internal contradictions that promote substantial changes to the system. This idea seems fully compatible with the theory of cultural systems suggested in this paper. However, we are not aware of any theoretical work demonstrating for instance a correlation between the rate of change and the degree of internal conflict or incompatibility in a system.

Evolving traits relationships may, under certain assumptions, give rise to very high rates of change typical of fashion or fashion-like phenomena (Acerbi et al., 2012 , Michaud, 2019). To see this, suppose that there are two kinds of traits, both of which can be transmitted between individuals. The first kind is composed of display traits like a colour or a style, and the other preference traits, which are linked to specific display traits. During their lifetime, individuals acquire and display traits and preferences through their interactions with other individuals. This set-up has two consequences for the individual. First, the acquisition of display traits will increase or modify the individual’s efficacy as a cultural model. If an individual’s display traits are popular, then that individual will be copied more frequently than an individual with less popular display traits. Second, preferences acquired by the individual determines which individuals it will tend to learn from. Note that with these assumptions, there are no absolute or permanent standards for what makes a display trait popular.

Among a group of individuals, these social learning processes will give rise to a highly unstable but clearly patterned scenario of cultural evolution, in which systems of preferences and display traits change quickly in cycles of outburst and decay (Acerbi et al., 2012 ). A cycle starts with a preference for a particular display trait stochastically becoming common among currently popular individuals. This increases the spread of the preference in the population, which in turn spreads the trait. However, as soon as the trait starts to be common, the preference starts to disappear. The reason for this is that individuals with the preference change faster than individuals without the preference (see the discussion about evolution of conservatism above). Thus, more individuals with the preference will lose it than individuals without the preference will gain it. An example from the model of Acerbi and colleagues is shown in Fig. 6 with the lag between preference and the corresponding display trait. The changes that occur in this model can be faster than the rate of change occurring when traits evolve independently of each other, because both the rise and fall of the display trait are actively driven by the fast changes in preferences.

figure 6

Example of a fashion cycle generated by an evolving mixture of display and preference traits. The example is based on the model of Acerbi et al. ( 2012 )

In most examples in this paper, trait relationships are assumed to be fixed and exogenously given, for instance by the nature of traits, logical constraints, interaction with reality or genetic predispositions. However, in the simulation model of Fig. 6 , the relationships between traits themselves are subject to cultural evolution.

Group phenomena

Cultural systems may also help to explain the emergence of cultural groups. A single trait may suffice to distinguish between members of different groups. Yet for the existence of such group-defining traits to be a causal factor influencing the behaviour of others—for instance, to serve as a signal for intra-group and inter-group biases or for overt prejudice towards other groups—and for the existence of the trait to be formed and maintained, such a trait needs to be interdependent with those that induce the relevant behaviour.

There are numerous examples of how important groups are for dispersal of ideas about the world and our behaviours towards other people, and how easily they form. A famous example is the minimal group paradigm (Tajfel, 1970 ; Tajfel et al., 1971 ), where discrimination emerges between groups based on arbitrary divisions. There is a vast empirical literature on group phenomena that we cannot cover here, but there are for example metastudies on group biases across cultures (Balliet et al., 2014 ; Romano et al., 2017 ), and surveys on how opinions and beliefs are reinforced in groups, polarising views on the societal level (Lamm and Myers, 1978 ; Isenberg, 1986 ; Abrams and Hogg, 1990 ) and on the importance of sharing several cultural traits for emotional closeness between individuals (Curry and Dunbar, 2013 ), showing that a cultural systems approach to understanding group formation may be viable.

Small systems of two or a few more traits may explain the maintenance of pre-existing groups in specific situations. In the modelling literature, there is typically an underlying strategic situation in the specified form of a game, usually a prisoners’ dilemma, where the group structure supposedly facilitates altruistic behaviour by coupling the trait of a group marker with cooperative behaviour towards individuals with that marker. The objective of such studies is to find a mechanism for an ingroup bias. Typically such models are based on a biologically inherited marker (e.g., Riolo et al., 2001 ; Hammond and Axelrod, 2006 ; Jansen and van Baalen, 2006 ) that also requires spatial assortment and kin selection (see Read, 2010 ; Jansson, 2013 ) or rapidly changing markers (Fu et al., 2012 ). Changing the underlying game can replace the spatial assortment (Jansson, 2015 ) and also allow for cultural nonstatic markers to coevolve with behaviour (McElreath et al., 2003 ; Efferson et al., 2008 ). Typically, the models are based on strategic situations and try to explain cooperative behaviour exclusively to members of your group through some kind of greenbeard effect (Dawkins, 1976 , 1982 ), group selection (Choi and Bowles, 2007 ), direct reciprocity through spatial structure or making group traits more flexible than behavioural traits, or reputation (Masuda and Ohtsuki, 2007 ; Grey et al., 2014 ) (for a review, see Masuda and Fu, 2015 ). As will be illustrated below, a cultural systems approach may contrast with such approaches by moving beyond strategic situations, pre-existing groups and biological inheritance.

There are also models implicitly based on simple cultural systems. Examples include polarisation and clustering based on shared traits (Schelling, 1971 ; Axelrod, 1997 ), set structured populations (Tarnita et al., 2009 ), and individuals structuring into groups (Grey et al., 2014 ). Schelling’s ( 1971 ) segregation model, for instance, entails simple systems that can maintain homogeneous views among actors, or opposing views that are somewhat balanced in numbers of advocates. The latter systems are unstable, and the population ends up segregating into cliques of homogenous sub-populations. Similar patterns emerge when agents copy more from the agents in the vicinity with whom they already have the most in common (Axelrod, 1997 ). This idea is a bit more generalised and explicitly connected to cultural systems in what is referred to as evolutionary set theory (Tarnita et al., 2009 ). Here, agents can become and stop being members of any number of available sets, that is, they have a number of cultural traits, and they interact more with agents with whom they share many traits, again in a strategic situation facilitating cooperation between similar agents. An even more bottom-up approach to group formation is one where the ideas are about the other agents, and agents interact more when they gain positive experience from previous interactions, and where they also exchange views on third parties, leading to clustering (Grey et al., 2014 ). Apart from cooperative interactions, there are also models of how social network structure emerges from similarity in several cultural traits (Centola et al., 2007 ; Centola, 2015 ).

Using the framework of cultural systems suggested here, we can potentially generalise mechanisms of group formation, polarisation and prejudice further, as by-products of relationships between the traits that agents possess.

Consider the previous example of a cultural system with simple attraction between traits A and B, and C and D, and repulsion between all other pairs (Fig. 1 ). We saw that the cultural evolution of such a system leads to a dominance of compatible traits. Looking at the prevalence of the individual traits over time (Fig. 7 ), we see that, typically, the population has not converged on sharing the same pair of compatible traits, but the two systems, AB and CD, tend to coexist (for further details on the simulation, see the Supplementary information). The relationships between traits have thus led to the spontaneous formation of two incompatible cultural groups.

figure 7

Prevalence of cultural systems over time. Most agents have either traits A and B, or C and D

Contrasting with the previous modelling approaches described above, there are no utilities involved. Groups have not been formed because it is rational or there is a selective pressure on the individuals, nor of spatial or social assortment, and the groups are defined only by cultural traits. This illustration merely points at the potential of explaining various group phenomena, and this particular example pertains more to polarisation into two camps than ethnic groups. However, with more clusters of mutually compatible traits, the population could polarise into several, and potentially overlapping, groups.

Relationships can also vary and be endogenous. A recent and related model specifically representing preferences (Goldberg and Stein, 2018 ) finds cultural variation divided into two clusters also when the compatibility between traits evolves culturally, through associative diffusion that takes place by pairwise displays and observations of cultural traits. When agents see two traits used together, they increase their association between them and make their preferences for them more similar, resulting in a cluster of traits that a part of the population likes and another cluster that they dislike, with the other part of the population having opposite preferences.

These cultural systems also provide opportunities for path dependence at the group level. New traits that enter the population might be absorbed by individual members of only one of the groups, depending on how they relate to existing traits, or even more groups may form. When clusters of compatible traits grow, the groups that are defined by them become more stable, and limit exchange between groups. Trait dependencies should thus not only allow for groups to form, but also for them to be maintained over time, and eventually be associated with beliefs, as well as behavioural and phenotypic traits. Examples may include prejudice, group biases and closeness between individuals.

At a more abstract level, this illustrates how multiple cultural systems can exist in parallel, also when the relationships between the traits are exogenously given (e.g., set by physical reality or logical constraints) and the potentially available traits are the same for all individuals in the population. At a higher-order level, cultural systems may themselves regulate the relationships between traits (e.g., having A and B may regulate how compatible C and D are). Cultural evolution may thus also give rise to multiple cultural systems that differ not only in what traits are included in a cluster, but also in how compatible those traits and potential traits outside that cluster are.

The cultural systems approach articulated here highlights a range of novel and emerging research areas in the cultural evolutionary literature. We have here focused on its implications for four such areas: (i) the cultural evolution of ‘filters’ that modulate processes involved in acquisition, invention, and transmission; (ii) the path dependent trajectories of cultural systems that carry signals of that system’s history; (iii) the rates of cultural change and diversification; and (iv) the formation and dynamics of cultural groups.

A noteworthy feature of these domains is that they display self-organisation: that the relationships between traits play a large part in which trait combinations are realised (in individuals, groups), and that these may, in turn, influence the downstream acquisition, innovation, and diffusion of traits. So, for instance, filters may themselves be culturally evolved decision rules aimed at optimising various goals, and path dependent explorations of trait pools may depend on the relationships holding between traits.

The modulating effects of self-organisation can be ephemeral, systematic, and everything in between—with the effect and duration of self-organised features contingent upon the vagaries of cultural evolution. Above we focused on the possibility of systematic influences, where cultural evolution itself provides the circumstances for the reliable acquisition of trait complexes and their effects in populations. This is for the simple reason that such complexes are likely to have pervasive and long-term effects, with broad implications.

The phenomenon of self-organisation is underappreciated in the modelling work of cultural evolutionary theory—even if the idea itself has some currency in the broader anthropological, philosophical, and evolutionary literature (e.g., Kauffman, 1993 ; Deacon, 1997 ; Sterelny, 2012 ). This may be because self-organising structures are only visible in approaches that represent multiple traits and their interrelationships. As we hope to have shown above, even when a few traits are employed, trait relationships can generate a wide range of interesting and novel dynamics. A cultural systems approach thus not only makes conspicuous self-organising phenomena, but provides a flexible set of tools for investigating and understanding them.

Another important feature of the systems approach is that it can address questions at multiple levels. We have here illustrated how cultural systems identify distinctive features at the trait, individual, and population level. As illustrated in Figs 5 – 7 , the consideration of relationships between traits can enrich the dynamics of population-level outcomes through microlevel models. We also saw how such relationships could channel the characteristics of individuals, modulate homogeneity and heterogeneity, and alter the pace of cultural change. Such processes might also bring about group formation in stable clusters or fashion cycles, and can explain aggregate measures at the group level that are difficult to generate with independent traits.

To take one example, when discussing rates of change in fashions or fads, we highlighted how the acquisition of preference and preferred display traits can generate rapidly fluctuating dynamics at the aggregate level. A cultural systems approach can thus complement already existing strategies at employing ‘population thinking’ (Lewens, 2015 ) by exploring how the endogenous links within cultural systems interact with individuals to realise population-level phenomena. Mapping the link between microlevel mechanisms and macrolevel outcomes to the scheme of Coleman ( 1986 ), cultural systems along with frequencies of traits in the population pose structural and situational constraints on agents, who adopt traits selectively through copying and filtering, producing updated frequencies and a new subset of associated structural and situational constraints.

Thus, a systems approach provides a framework for understanding how individual actions generate macroevolutionary causes, and how these can feed back to influence microevolutionary interactions, via their influence on trait availability and interrelationships (such as preferences). Our models have focused predominantly on the first of these mechanisms, pointing to areas where a systems approach can illuminate how individual-level behaviour generates population-level patterns. For instance, trait interrelationships can drive differentiation between cultural groups and modulate the tempo and mode of cultural evolution. We have further suggested that cultural filters may be an important mechanism at play to change macroevolutionary patterns by influencing and modifying these trait interrelationships.

At the same time, we pointed to empirical work showing how population-level causes can influence individual-level behaviour by modulating trait availability and desirability. Heidi Colleran’s work on the diffusion of contraceptive technology demonstrates how the influence of the average behaviour of the population (here, religiosity and education) can influence the availability and attitudes towards contraceptive knowledge and use. Group dynamics too can polarise and cause clustering of traits among distinct populations, further altering trait availability and desirability.

It is also possible that systems themselves may interact at the macro-level. Though we have not focused on such a possibility in this paper, we above highlighted the work of Erik Gjesfjeld and colleagues ( 2016 ) who explored the changing rates of origination and extinction in the production of car models. The system-level properties that feed into such origination and extinction rates—broad relationships between manufacturing strategies, state policies, demand cycles, oil production, and the like—provide yet another avenue of potential investigation for the systems approach.

As we hope to have stressed above, the idea of cultural systems is not a new one. It is not only a consensus view, but one that has long been subject to analysis and theorising in anthropological thought, especially where a range of thinkers have described cultures as systems subject to evolutionary change (Steward, 1955 ; Sahlins, 1960 ; Kroeber and Parsons, 1958 ; Geertz, 1973 ; Diener, 1980 ; for a general review, see Carneiro, 2003 ). Yet for the most part, these researchers deployed systems thinking in a qualitative way—often drawing a variety of analogies between cultures and specific systems like organisms or species. What is distinct about the approach motivated here—and what it adds to the already existing use of systems thinking—is that it employs the tools of formal modelling. The bottom-up style of systems modelling used in our examples is flexible and open-ended, providing the opportunity to explore a wide range of hypotheses by creatively modifying and combining different combinations of trait universes and agent properties.

This approach complements and generalises some recent models that have also adopted a strategy of modelling multiple traits and their interrelationships. Goldberg and Stein ( 2018 ), for instance, employ a similar framework to explore the role of what they call ‘constraint satisfaction’ in changing the trait interrelationships in a small trait pool (what they call a ‘semantic network’). This work explores how the compatibility and incompatibility of traits can be socially constructed and modified over time. In a different vein, Claidière and colleagues ( 2014 ) employ ‘evolutionary causal matrices’ to explore the effect that trait types have on the absolute number of said trait types over time. This is mostly analogous to what we have discussed as selective trait filters. Without explicitly representing the compatibility or incompatibility between traits, or the specific decision rules that determine the acquisition or modification of traits, these matrices directly model the filtering effects that traits have on the downstream composition of both individual and group systems.

Speaking generally, we have here illustrated how a systems approach—particularly one that builds upon the strategy of investigating the strategies of acquiring, innovating, and diffusing culture in a rich trait universe—generates new tools for explaining cultural evolutionary phenomena. Already, the results given above reveal multiple areas for future enquiry. In particular, exploration that goes beyond disjunctive compatibility or incompatibility has the potential to generate a richer set of dynamics. At the same time, building in different kinds of trait relationships—such as those necessitating the sequential acquisition of certain traits—offers the possibility of exploring more realistic trait universes.

As we have suggested in numerous places above, a systems approach also has the potential to connect with, and help to explore, other issues in cultural evolutionary theory. In particular, it seems apt for exploring issues at the intersection of demography, population size, and the size of population-level cultural systems (Henrich, 2004 ; Powell et al., 2009 . Cf. Vaesen et al., 2016 ). Along the same lines, it seems apt for connecting with the palaeoanthropological literature on the rates of change in cultural traits over time, where this includes both stasis and rapid change. The radical stasis of lithic technologies in the lower and middle Pleistocene and the radical change in culture that occurs in the Holocene (Mithen, 2005 . Cf. McBrearty and Brooks, 2000 ) provide a rich set of phenomena for exploration by a systems approach.

As we noted above, many of the extant cultural evolutionary models are based on those developed in evolutionary biology. Researchers in cultural evolution motivate the adoption of such models by means of analogy: the seeming similarity of transmission processes in cultural and genetic evolution has given warrant for the exploration of cultural evolutionary dynamics based on models using replicator dynamics or other population-genetic tools. We do not here wish to contribute to the growing literature that explores how researchers have developed analogy (e.g., Sperber, 1996 ; Lewens, 2015 ). Instead, we merely wish to point out that analogies often function to highlight salient avenues of empirical research, and that there are many such fruitful avenues.

We have here been inspired in part by work in systems biology—particularly that which describes the evolution of organisation and constraint within complex dynamic systems (Kauffman, 1993 ). To illustrate this analogy, consider HOX genes—an important class of deeply conserved homeobox genes that regulate patterns of development across almost all eukaryotes (Bürglin and Affolter, 2016 ). HOX genes regulate the site-specific development of morphology, so that limbs grow in species-typical fashion (Krumlauf, 1994 ), and manipulation of these genes can lead to odd mutations, such as Drosophila with legs where antennae normally form (Carroll, 2005 ). HOX genes are one instance of a structure that, once it has arisen, persists over time—forming a set of tools that can be tweaked to generate diversity. They serve as a signal and explanation for the similarity in body plan across different evolutionary groups.

Systems biology studies such homeobox genes as an instance of ‘constraint-based generality’ (Green, 2015 )—here understood as the ways in which systems tend to self-assemble a structure that constrains the possibilities in which it can change in the future (O’Malley, 2012 ). Some of these structural constraints that researchers have identified include core components and weak regulatory linkage (Kirschner and Gerhart, 2006 ), generative entrenchment (Wimsatt, 2001 ), and network robustness (Jaeger et al., 2015 ). These are structures that limit the evolutionary trajectories likely to occur, but in so doing, minimise the risk of lethal mutations, and, perhaps, increase the tempo of evolution.

Our guiding thought is that similar kinds of constraint-based principles and self-assembling features can help in understanding cultural systems. Like HOX genes, it seems likely that at least some cultural traditions are tightly integrated in virtue of their role in ensuring the socioeconomic viability of cultures over time (cf. Boyd et al., 1997 ). We expect these ‘cultural cores’ (Steward, 1955 ) to share several features, given their role in mitigating recurrent socioecological problems concerning resource allocation, free-rider problems, warfare, and the like (Sterelny, 2012 , 2016 ). Such cores could be usefully explored using a constraint-based approach that investigates the likely trajectories that populations will traverse over evolutionary time frames.

Yet here we also urge caution. Along with other researchers (e.g., Richerson and Boyd, 2005 ; Mesoudi, 2011 ), we would stress that cultural evolution works differently from biological evolution. As has often been noted, the social nature of culture means that ideas, traditions, beliefs, and technologies can readily diffuse between populations. The free-flowing transmissibility of culture—though analogous to a limited extent with horizontal gene transfer—is likely to generate unique dynamics and rates of change. Cultural traits are not necessarily transmitted as one package, but are acquired and lost in multiple steps, with the consequence that they can be individually selected on how compatible they are to other acquired traits. This suggests that realised cultural traits in a population may have radically different histories and transmission dynamics.

Beyond developing new analogies to on-going empirical research, a systems approach to culture has the potential to connect with the wide range of humanities and social science literature that have made general hypotheses about the formation, nature, and dynamics of cultural change. As we suggested above, the idea that culture can be understood as a system has been a mainstay of anthropological thinking in the twentieth century. Yet these ideas are also found in the classical works of sociology, linguistics, and economics (Marx, 1867 /1990, Saussure, 1959 , Durkheim, 1995 ), and are now widespread throughout the humanities and social sciences. To touch on just a few areas, systems thinking seems to be implicated in understanding gender structures (e.g., Walby, 1989 ), social norms, attitudes and ideology (e.g., Boutyline and Vaisey, 2017 ; Inglehart, 2018 ; Jansson et al., 2013 ; Strimling et al., 2019 ), and systems of language (e.g., Greenhill et al., 2017 ), technology (e.g., Franklin, 1999 ), economy (e.g., Wallerstein, 1974 ), and religion (e.g., Geertz, 1973 ). A systems approach provides a promising bridge to the as of yet unexplored wealth of theorising about culture coming from within the humanities and social sciences.

Of course, a systems approach brings with it a distinct set of challenges. Compared to population-level models of single or independent traits, incorporating relationships between traits introduces a higher level of complexity. This decreases the tractability of cultural evolution models, while simultaneously increasing degrees of freedom. Given the wide variety of possible outcomes, modifying parameters might induce significant changes to modelling results.

It is uncontroversial that culture is an organised system. What we have argued here is that explanations of several cultural phenomena are sensitive to the relationships between traits, and, further, that empirical and theoretical research suggests that these phenomena are central to culture and cultural change. In other words, acknowledging trait interrelationships opens up rich dynamics that can generate empirically observable patterns unattainable for models that represent traits in isolation. This suggests that we should not shy away from the challenge of adding this extra layer of complexity to our cultural evolutionary models.

Simulation model

In the simulations, there is a universe of cultural traits with relationships between them. A relationship between two traits consists of a compatibility score of 1 if the traits are compatible, and −1 if they are incompatible. A universe is specified for each illustration in the manuscript (see below). There are 100 agents, each with an individual cultural repertoire, consisting of a subset of traits from the universe. At the outset, the agents are naive, with empty repertoires, but acquire traits through innovation (of traits from the universe) and copying from other agents.

The agents meet in random interactions. One round of interactions, or a time step, includes copying, invention and a birth death process. First, each agent, the receiver, samples one other agent as a cultural model. The model randomly selects one of the traits, i, in its repertoire for display to the receiver. The receiver copies the trait with a probability determined by the average compatibility score s of the trait with the receiver’s current repertoire, that is,

where \(c_{ij}\) is the compatibility between i and j , and \(R \;\ne\; \emptyset\) is the set of traits in the receiver’s repertoire. If the receiver has no traits, \(R = \emptyset\) , then s  ≔  0. The probability of copying is determined by the logistic equation

The constant 10 was arbitrarily chosen, but values below around 5 give the score a small influence and the results were not sensitive to scores above that value.

Each agent then invents a new trait with probability 0.001; that is, it randomly selects a trait from the universe and adds it to its repertoire (if the agent does not already possess the trait). Finally, each agent dies with probability 0.01 (0.0025 in Section 6 – the lower rate provides more stability), and is replaced by a new naive agent.

In the sections ‘What is a cultural system?’ and ‘Group phenomena’, the universe consists of four traits, A, B, C and D, where A and B are mutually compatible, and C and D are also mutually compatible, but all other pairs of traits are mutually incompatible.

In the section ‘Stability versus change’, the universe consists of 20 traits. Four of these are named, A, B, C and D. In the first simulation A and B are compatible, in the second A, B and C are all compatible, and in the third all four are compatible. The remaining trait pairs (including C and D in the first, and D in the second simulation) are set to be mutually compatible with probability 0.1, and otherwise they are mutually incompatible.

See data availability to access the code (in Python).

Data availability

The models used in this paper were implemented in Python. The program along with code to generate data for the figures are available in a Dataverse repository: https://doi.org/10.7910/DVN/KKDZX8 .

For some recent reviews of this interdisciplinary literature and the variety of empirical and theoretical methods employed, see Mesoudi, 2011 , and Henrich, 2016 .

While the cognitive capacity for such ‘filters’ would be genetically evolved, as cultural traits are acquired, they will be increasingly shaped by cultural evolution. However, this is not to discount the likely existence and relevance of innate biases that modulate and influence processes involved in cultural acquisition, innovation, and change. For some discussion of these issues, see Cowie, 1999 , Sterelny, 2012 , Lewens, 2015 , and Heyes, 2018 .

Losing a trait can represent different actions depending on what is being studied. An individual can forget a piece of information, lose a skill or a preference, or suppress the use and display of the trait (if the focus is on visible culture). If, for example, trait A is a preference for X and B a preference against it, then B replaces A.

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This work was supported by the Knut and Alice Wallenberg Foundation (grant number 2015.0005), the Leverhulme Trust (grant number RG95309), and the Isaac Newton Trust (grant number G101655). Open access funding provided by Stockholm University.

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Introduction

Understand obstacles that prevent change, experiment with different ideas and approaches at all levels, create a shared vision, looking ahead, article and author information.

The San Francisco Declaration on Research Assessment (DORA) was published in 2013 and described how funding agencies, institutions, publishers, organizations that supply metrics, and individual researchers could better evaluate the outputs of scientific research. Since then DORA has evolved into an active initiative that gives practical advice to institutions on new ways to assess and evaluate research. This article outlines a framework for driving institutional change that was developed at a meeting convened by DORA and the Howard Hughes Medical Institute. The framework has four broad goals: understanding the obstacles to changes in the way research is assessed; experimenting with different approaches; creating a shared vision when revising existing policies and practices; and communicating that vision on campus and beyond.

Declarations can inspire revolutionary change, but the high ideals inspiring the revolution must be harnessed to clear guidance and tangible goals to drive effective reform. When the San Francisco Declaration on Research Assessment (DORA) was published in 2013, it catalogued the problems caused by the use of journal-based indicators to evaluate the performance of individual researchers, and provided 18 recommendations to improve such evaluations. Since then, DORA has inspired many in the academic community to challenge long-standing research assessment practices, and over 150 universities and research institutions have signed the declaration and committed to reform.

But experience has taught us that this is not enough to change how research is assessed. Given the scale and complexity of the task, additional measures are called for. We have to support institutions in developing the processes and resources needed to implement responsible research assessment practices. That is why DORA has transformed itself from a website collecting signatures to a broader campaigning initiative that can provide practical guidance. This will help institutions to seize the opportunities created by the momentum now building across the research community to reshape how we evaluate research.

Systemic change requires fundamental shifts in policies, processes and power structures, as well as in deeply held norms and values. Those hoping to drive such change need to understand all the stakeholders in the system: in particular, how do they interact with and depend on each other, and how do they respond to internal and external pressures? To this end DORA and the Howard Hughes Medical Institute (HHMI) convened a meeting in October 2019 that brought together researchers, university administrators, librarians, funders, scientific societies, non-profits and other stakeholders to discuss these questions. Those taking part in the meeting ( https://sfdora.org/assessingresearch/agenda/ ) discussed emerging policies and practices in research assessment, and how they could be aligned with the academic missions of different institutions.

The discussion helped to identify what institutional change could look like, to surface new ideas, and to formulate practical guidance for research institutions looking to embrace reform. This guidance – summarized below – provides a framework for action that consists of four broad goals: i) understand obstacles that prevent change; ii) experiment with different ideas and approaches at all levels; iii) create a shared vision for research assessment when reviewing and revising policies and practices; iv) communicate that vision on campus and externally to other research institutions.

Most academic reward systems rely on proxy measures of quality to assess researchers. This is problematic when there is an over-reliance on these proxy measures, particularly so if aggregate measures are used that mask the variations between individuals and individual outputs. Journal-based metrics and the H-index, alongside qualitative notions of publisher prestige and institutional reputation, present obstacles to change that have become deeply entrenched in academic evaluation. This has happened because such measures contain an appealing kernel of meaning (though the appeal only holds so long as one operates within the confines of the law of averages) and because they provide a convenient shortcut for busy evaluators. Additionally, the over-reliance on proxy measures that tend to be focused on research can discourage researchers from working on other activities that are also important to the mission of most research institutions, such as teaching, mentoring, and work that has societal impact.

Rethinking research assessment therefore means addressing the privilege that exists in academia, and taking proper account of how luck and opportunity can influence decision-making more than personal characteristics such as talent, skill and tenacity.

The use of proxy measures also preserves biases against scholars who still feel the force of historical and geographical exclusion from the research community. Progress toward gender and race equality has been made in recent years, but the pace of change remains unacceptably slow. A recent study of basic science departments in US medical schools suggests that under current practices, a level of faculty diversity representative of the national population will not be achieved until 2080 ( Gibbs et al., 2016 ).

Rethinking research assessment therefore means addressing the privilege that exists in academia, and taking proper account of how luck and opportunity can influence decision-making more than personal characteristics such as talent, skill and tenacity. As a community, we need to take a hard look – without averting our gaze from the prejudices that attend questions of race, gender, sexuality, or disability – at what we really mean when we talk about 'success' and 'excellence' if we are to find answers congruent with our highest aspirations.

This is by no means easy. Many external and internal pressures stand in the way of meaningful change. For example, institutions have to wrestle with university rankings as part of research assessment reform, because stepping away from the surrogate, selective, and incomplete 'measures' of performance totted up by rankers poses a reputational threat. Grant funding, which is commonly seen as an essential signal of researcher success, is clearly crucial for many universities and research institutions: however, an overemphasis on grants in decisions about hiring, promotion and tenure incentivizes researchers to discount other important parts of their job. The huge mental health burden of hyper-competition is also a problem that can no longer be ignored ( Wellcome, 2020a ).

Culture change is often driven by the collective force of individual actions. These actions take many forms, but spring from a common desire to champion responsible research assessment practices. At the DORA/HHMI meeting Needhi Bhalla (University of California, Santa Cruz) advocated strategies that have been proven to increase equity in faculty hiring – including the use of diversity statements to assess whether a candidate is aligned with the department's equity mission – as part of a more holistic approach to researcher evaluation ( Bhalla, 2019 ). She also described how broadening the scope of desirable research interests in the job descriptions for faculty positions in chemistry at the University of Michigan resulted in a two-fold increase of applicants from underrepresented groups ( Stewart and Valian, 2018 ). As a further step, Bhalla's department now includes untenured assistant professors in tenure decisions: this provides such faculty with insights into the tenure process.

The seeds planted by individual action must be encouraged to grow, so that discussions about research assessment can reach across the entire institution.

The actions of individual researchers, however exemplary, are dependent on career stage and position: commonly, those with more authority have more influence. As chair of the cell biology department at the University of Texas Southwestern Medical Center, Sandra Schmid used her position to revise their hiring procedure to focus on key research contributions, rather than publication or grant metrics, and to explore how the applicant's future plans might best be supported by the department. According to Schmid, the department's job searches were given real breadth and depth by the use of Skype interviews (which enhanced the shortlisting process by allowing more candidates to be interviewed) and by designating faculty advocates from across the department for each candidate ( Schmid, 2017 ). Another proposal for shifting the attention of evaluators from proxies to the content of an applicant's papers and other contributions is to instruct applicants for grants and jobs to remove journal names from CVs and publication lists ( Lobet, 2020 ).

The seeds planted by individual action must be encouraged to grow, so that discussions about research assessment can reach across the entire institution. This is rarely straightforward, given the size and organizational autonomy within modern universities, which is why some have set up working groups to review their research assessment policies and practices. At the Universitat Oberta de Catalunya (UOC) and Imperial College London, for example, the working groups produced action plans or recommendations that have been adopted by the university and are now being implemented ( UOC, 2019 ; Imperial College, 2020 ). University Medical Center (UMC) Utrecht has gone a step further: in addition to revising its processes and criteria for promotion and for internal evaluation of research programmes ( Benedictus et al., 2016 ), it is undertaking an in-depth evaluation of how the changes are impacting their researchers (see below).

To increase their chances of success these working groups need to ensure that women and other historically excluded groups have a voice. It is also important that the viewpoints of administrators, librarians, tenured and non-tenured faculty members, postdocs, and graduate students are all heard. This level of inclusion is important because when communities impacted by new practices are involved in their design, they are more likely to adopt them. But the more views there are around the table, the more difficult it can be to reach a consensus. Everyone brings their own frame-of-reference, their own ideas, and their own experiences. To help ensure that working groups do not become mired in minutiae, their objectives should be defined early in the process and should be simple, clear and realistic.

Aligning policies and practices with an institution's mission

The re-examination of an institution's policies and procedures can reveal the real priorities that may be glossed over in aspirational mission statements. Although the journal impact factor (JIF) is widely discredited as a tool for research assessment, more than 40% of research-intensive universities in the United States and Canada explicitly mention the JIF in review, promotion, and tenure documents ( McKiernan et al., 2019 ). The number of institutions where the JIF is not mentioned in such documents, but is understood informally to be a performance criterion, is not known. A key task for working groups is therefore to review how well the institution's values, as expressed in its mission statement, are embedded in its hiring, promotion, and tenure practices. Diversity, equity, and inclusion are increasingly advertised as core values, but work in these areas is still often lumped into the service category, which is the least recognized type of academic contribution when it comes to promotion and tenure ( Schimanski and Alperin, 2018 ).

A complicating factor here is that while mission statements publicly signal organizational values, the commitments entailed by those statements are delivered by individuals, who are prone to unacknowledged biases, such as the perception gap between what people say they value and what they think others hold most dear. For example, when Meredith Niles and colleagues surveyed faculty at 55 institutions, they found that academics value readership most when selecting where to publish their work ( Niles et al., 2019 ). But when asked how their peers decide to publish, a disconnect was revealed: most faculty members believe their colleagues make choices based on the prestige of the journal or publisher. Similar perception gaps are likely to be found when other performance proxies (such as grant funding and student satisfaction) are considered.

Bridging perception gaps requires courage and honesty within any institution – to break with the metrics game and create evaluation processes that are visibly infused with the organization's core values. To give one example, HHMI tries to advance basic biomedical research for the benefit of humanity by setting evaluation criteria that are focused on quality and impact. To increase transparency, these criteria are now published ( HHMI, 2019 ). As one element of the review, HHMI asks Investigators to "choose five of their most significant articles and provide a brief statement for each that describes the significance and impact of that contribution." It is worth noting that both published and preprint articles can be included. This emphasis on a handful of papers helps focus the review evaluation on the quality and impact of the Investigator's work.

Generic terms like 'world-class' or 'excellent' appear to provide standards for quality; however, they are so broad that they allow evaluators to apply their own definitions, creating room for bias.

Arguably, universities face a stiffer challenge here. Institutions striving to improve their research assessment practices will likely be casting anxious looks at what their competitors are up to. However, one of the hopeful lessons from the October meeting is that less courage should be required – and progress should be faster – if institutions come together to collaborate and establish a shared vision for the reform of research evaluation.

Finding conceptual clarity

Conceptual clarity in hiring, promotion, and tenure policies is another area for institutions to examine when aligning practices with values ( Hatch, 2019 ). Generic terms like 'world-class' or 'excellent' appear to provide standards for quality; however, they are so broad that they allow evaluators to apply their own definitions, creating room for bias. This is especially the case when, as is still likely, there is a lack of diversity in decision-making panels. The use of such descriptors can also perpetuate the Matthew Effect, a phenomenon in which resources accrue to those who are already well resourced. Moore et al., 2017 have critiqued the rhetoric of 'excellence' and propose instead focusing evaluation on more clearly defined concepts such as soundness and capacity-building. (See also Belcher and Palenberg, 2018 for a discussion of the many meanings of the words 'outputs', 'outcomes' and 'impacts' as applied to research in the field of international development).

Establishing standards

Institutions should also consider conceptual clarity when structuring the information requested from those applying for jobs, promotion, or funding. There have been some interesting innovations in recent years from institutions seeking to advance more holistic forms of researcher evaluation. UMC Utrecht, the Royal Society, the Dutch Research Council (NWO), and the Swiss National Science Foundation (SNSF) are also experimenting with structured narrative CV formats ( Benedictus et al., 2016 ; Gossink-Melenhorst, 2019 ; Royal Society, 2020 ; SNSF, 2020 ). These can be tailored to institutional needs and values. The concise but consistently formatted structuring of information in such CVs facilitates comparison between applicants and can provide a richer qualitative picture to complement more the quantitative aspects of academic contributions.

DORA worked with the Royal Society to collect feedback on its 'Resumé for Researchers' narrative CV format, where, for example, the author provides personal details (e.g., education, key qualification and relevant positions), a personal statement, plus answers to the following four questions: how have you contributed to the generation of knowledge?; how have you contributed to the development of individuals?; how have you contributed to the wider research community?; how have you contributed to broader society? ( The template also asks about career breaks and other factors "that might have affected your progression as a researcher"). The answers to these questions will obviously depend on the experience of the applicant but, as Athene Donald of Cambridge University has written: "The topics are broad enough that most people will be able to find something to say about each of them. Undoubtedly there is still plenty of scope for the cocky to hype their life story, but if they can only answer the first [question], and give no account of mentoring, outreach or conference organization, or can't explain why what they are doing is making a contribution to their peers or society, then they probably aren't 'excellent' after all" ( Donald, 2020 ).

It is too early to say if narrative CVs are having a significant impact, but according to the NWO their use has led to an increased consensus between external evaluators and to a more diverse group of researchers being selected for funding ( DORA, 2020 ).

Even though the imposition of structure promotes consistency, there is a confounding factor of reviewer subjectivity. At the meeting, participants identified a two-step strategy to reduce the impact of individual subjectivity on decision-making. First, evaluators should identify and agree on specific assessment criteria for all the desired capabilities. The faculty in the biology department at University of Richmond, for example, discuss the types of expertise, experience, and characteristics desired for a role before soliciting applications.

This lays the groundwork for the second step, which is to define the full range of performance standards for criteria to be used in the evaluation process. An example is the three-point rubric used by the Office for Faculty Equity and Welfare at University of California, Berkeley, which helps faculty to judge the commitment of applicants to advancing diversity, equity, and inclusion ( UC Berkeley, 2020 ). A strong applicant is one who "describes multiple activities in depth, with detailed information about both their role in the activities and the outcomes. Activities may span research, teaching and service, and could include applying their research skills or expertise to investigating diversity, equity and inclusion." A weaker candidate, on the other hand, is someone who provides "descriptions of activities that are brief, vague, or describe being involved only peripherally."

Recognizing collaborative contributions

Researcher evaluation is rightly preoccupied with the achievements of individuals, but increasingly, individual researchers are working within teams and collaborations. The average number of authors per paper has been increasing steadily since 1950 ( National Library of Medicine, 2020 ). Teamwork is essential to solve the most complex research and societal challenges, and is often mentioned as a core value in mission statements, but evaluating collaborative contributions and determining who did what remains challenging. In some disciplines, the order of authorship on a publication can signal how much an individual has contributed; but, as with other proxies, it is possible to end up relying more on assumptions than on information about actual contributions.

More robust approaches to the evaluation of team science are being introduced, with some aimed at behavior change. For example, the University of California Irvine has created guidance for researchers and evaluators on how team science should be described and assessed ( UC Irvine, 2019 ). In a separate development, led by a coalition of funders and universities, the Contributor Roles Taxonomy (CRediT) system ( https://credit.niso.org ), which provides more granular insight into individual contributions to published papers, is being adopted by many journal publishers. But new technological solutions are also needed. For scientific papers, it is envisioned that authorship credit may eventually be assigned at a figure level to identify who designed, performed, and analyzed specific experiments for a study. Rapid Science is also experimenting with an indicator to measure effective collaboration ( http://www.rapidscience.org/about/ ).

Communicate the vision on campus and externally

Although many individual researchers feel constrained by an incentive system over which they have little control, at the institutional level and beyond they can be informed about and involved in the critical re-examination of research assessment. This is crucial if policy changes are to take root, and can happen in different ways, during and after the deliberations of the working groups described above. For example, University College London (UCL) held campus-wide and departmental-level consultations in drafting and reviewing new policies on the responsible use of bibliometrics, part of broader moves to embrace open scholarship ( UCL, 2018 ; Ayris, 2020 ). The working group at Imperial College London organized a symposium to foster a larger conversation within and beyond the university about implementing its commitment to DORA ( Imperial College, 2018 ).

Other institutions and departments have organized interactive workshops or invited speakers who advocate fresh thinking on research evaluation. UMC Utrecht, one of the most energetic reformers of research assessment, hosted a series of town hall meetings to collect faculty and staff input before formalizing its new policies. It is also working with social scientists from Leiden University to monitor how researchers at UMC are responding to the changes. Though the work is yet to be completed, they have identified three broad types of response: i) some researchers have embraced change and see the positive potential of aligning assessment criteria with real world impact and the diversity of academic responsibilities; ii) some would prefer to defend a status quo that re-affirms the value of more traditional metrics; iii) some are concerned about the uncertainty that attends the new norms for their assessment inside and outside UMC ( Benedictus et al., 2019 ). This research serves to maintain a dialogue about change within the institution and will help to refine the content and implementation of research assessment practices. However, the changes have already empowered PhD students at UMC to reshape their own evaluation by bringing a new emphasis on research competencies and professional development to the assessment of their performance ( Algra et al., 2020 ).

We encourage institutions and departments to publish information about their research assessment policies and practices so that research staff can see what is expected of them and, in turn, hold their institutions to account.

The Berlin Institute of Health (BIH) has executed a similarly deep dive into its research culture. In 2017, as part of efforts to improve its research and research assessment practices, it established the QUEST (Quality-Ethics-Open Science-Translation) Center in and launched a programme of work that combined communication, new incentives and new tools to foster institutional culture change ( Strech et al., 2020 ). Moreover, a researcher applying for promotion at the Charité University Hospital, which is part of BIH, must answer questions about their contributions to science, reproducibility, open science, and team science, while applications for intramural funding are assessed on QUEST criteria that refer to robust research practices (such as strategies to reduce the risk of bias, and transparent reporting of methods and results). To help embed these practices independent QUEST officers attend hiring commissions and funding reviewers are required to give structured written feedback. Although the impact of these changes is still being evaluated, lessons already learned include the importance of creating a positive narrative centered on improving the value of BIH research and of combining strong leadership and tangible support with bottom-up engagement by researchers, clinicians, technicians, administrators, and students across the institute ( Strech et al., 2020 ).

Regardless of format, transparency in the communication of policy and practice is critical. We encourage institutions and departments to publish information about their research assessment policies and practices so that research staff can see what is expected of them and, in turn, hold their institutions to account. While transparency increases accountability, it has been argued that it may stifle creativity, particularly if revised policies and criteria are perceived as overly prescriptive. Such risks can be mitigated by dialogue and consultation, and we would advise institutions to emphasize the spirit, rather than the letter, of any guidance they publish.

Universities should be encouraged to share new policies and practices with one another. Research assessment reform is an iterative process, and institutions can learn from the successes and failures of others. Workable solutions may well have to be accommodated within the traditions and idiosyncrasies of different institutions. DORA is curating a collection of new practices in research assessment that institutions can use as a resource (see sfdora.org/goodpractices ), and is always interested to receive new submissions. Based on feedback from the meeting, one of us (AH) and Ruth Schmidt (Illinois Institute of Technology) have written a briefing note that helps researchers make the case for reform to their university leaders and helps institutions experiment with different ideas and approaches by pointing to five design principles for reform ( Hatch and Schmidt, 2020 ).

DORA is by no means the only organization grappling with the knotty problem of reforming research evaluation. The Wellcome Trust and the INORMS research evaluation group have both recently released guidance to help universities develop new policies and practices ( Wellcome, 2020b ; INORMS, 2020 ). Such developments are aligned with the momentum of the open research movement and the greater recognition by the academy of the need to address long-standing inequities and lack of diversity. Even with new tools, aligning research assessment policies and practices to an institution's values is going to take time. There is tension between the urgency of the situation and the need to listen to and understand the concerns of the community as new policies and practices are developed. Institutions and individuals will need to dedicate time and resources to establishing and maintaining new policies and practices if academia is to succeed in its oft-stated mission of making the world a better place. DORA and its partners are committed to supporting the academic community throughout this process.

DORA receives financial support from eLife, and an eLife employee (Stuart King) is a member of the DORA steering committee.

  • Palenberg M
  • Google Scholar
  • Benedictus R
  • Ferguson MW
  • Zuijderwijk J
  • Broniatowski DA
  • Gossink-Melenhorst K
  • Imperial College
  • McKiernan EC
  • Schimanski LA
  • Muñoz Nieves C
  • O'Donnell DP
  • Pattinson D
  • National Library of Medicine
  • Royal Society
  • Weissgerber T
  • QUEST Group
  • UC Berkeley

Author details

Anna Hatch is the program director at DORA, Rockville, United States

For correspondence

Competing interests.

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Stephen Curry is Assistant Provost (Equality, Diversity & Inclusion) and Professor of Structural Biology at Imperial College, London, UK. He is also chair of the DORA steering committee

No external funding was received for this work.

Acknowledgements

We thank the attendees at the meeting for robust and thoughtful discussions about ways to improve research assessment. We are extremely grateful to Boyana Konforti for her keen insights and feedback throughout the writing process. Thanks also go to Bodo Stern, Erika Shugart, and Caitlin Schrein for very helpful comments, and to Rinze Benedictus, Kasper Gossink, Hans de Jonge, Ndaja Gmelch, Miriam Kip, and Ulrich Dirnagl for sharing information about interventions to improve research assessment practices at their organizations.

Publication history

  • Received: May 7, 2020
  • Accepted: August 6, 2020
  • Version of Record published: August 12, 2020 (version 1)

© 2020, Hatch and Curry

This article is distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use and redistribution provided that the original author and source are credited.

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Culture Change Research Paper

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What is culture change? In a way, the phrase itself is problematic; after all, culture was formulated as a scientific concept partly for the very reason that customs seemed resistant to change—at least compared with the confusing blur of particular people and events traditionally studied by historians (Tylor, 1871/1924, p. 5). Indeed, some anthropologists have tried to analyze cultures as if they did not change at all; such approaches, however, seem ever less relevant in the rapidly globalizing world of the 20th century.

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In the phrase “culture change,” change has its usual meaning; culture, however, is being used in a sense technical enough to need a bit more discussion here at the outset. Culture, as classically defined by Edward B. Tylor in 1871, refers to “that complex whole which includes knowledge, belief, art, morals, law, custom, and any other capabilities and habits acquired by man as a member of society” (1871/1924, p. 1). Once we realize that by the word “art” Tylor meant all the artifacts customarily made and used by a society, we see that this is a broad definition indeed: It includes the customary things with which people surround themselves, the customary ways they interact with one another behaviorally, and the ideas that are more or less shared among them.

There are anthropologists, it should be said, who consider culture to be things that only an individual can acquire by virtue of being a member of society. One problem with this is that it excludes features that inherently characterize groups rather than individuals—some of which certainly would seem to be fundamental features of a society’s way of life, such as economic inequality, an elaborate division of labor, or (group) religious ritual.

Some anthropologists think of culture not only as an acquisition of individuals but also as a particular kind of individual acquisition, namely, mental. Culture, for them, is strictly in our heads. From their standpoint, neither the automobile nor the computer, say, would be part of American culture in the early 21st century; rather, only the underlying ideas of which the things themselves (they maintain) are realizations deserve to be considered culture. This, however, makes culture difficult to study empirically by making it outwardly unobservable.

Defining culture as strictly mental also encourages an oversimplified and misleading conception of culture change. Anthropologists who think of culture as essentially mental tend to think of culture change as essentially due to new ideas. This focus distracts our attention from, if it does not quite deny, three key points about culture change. First, what ideas are “thinkable” depends partly on existing cultural arrangements. Ideas do not really come “out of the blue”; there is cultural wisdom, then, in the scriptural claim that there is “nothing new under the Sun”—nothing totally new at least. Second, new ideas are by no means sufficient in themselves to bring about culture change. The greatest idea in the world must somehow be acted on before it has any chance to change culture. Ideas that remain trapped in their thinkers’ heads, issuing in neither new behaviors nor new artifacts, are of no cultural consequence whatever. Third, behavioral or artifactual consequences are also insufficient for culture change. These consequences must be greeted by significant social acceptance; and this, like the occurrence of the new ideas in the first place, depends to some degree on existing cultural arrangements.

In any case, when the subject is culture change, it seems that anthropologists (and journalists) today usually use—whether they admit it or not—a more general definition along Tylor’s lines; and this appears to have been true in the past as well. For present purposes, then, the constituents of culture are not only ideas about things but also about the things themselves—objectively observable artifacts and behaviors. By artifactual is meant the world around us insofar as it is built or manufactured by humans: T-shirts and tuxedos, furniture and appliances and buildings, cornfields and computers, automobiles and highways, pencils and power plants, cell phones, baseball bats, factories, and baptismal fonts. By behavioral is meant the observable motion of our bodies through space, usually oriented to the artifactual world and/or literally manipulating artifacts. By ideational is meant everything that goes on in our heads: thoughts about artifacts and behaviors (of one another and ourselves), about thoughts (again, of one another and ourselves) and even thought itself, and about the rest of the universe. (Feelings, which also may be said to go on in our heads, are important in social interaction and are influenced by culture; they are not, however, properly considered as themselves constituents of culture.) Because this trichotomy is essential in understanding a current approach to culture change, we shall return to it after examining past approaches.

Past Approaches to Culture Changes

Although its roots naturally lie deeper in the past, anthropology took shape as a scholarly discipline in the 19th century. From the late 15th century on, exploration and colonization—led by Spain, Portugal, the Netherlands, France, and Great Britain—had produced a large and growing body of information about how different were the customs in the various parts of the world. Much of this consisted of reports by explorers and missionaries; systematic anthropological fieldwork was an achievement largely of the 20th century. Not entirely wide of the mark, then, is the image of the so-called 19th-century evolutionists as scholars in their studies poring over fanciful accounts of exotic peoples in faraway places. It sounds a rather far cry from scientists in their laboratories conducting meticulous experiments; indeed, critics later would charge that it in fact had been nothing more than “armchair speculation.” Yet real progress was made. Judicious handling of the material, after all, could go some way in separating truth from falsehood. Tylor pointed out that when two or more visitors independently of each other had reported the same custom in the same place, it was unlikely to be a fabrication—especially if the custom seemed odd.

In terms of theory, Tylor and others found themselves facing degenerationism. Inspired by the biblical book of Genesis, the idea was that all humans had practiced agriculture and achieved a modest level of civilization not too long after creation itself. Then, with the dispersion of people throughout the world, some of them degenerated to lower levels (some forgetting even how to grow food), while others rose to higher levels. Degenerationism, one might say, was the first grand theory of culture change. Foremost among scholars putting it to rest was Edward B. Tylor. Using his extensive knowledge of the anthropological evidence that already had accumulated by around 1865, Tylor showed that “high” cultures quite certainly had originated in a state resembling that of the “low” cultures still observable in some parts of the world and that there was no evidence that any of the latter had come into being by degeneration from a higher condition of culture (Tylor 1865/1964).

The 19th Century: Beyond Degeneration’s Defeat

Strictly speaking, the defeat of degenerationism was perhaps more a step in separating science from religion than a step in science itself. Quite different in this respect were the debate over the relative importance of diffusion and independent invention and attempts to characterize the cultural past as a series of stages.

Independent Invention and Diffusion

Tylor and other leading 19th-century evolutionists were united against degenerationism but divided on this question. The issue arose when among the glaring differences between human cultures, striking similarities also appeared. Boomerangs, for example, were reported not only for Australia but also for regions of India and Egypt. How was this distribution to be explained? Had this weapon been invented only once, then spread to the other two regions, or had it been invented independently three times? Those inclined to stress the importance of diffusion would prefer the former explanation, claiming that it is much easier for humans to copy something than to invent it. Those favoring independent invention would prefer the latter explanation, claiming that the human mind is sufficiently alike everywhere (“psychic unity”) that it will tend, when faced with similar problems under similar conditions, to produce similar solutions.

Toward the extremes were two German scholars: Adolf Bastian argued that independent invention should be presumed unless strong evidence for diffusion could be produced, while Henry Balfour argued that diffusion should be presumed until overwhelming evidence for independent invention was put forth (Lowie, 1937). Most of the 19th-century evolutionists were less extreme. In the case of the boomerang, for instance, they would by no means rule out the possibility that it had originated independently in two of the regions and diffused from one of these to the third region.

Associated rather closely with a stress on independent development was the idea that human culture everywhere tended to advance through broadly similar stages. The most famous formulation was Lewis Henry Morgan’s (1877/1985) sequence, savagery, barbarism, civilization. (Morgan subdivided the first two of these stages into lower, middle, and upper for a total of seven stages.) While debates over independent invention versus diffusion often centered on particular cultural features (as in the boomerang example), the concept of a stage involved a vast pattern of cultural features—that is, an entire kind of cultural system. Still, the defining of such stages did require reference to at least some particular features; and Morgan chose, for this purpose, mainly items of material technology. The transition from lower savagery to middle savagery, for example, was marked in part by the use of fire and from upper savagery to lower barbarism by the invention of pottery. Civilization was reached, in Morgan’s view, not with a technological achievement but rather with the development of a phonetic alphabet. His reliance on primarily technological markers helped make the stages more objectively identifiable and was quite convenient for archaeologists, who after all can recover neither behavioral nor ideational evidence but material evidence alone. Though the terms savage and barbarian sound ethnocentric today, anthropology still recognizes general stages through which culture change has tended to pass; and they are not entirely different from Morgan’s. Pottery, for example, being heavy and fragile, is not highly functional for the mobile way of life characteristic of foragers; Morgan’s use of pottery to mark the end of savagery therefore makes this stage broadly comparable to the long period (evidently around 99.8% of our evolutionary past) before settling into villages and growing food— what is today termed the hunting-gathering era, or Paleolithic (Old Stone Age) (Harris, 1968, pp. 185–186).

Some 19th-century evolutionists proposed stage sequences of other kinds. Herbert Spencer (1897) proposed that human political culture had advanced through four progressive stages: simple, compound, doubly compound, and trebly compound. These stages resemble more recent sequences such as band, tribe, chiefdom, and state (Service, 1962) and village, chiefdom, state, and empire (Carneiro, 2003). More important than the specific stages delineated, however, are these two facts stressed by Spencer: First, political evolution does not occur by the simple increase in population of a small society (a band or village) until it has became a large one (a state or empire); rather, it occurs by the combining of smaller societies. Second, this combining is stepwise, with little room for skipping steps. That is, we know of no cases in which bands or villages have combined directly into states or empires; rather, they combine into chiefdoms, which then may (or may not) combine into states. Similarly, chiefdoms do not combine directly into empires but into states, which then may (or may not) combine into empires.

Political evolution thus has a unilinear quality: Any society reaching a later stage will have done so by having passed thorough earlier stages. This assuredly does not mean that all societies at an earlier stage will advance to a later stage! In the human past, there must have been, after all, vastly more bands and villages that never helped compose chiefdoms than those that did, far more chiefdoms that never helped compose states than those that did, and many more states that never helped compose empires than those that did. The unilinearity of political evolution, with respect to a given society, we might well say, is not predictive but retrodictive: Though we cannot be sure a given small society will ever become part of a larger one, we can be sure a large society originally became large by the compounding of smaller ones. Spencer’s picture of political evolution as having progressed by the stepwise unification of units (mainly through military conquest) remains influential today (Carneiro, 2003).

Other stage sequences have not held up so well; their main role proved to be stimulating research that led to their own rejection. The greatest is J. J. Bachoffen’s (Partenheimer, 1861/2007) set of stages based on gender relations. He argued that humans originally lived in a state of unregulated sexual promiscuity. Females, finding themselves too much at the mercy of the physically stronger males, managed somehow to gain control and institute religion and marriage; but the “male principle” ultimately proved even higher and purer, and the stage of matriarchal culture gave way to patriarchal culture. By around 1900, this theory of culture change as an epic three-stage battle between the sexes had proven untenable: It had been based on conflating matrilineality (tracing family lines through females) and matriarchy (sociopolitical rule by females) and on Bachoffen’s having relied heavily on Greco-Roman myths to reconstruct the past. Still, the idea that humans had passed through a matriarchal stage had been embraced by the leading cultural evolutionists of the late 19th century: Edward B. Tylor, Herbert Spencer, and Lewis Henry Morgan.

Errors such as this rather glaring one, a growing suspicion that delineating evolutionary stages was inherently ethnocentric anyway, the misconception that the evolutionists had argued for a kind of rigid unilinearity in all aspects of culture change, and probably increasing contact between societies thanks to dramatically improved means of transportation and communication were among the forces that moved 20th-century anthropology to approach culture change in new ways.

The Early 20th Century

Dissatisfaction with the 19th-century orientation to culture change appeared earlier in the United States than in Europe. Sometimes, it presented itself as choosing a new battle instead of taking sides in the old one. In a highly influential paper of 1920, Franz Boas wrote as follows:

American scholars are primarily interested in the dynamic phenomena of cultural change, and try to elucidate cultural history by the application of the results of their studies. . . . They relegate the solution of the ultimate question of the relative importance of parallelism of cultural development in distant areas, as against worldwide diffusion . . . to a future time when the actual conditions of cultural change are better known. (p. 314)

This sounds evenhanded enough; but in fact, the concept of independent invention (or as Boas here calls it, parallel development) was intimately bound up with that of cultural evolutionism. Part and parcel of discrediting the latter, then, was a growing stress on contact between societies as key to understanding culture change. Boas (1920) went on in the very same paper to admit this stress; but he carefully ascribed it to methodological considerations rather than to any animosity toward cultural evolutionism: “It is much easier to prove dissemination than to follow up developments due to inner forces, and the data for such a study are obtained with much greater difficulty” (p. 315). Boas seems here to have been thinking of the contrast between directly observing how cultures vary over space and using archaeological evidence—laborious to obtain and relatively fragmentary at best—to try to piece together how a culture has changed over time.

By 1924, Boas seems to have decided that more than methodological considerations were involved. A paper titled “Evolution or Diffusion?” argued in effect that when societies appear to be culturally mixed, intermediate, or transitional, this nearly always should be taken as evidence of diffusion of traits from less culturally mixed societies, not as evidence of evolution from an earlier to a later condition of culture; to exemplify the danger of the evolutionary assumption, he discussed the old—and evidently misguided—interpretation of matrilineal customs as indicating transitionality between supposed matriarchal and patriarchal stages.

The Diversity of Diffusion

As part of the general reaction of the anthropological world against cultural evolutionism, then, culture change came to be thought of by anthropologists as primarily a matter of diffusion. In one of the more famous passages ever penned by an anthropologist, Ralph Linton (1936) wrote of how a typical adult male in the United States (of the 1930s) started his day. The flavor—if not the full effect—of this virtuoso performance can be appreciated from the final paragraph:

When our friend has finished eating he settles back to smoke, an American Indian habit, consuming a plant domesticated in Brazil in either a pipe, derived from the Indians of Virginia, or a cigarette, derived from Mexico. If he is hardy enough he many even attempt a cigar, transmitted to us from the Antilles by way of Spain. While smoking he reads the news of the day, imprinted in characters invented by the ancient Semites upon a material invented in China by a process invented in Germany. As he absorbs the accounts of foreign troubles he will, if he is a good conservative citizen, thank a Hebrew deity in an IndoEuropean language that he is 100 percent American. (p. 327)

It is one thing to think of a culture as a product of diffusion; it is another to think about the process of diffusion itself. One can usefully distinguish four forms: direct contact, immigrant diffusion, intermediate contact, and stimulus diffusion.

Direct contact describes the case in which a cultural feature spreads from one society to adjacent societies and from those to other more distant ones. The basic type of medieval castle (“motte-and-bailey,” in which the structure stands atop a mound [the motte] surrounded by a ditch, surrounded in turn by a palisaded courtyard [the bailey]), for example, originated in northern France in the 10th century and gradually spread through most of western Europe. On a larger geographical scale, paper, having originated centuries before in China, underwent diffusion from the 8th century through the 15th to the Arab world and then to Europe. Three recurrent steps (in this as in other cases where the feature is a commodity) were (1) importation of small amounts as a luxury item, (2) importation of larger amounts as the item became widely used, followed eventually by (3) internal manufacture supplementing or replacing importation.

A particularly important way that diffusion occurs, often overlooked, is along with the expansion or migration of populations. One example of this immigration diffusion is the availability in American cities of “ethnic” options when people are choosing a restaurant. Very often this availability reflects the immigration of people who have opened restaurants serving the cuisine of the nations from which they have come. Another example of immigrant diffusion would be the enormous number of English cultural features—implements, customs, and beliefs (and the language)—that came to North America as a matter of course along with the colonists themselves. Immigrant migration is easily overlooked perhaps because the word “diffusion” conjures up an image of a cultural feature spreading mainly between people rather than mainly with them. In fact, without historical records it is often difficult to tell whether a cultural feature long ago moved across a resident population or simply along with an expanding one; the spread of motte-and-bailey castles, for example, seems to have been more or less closely associated with the geographic expansion of the ethnic group known as the Normans.

Ethnic foods nicely exemplify another important point. Though some food critics may complain about, say, the amount of beef in our “Mexican” food, the sugary sauces in many “Chinese” dishes, or the quantities of sour cream in our “Japanese” sushi, such changes seem to appeal to the American palate (so to speak). And for cultural features to undergo such modification as they become accepted in a new social environment is more the rule than the exception when it comes to diffusion. Of course, this often involves cultural features more important than details of cuisine; a good example here would be the changes undergone by capitalism as it was culturally incorporated by Japan after the Second World War (Okumura, 2000).

Intermediate contact refers to the spread of cultural features by such agents as explorers, sailors, traders, or missionaries. This kind of diffusion reflects the fact that by the time societies have grown large enough to have an elaborate division of labor, some occupational specialties routinely position individuals to serve as diffusers of cultural elements. In the 1500s, for example, sailors, having gotten tobacco (and the practice of smoking it) in the New World, introduced it into the great port cities of Europe. Meanwhile, many European things were being introduced into the New World—notably, horses by Spanish explorers and Christianity by the missionaries. (The first Catholic missionaries arrived within a few years of Columbus’s initial voyage.) Another famous example, very important in the evolution of science and technology, was the diffusion of India’s decimal system (along with “Arabic numerals”) into Europe by way of a small number of books imported from the Middle East. Though written before CE 1000, these books’ influence was not widely felt in Europe— where Roman numerals remained customary—until the advent of the printing press centuries later.

Stimulus diffusion refers to situations in which an idea from outside triggers a society to develop and incorporate something new into its culture. A classic case is the development of writing among the Cherokee stimulated by a man named Sequoya from his observations of Europeans. Though the system used some symbols from the English alphabet, they represented not individual sounds but entire syllables; the writing system, that is, was syllabic rather than alphabetic. A “mere” idea from outside had sufficed to inspire a novel cultural development. But some degree of modification in a new environment is, as we have seen, a common aspect of diffusion; therefore, stimulus diffusion can be understood essentially as taking this aspect to an extreme.

Competition among peoples has given rise to important examples of stimulus diffusion. The ancient Hittites, first to develop iron chariots for war, tried to keep iron smelting a military secret and of course were not about to export iron chariots to surrounding societies; but eventually, the other societies developed (or otherwise acquired) them on their own. Fear of being conquered is a powerful stimulus! Some 4,000 years later, biological weapons, space programs, and nuclear power often have been developed more by stimulus diffusion than by direct diffusion though it seems likely that indirect contact by way of espionage has played no small role as well.

Intrasocietal Diffusion

In anthropology, diffusion traditionally has been thought of as between social groups, especially between entire societies—typically nations. This form of diffusion may be termed intersocietal; as such, it contrasts with intrasocietal diffusion. Intrasocietal diffusion refers to the spread of an innovation within one group rather than from one group to another. Disciplines such as economics and sociology have given more attention to intrasocietal diffusion than have anthropologists. One of the most interesting things to emerge is a characteristic S-shaped curve describing the extent of an innovation’s adoption with respect to time. Some authorities consider this curve to result from innovativeness being a normally distributed trait (that is, a trait fitting the “bell curve”) within human populations. An innovation diffuses slowly at first because early-adopter types are fairly rare, gains “speed” as less atypical people adopt it, and levels off as the later-adopting, relatively rare “laggards” finally adopt it. There is evidence that early adopters tend to be higher in terms of education and income than do later ones (Rogers, 2003). The anthropologist H. G. Barnett (1953) suggested several “ideal types” of innovator or early adopter: the dissident, who is simply a nonconforming kind of individual; the indifferent, who is for some reason—perhaps merely by virtue of still being young—not strongly committed to conventionality; the disaffected, for whom certain experiences have loosened the commitment to conventionality (e.g., leaving home to go to college); and the resentful, embittered by having failed to achieve success in conventional terms. The final three of these would seem somewhat age graded in the sense that young, middle-aged, and older individuals, respectively, would be most likely to fit the description. By reminding us that people of all ages can have reasons for desiring change, Barnett’s typology perhaps helps account for the otherwise surprising failure to find a general tendency for innovators to be relatively young.

The Limits of Diffusionism

While diffusion has been and remains an important process of culture change, it can be overemphasized. Its easy comprehensibility may help explain the popularity, with the public, of fanciful images of lost continents or intercontinental raft voyages. In a somewhat more scholarly vein, the English biologist G. Eliot Smith (1928) tried to show that civilization had originated only once, in ancient Egypt; significant signs of civilization anywhere else in the world he attributed to diffusion from the fertile floodplain of the Nile. The German priest Wilhelm Schmidt (1939) attempted to account for particular cultures as the intermingling of customs resulting from the overlapping of cultural “circles” radiating from a small number of centers. Another feature of diffusionism was its almost studied neglect of the systemic aspect of culture as if a culture were not so much a system of interrelated elements as a mere collection of juxtaposed borrowings—a “thing of shreds and patches” (cf. Harris, 1968, pp. 353–354).

There is at least one respect in which it is instructive to think of culture as a collection or stock of elements. As early as 1877, Lewis Henry Morgan suggested that culture change naturally tends to accelerate over time because any element of “knowledge gained” has the potential to become a “factor in further acquisitions” (1877/1985, p. 38). Innovations, that is, often involve combinations of preexisting elements; therefore, the more cultural “material” there is available, the more innovations there will be. Culture, then, is somewhat like a snowball: The more of it there is, the faster it grows. It is important to remember, however, that this “growth” should not be presumed to constitute progress, at least morally, and that this snowballing tendency does not mean that “culture changes itself ” since the innovations involved in the process are not themselves cultural unless and until they have been incorporated into a group’s way of life.

Acculturationism and Its Limits

Professional anthropologists, of whom there were by now a growing number (due especially to Boas’s efforts at Columbia University), tended to be skeptical of such extremes; they were more bothered by the observable facts that diffusion was not inevitable when cultures came into contact (whether indirect or direct) and that it was, in any case, only one of several possible results of such contact. The emphasis accordingly shifted from diffusion to acculturation, authoritatively defined as “those phenomena which result when groups of individuals giving different cultures come into continuous firsthand contact, with subsequent changes in the original cultural patterns of either or both groups” (Redfield, Linton, & Herskovits, 1936, p. 149).

This broadening of emphasis was to some extent a matter of convenience for American graduate students studying native peoples since these peoples by then had been long subject to the shattering effects of the Euro-American expansion into the New World. But the broadening also redirected attention from cultural elements as such to situations (and even particular events) on the one hand and to groups and individuals and their reactions on the other. Thus, studying acculturation so defined might entail as much attention to history and psychology as to culture!

As a significant example of how the study of acculturation leads to psychological issues, we might begin by observing that people seem in most times and places to have found it easy to assume that their own culture or subculture is somehow essentially better than most or all other ways of life. Since this interpretation places one’s own culture at the center of the moral universe, it is termed ethnocentrism. Ethnocentrism ordinarily brings with it judgmental attitudes; sometimes, it even brings feelings of disgust. Presumably, all humans have ethnocentric tendencies, unconscious if not conscious; these perhaps stem from the fact that each of us is necessarily enculturated from infancy on in some particular way of life rather than in all possible ways.

Scientists, including anthropologists, generally agree in defining culture as a social rather than a genetic acquisition; and they generally regard ethnocentrism, whatever else it may be, as a barrier to the successful study of other cultures or subcultures. Arrogance, judgmentalism, and disgust reduce one’s chances of gaining a more accurate and deeper understanding of other ways of life. To counteract their own ethnocentric tendencies, anthropologists adopt the assumption that no culture or subculture is basically better or worse than any other. This assumption is known as cultural relativism. In reference to culture change, ethnocentrism would be expected to create resistance to diffusion. Other things being equal, unfamiliar cultural elements from outside might appear undesirable or threatening simply because they are unfamiliar. There also may be outright hostility toward the out-group itself that would foster a desire to be as different from them culturally as possible. Thus it is that acculturation phenomena include not only diffusion but also intentional resistance to diffusion (Loeb & Devereux, 1943).

Much depends, however, on the attitude of the borrowing society toward the lending one. Although it is common for in-groups to look down on out-groups and their ways, it can happen that an in-group actually looks up to an outgroup. Prestige attaching to an out-group of course would facilitate adoption of its cultural elements by an in-group, thus promoting diffusion.

It is sometimes argued that acculturation studies were ideologically tainted by denying or glossing over the effects of exploitation on indigenous peoples and cultures. It is important to recognize, however, that many anthropologists were not only acutely aware of this danger but also actually engaged in lively, open debate about it; an excellent example is the exchange between Victor Barnouw, Bernard J. James, and Harold Hickerson about Chippewa personality (Barnouw, 1979).

The Mid-20th Century

The limitations of acculturation as a focus for studying culture change were sufficiently grave that by the time the concept was achieving clear formulation, some younger anthropologists already were heading in a different direction—a direction reasserting the importance of focusing on culture itself rather than on psychology or history and on culture as a system of interacting elements. Julian Steward (1955) stressed that a culture’s first order of business, so to speak, was to adapt a human group successfully enough to its environment for the society to survive; he paid special attention to the way in which specific environments called forth specific kinds of cultural adaptations. Leslie A. White (1949) shared Steward’s stress on culture as a survival mechanism but was more interested than Steward in the trajectory of human culture as a whole—a contrast sometimes connoted by “Culture” compared with “cultures.” The notion of a single human culture may seem odd. Yet it seems likely that no human group is or ever has been completely isolated from all others; so if humans are connected, even if only indirectly by patterned interaction, it makes sense to consider us a single social group; in which case, the concept of a or the socially acquired human way of life, no matter how diverse, finds justification. White argued forcibly that the most important innovations in cultural evolution have been those that led to greater control and consumption of energy; indeed, he wrote of culture as being at heart an energy-capturing system. White and Steward often were termed “neoevolutionists” because their work in some respects constituted a return to the search for scientific laws that had inspired the 19th-century evolutionists.

Leslie White (1949) and Julian Steward (1955) engaged in vigorous debates that tended to enlarge on their differences and minimize their similarities. This situation was to some extent clarified when Marshall Sahlins (1960) proposed calling Steward’s focus “specific cultural evolution” and White’s, “general cultural evolution.” In a highly influential book, The Rise of Anthropological Theory, Marvin Harris (1968) argued convincingly that Steward and White actually had in common what was important and fundamental and new: not that they both believed culture evolved but that they both believed the best way to analyze culture was to begin with the tools and techniques through which people met their everyday survival needs in the environment they inhabited. Changes in (or differences of) environment would mean technological change; technological change would bring change in how people interacted and even in the kinds of groups they lived in; and these changes would trigger changes in how people thought about the world, one another, and themselves. To understand culture change, these materialists taught, we need to acknowledge the primacy of the technological linkage between people and environment; changes in that linkage will be the most potent innovations of all.

Contemporary Approaches to Culture Change

For a time in the years leading up to 1970, it appeared that the anthropological study of culture (and culture change) might be unified under the evolutionist/materialist banner. The approach was especially appealing to archaeologists (Steward had begun his career as one); the artifactual evidence to which they have direct access is material indeed, so according theoretical priority to technology appealed to them. And indeed, the evolutionist-materialist approach, looking at cultures as adaptive systems, has vanished from the anthropological landscape. But something quite different was also astir, especially among cultural and linguistic anthropologists affected by certain countercultural trends of the 1960s. Thus, we may think of two broad contemporary approaches to culture change: the new acculturationism and the continuation of evolutionism/materialism.

The New Acculturationism

Published only a year after The Rise of Anthropological Theory was a very different book indeed: an edited volume titled Reinventing Anthropology (Hymes, 1969). Here was revealed a profound skepticism toward and even indictment of the effort to study human culture scientifically. Science, reason, and anthropology (and anthropologists) were associated not with the liberation of human minds but with the exploitation of colonized populations.

Of special importance for the study of culture change was the idea that cultural anthropologists were mistaken about what they had been studying. Though they thought the hunting-gathering people, the pastoralists, the villagers, or the peasants they observed provided glimpses into more ancient ways of life, what they principally offered, it is proposed, are insights into the effects of colonialism and capitalist exploitation. In a sense, the argument is that we have always been essentially studying acculturation, whether we knew and admitted it or not. In part, this is because the anthropologist herself or himself is—and must to some extent remain—a stranger; and whatever he or she writes is not so much an objective picture of the observed by an observer but a subjective account of an interaction between the two.

We noted that diffusionism was taken to its greatest extremes not by professional anthropologists but by a biologist and a priest; similarly, the extreme of this new acculturationism was reached by a journalist, Patrick Tierney (2000), who argued that it was anthropologists themselves (along with journalists) who were responsible for the devastation—by the outside world—of the Amazon and its native peoples. Anthropologists have argued, more modestly, that in past studies the effects of contact (colonization and exploitation) sometimes have been seriously underestimated (e.g., Ferguson & Whitehead, 1992); and many have been at pains, in their own recent work, to highlight rather than ignore the inequality built into the contact situations they study (and in which they participate). Sherry Ortner (1999), for example, introduces her study of mountain climbers and their Sherpa guides by noting that one group has “more money and power than the other.” She goes on to suggest that whether one is dealing with a colonial, postcolonial, or globalizing context, “what is at issue are the ways in which power and meaning are deployed and negotiated, expressed and transformed, as people confront one another within the frameworks of differing agendas” (p. 17). Greater sensitivity to such issues is an important development. At the same time, declaring that nothing about earlier human cultures can be learned by studying recent band, pastoral, and village peoples seems at least as extreme and implausible as considering them to be perfectly preserved “fossils” of those cultures.

Evolutionism-Materialism

Evolutionism-materialism continues to see cultures as adaptive systems and to see this as the key to understanding culture change. There have been ongoing efforts, however, to demarcate subsytems of the system and to interpret culture change as resulting from interaction of these subsystems.

A system is a set of related parts such that change in one part can bring about change in another part. Is culture a system? Here is an example suggesting that it is. Prior to around 1850, most American families lived on farms. On the farm, children were an economic asset because they enlarged the “work force” for what was essentially a family-owned, family-operated business. Children became economically productive at an early age by doing chores such as gathering eggs and feeding animals and of course became more valuable as they matured. One’s children also provided one’s care in old age. Urban life, however, converted children from economic assets to economic liabilities; to feed, clothe, and educate each one takes a lot of money. Parenting of course has its rewards in urban society, but those rewards do not usually include economic profitability! As a result, large families and therefore large households were far more common 2 centuries ago than they are today. On the farm, children commonly grew up alongside their parents and several siblings and sometimes grandparents, too. Today, households on the average are much smaller. One- or two-children households are common, and indeed, about one fourth of American households contain only one person. Thus, the shift in what people do for a living has brought dramatic changes in how children grow up and in home life more generally. Yet one can think of changes in one part of culture that have little or no apparent effect on other parts of culture. In recent decades, for example, the technology for recording and listening to music has changed rapidly from vinyl records to tapes to compact discs; yet it is difficult to think of significant changes in our way of life that have been triggered by these changes. Another contrast of this kind is the transformative effect that the acquiring of horses famously had on the cultures of the American Great Plains compared with the relatively modest effect that acquiring tobacco had on the cultures of Europe. Such contrasts raise the possibility that there are certain kinds of culture changes that tend to be more potent than other kinds in triggering further cultural changes. In other words, considering a culture as a partially integrated system, are some subsystems more determinative than others of the characteristics of the system as a whole? If so, which one or ones?

Several divisions of cultural systems into subsystems have been suggested; especially important and illuminating has been a division into three subsystems designated most simply as technology, social organization, and ideology. Karl Marx (1867/1906), who usually distinguished only two subsystems called base and superstructure, suggested this one in a footnote to Chapter 15 of Capital:

Technology discloses man’s mode of dealing with Nature, the process of production by which he sustains his life, and thereby also lays bare the mode of formation of his social relations, and of the mental conceptions that flow from them. (p. 406, note 2)

Note that “technology” here does not refer to everything to which we might commonly apply the term such as the latest leisure devices for watching movies or listening to music but to artifacts and processes more essential to our survival: the technology involved in “dealing with Nature” so as to sustain the lives of human beings—that is, the means by which food is produced and by which raw materials are extracted and made into the things we need and want. Especially fundamental is the tapping of energy sources: getting food to fuel our own bodies, gathering and burning firewood, domesticating plants and animals, mining and burning coal, drilling and burning oil, trapping sun or wind, and even the controlled splitting of atoms (White, 1949).

Note that this seminal sentence not only suggests three subsystems but also places technology in the “driver’s seat” or in the role of what is sometimes called, in analogy to energy production, the “prime mover.” This idea, that how people use the physical environment in order to survive is basic to understanding entire cultural systems, is often known as the principle of infrastructural primacy as suggested by Marvin Harris in his extensive writings on the subject.

But technology includes also the means we use for literally moving ourselves from place to place physically and for staying in touch; thus, there are technologies of transportation and communication. Technology includes, too, some of the means we apply directly to ourselves as physical beings to foster health and control reproduction; there is, then, such a thing as medical technology. And of course when societies pursue their own interests—at least as defined by leaders—as over against those of other societies, they may resort to the weapons of war and hence the importance of military technology.

We might be tempted to think of technology as essentially artifactual; but note that technology here refers not only to the kinds of artifacts employed as societies go about the business of surviving but also to the behavior patterns required for making and using the artifacts involved: it was not only just stone tools long ago, for example, but also the ways of making and using them; not only just the food—then or now—but also the ways of finding or growing it; not only just the oil drills but also the ways of finding, drilling, and refining the oil.

A complementary point must be stressed regarding social organization: Though we might be tempted to think of it as entirely behavioral (consisting of the patterned ways people interact with one another), “social organization” nearly always takes place in a more or less humanaltered (artifactual) environment and often directly involves artifacts, whether a frisbee thrown between friends, the money exchanged in a cash transaction, or the paraphernalia used in a church service. Admittedly, we might say that technology has a kind of artifactual “focus,” social organization a behavioral one; but as cultural subsystems, both technology and social organization are simultaneously artifactual and behavioral.

The situation is different with ideology. Widely shared ideas and beliefs can be associated to a certain extent with artifacts in the form of such documents as constitutions or holy books; but so long as we are thinking of behavior in physical rather than mental terms, the ideological subsystem is inherently nonbehavioral. This subsystem is best thought of as essentially neither artifactual nor behavioral but ideational—though it certainly includes ideas about artifacts and behaviors. (The idea that cars have four wheels is an obvious example of the former, that people should treat others as they would like to be treated of the latter.) It is important to remember, however, that as a subsystem of culture, it includes not any and all ideas but only those we would be willing to say have become part of a way of life—that is, that have undergone cultural incorporation.

At first glance, then, the trichotomy of technology, social organization, and ideology sounds rather like that of artifacts, behaviors, and ideas; it turns out, however, that the trichotomy of artifacts, behaviors, and ideas, helpful as it is for thinking about innovations and about the kinds of things that constitute culture, differs quite significantly from this new trichotomy. We are thinking now not so much about the kinds of elements that compose a system as about the kinds of subsystems whose interaction constitutes the functioning of the system. A biochemical analogy may he helpful: The constituents of a single-celled organism are atoms and molecules, but understanding the organism as a functioning system requires identification of major subsystems, such as the cell wall, the nucleus, and the cytoplasm. Serving different purposes, the classifications are complementary rather than contradictory. (The terms technology, social organization, and ideology as used largely this way are from Gerhard Lenski [1970], which closely resemble Leslie A. White’s [1949] technological, sociological, and ideological systems; Marvin Harris [1979] coined infrastructure-structure-superstructure while I and my coauthors have offered interfaces-interactions-interpretations [Graber, Skelton, Rowlett, Kephart, & Brown, 2000].)

Among the various contexts in which customary social organization expresses itself (e.g., economic, political, domestic, and ritual), political organization holds a place of special interest with regard to culture change. For one thing, political leaders in large societies can legislate— and have legislated—programs aimed at making individuals or groups who differ culturally from the wider society “fit in.” Such programs, often involving reservations and/or missions and schools for educating children and young people on a nonvoluntary basis, may be termed “forced assimilation”; it cannot be said they have a very proud history.

A very different effect of political organization on culture change occurs when a revolutionary government seeks not to adapt individuals to the prevailing culture but to bring dramatic change to the prevailing culture itself. In the 20th century, for example, several peasant societies underwent rapid industrialization in what may well be termed, after the Chinese case, “cultural revolutions” (Wolf, 1969). This reminds us that culture, though by definition relatively resistant to change, not only does change but also can even do so quite rapidly.

The Course of Culture Change

When we turn to consider the overall course followed by the development of human culture, we find that both the evolutionist-materialist and acculturationist approaches are illuminating.

The earliest solid evidence of human culture consists of simple stone tools dating back to between 2 and 3 million years ago. Our closest living relatives, the chimpanzees, exhibit elementary cultures; but their artifacts are fashioned of perishable materials and therefore would not be archaeologically recognizable. It seems quite likely, then, that culture itself is even older than the stone tools left to us by our early ancestors.

Between 2 million and 1 million years ago, early humans expanded from the tropics of Africa into the rest of the Old World. Because this expansion was chiefly into colder environments, it must have been greatly facilitated by the control of fire, which probably had been attained by half a million years ago and possibly had been attained much earlier. Judging from fire’s centrality— literally as well as figuratively in terms of domestic interaction—in the culture of recent hunting-gathering peoples, we can imagine that the acquisition of fire was of enormous significance.

Although our ancestors all remained hunter-gatherers for over 99% of the time since the appearance of the first stone tools, they expanded into many different environments. This expansion was made possible not only by control over fire but also by the development, probably generally over many generations of trial and error, of different kinds of tools suited to gathering, hunting, and fishing whatever the local physical environment offered. The considerable extent to which culture change was driven by radiation of humans into new environments—achieved, among other life-forms, overwhelmingly by biological rather than by cultural change—goes far to vindicate the evolutionistmaterialist view of culture as essentially an adaptive system. (Further vindications come from the fact that anthropologists, when they write descriptions not only of bands but also of pastoralists and village peoples, nearly always deem it most enlightening to begin with the physical environment and how the people interface with it to survive; then, they proceed to describe how people interact with one another and only then to focus on how the people interpret reality—their religious and philosophical conceptions. Ethnographically, it works better, as a Marxian metaphor puts it, to ascend from the earth to the heavens than to descend from the heavens to the earth.)

Between 10,000 and 15,000 years ago, populations had grown sufficiently dense in some parts of the world that people had begun settling into villages and growing food in addition to hunting and gathering it. In some places, the natural environment created population “pressure cookers” in which competition for ever scarcer farmland led to warfare between societies, followed by the displacement, destruction, or subjugation of the vanquished. Culture then not only had to accommodate the physical environment but also had to allow for the existence of human groups large enough and well coordinated enough to compete successfully with surrounding groups (Carneiro, 1970). Thus began the process of transforming a large number of small societies into a small number of large ones (Carneiro, 1978; Graber, 1995). With this growth in the size of societies came the complex division of labor and the stratification into rich and poor, powerful and powerless that still characterize human culture today.

By 500 years ago, a few societies had grown large and technologically advanced enough to cross oceans. What we know as the modern system of nations began taking shape. Soon, the steam engine was powering the Industrial Revolution. Transportation and communication accelerated, bringing people together even more than did the increasing density of the population itself; and increased trade made a society’s culture less and less dependent on its own physical environment. Spurred by warfare and the threat of war, science and technology advanced so rapidly that nuclear war, and perhaps other threats of which we are not even aware, confront us with the possibility of selfextinction; and recently, we have learned that centuries of burning hydrocarbons have contributed to depleting earth’s ozone layer and are significantly altering the climate. Fortunately, we also have much greater (and constantly growing) knowledge of our effects on the physical environment, of how the ever more integrated global economy works, and of how societies and cultures have affected—and continue to affect—one another, reflected in the greater sophistication and sensitivity of the new acculturationism. If this growing knowledge (perhaps aided by good luck) allows us to avoid disaster, we bid passage to continue on the path to becoming a single world society (Carneiro, 1978; Graber, 2006).

Stone tools, agriculture, the steam engine and industrialization, nuclear power—these changes in the technological subsystem of human culture have triggered vast changes throughout all three subsystems. Already making their mark are computers and genetic engineering; on the horizon are, for example, developments including nanotechnology and controlled nuclear fusion. For better or worse, technology seems destined to play a major role in future culture change; but—as Leslie White (1949) observed— whether as hero or villain, we do not know.

To sum up, then, by definition (1) culture resists change; but in fact, (2) it does change; indeed, (3) it can even change rapidly; (4) its overall rate of change appears to have increased; and (5) it differentiated as humans expanded into and exploited different environments and then began integrating as global population density increased; (6) integration continues to dominate the culturechange picture as we enter the 21st century as a major dimension of “globalization.”

Will cultural integration eventually eradicate all cultural differences? This seems unlikely. After all, different households even of the same social class and in a single neighborhood acquire rather different ways of going about the business of everyday life—differences that become quite clear when, say, schoolmates visit each other’s homes; even greater is this impression when new roommates or couples first attempt setting up a new household of their own! The deep similarities of human beings placed limits on the cultural differentiation that allowed our ancestors to occupy our planet; our persisting individual differences place limits on the cultural integration that will allow us, we hope, to live together on it for a long time to come.

Bibliography:

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culture change research paper

ORIGINAL RESEARCH article

A comparison of change blindness in real-world and on-screen viewing of museum artefacts.

\r\nJonathan E. Attwood

  • 1 NeuroMetrology Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
  • 2 Medical Sciences Division, University of Oxford, Oxford, United Kingdom
  • 3 Ashmolean Museum Engagement Programme, Ashmolean Museum of Art and Archaeology, University of Oxford, Oxford, United Kingdom
  • 4 Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom

Change blindness is a phenomenon of visual perception that occurs when a stimulus undergoes a change without this being noticed by its observer. To date, the effect has been produced by changing images displayed on screen as well as changing people and objects in an individual’s environment. In this experiment, we combine these two approaches to directly compare the levels of change blindness produced in real-world vs. on-screen viewing of museum artefacts. In the real-world viewing condition, one group of participants viewed a series of pairs of similar but slightly different artefacts across eye saccades, while in the on-screen viewing condition, a second group of participants viewed the same artefacts across camera pans on video captured from a head-mounted camera worn by the first set of participants. We present three main findings. First, that change blindness does occur in a museum setting when similar ancient artefacts are viewed briefly one after another in both real-world and on-screen viewing conditions. We discuss this finding in relation to the notion that visual perceptual performance may be enhanced within museums. Second, we found that there was no statistically significant difference between the mean levels of change blindness produced in real-world and on-screen viewing conditions (real-world 42.62%, on-screen 47.35%, X 2 = 1.626, p > 0.05 1 d.f.). We discuss possible implications of these results for understanding change blindness, such as the role of binocular vs. monocular vision and that of head and eye movements, as well as reflecting on the evolution of change detection systems, and the impact of the experimental design itself on our results. Third, we combined the data from both viewing conditions to identify groups of artefacts that were independently associated with high and low levels of change blindness, and show that change detection rates were influenced mainly by bottom-up factors, including the visible area and contrast of changes. Finally, we discuss the limitations of this experiment and look to future directions for research into museum perception, change blindness, real-world and on-screen comparisons, and the role of bottom-up and top-down factors in the perception of change.

Introduction

Change blindness is defined as the failure to detect when a change is made to a visual stimulus ( Simons and Levin, 1997 ). It occurs when the local visual transient produced by a change is obscured by a larger visual transient, such as an eye blink ( O’Regan et al., 2000 ), saccadic eye movement ( Grimes, 1996 ; McConkie and Currie, 1996 ), screen flicker ( Rensink et al., 1997 ), or a cut or pan in a motion picture ( Simons, 1996 ; Levin and Simons, 1997 ); or when the local visual transient produced by a change coincides with multiple local transients at other locations, known as mud-splashes, which act as distractions, causing the change to be disregarded ( O’Regan et al., 1999 ). Change blindness is distinct from inattentional blindness, which occurs when an individual is blind to the presence of an entire object while performing a distracting task [as in the well-known “gorilla in the room” experiment ( Simons and Chabris, 1999 )]. In contrast, change blindness occurs when an individual is blind to changes occurring to an object with which they are actively engaged. Because of this, when missed changes are later pointed out to the observer, they are usually met with a sense of disbelief at how something could ever have been missed. The surprising nature of change blindness results from a disconnect between the assumption that our visual perceptions are so detailed as to be virtually complete, and the actual ability of the visual system to represent and compare scenes moment-to-moment. In this way, change blindness is a testable phenomenon that can be used to investigate the nature of visual representations in different conditions ( Simons and Rensink, 2005 ).

In most of the studies published to date, change blindness has been produced using altered photographs or videos of natural scenes displayed on computer screens. More recently, change blindness has also been shown to take place in more naturalistic scenarios. For example, in one real-world experiment, more than half of participants failed to notice the changing of a conversation partner in front of them ( Simons and Levin, 1998 ; Levin et al., 2002 ), and in another, more than half of participants were blind to the changing of an object’s colour or a printed word’s font ( Varakin et al., 2007 ).

In the current experiment, we sought first to demonstrate whether change blindness could be produced inside a museum, using ancient museum artefacts as visual stimuli. It has been suggested that the visual interactions taking placed within museums involve enhanced perception compared to everyday visual interactions ( O’Neill and Dufresne-Tassé, 1997 ), raising the question of whether change blindness is still a demonstrable phenomenon under such conditions. Inattentional blindness has been previously investigated in a museum setting ( Levy, 2011 ), but as far as we are aware this is the first attempt to produce change blindness inside a museum.

Once it has been produced, we will directly compare the levels of change blindness produced by a single set of visual stimuli viewed in both on-screen and real-world conditions. In the real-world condition, one group of participants viewed a series of pairs of similar but slightly different artefacts across eye saccades, while in the on-screen condition, a second group of participants viewed the same series of artefacts across camera pans on video captured from a head-mounted camera worn by the first set of participants. It is important to know whether or not this shift to more on-screen interaction has negative consequences such as increased change blindness. To the best of our knowledge, this is the first attempt to directly compare change blindness levels produced in on-screen and real-world viewing conditions.

Our motivation for making this comparison was twofold. First, as a response to the relative lack of comparisons between on-screen and real-world perception made to date, despite the extensive use of both conditions across human visual perception research. Because non-stereoscopic cameras capture and display light from a single perspective, on-screen viewing conditions provide only monocular cues to visual depth. These depth cues include linear perspective, object occlusion, and motion parallax ( Cutting, 1997 ; Albertazzi et al., 2010 ). By contrast, because in real-world viewing conditions light reflected from the three-dimensional environment is captured from the perspective of both eyes without passing through a camera, binocular depth cues, including binocular disparity and ocular convergence, become available in addition to the monocular cues. There is evidence to suggest that binocular stereoscopic vision confers an advantage over monocular vision in certain perception performance tasks, including the analysis of complex visual scenes ( Jones and Lee, 1981 ), surface visualisation ( Wickens et al., 1994 ), and the programming of prehensile movements ( Servos et al., 1992 ). However, evidence of preserved function without stereopsis also exists, most notably amongst pilots ( Snyder and Lezotte, 1993 ), and the overall functional significance of binocular stereopsis remains unclear ( Fielder and Moseley, 1996 ). Based on this evidence and our own observations, our hypothesis is that change blindness levels will be lower in the real-world condition than in the on-screen condition, because the perceptual advantages of binocular over monocular vision will produce a greater rate of change detection in the real-world scenario.

We were also motivated to make this comparison by the increasing frequency and importance of on-screen visual interactions alongside real-world interactions in modern working and social life. The growing accessibility of high-speed internet and the capability of smart portable devices has already significantly changed the way that many people exchange visual information. A recent report found that adults in the United States spend an average of more than 8 h a day accessing media through a device with a screen ( The Nielsen Total Audience Report - Q1 2016, 2016 ). For many people, this amount of time will account for the majority of their waking day and such a significant shift in behaviour warrants further investigation in its own right.

Materials and Methods

We recruited 62 participants through an advertisement describing a neuropsychological experiment taking place at the Ashmolean Museum in Oxford. The group of participants consisted of students and employees of the University of Oxford, covering a wide range of disciplines from Art History and Fine Art to Law and Medicine. While none of the participants were artists, they might all be considered to hold some form of interest in art, or art history, given that they responded to our advert. The participants were allocated using a random number generator to either real-world or on-screen viewing conditions. 31 participants were allocated to each group. The mean age of participants in the real-world group was 22.8 years (SD ± 5.3 years) and 58.1% were female. The mean age of participants in the on-screen group was also 22.8 years (SD ± 5.9 years) and 58.1% were female. No attempt was made to match the groups. The exact sex matching occurred by chance. The close age matching results from the participants predominantly being university students. This study was carried out with permission from the Central University Research Ethics Committee (CUREC), and all subjects gave their written informed consent after the experimental procedures had been explained to them, in accordance with the Declaration of Helsinki.

Museum Setting

The experiment was conducted in the Ashmolean Museum of Art and Archaeology, part of the University of Oxford. Twelve pairs of artefacts from the museum’s collection were used, including three pairs of Japanese woodblock prints, one pair of Chinese porcelain bowls, two pairs of Iranian tiles, one pair of Athenian lekythoi, one pair of Renaissance bronze medals, two pairs of Anglo-Saxon brooches, and two pairs of English silverware. These artefacts were chosen because although they had originally been designed to appear identical in their pairs, through their individual manufacture and subsequent usage they had all come to exhibit differences, ranging from relatively subtle to more major differences in appearance, including differences in colour, shape, and design. There were differences between all 12 pairs of artefacts used in the experiment.

Change Blindness Paradigm

Twelve pairs of artefacts were displayed in a fixed order before each participant. For each pair of artefacts, a participant observed one item for a short period of time before looking to the second item and observing it for the same length of time as the first. As participants looked from one item to the next, the differences between their appearances generated local visual transients. However, the transition of looking from one item to the other generated a larger visual transient which would to a certain extent obscure the local transients, and thus produce a corresponding degree of change blindness. This degree was measured by participants responding to the question: Did you notice any differences between the two objects? They were then required to describe any differences they did notice in writing after viewing each pair of artefacts. Subsequently, the participants’ descriptions were marked as either correct or incorrect according to the actual differences manifest between the objects. If none of the changes existing between a pair of artefacts were correctly identified, the participant was recorded as being change blind with respect to that pair. If a single change was correctly identified, they were recorded as not being change blind. The degree of change blindness recorded was therefore a reflection of the balance of local and large visual transients that were produced by observing these pairs of museum artefacts in real-world and on-screen viewing conditions.

The length of time for which participants observed each artefact was set at a duration that would produce a change blindness effect appropriate to allow for a comparison to be made between the two conditions. The requisite duration was determined through a series of trials in which photographs of the pairs of artefacts were observed in series on a monitor for different lengths of time. An observation time of 5 s per artefact separated by an interval of 2 s resulted in change blindness in 15% of the pairs. Observation time of 2 s with an interval of 0.5 s produced change blindness in 20%, and an observation time of 0.25 s with an interval of 0.25 s produced 57% change blindness. Given that the motion of turning to look from one artefact to another would produce an interval between fixations of less than 100 ms ( Grossman et al., 1988 ), an observation time of 1 s was chosen in order to achieve approximately 50% mean change blindness in the on-screen condition. This was thought to be optimal in allowing for a comparison to be made between this and the real-world condition.

Both viewing conditions were similarly controlled to standardise the nature and duration of the periods of observation, and the transition from one artefact to another. The artefacts were placed in their pairs on a table in a room within the museum (Figure 1A ). They remained covered for the majority of the experiment, and members of museum staff were present to ensure their safekeeping throughout. The items in each pair were placed 40 cm apart, and a chair was placed in front of each pair of artefacts to provide a viewing distance of 75 cm. A high definition 32-inch LCD screen was also present in the room with a chair placed in front of it. The real-world viewing condition consisted of participants sitting in front of and viewing the artefacts on the table before them (Figure 1B ). The on-screen condition consisted of a separate group of participants sitting in front of the screen and viewing the artefacts on its display (Figure 1C ). Both participants were aware of each other and their roles throughout the course of the experiment.

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FIGURE 1. (A) The experimental setup within the museum, showing the artefacts (covered), two participants, and an experimenter. (B) The real-world viewing condition: the participant is sat in front of a pair of artefacts, wearing a pair of modified goggles and head-mounted camera. (C) The on-screen viewing condition, the participant is sat in front of a monitor, wearing a pair of modified goggles and watching a live feed from the head-mounted camera. Images reproduced with permission from Ashmolean Museum, University of Oxford. All the persons depicted on this picture gave their consent for publication.

All participants’ visual fields were restricted by wearing a pair of goggles that were modified for the purposes of this study. Opaque inserts were fitted to the inside of the goggles to leave a window of 3 cm diameter in front of each eye. This restricted the binocular field of view to 45.56° (0.79°rad) horizontally and 48.14° (0.84 rad) vertically at the 75 cm viewing distance. The field was sufficient to contain the full surface of the largest artefact while also not allowing both of the smallest artefacts to be viewed when the visual field was centred on one of them, in both the real-world (Figure 2A ) and on-screen conditions (Figure 2B ). These steps were taken to ensure that participants would not be able to make multiple eye saccades between the items in front of them, which would have added a significant uncontrolled variable.

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FIGURE 2. (A) The real-world participants’ views of the largest (top) and smallest (bottom) artefacts through the modified goggles. The whole surface of the largest artefact was visible, but both items of the smallest pair of artefacts were not visible at the same time (to scale). (B) The on-screen participants’ views of the largest (top) and smallest (bottom) artefacts through the modified goggles on the screen (to scale). Images reproduced with permission from Ashmolean Museum, University of Oxford.

Real-World Viewing Condition

Once sat in front of the first pair of artefacts, the real-world participant was instructed to start with their head toward the item on their left, so that their visual field would be centred on the first artefact. The artefact was initially obscured by a small screen. On an auditory cue the screen was manually removed by an experimenter so that the participant could view the first artefact. This period of observation lasted for 1 s, after which another cue sound signalled for the participant to turn their head and eyes to look at the second item to their right, so that their visual field would now be centred on the second artefact. This period of observation lasted for a further 1 s, after which a small screen was placed between the participant and the second item by an experimenter so that it could no longer be seen. In this way, both artefacts were viewed for a duration of 1 s, with a brief visual transition interrupting the viewings.

The visual transition which occurred in the real-world viewing condition consisted of a combination of a head rotation and a saccadic eye movement. This combination has been defined elsewhere as a gaze shift ( Binder et al., 2009 ), where gaze is defined as the sum of eye position with respect to the head and head position with respect to the body. When the visual field shifts more than 15–20°, an eye saccade is normally accompanied by a head rotation in order to return the eyes to a neutral position within the orbits and allow the extra-ocular muscles to relax. In this case, the shift was 28.1° (0.49 rad), and participants in the real-world condition were specifically instructed to turn both their head and eyes to view the second artefact in each pair.

The coordination of gaze shifts is complex but the basic elements are well-understood ( Pelisson and Guillaume, 2009 ). As the head initially rotates and the eyes stay fixed on the first target, eye movement is under the control of the vestibulo-ocular reflex (VOR). Once head rotation has brought the new target into the visual field, an endogenous eye saccade occurs to move the point of foveation from the first target to the second. Following this, though the second target is now foveated, there is still residual head rotation due to a lag in the control of head movement relative to that of the eyes, and this is compensated for by a further period of VOR eye movement. The components of the gaze shift are therefore an initial period of VOR, an exogenous eye saccade, followed by a further period of VOR. It is not yet known whether VOR eye movements are able to induce change blindness by themselves, but that eye saccades are able to is well-established ( Grimes, 1996 ; McConkie and Currie, 1996 ). Thus, in the real-world viewing condition in this experiment, the large visual transient consisted of an eye saccade which was preceded and followed by a period of VOR eye movement.

On-Screen Viewing Condition

While the above processes were taking place, a small head-mounted high definition video camera was attached to the goggle strap of the participant in the real-world viewing condition. The camera used was a Contour+2 HD with 170° wide-angle lens, operating at a frame rate of 30 fps and 1920 × 1080 resolution, weighing 156 g, and measuring 98 mm × 60 mm × 34 mm. It was connected by an HDMI cable to 1080p high definition 32-inch LCD screen, producing a live video feed on the screen in front of the participant in the on-screen condition. The acuity achievable when viewing this screen was 20/70, which, although inferior to 20/20 vision, was significantly greater than the level required to resolve the smallest change detected by any participant in the real-world condition, which was measured to be 20/180 (a change of 2 mm diameter viewed at 75 cm). The on-screen participant wore an identical pair of modified goggles to their counterpart in the real-world group (except without a camera attached to the goggle strap), which, as in the real-world group, prevented multiple eye saccades being made between artefacts.

Unlike participants in the real-world viewing condition, however, on-screen participants did not have to follow instructions to move their head or eyes on auditory cues. Instead, as the real-world participant rotated their head to look from the first item to the second, the head-mounted camera also rotated and the footage on the screen panned across to reveal the second artefact to the on-screen participant. An equivalent change to the contents of the visual field was therefore produced without an equivalent gaze shift taking place. Thus, in the on-screen viewing condition, the large visual transient consisted of a camera pan rather than an eye saccade preceded and followed by a period of VOR. Of course, the other difference between viewing conditions was that artefacts were viewed directly by participants in the real-world group, while they were viewed on an LCD display in the on-screen group. On-screen participants viewed the screen from a distance of 75 cm, and the camera and screen were calibrated so that the representations of the artefacts were displayed at life-size in order to match conditions in the real-world conditions. In both real-world and on-screen viewing conditions, therefore, the artefacts subtended the same visual angle.

The only differences between the conditions, then, were the nature of the large visual transient and the format of display. We suggest that these variables constitute the defining differences between all real-world and on-screen visual interactions, in that they represent both the behaviour of the subject who is viewing and the nature of the object that is being viewed in these scenarios. Thus, the results of this experiment reflect a comparison of the levels of change blindness produced by a single set of visual stimuli in real-world and on-screen viewing, as defined by the nature of the large visual transient and the format of display typical of these conditions.

We present three main findings. First, that change blindness does occur in a museum setting when similar ancient artefacts are viewed briefly one after another in both real-world and on-screen viewing conditions (Table 1 and Figure 3 ).

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TABLE 1. Table of results.

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FIGURE 3. Levels of change blindness in real-world and on-screen viewing conditions produced by each pair of artefacts and the overall mean. Asterisks denote level of significance ( X 2 test with one degree of freedom. No asterisk = p > 0.05; ∗ = 0.05 > p > 0.02; ∗∗ = 0.02 > p > 0.01; ∗∗∗ = 0.01 > p > 0.001; ∗∗∗∗ = 0.001 > p ).

Second, we found that there was no statistically significant difference between the mean levels of change blindness produced in real-world and on-screen viewing conditions [real-world 42.62%, on-screen 47.35%, X 2 = 1.626, p > 0.05 1 d.f. (Table 1 and Figure 3 )]. The total number of trials per pair of artefacts ranged from 29 to 31 due to a small number of failures by participants to follow the experimental procedure described above (13 failures from 371 trials = 3.5%). The mean level of change blindness produced in the on-screen condition was close to 50%, as intended to allow comparison between the two conditions. One pair of artefacts produced a significantly higher degree of real-world change blindness than on-screen change blindness (Pair 2: real-world 86.7%, on-screen 46.7%, X 2 = 10.800, 0.01 > p > 0.001), while three pairs produced a significantly higher degree of on-screen change blindness than real-world change blindness (Pair 4: real-world 20.0%, on-screen 50.0%, X 2 = 5.934, 0.02 > p > 0.01; Pair 10: real-world 3.5%, on-screen 41.4%, X 2 = 11.997, 0.001 > p ; Pair 12: real-world 70.0%, on-screen 93.3%, X 2 = 5.455, 0.02 > p > 0.01). But in the other eight pairs, and overall, there was no significant difference between the levels of change blindness produced.

Third, following the finding of no significant difference between the levels of change blindness produced in real-world and on-screen conditions, we combined the data from both groups to compare the levels of change blindness produced by each pair of artefacts independently (Figure 4 ). From these results, we consider in particular three pairs of artefacts which produced a level of change blindness greater than 75% (pairs 1, 7, and 12, 79.31–83.33%), and three pairs of artefacts which produced a level of change blindness lower than 15% (pairs 3, 5, and 6, 4.84–12.90%).

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FIGURE 4. Levels of change blindness combined from real-world and on-screen viewing conditions produced by each pair of artefacts and the overall mean.

Change Blindness in a Museum Setting

Our first finding, that change blindness does occur in a museum setting when similar ancient artefacts, in this case some more than 2,000 years old, are viewed briefly one after another in both real-world and on-screen viewing conditions, is a significant addition to the body of evidence demonstrating that change blindness can be produced in more naturalistic environments outside of the laboratory.

Change blindness experiments have established that details considered to be important are detected more readily than those that are less important ( Rensink et al., 1997 ; O’Regan et al., 2000 ), even when the changes are of equivalent physical salience ( Kelley et al., 2003 ). These findings suggest that attention plays an important role in prioritising the elements of a visual scene, and in determining what is represented and compared between scenes and what is not ( Simons and Rensink, 2005 ). However, even changes that are made to attended objects can still be missed ( Ballard et al., 1995 ; Simons, 1996 ), which leads to the conclusion that attention is necessary, but not sufficient, for change detection to occur. The other determinants of change detection can be divided between bottom-up, or stimulus-driven, factors, such as visual salience, and top-down, or goal-driven, factors, such as context, gist and motivation ( Borji and Itti, 2013 ). It has been suggested that both bottom-up and top-down factors are enhanced in the visual interactions that take place within museums, due to the exceptional and exemplary nature of the objects being viewed, and the intensity of observation and motivation to form interpretations from what is seen, respectively ( O’Neill and Dufresne-Tassé, 1997 ). However, later discussions have warned against ‘uncritical acceptance of the distinction between utilitarian (ordinary) and the aesthetic (museum) seeing,’ and while it is acknowledged that ‘the notion of the distinction…is, in one form or another, firmly embedded in many account of vision and aesthetic experience,’ in fact, ‘cognitive neuroscience does not supply any facts that could substantiate the sharp divide between the ‘normal’ and the aesthetic perception’ ( Kesner, 2006 ). In the present experiment, we did not compare the levels of change blindness produced within and outside of the museum setting. Instead, we have merely demonstrated that change blindness can be produced among participants viewing artefacts inside a museum. However, in light of this finding and the unresolved questions that surround it, such a comparison would be an appropriate next step.

Change Blindness in Real-World and On-Screen Viewing Conditions

Our second finding was that there was no statistically significant difference between the mean levels of change blindness produced in real-world and on-screen viewing conditions. This means that altering both the format of display from the objects themselves to an on-screen virtual object representation, and the nature of the large visual transient from a camera pan to an eye saccade preceded and followed by VOR, did not significantly affect the rate of change detection in this experiment.

The possible interpretations of this finding are: (1) that neither the format of display nor the nature of the large visual transient had a significant effect on change detection, (2) that the format of display and nature of the large visual transient had equal and opposite effects on change detection, resulting in no combined effect overall, or (3) that the similarities between the two conditions were so great compared to the differences, that any effects produced by either the format of display or the nature of the large visual transient were masked by the intrinsic design of the experiment.

Regarding the first part of the first interpretation, if the format of display had no significant effect on change detection, then this finding provides no support for our hypothesis, which was that the perceptual advantages of binocular stereoscopic vision would produce a greater rate of change detection in the real-world condition compared to the on-screen condition.

Our hypothesis was formulated based on evidence that binocular stereoscopic vision confers an advantage over monocular vision in certain perception performance tasks, including the analysis of complex visual scenes ( Jones and Lee, 1981 ), surface visualisation ( Wickens et al., 1994 ), and the programming of prehensile movements ( Servos et al., 1992 ). We also made reference to the fact that in the real-world condition, binocular stereopsis would provide additional depth cues of binocular disparity and ocular convergence, compared to in the on-screen condition where only monocular depth cues of linear perspective, object occlusion, and motion parallax would be available ( Cutting, 1997 ; Albertazzi et al., 2010 ). According to our first interpretation, this finding runs contrary to the evidence supporting our initial hypothesis. However, due to the equal plausibility of the other interpretations, we cannot reliably contrast our finding with those drawn elsewhere. As previously discussed, evidence of preserved visual performance without stereopsis does exist ( Snyder and Lezotte, 1993 ), and so the overall functional significance of binocular stereopsis must unfortunately remain unclear.

In the absence of clear evidence either way, it is interesting and perhaps instructive to consider the evolutionary arguments for why we might expect change detection to be enhanced by binocular stereoscopic vision. One could argue that a real-world object, with the potential to act upon its viewer and itself to be acted upon, should be perceived more strongly than an on-screen object, which ultimately remains virtual (although the screen which displays it is itself a real-world object). However, as some of the artefacts used in this experiment demonstrate, the human visual system has been processing two-dimensional representations of three-dimensional objects for thousands of years. The earliest cave paintings discovered date back to 15,000–10,000 BC. Human beings, and especially the human nervous system, have undergone significant changes over hundreds of generations in this time, but we reason that in this period there will have been no drive to either significantly strengthen or weaken the local visual transients formed from the observation of two-dimensional images relative to three-dimensional objects. The subjects of the earliest two-dimensional representations were bison, mammoth, and reindeer, the prey of those who depicted them on the walls of their dwellings. This alone is testament to the fact that the ability to create and understand representations of the surrounding environment and the messages being communicated about them is likely to have conferred a selective advantage over the recent course of our evolution. Indeed, while objects that can act upon us and that we can act upon have remained important for our survival, one can argue that images have come to be just as important to the modern human.

Returning to the second part of the first interpretation of our finding, it is possible that the nature of the large visual transient had no significant effect on change detection. We are not aware of any previous attempts to compare the effect of eye saccades and VOR vs. camera pans on visual performance. And, as above, due to the equal plausibility of the other interpretations, we cannot present this interpretation as a reliable conclusion. Theoretically, one could reasonably argue that activation of neural systems controlling head and eye movements might either enhance or impair the parallel systems involved in change detection. Once again, in the absence of evidence, we might have recourse to consider evolutionary arguments. However, in this situation this seems hardly relevant. Before motion pictures were developed at the turn of the twentieth century, the human visual system would never have been exposed to a change to the contents of the visual field in the absence of head or eye movements – such a thing would simply not have been possible. Consequently, there has been almost no time for natural selection to affect the mechanisms of change detection operating in the context of a camera pan compared to an eye saccade accompanied by VOR eye movement.

The second possible interpretation of our finding is that the format of display and nature of the large visual transient had equal and opposite effects on change detection, resulting in no combined effect overall. Because of the difficulties in drawing conclusions about either variable discussed above, the uncertainty of following this interpretation would be even greater, and as such need not be discussed further.

The third possible interpretation of our main finding was that the similarities between the two conditions were so great compared to the differences, that any effects produced by either the format of display or the nature of the large visual transient were masked by the intrinsic design of the experiment. In any experiment, the pattern of findings will be determined by a balance between both the controls and the variables that constitute the experimental paradigm ( Gozli, 2017 ). Our paradigm included a relatively large number of controls and restrictions: a fixed viewing time, a restricted field of view, proscribed head and eye movements, and a specific set of visual stimuli. It was necessary to institute these limitations to reliably isolate our two experimental variables from a complex naturalistic scenario. However, it is possible that, such was the impact of these controls relative to the difference between the experimental variables, that our two viewing conditions were in effect much more similar than they were different. In this way, it is possible that the similarity in task performance across the two conditions could have masked effects produced by the differences between on-screen and real-world viewing conditions. It is perhaps not possible to determine to what degree any effects may have been masked. However, with this in mind, we can only state that the differences between on-screen and real-world viewing conditions were not large enough to produce a significant difference in participant performance in the context of this experiment.

In summary, then, it is difficult to interpret our finding that there was no statistically significant difference between the mean levels of change blindness produced in real-world and on-screen viewing conditions. The effects of altering the format of display and the nature of the visual transient in this experiment cannot be separated, the possibility of equal and opposite effects cannot be excluded, and the possibility that effects were masked by the overall similarity of the viewing conditions must be considered.

Bottom-Up and Top-Down Factors

The third and final finding of this study came after combining the data across both conditions to compare the levels of change blindness produced by each pair of artefacts independently. We consider in particular three pairs of artefacts which produced a level of change blindness greater than 75% (pairs 1, 7, and 12, 79.31–83.33%), and three pairs of artefacts which produced a level of change blindness lower than 15% (pairs 3, 5, and 6, 4.84–12.90%). Given that the nature of the large visual transients was controlled across the experiment, it follows that these data reflect the fact that local visual transients produced by the changes between the artefacts in pairs 1, 7, and 12 were weaker than those produced by the changes between the artefacts in pairs 3, 5, and 6. These local transients arose from the differences in appearance of the pairs of artefacts.

Taking pairs one and three, both Japanese woodblocks prints, as an example, the artefacts in both pairs are the same size as each other and share the same designs (Figures 5A , 6A ). Pair one, the wave prints, also share very similar colouring (Figure 5A ). The only differences in colouring between this pair are the subtle changes in hue to the border and box containing script. These changes in colour are slight and cover a small proportion of the visible surface of the artefacts. By contrast, pair three, the eagle prints, are more obviously different in colour (Figure 6A ). For instance, the colour of the sky changes from dark blue to light blue between the two prints, and the colour of the boxes containing script changes from pink and red to green and orange. Collectively, these changes represent a more significant colour change and cover a larger proportion of the artefact’s visible surface, compared to the wave prints. It is these local visual transients which account for the lower level of change blindness amongst participants viewing pair three compared to pair one (12.90% vs. 79.31%, respectively).

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FIGURE 5. (A) Pair 1: Utagawa Hiroshige, The Sea at Satta in Suruga Province from Thirty Six Views of Mount Fuji . Woodblock prints with bokashi (tonal gradation). 1858-9 AD. 22.4 cm × 34.0 cm. (B) Pair 7: Athenian red-figure lekythoi. Nike flying with phiale (left). Nike flying with thurible (right). 490–480 BC. 32.4 cm (left) and 31.8 cm (right) tall. Images reproduced with permission from Ashmolean Museum, University of Oxford. (C) Pair 12: Isaac Dighton, Silver toilet dressing table service, 2 of 14. 1699–1700 AD. 10.3 cm (left) and 10.5 cm (right) tall. Images reproduced with permission from Ashmolean Museum, University of Oxford.

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FIGURE 6. (A) Pair 3: Utagawa Hiroshige, Jûmantsubo Plain at Susaki, near Fukagawa from One Hundred Famous Views of Edo . Woodblock prints with bokashi (tonal gradation). 1856-8 AD. 22.0 cm × 32.8 cm. Images Ashmolean Museum, University of Oxford. (B) Pair 5: Iranian star tiles. Late 13th–14th century AD. 16.0 cm × 6.5 cm, 15.0 cm × 15.0 cm (left), 13.0 cm × 15.0 cm (right). (C) Pair 6: Iranian tiles with interlacing pattern. 13th century AD. Images reproduced with permission from Ashmolean Museum, University of Oxford.

Pair seven, the Athenian lekythoi, produced a high level of change blindness similar to that produced by pair one, the wave prints. The artefacts in this pair are the same size as each other, share the same red-figure colouring, and have near-identical designs, except for the depiction of the object in the figure’s right hand, which changed from a phiale to a thurible (Figure 5B ). As for pair one, this change covers a small area of the visible surface of the artefacts, and so represents a relatively small local visual transient, which translates to a high level of change blindness (83.33%). Similarly, pair 12, the silver flasks, also produced a high level of change blindness (81.67%). The two flasks are practically identical, save only for the uppermost tip which has been displaced atop the first item (Figure 5C ). Again, this change represents a small area of the artefact’s visible surface, and so only produced a small local visual transient.

The artefacts in pair six, the Iranian tiles with interlacing pattern, are the same size as each other and share the same design and colouring (Figure 6C ). However, the first tile has an area of damage to its corner and the second tile carries an extra piece of cement on its front. These changes together account for a large area of the artefacts’ visible surfaces, and as such constitute large local visual transients responsible for a low level of change blindness (11.29%). Pair five, the Iranian star tiles, produced the lowest level of change blindness of all (4.84%), with only three of the 62 participants not noticing any changes between them. These artefacts manifest differences in both the design of their central area, and also in that one of the points on the second tile has been broken off (Figure 6B ). These two changes constitute large local visual transients accounting for a very low level of change blindness.

The characteristics of changes that produced the most easily detected local visual transients include a large visible area of change and high contrast changes in colour. Both of these characteristics, area and contrast, can be directly related to the retina, where light from the visual field is transduced by photoreceptor cells, and contrast is enhanced by lateral inhibition of neurons in the layers between the photoreceptors and retinal ganglion cells. Because these characteristics are amongst the first to be encoded by the visual system, they are possible candidates for bottom-up influences on the prioritisation of what is represented and compared during the process of conscious change perception. In line with this, it has been shown that highly salient objects, where salience includes colour, intensity, and orientation ( Koch and Ullman, 1985 ; Itti and Koch, 2000 ), attract visual fixations earlier than less salient objects ( Underwood and Foulsham, 2006 ; Underwood et al., 2006 ), and it is well-established that the larger a surface is within the visual field the more likely it is to be fixated ( Peschel and Orquin, 2013 ).

However, it is clear that areas undergoing change can be fixated within a change blindness paradigm without the change itself being perceived ( O’Regan et al., 2000 ). It has also been shown that bottom-up factors can at times be overridden by top-down cognitive influences, such as the consistency of an object within the gist of a scene ( Underwood and Foulsham, 2006 ; Stirk and Underwood, 2007 ), and the specific task the viewer is asked to perform when observing a stimulus ( Underwood et al., 2006 ). In this experiment, there are likely to have been many top-down influences derived from the artefacts themselves, such as the prior knowledge that ancient pottery is more likely to exhibit differences in terms of damage, while prints may be more likely to exhibit colour differences. However, the two groups of artefacts which produced the lowest and highest levels of change blindness, respectively, both exhibited differences of colour, design, and damage. This suggests that top-down influences concerning types of changes had a minimal effect on the level of change blindness produced by each artefacts. For this reason, we suggest that bottom-up factors were relatively spared from being over-ridden by top-down effects, and were therefore able to exert their own influence on the processes of representation and comparison, and ultimately change blindness. In this way, our findings support a role for bottom-up factors including a large visible area of change and high contrast colour change in determining which elements in a visual scene are represented and compared in the process of conscious change perception, in both real-world and on-screen viewing conditions.

Limitations

The methods used in this study carry their own limitations. We will discuss them in relation to the two main comparisons performed in this experiment. Namely, the comparison of real-world and on-screen viewing conditions, and the comparison of the 12 pairs of artefacts. Regarding the former, first, by comparing the performance of two different groups of participants in real-world and on-screen conditions, we introduced the potential for selection bias. We saw no practicable alternative to this, as a change cannot be shown to the same participant more than once in a change blindness experiment. To mitigate this bias, we recruited over 30 participants that we randomly allocated to each group, which resulted in near-identical demographics being represented in both.

Second, while it was important to control the conditions in which the artefacts were observed, this was at the expense of the naturalism of the viewing experience. The viewing distance and placements of the objects were similar to what would be found in a natural museum environment, but the brief periods of observation and the removal of peripheral vision using modified goggles were both unnatural. However, the conditions were the same for participants in both groups. Third, by recording changes which participants described incorrectly in the same way as changes that were not described at all, we set a relatively high threshold for change detection to be achieved. Our methodology did not distinguish between the experience of completely missing a change and the experience of sensing that a change had occurred but not being able to describe that change correctly. It is also possible that the head movement of the real-world observer provides an extra attentional cue to the on-screen observer by centering on the change.

Regarding the comparison between the 12 pairs of artefacts, first, it is possible that the performance of participants changed over the course of the experiment as they advanced through the 12 sets of observations. It is both conceivable that their performance may have improved due to a learning effect, or conversely have worsened due to fatigue. We expect that because each observation was only brief (less than 3 s), and the number of observations was relatively few, neither of these effects are likely to have impacted significantly on the levels of change blindness recorded over the course of the experiment. Each set of 12 trials took less than 10 min to perform. Although the order in which the artefacts were viewed was not varied between participants (which could have mitigated any such effects), the levels of change blindness produced from pair one to pair 12 bear no relation to either an increasing or decreasing trend. Finally, the collection of artefacts used as visual stimuli did not contain a control pair, in that there was no pair of artefacts that were truly identical to each other. If such a pair had produced a change blindness level of 100% it would have strengthened the confidence with which we can draw conclusions from our data.

Change blindness is a testable phenomenon of visual perception that can be used to investigate the nature of visual perception in different conditions. It has been produced in naturalistic scenarios outside of the laboratory before using everyday objects, but until now it has not been produced in a setting such a museum, where visual perception may be enhanced. We have for the first time demonstrated that change blindness can be produced inside a museum, using ancient museum artefacts as visual stimuli, under both real-world and on-screen viewing conditions. We anticipate further experiments will be required to fully investigate the notion of altered visual perception inside museums.

While in society, on-screen interactions are increasingly coming to replace real-world ones, there is a relative lack of experimental comparisons between visual perceptual performance in real-world and on-screen conditions. We have for the first time directly compared the levels of change blindness produced by a single set of visual stimuli viewed in both on-screen and real-world conditions, and found that there was no statistically significant difference between the levels of change blindness produced in the two conditions. This does not appear to support our original hypothesis that change detection would be enhanced in real-world conditions relative to on-screen due to the perceptual advantages of binocular stereoscopic vision. We discuss the difficulty of interpreting this finding and caution against generalising the result of this experiment too readily.

In light of this finding, we combined the data from both viewing conditions to identify groups of artefacts that were independently associated with high and low levels of change blindness, and found that change detection rates were influenced mainly by bottom-up factors, including the visible area and contrast of changes, more than top-down factors. In this way, our findings support a role for bottom-up factors in determining which elements in a visual scene are represented and compared in the process of conscious change perception, in both real-world and on-screen viewing conditions. Finally we discuss the intrinsic limitations of this experiment which must be considered alongside its results. We hope, nevertheless, that our attempt to add to the understanding of visual perception within museums, the phenomenon of change blindness, perceptual performance in real-world and on-screen conditions, and the role bottom-up and top-down factors in change detection will motivate further research into these increasingly relevant questions.

Author Contributions

JA, CK, JH, GH, and CAA designed the research, revised and improved the manuscript. JA and CAA analysed the data and prepared the figures. JA, CK, JH, and CAA discussed the results, advised on interpretation and contributed to the final draft of the manuscript. All authors contributed to and had approved the final manuscript.

JH is funded by the Andrew Mellon Foundation and CAA is supported by the Dementias and Neurodegenerative Diseases Research Network (DENDRON), the NIHR and UCB.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank all of our participants, whose enthusiasm and encouragement have been a great support to us throughout our work on this project. We are also very grateful to the staff of the Ashmolean Museum of Art and Archaeology for their generosity and guidance in making this unique experiment possible. This manuscript is dedicated to the memory of Professor GH.

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Keywords : change blindness, vision, perception, real-world, on-screen, binocular, museum, artefact

Citation: Attwood JE, Kennard C, Harris J, Humphreys G and Antoniades CA (2018) A Comparison of Change Blindness in Real-World and On-Screen Viewing of Museum Artefacts. Front. Psychol. 9:151. doi: 10.3389/fpsyg.2018.00151

Received: 26 July 2017; Accepted: 29 January 2018; Published: 16 February 2018.

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Copyright © 2018 Attwood, Kennard, Harris, Humphreys and Antoniades. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Chrystalina A. Antoniades, [email protected]

† Deceased

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Research Paper On Culture Change

Type of paper: Research Paper

Topic: Workplace , Culture , Company , Development , Employee , Organization , Goals , Belief

Words: 1600

Published: 01/29/2020

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Organizational culture is the deeply held beliefs, behaviours and a certain way of working or conduct in a given company. Organization culture is something that has been in the company for the longest time period and can go for as long as the company started to the extent that some people do not even know where it came from. These cultures are so deeply held and thus this stands to mean that they cannot be easily changed or altered in the company in the normal course of operations. It is therefore important for the company to try and see how they can work around them to improve on their productivity even in these long standing cultures (McKelvey, 2000). Despite this fact, many companies have gone all the way to change their organization culture all with the sole purpose of improving how people work and also their perception towards things and people (Ashkanasy, 2000). For the purpose of this paper, we are going to analyse TriZetto Corporation, a company that is based in the United States of America. This is a company that provides medical care solutions to those health centres around the world that wish to improve on their efficiency and work better towards the betterment of their operations. The company serves more than 200,000 care providers and more than half of the population in the USA. This demonstrates the strength of the company and the fact that it is crucial to realize the culture in place is adhered to by incumbent workers, therefore newly hired employees should also follow the same. As a company that is operating in a dynamic environment, it has goals and objectives that the company wishes to accomplish. These goals and objectives are stated as to fix the problems currently existing in the healthcare sector, and to impact positively the lives of patients in positive ways through affordable costs of accessing medical care and its efficient administration. The mission of this company is to provide solutions to health care problems and improve on the degree at which the service is offered. In its vision statement, the company wishes to make health care as simple as possible to all the people irrespective of their attitude, religion, race or colour (Ashkanasy, 2000). For a long period of time, TriZetto had operated very comfortably and sat in this zone for a long period. Though this changed when a new CEO, Trace Devanny, joined the company in 2010. Trace realized that the company had major opportunities to grow but it had a major weakness that could reverse all this achievemnt. This opportunity was based on the fact that the American government had amended so much law and clauses that governed the governed the medical centres and their provision of the services to their clients. These amendments enabled TriZetto to become very efficient over the years and the provision of the information became simpler for them. This also ensured that the company offered as many services and information as possible to the clients and also acquired other entities in merger and partnership deals. This shows how successful this entity was and how well it had mastered its field and became the top player in that industry. Despite these strengths and opportunities, Trace discovered that there was one culture that evident in the organization that needed to be changed; comfortable mentality. This was an issue that meant that people were satisfied with what they offered and thus they did not think that there was much that could be done apart from what they were already doing. This did not get down well with Trace and he wanted to change this culture for the better whereby the employees could always aim higher and beyond their abilities. Changing organizational culture is not as simple as it may sound but it needs so much commitment and dedication of the entire team. To begin with, Trace formulated several changes that he felt were necessary to introduce the needed change. First, he made the workers realize that customers were the most important people in their business and they needed to be very customer focused. This meant they had to devise newer ways of approaching at customers needs to ensure they realize their goal of becoming success agents of the company initiatives (Ashkanasy, 2000). Besides these, TriZetto introduced other products and services that go hand to hand with the medical services, for example, consultancy. There are a number of factors that a person needs to consider before embarking to change the organizational culture of any organization. In the first place, the manager should focus on the company’s external and internal environment of operation and the ease it can change within its desired limits. The environment will determine where the company is placed in the market share and the possible effects on the change (Ashkanasy, 2000). It is the most important factor that TriZetto began with when staring this course of action. Secondly, the company needs to consider the values that are held dear by the company. The management needs to not necessarily change the values but change them to be used towards the new direction so as to achieve the objectives. These values will be more realistic if the company built on their strong points and ensure that there is the minimum deviation from the normal. In any setting, attitude of the people that are mandated with a certain activity is very important, whether positive or negative, as it affects the success or failure of the mission at hand. It was important for Trace to look at the attitude of the employees and try as much as possible to influence them positively (McKelvey, 2000). This could be hard and but it is an important ingredient in the process of changing the culture of the firm. TriZetto had to influence people to come out of their comfort mentality and focus on the customers as the boss and the major contributors to the success of the company. If he could manage to change this, he was assured that changing of the culture was also an easy task. Once the attitude is changed, the company needs to focus on how to change the relationships between the employees and other stakeholders. Devanny led by example and always strived to establish the desired relationships with all the people he interacted with before he could reach a conclusion on what he wanted to achieve. This is what is known as participative leadership. Good relationships will enable everyone to feel at home and in a better position to change how they look at things because everybody wants to feel appreciated and loved but above all recognized in their own capacities (McKelvey, 2000). When the company has been able to positively influence the above four steps, it is now the time to hit on the last step that will be crucial in the implementation of organizational culture change. This is what is known as the behaviour change (Ashkanasy, 2000). TriZetto Company had this desire to change the way people behave and their attitude towards things and people was intended to change in the shortest time possible. The change in behaviour was the necessary step that would lead to the organization achieving the customer based focus as earlier stated. At long last, TriZetto Company was able to change its culture despite the critics that organizational cultures are deeply held and cannot be changed with ease at any one given time. Changing of the culture does not come without its fair share of challenges on the part of the employees and other people for whom the change is being introduced. The workers of this company felt as if the new CEO had come up with so much demand and he was up to manipulate their long standing status quo. This was an issue that needed to be looked into and Devanny was already too focused to ensure that the company was a better place to be and also ensure that the set goals were achieved. These critics were mostly contributed towards by the internal politics that are there in every organization and they are at times heavy. This change was however fruitful as the company workers were able to provide specialized services to their clients and they always aimed for higher goals thus leading to even some very innovative ideas among the people. This translated to the general success of the company and efficiency in the provision of the mission, vision, objectives and goals of the company. In conclusion, an organization that is intended to change its culture should not be afraid to go forward to ensure they meet their aspirations provided they are for the betterment of the company. The management should follow the right steps to reduce the stress levels among the employees who are to make the change effective. Change in organizational culture is important especially those that does not support the growth of the company or are influencing the performance of the employees negatively.

Ashkanasy, N. M., Wilderom, C., & Peterson, M. F. (2000). Handbook of organizational culture & climate. Thousand Oaks, Calif.: Sage Publications. McKelvey, M. D. (2000). Evolutionary innovations: The business of biotechnology. New York: Oxford University Press.

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How technology is reinventing education

Stanford Graduate School of Education Dean Dan Schwartz and other education scholars weigh in on what's next for some of the technology trends taking center stage in the classroom.

culture change research paper

Image credit: Claire Scully

New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of Stanford Graduate School of Education (GSE), who is also a professor of educational technology at the GSE and faculty director of the Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately worried that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or coach students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the AI + Education initiative at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of CRAFT (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the Digital Learning initiative at the Stanford Accelerator for Learning, which runs a program exploring the use of virtual field trips to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”

Gamification

Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”

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Creating a research culture in which our research and research community can thrive

13 February 2024

Professor Geraint Rees, Vice-Provost (Research, Innovation and Global Engagement) and Emma Todd, Director of Research Culture, share their thoughts on research culture at UCL, current initiatives and challenges, and progress made so far.

UCL Quad with Welcome flags

Improving our research culture involves tackling obvious as well as more subtle and intangible aspects of the research environment. For example, we need the right infrastructure and systems in place for people to do their best work – which is something that the UCL Strategic Plan 2022-27 prioritises – but we also need to consider how we conduct and share our research and the experience and careers of the individuals within our research community.

Research culture is a broad and contested term and will mean different things to different people. This is evidenced by the plans set out by peer institutions which all have a slightly different flavour, dependent on local priorities. But where there is agreement is on the importance of research culture to the academic and societal mission of universities. If we are to continue to deliver world-leading research, we need to continue to attract and retain the most talented people; and we need to ensure the conditions are in place for those people to do their best work.

There are also some very real financial reasons why we need to take this seriously; principally that research funders are increasingly including an assessment of research culture in funding decisions. If you’ve applied for a Wellcome or UKRI research grant in the past few years, or supported someone through the process, you’ve probably had to evidence how both UCL and you as an individual are actively supporting a positive research culture.

Similarly, REF2029 will also require us to provide not just a snapshot of our research culture through data, but evidence of how we’ve proactively improved elements of it. A chunk of our QR funding – around £40m p.a. based on current income – will depend on how well we can evidence that account.

A collaborative approach to enhancing our research culture

We started in 2021 by asking members of our research community what their vision of a positive research culture was, where we’re doing well and where there is room for improvement. And we’ve articulated this as a shared roadmap for change . We’re now working with colleagues across UCL – in faculties and functions – to actively progress against the goals in the roadmap.

Whilst we consistently perform well in national exercises such as REF and KEF – which suggests we’ve got many of the fundamentals right – you told us that research excellence can come at the expense of workplace experience and wellbeing. Some of the other things we heard are that research career progression is not as clear, equitable and merit-based as it should be; that research management and leadership is undervalued and not incentivised; and that research workloads are at times unmanageable.

We’re committed to making improvements in these areas, and more. And we’re committed to doing it in partnership with our research community. We have created a Community Steering Group, chaired by Dr Natalie Marchant , Associate Professor in Brain Sciences, and made up of a cross-section of the research community – who will act as the voice of the research community and champions for change.

Concrete steps we are taking to deliver change

While ‘research culture’ is a relatively new term, activity to support a fair, collaborative and inclusive research environment that underpins excellent research has been happening at UCL for many years, at all levels of the institution. Here are a few illustrative examples:

  • The BEAMS Research Coordination Office launched the Women and Large Grant Leadership training programme – now being extended to Life and Medical Sciences – which equips female academics with the skills, knowledge, support, and networks to successfully apply for large, complex and collaborative grants.
  • The Collaborative Social Science Domain Early Career Network – one of 18 grassroots ECNs at UCL – has developed a Collaborative Research Manifesto which includes 18 statements they hope will help researchers navigate a way forward together.
  • Professor Jo Van Herwegen in the IOE Centre for Education Policy and Equalising Opportunities has developed an Individual Development Plan (IDP) process that was first created to meet her own academic career needs and goals – but has grown to be used by over 70% of her departmental colleagues.  

You can read more case studies on the Research Culture website .

In addition to ensuring all this activity continues, we’re delivering a range of targeted initiatives. Here are just a few ways we’re responding to the issues you raised with us. We’re working on our web pages where we’ll endeavour to share progress updates on these initiatives and more.

We heard in our survey work that researcher promotion pathways are not as clear, equitable and merit-based as they should be. 17% fewer women agreed that they had equitable opportunities for career progression. 38% of ECRs versus 80% of established academics agreed that pathways were clear.

To address this, we are working with colleagues to review researcher promotions processes and practices, starting with grades 6-8, and will be developing guidance and making recommendations on good practice as a first step.

People management – which is critical to running research groups and setting local culture – is undervalued and not incentivised as it should be. Participants in focus groups and interviews referenced the large number of staff in leadership roles who have had no management training, and staff managing researchers expressed low confidence dealing with issues such as underperformance, with only 54% agreeing they felt confident with this aspect of management.

To respond to this, colleagues in Organisational Development set up the Experienced Principal Investigators programme , using research culture funding, and the Advancing Principal Investigators programmes to cater for the different leadership needs of both senior and emerging Principal Investigators. The team has also developed the People Management Essentials programme , which is open to all UCL colleagues with line-management responsibilities. We want to continue to discuss with colleagues across the institution how we can jointly incentivise and enable the prioritisation of people management in research groups and teams.

UCL is innovative, excellent and ambitious, but this excellence sometimes comes at the expense of individuals’ workplace experience and wellbeing. Only 59% of respondents in our institutional survey agreed that their working environment supports their mental health and wellbeing, which is lower than we would like, even though it is 12% higher than the sector average.

In December 2022 UCL was one of the first five institutions to be appointed the University Mental Health Charter . The recommendations from Student Minds highlighted the need for university-wide models of support for researchers to be developed. To respond to this, the Wellbeing team will be using research culture funding to pilot 90-minute training webinars for supervisors of PhD students. They’ll also be deepening their understanding of the wellbeing needs of UCL’s researcher community, which will enable them to design a tailored programme covering mental health, wellbeing and psychological safety in research contexts. The recently appointed Pro-Vice-Provosts for the Grand Challenge of Mental Health and Wellbeing will also be working to harness UCL’s rich collective expertise in this area for the benefit of our own university community, as well as society more broadly. 

Many of you are experiencing role expansion and a multitude of pressures that make it difficult to protect time for research and innovation and that have an impact on your work-life balance and wellbeing. Benchmarking data shows that UCL staff are 9% less likely than colleagues from peer institutions to agree that workloads are fair. 

To address this, a working group bringing together academic Heads of Department and colleagues from VP and Provost’s Offices – chaired by Professor Eloise Scotford, Dean of Laws – is working to better understand the 'excessive workload' phenomena. A survey of all Heads of Department in summer 2023 has provided a huge amount of useful data which will be used to understand academic workload causes at UCL. The working group has developed draft principles for academic workload and is developing recommendations to align UCL policies and tools to help empower departments to address excessive academic workload more effectively. The group will use this and other data to inform a set of recommendations for both short- and long-term action to address workload. These recommendations will be presented to the Academic Leadership Group for informal initial discussion and input in spring. Wider consultation on priorities and implications will be planned as appropriate and a final report will be presented to UMC in the summer.

A shared endeavour

Improving our research culture will require continued efforts from across UCL’s faculties and functions. It will require people with formal and informal authority to think about what they might do differently and how they can contribute to local research culture.

Some of the changes we can make as an institution – with your engagement and support of course – others are beyond the direct control of any single institution and will require joined-up work across the sector, with funders, publishers and other research bodies. We are committed to enhancing our research culture and we hope you’ll join us in this shared endeavour.

Find out more on the Research Culture website  or get in touch with the Research Culture team .

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Today's Paper | February 19, 2024

Cultural change for economy.

culture change research paper

THE topic of Pakistan’s non-ending economic quagmire elicits many proposals, some good, some reworded, and some repetitive. Rarely, if ever, is there a discussion on cultural aspects impinging on economic outcomes.

I argue that, for the economic transformation of the country, cultural change is as important as factors like investment, savings, fiscal policy, etc. Importantly, the argument centres not just on government and governance — which are the usual targets, for good reason — but also on society. What follows is a brief description of where and what aspects need a change.

Let us start with the sensitive topic of religion. When we talk of the economic performance we would like to emulate, one inescapable conclusion is that religion takes a back seat in the affairs of the state and society and has a temperate, minor influence. Pakistan, however, represents the case of a country and society where faith and extreme attitudes are deeply woven into the fabric of society, as shown recently in an excellent survey by Dr Durre-e-Nayab and her team (PIDE Basics Survey).

This leads to several repercussions. Take the example of the institution of insurance, one of the most effective instruments devised to lessen risks surrounding our lives. Pakistan’s insurance penetration rate is hardly 0.91 per cent, lower even among regional peers (in contrast, 90pc of households in Japan are covered by life insurance), mainly because the majority consider it to be against their religious beliefs. This, in turn, leads to spillovers that go far beyond economics. The search for physical and financial security to counter risks, for example, often leads individuals into the hands of extractive actors, who use them to get their illegal deeds done.

Pakistani society and our governments must give up their ‘let’s kill the rich’ attitude.

Ironically, it’s a form of slavery that religion prohibits, but the same beliefs are cited as a hindrance to accepting instruments like insurance or savings accounts. This mental outlook needs to change.

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The second aspect where a cultural change needs to take root is the acceptance of divergent points of view. Modern economic growth did not come about merely through erecting cemented structures. In fact, it was a long, protracted battle of ideas from which theories and practices of modern growth emerged. That transformation was underpinned by intellectual humility and tolerance of others’ opinion.

Such humility, unfortunately, is largely lacking in Pakistan. Across the country, what one usually encounters is the ‘my way or the highway’ approach. People frequently give opinions as if they know everything, repulsed by even the slightest impression that they may be wrong on some issue.

Sadly, this attitude extends to the economist community of Pakistan (generally speaking), their senseless grandstanding being based on limited reading and understanding of historical circumstances, all the while answering to their own inert biases.

Third, Pakistani society and our governments have to give up their ‘let’s kill the rich’ attitude. There is a dire need to realise that not every person in Pakistan who has made his/ her way to riches is a haramkhor . This attitude is anathema to wealth creation. There are enough examples of individuals who have become rich by dint of their hard work, dedication and industriousness.

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Yes, there is ample corruption as well as leeches who have accumulated wealth through dubious means. But more often than not, we find that the basis of their accumulation is support from governance structures (subsidies, fat contracts, lax application of laws that help them get away, etc.), which in turn perpetuates a culture of impunity, theft and corruption.

So let’s realise that wealth and wealth creation by genuine means is something to celebrate rather than scoff at.

Fourth, people need to recognise that there is no substitute for hard work (physical and mental). Shortcuts ( jugaar ) can only take you so far, and are never a good strategy to gain long-term success and credibility. Examples abound, from ‘professors’ who achieved the position through plagiarising papers to ‘double shah’-type characters, but a recent one would do.

Last year, Amazon suspended thousands of accounts from Pakistan for fraudulent business practices. It never occurred to the perpetrators that their behaviour would not only shunt them out of the largest online marketplace, but also severely diminish the chances of other honest entrepreneurs/ businessmen in Pakistan trying to establish themselves there. Neither did the government attempt to prosecute them for tarnishing the country’s already low repute.

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Put another way, the attempted jugaar may have severely dented our access to one of the largest firms in the world (Amazon’s market cap stands at $1.77 trillion). These kinds of jugaars and rip-offs are common across the country, in every sphere of life, and are basically a reflection of a lack of ethical and moral standing upon which a healthy, trust-enhancing society is built.

No wonder whether it is the life of Prophet Muhammad (PBUH) or the writings of Adam Smith (who wrote Theory of Moral Sentiments before his magnum opus, Wealth of Nations ), one finds heavy stress on fair, just and ethical dealings in matters of commerce and the economy.

Last, but not least, Pakistan cannot hope to have a transformed economy without women being an active part of the labour force. Its female labour force participation rates are lowest even amongst regional peers. The participation rates in KP and Balochistan are not even 12 per cent, with prevalent culture being a huge block. High fertility rates are one outcome of this low participation.

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The points and issues to ponder are many, but I will end by suggesting that both society and the government need to realise that economic growth and change is not merely about endless amounts of cement, steel, brick and mortar.

Nor would merely increasing the Public Sector Development Programme ensure economic prosperity (an issue to be taken up later). Breaking the cycle of economic backwardness has to be complemented by a cultural change that values ethics, morality and emancipation of mind from the clutches of obscurantism and mediocrity.

The writer is an economist and presently a Research Fellow at PIDE. The article constitutes his personal opinion.

[email protected]

X: ShahidMohmand79

Published in Dawn, February 16th, 2024

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MIT researchers remotely map crops, field by field

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Four Google Street View photos show rice, cassava, sugarcane, and maize fields.

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Four Google Street View photos show rice, cassava, sugarcane, and maize fields.

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Crop maps help scientists and policymakers track global food supplies and estimate how they might shift with climate change and growing populations. But getting accurate maps of the types of crops that are grown from farm to farm often requires on-the-ground surveys that only a handful of countries have the resources to maintain.

Now, MIT engineers have developed a method to quickly and accurately label and map crop types without requiring in-person assessments of every single farm. The team’s method uses a combination of Google Street View images, machine learning, and satellite data to automatically determine the crops grown throughout a region, from one fraction of an acre to the next. 

The researchers used the technique to automatically generate the first nationwide crop map of Thailand — a smallholder country where small, independent farms make up the predominant form of agriculture. The team created a border-to-border map of Thailand’s four major crops — rice, cassava, sugarcane, and maize — and determined which of the four types was grown, at every 10 meters, and without gaps, across the entire country. The resulting map achieved an accuracy of 93 percent, which the researchers say is comparable to on-the-ground mapping efforts in high-income, big-farm countries.

The team is applying their mapping technique to other countries such as India, where small farms sustain most of the population but the type of crops grown from farm to farm has historically been poorly recorded.

“It’s a longstanding gap in knowledge about what is grown around the world,” says Sherrie Wang, the d’Arbeloff Career Development Assistant Professor in MIT’s Department of Mechanical Engineering, and the Institute for Data, Systems, and Society (IDSS). “The final goal is to understand agricultural outcomes like yield, and how to farm more sustainably. One of the key preliminary steps is to map what is even being grown — the more granularly you can map, the more questions you can answer.”

Wang, along with MIT graduate student Jordi Laguarta Soler and Thomas Friedel of the agtech company PEAT GmbH, will present a paper detailing their mapping method later this month at the AAAI Conference on Artificial Intelligence.

Ground truth

Smallholder farms are often run by a single family or farmer, who subsist on the crops and livestock that they raise. It’s estimated that smallholder farms support two-thirds of the world’s rural population and produce 80 percent of the world’s food. Keeping tabs on what is grown and where is essential to tracking and forecasting food supplies around the world. But the majority of these small farms are in low to middle-income countries, where few resources are devoted to keeping track of individual farms’ crop types and yields.

Crop mapping efforts are mainly carried out in high-income regions such as the United States and Europe, where government agricultural agencies oversee crop surveys and send assessors to farms to label crops from field to field. These “ground truth” labels are then fed into machine-learning models that make connections between the ground labels of actual crops and satellite signals of the same fields. They then label and map wider swaths of farmland that assessors don’t cover but that satellites automatically do.

“What’s lacking in low- and middle-income countries is this ground label that we can associate with satellite signals,” Laguarta Soler says. “Getting these ground truths to train a model in the first place has been limited in most of the world.”

The team realized that, while many developing countries do not have the resources to maintain crop surveys, they could potentially use another source of ground data: roadside imagery, captured by services such as Google Street View and Mapillary, which send cars throughout a region to take continuous 360-degree images with dashcams and rooftop cameras.

In recent years, such services have been able to access low- and middle-income countries. While the goal of these services is not specifically to capture images of crops, the MIT team saw that they could search the roadside images to identify crops.

Cropped image

In their new study, the researchers worked with Google Street View (GSV) images taken throughout Thailand — a country that the service has recently imaged fairly thoroughly, and which consists predominantly of smallholder farms.

Starting with over 200,000 GSV images randomly sampled across Thailand, the team filtered out images that depicted buildings, trees, and general vegetation. About 81,000 images were crop-related. They set aside 2,000 of these, which they sent to an agronomist, who determined and labeled each crop type by eye. They then trained a convolutional neural network to automatically generate crop labels for the other 79,000 images, using various training methods, including iNaturalist — a web-based crowdsourced  biodiversity database, and GPT-4V, a “multimodal large language model” that enables a user to input an image and ask the model to identify what the image is depicting. For each of the 81,000 images, the model generated a label of one of four crops that the image was likely depicting — rice, maize, sugarcane, or cassava.

The researchers then paired each labeled image with the corresponding satellite data taken of the same location throughout a single growing season. These satellite data include measurements across multiple wavelengths, such as a location’s greenness and its reflectivity (which can be a sign of water). 

“Each type of crop has a certain signature across these different bands, which changes throughout a growing season,” Laguarta Soler notes.

The team trained a second model to make associations between a location’s satellite data and its corresponding crop label. They then used this model to process satellite data taken of the rest of the country, where crop labels were not generated or available. From the associations that the model learned, it then assigned crop labels across Thailand, generating a country-wide map of crop types, at a resolution of 10 square meters.

This first-of-its-kind crop map included locations corresponding to the 2,000 GSV images that the researchers originally set aside, that were labeled by arborists. These human-labeled images were used to validate the map’s labels, and when the team looked to see whether the map’s labels matched the expert, “gold standard” labels, it did so 93 percent of the time.

“In the U.S., we’re also looking at over 90 percent accuracy, whereas with previous work in India, we’ve only seen 75 percent because ground labels are limited,” Wang says. “Now we can create these labels in a cheap and automated way.”

The researchers are moving to map crops across India, where roadside images via Google Street View and other services have recently become available.

“There are over 150 million smallholder farmers in India,” Wang says. “India is covered in agriculture, almost wall-to-wall farms, but very small farms, and historically it’s been very difficult to create maps of India because there are very sparse ground labels.”

The team is working to generate crop maps in India, which could be used to inform policies having to do with assessing and bolstering yields, as global temperatures and populations rise.

“What would be interesting would be to create these maps over time,” Wang says. “Then you could start to see trends, and we can try to relate those things to anything like changes in climate and policies.”

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BREAKING: Navalny's widow accuses Kremlin of hiding opposition leader's body to cover up his murder

A once-ignored community of science sleuths now has the research community on its heels

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A community of sleuths hunting for errors in scientific research have sent shockwaves through some of the most prestigious research institutions in the world — and the science community at large.

High-profile cases of alleged image manipulations in papers authored by the former president at Stanford University and leaders at the Dana-Farber Cancer Institute have made national media headlines, and some top science leaders think this could be just the start.

“At the rate things are going, we expect another one of these to come up every few weeks,” said Holden Thorp, the editor-in-chief of the Science family of scientific journals, whose namesake publication is one of the two most influential in the field. 

The sleuths argue their work is necessary to correct the scientific record and prevent generations of researchers from pursuing dead-end topics because of flawed papers. And some scientists say it’s time for universities and academic publishers to reform how they address flawed research. 

“I understand why the sleuths finding these things are so pissed off,” said Michael Eisen, a biologist, the former editor of the journal eLife and a prominent voice of reform in scientific publishing. “Everybody — the author, the journal, the institution, everybody — is incentivized to minimize the importance of these things.” 

For about a decade, science sleuths unearthed widespread problems in scientific images in published papers, publishing concerns online but receiving little attention. 

That began to change last summer after then-Stanford President Marc Tessier-Lavigne, who is a neuroscientist, stepped down from his post after scrutiny of alleged image manipulations in studies he helped author and a report criticizing his laboratory culture. Tessier-Lavigne was not found to have engaged in misconduct himself, but members of his lab appeared to manipulate images in dubious ways, a report from a scientific panel hired to examine the allegations said. 

In January, a scathing post from a blogger exposed questionable work from top leaders at the Dana-Farber Cancer Institute , which subsequently asked journals to retract six articles and issue corrections for dozens more. 

In a resignation statement , Tessier-Lavigne noted that the panel did not find that he knew of misconduct and that he never submitted papers he didn’t think were accurate. In a statement from its research integrity officer, Dana-Farber said it took decisive action to correct the scientific record and that image discrepancies were not necessarily evidence an author sought to deceive. 

“We’re certainly living through a moment — a public awareness — that really hit an inflection when the Marc Tessier-Lavigne matter happened and has continued steadily since then, with Dana-Farber being the latest,” Thorp said. 

Now, the long-standing problem is in the national spotlight, and new artificial intelligence tools are only making it easier to spot problems that range from decades-old errors and sloppy science to images enhanced unethically in photo-editing software.  

This heightened scrutiny is reshaping how some publishers are operating. And it’s pushing universities, journals and researchers to reckon with new technology, a potential backlog of undiscovered errors and how to be more transparent when problems are identified. 

This comes at a fraught time in academic halls. Bill Ackman, a venture capitalist, in a post on X last month discussed weaponizing artificial intelligence to identify plagiarism of leaders at top-flight universities where he has had ideological differences, raising questions about political motivations in plagiarism investigations. More broadly, public trust in scientists and science has declined steadily in recent years, according to the Pew Research Center .

Eisen said he didn’t think sleuths’ concerns over scientific images had veered into “McCarthyist” territory.

“I think they’ve been targeting a very specific type of problem in the literature, and they’re right — it’s bad,” Eisen said. 

Scientific publishing builds the base of what scientists understand about their disciplines, and it’s the primary way that researchers with new findings outline their work for colleagues. Before publication, scientific journals consider submissions and send them to outside researchers in the field for vetting and to spot errors or faulty reasoning, which is called peer review. Journal editors will review studies for plagiarism and for copy edits before they’re published. 

That system is not perfect and still relies on good-faith efforts by researchers to not manipulate their findings.

Over the past 15 years, scientists have grown increasingly concerned about problems that some researchers were digitally altering images in their papers to skew or emphasize results. Discovering irregularities in images — typically of experiments involving mice, gels or blots — has become a larger priority of scientific journals’ work.   

Jana Christopher, an expert on scientific images who works for the Federation of European Biochemical Societies and its journals, said the field of image integrity screening has grown rapidly since she began working in it about 15 years ago. 

At the time, “nobody was doing this and people were kind of in denial about research fraud,” Christopher said. “The common view was that it was very rare and every now and then you would find someone who fudged their results.” 

Today, scientific journals have entire teams dedicated to dealing with images and trying to ensure their accuracy. More papers are being retracted than ever — with a record 10,000-plus pulled last year, according to a Nature analysis . 

A loose group of scientific sleuths have added outside pressure. Sleuths often discover and flag errors or potential manipulations on the online forum PubPeer. Some sleuths receive little or no payment or public recognition for their work.

“To some extent, there is a vigilantism around it,” Eisen said. 

An analysis of comments on more than 24,000 articles posted on PubPeer found that more than 62% of comments on PubPeer were related to image manipulation. 

For years, sleuths relied on sharp eyes, keen pattern recognition and an understanding of photo manipulation tools. In the past few years, rapidly developing artificial intelligence tools, which can scan papers for irregularities, are supercharging their work. 

Now, scientific journals are adopting similar technology to try to prevent errors from reaching publication. In January, Science announced that it was using an artificial intelligence tool called Proofig to scan papers that were being edited and peer-reviewed for publication. 

Thorp, the Science editor-in-chief, said the family of six journals added the tool “quietly” into its workflow about six months before that January announcement. Before, the journal was reliant on eye-checks to catch these types of problems. 

Thorp said Proofig identified several papers late in the editorial process that were not published because of problematic images that were difficult to explain and other instances in which authors had “logical explanations” for issues they corrected before publication.

“The serious errors that cause us not to publish a paper are less than 1%,” Thorp said.

In a statement, Chris Graf, the research integrity director at the publishing company Springer Nature, said his company is developing and testing “in-house AI image integrity software” to check for image duplications. Graf’s research integrity unit currently uses Proofig to help assess articles if concerns are raised after publication. 

Graf said processes varied across its journals, but that some Springer Nature publications manually check images for manipulations with Adobe Photoshop tools and look for inconsistencies in raw data for experiments that visualize cell components or common scientific experiments.

“While the AI-based tools are helpful in speeding up and scaling up the investigations, we still consider the human element of all our investigations to be crucial,” Graf said, adding that image recognition software is not perfect and that human expertise is required to protect against false positives and negatives. 

No tool will catch every mistake or cheat. 

“There’s a lot of human beings in that process. We’re never going to catch everything,” Thorp said. “We need to get much better at managing this when it happens, as journals, institutions and authors.”

Many science sleuths had grown frustrated after their concerns seemed to be ignored or as investigations trickled along slowly and without a public resolution.  

Sholto David, who publicly exposed concerns about Dana-Farber research in a blog post, said he largely “gave up” on writing letters to journal editors about errors he discovered because their responses were so insufficient. 

Elisabeth Bik, a microbiologist and longtime image sleuth, said she has frequently flagged image problems and “nothing happens.” 

Leaving public comments questioning research figures on PubPeer can start a public conversation over questionable research, but authors and research institutions often don’t respond directly to the online critiques. 

While journals can issue corrections or retractions, it’s typically a research institution’s or a university’s responsibility to investigate cases. When cases involve biomedical research supported by federal funding, the federal Office of Research Integrity can investigate. 

Thorp said the institutions need to move more swiftly to take responsibility when errors are discovered and speak plainly and publicly about what happened to earn the public’s trust.  

“Universities are so slow at responding and so slow at running through their processes, and the longer that goes on, the more damage that goes on,” Thorp said. “We don’t know what happened if instead of launching this investigation Stanford said, ‘These papers are wrong. We’re going to retract them. It’s our responsibility. But for now, we’re taking the blame and owning up to this.’” 

Some scientists worry that image concerns are only scratching the surface of science’s integrity issues — problems in images are simply much easier to spot than data errors in spreadsheets. 

And while policing bad papers and seeking accountability is important, some scientists think those measures will be treating symptoms of the larger problem: a culture that rewards the careers of those who publish the most exciting results, rather than the ones that hold up over time. 

“The scientific culture itself does not say we care about being right; it says we care about getting splashy papers,” Eisen said. 

Evan Bush is a science reporter for NBC News. He can be reached at [email protected].

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UK signals step change for regulators to strengthen AI leadership

The UK is on course for more agile AI regulation, as the government publishes its response to the AI Regulation White Paper consultation today.

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  • Over £100 million to support regulators and advance research and innovation on AI , including Hubs in healthcare and chemical discovery
  • Key regulators asked to publish plans by end of April for how they are responding to AI risks and opportunities
  • UK government makes case for introducing future targeted, binding requirements for most advanced general-purpose AI systems

The UK is on course for more agile AI regulation, backing regulators with the skills and tools they need to address the risks and opportunities of AI , as part of the government’s response to the AI Regulation White Paper consultation today (6 February).

It comes as £10 million is announced to prepare and upskill regulators to address the risks and harness the opportunities of this defining technology. The fund will help regulators develop cutting-edge research and practical tools to monitor and address risks and opportunities in their sectors, from telecoms and healthcare to finance and education. For example, this might include new technical tools for examining AI systems.

Many regulators have already taken action. For example, the Information Commissioner’s Office has updated guidance on how our strong data protection laws apply to AI systems that process personal data to include fairness and has continued to hold organisations to account, such as through the issuing of enforcement notices. However, the UK government wants to build on this by further equipping them for the age of AI as use of the technology ramps up. The UK’s agile regulatory system will simultaneously allow regulators to respond rapidly to emerging risks, while giving developers room to innovate and grow in the UK.

In a drive to boost transparency and provide confidence to British businesses and citizens, key regulators, including Ofcom and the Competition and Markets Authority, have been asked to publish their approach to managing the technology by 30 April. It will see them set out AI -related risks in their areas, detail their current skillset and expertise to address them, and a plan for how they will regulate AI over the coming year.

This forms part of the AI regulation white paper consultation response, published today, which carves out the UK’s own approach to regulation and which will ensure it can quickly adapt to emerging issues and avoid placing burdens on business which could stifle innovation. This approach to AI regulation will mean the UK can be more agile than competitor nations, while also leading on AI safety research and evaluation, charting a bold course for the UK to become a leader in safe, responsible AI innovation.

The technology is rapidly developing, and the risks and most appropriate mitigations, are still not fully understood. The UK government will not rush to legislate, or risk implementing ‘quick-fix’ rules that would soon become outdated or ineffective. Instead, the government’s context-based approach means existing regulators are empowered to address AI risks in a targeted way.

The UK government has for the first time, however, set out its initial thinking for future binding requirements which could be introduced for developers building the most advanced AI systems - to ensure they are accountable for making these technologies sufficiently safe.

Secretary of State for Science, Innovation, and Technology, Michelle Donelan said: 

The UK’s innovative approach to AI regulation has made us a world leader in both AI safety and AI development. I am personally driven by AI ’s potential to transform our public services and the economy for the better – leading to new treatments for cruel diseases like cancer and dementia, and opening the door to advanced skills and technology that will power the British economy of the future. AI is moving fast, but we have shown that humans can move just as fast. By taking an agile, sector-specific approach, we have begun to grip the risks immediately, which in turn is paving the way for the UK to become one of the first countries in the world to reap the benefits of AI safely.

Meanwhile, nearly £90 million will go towards launching nine new research hubs across the UK and a partnership with the US on responsible AI . The hubs will support British AI expertise in harnessing the technology across areas including healthcare, chemistry, and mathematics.

£2 million of Arts and Humanities Research Council ( AHRC ) funding is also being announced today, which will support new research projects that will help to define what responsible AI looks like across sectors such as education, policing and the creative industries. These projects are part of the AHRC ’s Bridging Responsible AI Divides ( BRAID ) programme.

£19 million will also go towards 21 projects to develop innovative trusted and responsible AI and machine learning solutions to accelerate deployment of these technologies and drive productivity. This will be funded through the Accelerating Trustworthy AI Phase 2 competition, supported through the UKRI Technology Missions Fund, and delivered by the Innovate UK BridgeAI programme.

The government will also be launching a steering committee in spring to support and guide the activities of a formal regulator coordination structure within government in the spring. 

These measures sit alongside the £100 million invested by the government in the world’s first AI Safety Institute to evaluate the risks of new AI models, and the global leadership shown by hosting the world’s first major summit on AI safety at Bletchley Park in November.

The groundbreaking International Scientific Report on Advanced AI Safety which was unveiled at the summit will also help to build a shared evidence-based understanding of frontier AI ,  while the work of the AI Safety Institute will see the UK working closely with international partners to boost our ability to evaluate and research AI models.

The UK further commits to this approach today with an investment of £9 million through the government’s International Science Partnerships Fund, bringing together researchers and innovators in the UK and the United States to focus on developing safe, responsible, and trustworthy AI .

The government’s response lays out a pro-innovation case for further targeted binding requirements on the small number of organisations that are currently developing highly capable general-purpose AI systems, to ensure that they are accountable for making these technologies sufficiently safe. This would build on steps the UK’s expert regulators are already taking to respond to AI risks and opportunities in their domains.

Hugh Milward, Vice-President, External Affairs Microsoft UK said:

The decisions we take now will determine AI ’s potential to grow our economy, revolutionise public services and tackle major societal challenges and we welcome the government’s response to the AI White Paper. Seizing this opportunity will require responsible and flexible regulation that supports the UK’s global leadership in the era of AI ”.

Aidan Gomez, Co-Founder and CEO of Cohere, said:

By reaffirming its commitment to an agile, principles-and-context based, regulatory approach to keep pace with a rapidly advancing technology the UK government is emerging as a global leader in AI policy. The UK is building an AI -governance framework that both embraces the transformative benefits of AI , while being able to address emerging risks.

Lila Ibrahim, Chief Operating Officer, Google DeepMind:

I welcome the UK government’s statement on the next steps for AI regulation, and the balance it strikes between supporting innovation and ensuring AI is used safely and responsibly. The hub and spoke model will help the UK benefit from the domain expertise of regulators, as well as provide clarity to the AI ecosystem - and I’m particularly supportive of the commitment to support regulators with further resources. AI represents an opportunity to drive progress for humanity, and we look forward to working with the government to ensure that the UK can continue to be a global leader in AI research and set the standard for good regulation.

Tommy Shaffer Shane, AI Policy Advisor at the Centre for Long-Term Resilience, said:

We’re pleased to see this update to the government’s thinking on AI regulation, and especially the firm recognition that new legislation will be needed to address the risks posed by rapid developments in highly-capable general purpose systems. Moving quickly here while thinking carefully about the details will be crucial to balancing innovation and risk mitigation, and to the UK’s international leadership in AI governance more broadly. We look forward to seeing the government work through this challenge at pace, and to further updates on the approach to new legislation in the coming weeks and months.

Julian David, CEO at techUK said:  

techUK welcomes the government’s commitment to the pro-innovation and pro-safety approach set out in the AI Whitepaper. We now need to move forward at speed, delivering the additional funding for regulators and getting the Central Function up and running. Our next steps must also include bringing a range of expertise into government, identifying the gaps in our regulatory system and assessing the immediate risks. If we achieve this the Whitepaper is well placed to provide the regulatory clarity needed to support innovation, and the adoption of AI technologies, that promises such vast potential for the UK.”  

Kate Jones, Chief Executive of the Digital Regulation Cooperation Forum (DRCF), said:

The DRCF member regulators are all keen to maximise the benefits of AI for individuals, society and the economy, while managing its risks effectively and proportionately. To that end, we are taking significant steps to implement the White Paper principles, and are collaborating closely on areas of shared interest including our forthcoming AI and Digital Hub pilot service for innovators.

John Boumphrey, UK Country Manager of Amazon said:

Amazon supports the UK’s efforts to establish guardrails for AI , while also allowing for continued innovation. As one of the world’s leading developers and deployers of AI tools and services, trust in our products is one of our core tenets and we welcome the overarching goal of the white paper. We encourage policymakers to continue pursuing an innovation-friendly and internationally coordinated approach, and we are committed to collaborating with government and industry to support the safe, secure, and responsible development of AI technology.

Markus Anderljung, Head of Policy, Centre for the Governance of AI said:

The UK’s approach to AI regulation is evolving in a positive direction: it heavily relies on existing regulators, takes concrete steps to support them, while also investing in identifying and addressing gaps in the regulatory ecosystem. I am particularly pleased that the response acknowledges the need to address one such gap that has become more apparent since the white paper’s publication: how the most impactful and compute-intensive AI systems are developed and deployed onto the market.

The consultation has highlighted the strong support for the five cross-sectoral principles which are the foundation of the UK’s approach and include safety, transparency, fairness, and accountability.

The publication of the AI Regulation White Paper last March laid the foundations for the UK’s approach to regulating AI by driving safe, responsible innovation. This common sense, pragmatic approach will now be further strengthened by robust regulator expertise, allowing people across the country to safely harness the benefits of AI for years to come.

Notes to editors

Read the full government response to the AI White Paper consultation .

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    DOI: 10.1177/1745691617699971 Authors: Michael E W Varnum Arizona State University Igor Grossmann University of Waterloo Abstract and Figures More than half a century of cross-cultural research...

  2. Cultural Change: The How and the Why

    Cultural Change: The How and the Why Michael E. W. Varnum and Igor Grossmann View all authors and affiliations Volume 12, Issue 6 https://doi.org/10.1177/1745691617699971 Contents Get access More Abstract

  3. Culture Creation and Change: Making Sense of the Past to Inform Future

    First published online April 11, 2022 Culture Creation and Change: Making Sense of the Past to Inform Future Research Agendas Yeun Joon Kim https://orcid.org/0000-0001-9400-1386, Soo Min Toh, and Sooyun Baik View all authors and affiliations Volume 48, Issue 6 https://doi.org/10.1177/01492063221081031 PDF / ePub More Abstract

  4. PDF Dissertation Individual Perceptions of Culture and Change: a Unifying

    INDIVIDUAL PERCEPTIONS OF CULTURE AND CHANGE: A UNIFYING PERSPECTIVE ON CHANGE-ORIENTED ORGANIZATIONAL CULTURES Submitted by James W. Weston ... 1519438), part of my role was to research and create theoretical papers to publish in the areas of organizational culture and change. As such, the idea and very preliminary research for this ...

  5. The concept of culture: Introduction to spotlight series on

    The papers encompass other issues as well (e.g., culture as dynamic and changing, culture as constructed by people, applied implications, methodological implications), and ultimately raise many further questions about culture and development that will hopefully inspire developmentalists to think deeply about the concept of culture and to ...

  6. A systems approach to cultural evolution

    Research in cultural evolution aims at understanding and explaining cultural change at multiple causal levels (e.g., Mesoudi, 2011; Colleran and Mace, 2015; Gjesfjeld et al., 2016). Culture, like ...

  7. Changing cultures, changing brains: A framework for integrating

    Volume 162, May 2021, 108087 Changing cultures, changing brains: A framework for integrating cultural neuroscience and cultural change research Jung Yul Kwon , Alexandra S. Wormley , Michael E.W. Varnum Add to Mendeley https://doi.org/10.1016/j.biopsycho.2021.108087 Get rights and content •

  8. Culture Creation and Change: Making Sense of the Past to Inform Future

    tant insights into culture creation and change. We first reviewed 10 leading journals in management, adapting the approach of past reviews (e.g., Tsui et al., 2007). We first read titles and abstracts to determine whether the papers examined antecedents of cultures. If it was not apparent from the titles and abstracts,

  9. Research Culture: Changing how we evaluate research is ...

    Culture change is often driven by the collective force of individual actions. ... This emphasis on a handful of papers helps focus the review evaluation on the quality and impact of the Investigator's work. ... has executed a similarly deep dive into its research culture. In 2017, as part of efforts to improve its research and research ...

  10. Cultural change Research Papers

    A second importance of this research is that it may be useful for future studies seeking to understand the processes of cultural change and cultural identity in the community and by extension in Belize. APA Citation: Manzaneres, M., & Cocom, R. (2015). Cultural change in Gales Point Manatee: Auto-ethnographic reflections from a community member.

  11. Culture Change Research Paper

    Culture Change Research Paper View sample culture research paper on culture change. Browse other research paper examples for more inspiration. If you need a thorough research paper written according to all the academic standards, you can always turn to our experienced writers for help. This is how your paper can get an A!

  12. Full article: How changing organizational culture can enhance

    As postulated earlier, this research aims to develop a framework that helps any given organization in enhancing its desired innovation type with the aim of improving its competitiveness in the market. Determining existing culture types and innovation types in the organization are the first and second steps of the framework, respectively.

  13. Corporate culture: Evidence from the field

    1. Introduction. Corporate culture is claimed to be an important driver of business value. However, there are many unanswered research questions, such as: how do we define and measure corporate culture, is it possible to assign a dollar value to culture, what is the relation between the elements that constitute a firm's culture and performance, do leaders invest enough in culture, and how do ...

  14. Frontiers

    Introduction. Change blindness is defined as the failure to detect when a change is made to a visual stimulus (Simons and Levin, 1997).It occurs when the local visual transient produced by a change is obscured by a larger visual transient, such as an eye blink (O'Regan et al., 2000), saccadic eye movement (Grimes, 1996; McConkie and Currie, 1996), screen flicker (Rensink et al., 1997), or a ...

  15. PDF National Safety Culture Change Initiative

    for a cultural change within the fire service relating to safety; incorporating leadership, management, supervision, accountability, and personal responsibility (NFFF, 2011), is an over-arching initiative, acknowledging that the organi-zational culture of the fire service must undergo a change to accept the other 15 recommendations.

  16. Cultural Research Paper Topics: 150+ Ideas for Students

    Blog Research paper Explore Our Top Cultural Research Paper Topics Updated 19 May 2023 Culture is a broad term that covers an endless number of possibilities for crafting research topics. You can view it as a global aspect and write a research paper about culture inherent in all of humanity.

  17. Culture Change Research Paper

    Research Paper On Culture Change Type of paper: Research Paper Topic: Workplace, Culture, Company, Development, Employee, Organization, Goals, Belief Pages: 6 Words: 1600 Published: 01/29/2020 ORDER PAPER LIKE THIS Organizational culture is the deeply held beliefs, behaviours and a certain way of working or conduct in a given company.

  18. How technology is reinventing K-12 education

    Study finds public pension plans on shaky ground. New research calls attention to a huge funding gap and growing risk exposure, raising alarms about the long-term viability of government pensions.

  19. Creating a research culture in which our research and research ...

    Concrete steps we are taking to deliver change. While 'research culture' is a relatively new term, activity to support a fair, collaborative and inclusive research environment that underpins excellent research has been happening at UCL for many years, at all levels of the institution. Here are a few illustrative examples:

  20. The determinants of organizational change management success

    Several studies have highlighted that most organizational change initiatives fail, with an estimated failure rate of 60-70%. 1, 5, 6 High failure rate raises the sustained concern and interest about the factors that can decrease failure and increase the success of organizational change. 7 Researchers and consultancy firms have developed several ...

  21. Cultural change for economy

    THE topic of Pakistan's non-ending economic quagmire elicits many proposals, some good, some reworded, and some repetitive. Rarely, if ever, is there a discussion on cultural aspects impinging ...

  22. 3 Truths for Ministering After the Chaos

    2. Remember change today is not a criticism of the past. One reason our churches reject change is the misunderstanding that we are looking at what we (or our family) did in the past and judging it as a mistake. As church leaders, we need to help our people recognize that those who went before us were serving a different culture and in a ...

  23. MIT researchers remotely map crops, field by field

    A new method can remotely map crop types in low- or middle-income countries where agricultural data are sparse. The maps will help scientists and policymakers track global food supplies and estimate how they might shift with climate change and growing populations.

  24. A once-ignored community of science sleuths now has the research

    A community of sleuths hunting for errors in scientific research have sent shockwaves through some of the most prestigious research institutions in the world — and the science community at large.

  25. UK signals step change for regulators to strengthen AI leadership

    Research and statistics. Reports, analysis and official statistics. Policy papers and consultations. Consultations and strategy. Transparency. Data, Freedom of Information releases and corporate ...