Articles on Developmental psychology

Displaying 1 - 20 of 30 articles.

research studies on developmental psychology

Advertising toys to children is an environmental nightmare – here’s how parents can deal with it

Michelle Cowley-Cunningham , Dublin City University

research studies on developmental psychology

Secure attachment to both parents − not just mothers − boosts children’s healthy development

Or Dagan , Long Island University Post and Carlo Schuengel , Vrije Universiteit Amsterdam

research studies on developmental psychology

Children, like adults, tend to underestimate how welcome their random acts of kindness will be

Margaret Echelbarger , Stony Brook University (The State University of New York)

research studies on developmental psychology

At what age are people usually happiest? New research offers surprising clues

Clare Mehta , Emmanuel College

research studies on developmental psychology

10 parenting strategies to reduce your kids’ pandemic stress

Amanda Sheffield Morris , Oklahoma State University and Jennifer Hays-Grudo , Oklahoma State University

research studies on developmental psychology

Teens are wired to resent being stuck with parents and cut off from friends during coronavirus lockdown

Catherine Bagwell , Emory University

research studies on developmental psychology

Isolating together is challenging – and relationship stresses can affect biological functioning

Hannah L. Schacter , Wayne State University

research studies on developmental psychology

Knowledge is a process of discovery: how constructivism changed education

Luke Zaphir , The University of Queensland

research studies on developmental psychology

Grudges come naturally to kids – gratitude must be taught

Nadia Chernyak , University of California, Irvine ; Peter Blake , Boston University , and Yarrow Dunham , Yale University

research studies on developmental psychology

Father’s Day: Lesser-known ways dads improve children’s lives

Audrey-Ann Deneault , L’Université d’Ottawa/University of Ottawa

research studies on developmental psychology

Adolescents have a fundamental need to contribute

Andrew J. Fuligni , University of California, Los Angeles

research studies on developmental psychology

Children are natural optimists – which comes with psychological pros and cons

Janet J. Boseovski , University of North Carolina – Greensboro

research studies on developmental psychology

When can you buy a gun, vote or be sentenced to death? Science suggests US should revise legal age limits

Laurence Steinberg , Temple University

research studies on developmental psychology

Teens aren’t just risk machines – there’s a method to their madness

Jessica Flannery , University of Oregon ; Elliot Berkman , University of Oregon , and Jennifer Pfeifer , University of Oregon

research studies on developmental psychology

More children are starting school depressed and anxious – without help, it will only get worse

Dr Amelia Shay , Australian Catholic University and Cen Wang , Charles Sturt University

research studies on developmental psychology

Lies about Santa? They could be good for your child

Kristen Dunfield , Concordia University

research studies on developmental psychology

For baby’s brain to benefit, read the right books at the right time

Lisa S. Scott , University of Florida

research studies on developmental psychology

How to combat racial bias: Start in childhood

Gail Heyman , University of California, San Diego

research studies on developmental psychology

Babies can learn the value of persistence by watching grownups stick with a challenge

Julia Leonard , Massachusetts Institute of Technology (MIT)

research studies on developmental psychology

Watching children learn how to lie

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The Place of Development in the History of Psychology and Cognitive Science

In this article, I analyze how the relationship of developmental psychology with general psychology and cognitive science has unfolded. This historical analysis will provide a background for a critical examination of the present state of the art. I shall argue that the study of human mind is inherently connected with the study of its development. From the beginning of psychology as a discipline, general psychology and developmental psychology have followed parallel and relatively separated paths. This separation between adult and child studies has also persisted with the emergence of cognitive science. The reason is due essentially to methodological problems that have involved not only research methods but also the very object of inquiry. At present, things have evolved in many ways. Psychology and cognitive science have enlarged their scope to include change process and the interaction between mind and environment. On the other hand, the possibility of using experimental methods to study infancy has allowed us to realize the complexity of young humans. These facts have paved the way for new possibilities of convergence, which are eliciting interesting results, despite a number of ongoing problems related to methods.

Introduction

In this paper, I intend to analyze how the relationship of developmental psychology to general psychology and cognitive science has unfolded. This historical analysis will provide a background for a critical examination of the present state of the art.

Psychology emerged as a scientific discipline with the founding of Wundt’s Laboratory in Leipzig at the end of the nineteenth century (1879) 1 . Wundt’s method, both experimental and introspective, was directed to the study of an adult’s mind and behavior. It is less well-known that only 10 years later, James Baldwin, who had attended Wundt’s seminars in Germany, founded a laboratory of experimental psychology in Toronto in which experiments devoted to the study of mental development were performed. If the occasion that aroused Baldwin’s interest was the birth of his first daughter, actually, “that interest in the problems of genesis–origin, development, evolution–became prominent; the interest which was to show itself in all the subsequent years” ( Baldwin, 1930 ). Baldwin’s work was a source of inspiration for Piaget, certainly one of the most prominent figures in developmental psychology ( Morgan and Harris, 2015 ).

From the origins of psychology as a discipline, general psychology and developmental psychology have followed parallel and relatively separate paths. Two questions are particularly relevant to explain this fact.

From a theoretical point of view, developmental psychology has all along been greatly influenced by biology and evolutionary theory. The founders of developmental psychology have widely analyzed the relation between ontogenesis and phylogenesis ( Baldwin, 1895 ; Piaget, 1928 ). This analysis resulted in accepting the challenge of explaining development in a broad sense. In his autobiography, Baldwin affirms that already in the 10 years that he spent in Princeton between 1893 and 1903, where he founded another laboratory of experimental psychology, “the new interest in genetic psychology and general biology had become absorbing, and the meagerness of the results of the psychological laboratories (apart from direct work on sensation and movement) was becoming evident everywhere.” Thus, developmental psychology has followed an approach that in general psychology appeared much later 2 .

A second question regards method. Developmental researchers, while manifesting their attachment to experimental procedures, have been confronted with their insufficiency in the study of development. Both for deontological and practical reasons, many aspects of development, in particular in infants and young children, can hardly be investigated experimentally. Thus, a great number of studies in developmental psychology make use of observational methods based on different techniques such as ethnographic methods or parent reports, and the reliability of these methods has been questioned.

This relative separation between studies of adults and children has also persisted with the emergence of cognitive science. Actually, the primary aim of cognitive science, at least at the outset, was to model what we could call an adult static mind. Given a certain output, for instance an action, the task of the psychologist was to reconstruct the inference processes that were at the origin of this same action.

At the beginning of the twenty-first century, psychology and cognitive science have enlarged their scope to include change processes and the interaction between mind and environment, including other minds. Developmental psychology, for its part, has developed nonverbal methods such as looking measures and choice measures that also make it possible to carry out experiments with infants. These facts have paved the way for new possibilities of convergence, which are eliciting interesting results, despite a number of ongoing problems related to methods.

Psychology, Cognitive Science and Artificial Intelligence

The beginning of cognitive science.

According to the American psychologist George Miller, cognitive science was born on September 11, 1956, the second day of the Second Symposium on Information Theory held at MIT. That day began with a paper read by Allen Newell and Herbert Simon on the state of art of the Logic Theory Machine: a proof on computer of theorem 2.01 of Whitehead and Russell’s Principia Mathematica . That very same day ended with the first version of Chomsky’s The Structures of Syntax . Miller left the symposium convinced that experimental psychology, theoretical linguistics, and computer simulation of cognitive processes could become parts of a wider whole and that the future of research would be found in the elaboration of this composite whole (reported in Bruner, 1983a ). It is Miller who in 1960, together with Eugene Galanter and Karl Pribram, authored a text that may be considered the manifesto of cognitive science and that proclaimed the encompassing of cognitive psychology within the more general framework of information processing ( Miller et al., 1960 ). The assumption was that newly born information science could provide a unifying framework for the study of cognitive systems ( Schank and Abelson, 1977 ).

From a theoretical point of view, the core of this project is the concept of representation. Intentional mental states, such as beliefs and perceptions, are defined as relations to mental representations. The semantic properties of mental representations explain intentionality ( Pitt, 2017 ). Representations can be computed and thus constitute the basis for some forms of logic systems. According to the Cognitive Science Committee (1978) , which drew up a research project for the Sloan Foundation, all those disciplines, which belong to cognitive science, share the common goal of investigating the representational and computational capacities of the mind and the structural and functional realization of these capacities in the brain.

This point of view constitutes the foundation for what has been called functionalism in the philosophy of mind, i.e., the hypothesis that what defines the mind are those features that are independent of its natural realization. The classic functionalist stance is expressed by Pylyshyn in his book on computation and cognition ( Pylyshyn, 1984 ). He maintains that a clear distinction must be made between the functional architecture of the cognitive system and the rules and representations that the system employs.

Functionalism has been greatly discussed and criticized from the beginning ( Block, 1978 ; Dreyfus, 1979 ). Harnad (1990) identified what has been defined as the symbol grounding problem : “How can the semantic interpretation of a formal symbol system be made intrinsic to the system?”

The most exhaustive and most deeply argued critique of functionalism was advanced by Searle, who developed his arguments over time, publishing a number of essays which have given rise to heated debate ( Searle, 1980 , 1990 , 1992 ). The position taken up by functionalism is that the relationship between the brain and its products, that is to say conscious processes, is mediated by an intermediate level of unconscious rules. This intermediate level is, for functionalists, the level of the program. It is postulated that the rules are computational and that, consequently, the aim of research in cognitive science is to reconstruct these rules. Searle’s objection is that there are only two types of natural phenomena, the brain and the mental states that the brain brings into being and that humans experience. The brain produces mental states due to its specific biological characteristics. When we postulate the existence of unconscious rules, according to Searle, we invent a construct whose aim is to highlight a function, which we believe is especially significant. Such a function is not intrinsic and has no causal power. This argument is particularly interesting because it is founded on the impassable biological nature of the mind. Neither logic nor mathematical or statistical procedures may replace brain as a biological organ.

From another perspective, some scholars have emphasized that functionalism leads to a new form of behaviorism. Putnam (1988) claimed that reducing mental processes exclusively to their functional descriptions is tantamount to describe such processes in behavioristic terms 3 . In psychology, one of the most polemical critics of functionalism as a dangerous vehicle toward a new form of anti-mentalism, which would render vain all the battles waged by cognitivists against classic behaviorism, was a developmental psychologist, Bruner (1990) . The centrality of computability as the criterion for the construction of models in cognitive science leads naturally, in Bruner’s opinion, to abandoning “meaning making,” which was the central concern of the “Cognitive Revolution.”

Thus, at least at the outset, cognitive science was devoted to constructing computational models of human inference processes and of the knowledge that is used in performing these inferences. This definition of the object of cognitive science has led at first to designing and implementing problem-solving systems, where the complexity was located in the inference mechanisms, supposed to be the same for all problems ( Newell and Simon, 1972 ). Later, systems were implemented where reasoning was associated with specific and articulated knowledge representation ( Levesque and Brachman, 1985 ).

Notably, the aspect that was absent from this view of cognitive science was learning. This lack, according to Gentner (2010) , could be partly explained as a reaction to behaviorism, which was completely centered on learning. In fact, there were also philosophical reasons. Chomsky and Fodor, who were among the most influential members of the cognitive science community, were highly critical of the concept of learning. In their view, learning as a general mechanism does not exist, and Fodor even went so far as to state explicitly that no theory of development exists either ( Fodor, 1985 ).

Thus, cognitive science was born essentially as a reaction to behaviorism and took its legitimacy from the use of methodologies developed within artificial intelligence. These methodologies were supposed to make explicit how mental representations produced human activity in specific domains. However, this approach had a price: it separated the mind from its biological basis and from the context in which human activity takes place. There was no place for development, interaction, and variation due to biological or social causes 4 . This theoretical choice explains Bruner’s disillusion. For Bruner, cognitive science had fallen back into the behaviorism against which it originated, and no interesting relation could be established with developmental psychology. Developmental psychology is founded on the premise that a human being develops in interaction with the physical world and the society of other humans.

Cognitive Science in the Twenty-First Century

Cognitive science has changed considerably from its beginning. An obvious novelty concerns the increased importance assumed by learning with the emergence of connectionism ( Hinton, 1989 ).

When connectionist models were introduced, there was much debate regarding the relation of neural networks with the functioning of the human brain and their ability to address higher forms of thought ( Fodor and Pylyshyn, 1988 ; Quinlan, 1991 ; Chalmers, 1993 ). Later, philosophical discussion was replaced by empirical considerations. Networks are an efficient computational tool in some domains and are often used jointly with symbolic computations ( Wermter and Sun, 2000 ). Moreover, in recent advancements of artificial Intelligence, neural networks have been largely replaced by a variety of techniques of statistical learning ( Forbus, 2010 ).

More interesting for my purpose is the changes that the general philosophy of cognitive science has undergone due to the problems that have emerged with classic symbolic models. At its origin, the core of cognitive science was the relation between psychology and artificial intelligence. In the original project, this marriage was to be fruitful for both disciplines. Artificial intelligence expected from psychology the analysis of high-level mental mechanisms that, once simulated on a computer, could improve the efficiency of artificial systems. With computer simulation, psychology was to acquire a method to validate its models. However, this marriage, which for a while has been very productive and has generated many interesting ideas, ultimately failed. Artificial intelligence has evolved computing techniques that produce efficient systems without asking anymore if these techniques replicate human mental processes more or less faithfully. In psychology, the constraint to produce computational models has again restricted its scope ( Airenti and Colombetti, 1991 ).

Thus, the results of cognitive science of the twentieth century have led to a shift in cognitive science that has emerged with this century. Some researchers have proclaimed that the theoretical hypothesis that minds functionalities can be modeled disregarding the fact that they operate on the external world through the body could no longer be accepted. This new approach implies accounting for the biology of the mind/body unity and the interaction with the external world, both physical and social. One source of inspiration for this new turn came from Varela et al. (1991) , who proposed the concept of the embodied mind . Actually, the concept of embodiment includes many rather disparate inspirations, from Merleau-Ponty and phenomenology to Buddhism. I do not analyze these questions here. What interests me is the mere assumption that cognition is grounded in the world.

This new turn corresponds to the major importance assumed by robotics. It might be exaggerated to say that the role played by artificial intelligence in the past is now assumed by robotics. However, it is clear that the aim of constructing artificial actors that interact with the world and/or with humans has again established a link between the study of humans and the production of artificial systems. With respect to the past, the focus is no longer on the symbolic function of the mind, but on the mind embedded into a physical device that interacts with the external world. This evolution is linked to the enlarged scope of present robotics that goes well beyond traditional tasks such as farm automation. The ambition is to construct robots that may cooperate with humans in a multiplicity of tasks, including, for instance, assisting aged or disabled people or interacting with autistic children. Social robotics has then evolved toward biologically inspired systems, based on the notions of self-organization and embodiment ( Pfeifer et al., 2007 ). This new development has led to question once again psychologists about those characteristics that make humans what they are. If robots must be able to interact with humans, they should show those same characteristics ( Kahn et al., 2007 ). Can robots be endowed with intentionality, emotions, and possibly empathy?

Here, again a functionalist position appears. For some authors, the fact that the robot’s internal mechanisms are grounded in physical interactions with the external environment means that they truly have the potentiality of intrinsic intentionality ( Zlatev, 2001 ). This means, for them, that a mind is embodied in a robot. To the question of whether robots can have emotions, Arbib and Fellous (2004) answer that a better knowledge of biological systems will allow us in the future to single out “brain operating principles” independent of the physical medium in which they are implemented. This new form of functionalism is currently contrasted with an approach that considers that mental states and emotions are not intrinsic but can only be attributed to robots by humans ( Ziemke et al., 2015 ). Robots’ embodiment does not overcome the objection that was addressed to traditional artificial intelligence, namely that mental states and emotions can only be produced by a biological brain ( Ziemke, 2008 ). This latter position maintains that the relevant question for human-robot interaction is not that robots must be intentional beings, but that they must be perceived as such by humans ( Airenti, 2015 ; Wiese et al., 2017 ).

In conclusion, we can say that cognitive science was born as a way to renew psychology through a privileged connection with artificial intelligence. In the present state of research, it is social robotics that is attempting to establish a connection with biological sciences, psychology, and neuroscience, in order to build into robots those functionalities that should allow them to successfully interact with the external physical and social world. However, the main fundamental philosophical problems remain unchanged. One could still argue, as Searle did, that human mentality is an emergent feature of biological brains and no logical, mathematical or statistical procedure can produce it.

Present Questions for Cognitive Science

The question that we may raise today is this: what is cognitive science for? The relation that psychology has established with the sciences of the artificial has hidden the fact that a number of phenomena, which are essential for explaining the functioning of the human mind, have been largely ignored. This failure in explanation, which has concerned, for instance, the managing of mental states and emotions, and many complex communicative phenomena, is fundamentally linked to the fact that the mind is constantly in interaction with the physical and social world in a process of development. The primitive idea of cognitive science was to go beyond traditional psychology to enrich the study of mind with the contributions of other disciplines that also investigated human mind, such as linguistics, philosophy, and anthropology. This approach, which concerns the definition of the field of cognitive science, has been quite early reinterpreted as a problem of formalism. The question posed has been: how could psychology produce scientific models of human thought? Hence, the importance assumed by computer modeling as a means of replacing more traditional logical, mathematical, and statistical models. However, this theoretical choice has generated a major ambiguity, because computer models that are founded on logical, mathematical, or statistical formalisms have been seen as possibly equivalent to the mind. Once the fallacy of this equivalence appears—because no artificial model may replace the causal power of the human brain—we are left with some formal models with very limited psychological significance. What has been lost is the richness that cognitive science was supposed to acquire by connecting different disciplines. In particular, for many years, this approach has prevented general psychology from connecting with developmental psychology, a field of studies that, since Baldwin, had already posed the problem of the construction of the human mind as the result of biological development and social interaction.

The Study of Development

Biology and development in the debate between piaget and chomsky.

Studying development necessarily implies considering the fact that humans are biological systems that are certainly particularly complex but also share many characteristics with other living beings. Thus, in the field of developmental psychology, many questions have emerged concerning the link between development and evolution, the relation between genetic endowment and the influence on acquisition of environment (a concept that includes physical environment, parenting, social rules, etc.), and the nature of learning.

For Piaget, who came to developmental psychology from natural sciences, development had to be seen in the light of the theories of evolution. Intelligence, for him, is a particular case of biological adaptation, and knowledge is not a state but a process. Through action, children explore space and objects in the external world, and in this way, for instance, they learn the properties of the objects and their relations. These ideas, which sound rather contemporary to us, were considered as problematic in the past and prevented the establishment of a relationship between the study of development and the study of cognition in general. It is only in this century that development has been integrated into evolution studies via the so-called evo-devo approach and that these ideas have given rise to an interest in psychology ( Burman, 2013 ).

Actually, some aspects of Piaget’s perspective were problematic. Piaget supported his theory using what was considered a Lamarckian vision of evolution that assumed the inheritance of acquired characteristics. He had a well-known debate at the end of his life (1975) with Noam Chomsky on language acquisition, and outstanding biologists who also participated to the debate contested the validity of his use of the concept of phenocopy ( Piattelli-Palmarini, 1979/1980 ). In fact, on this point, Piaget had been influenced by Baldwin, who proposed what is known as Baldwin’s effect ( Simpson, 1953 ). This effect manifests in three stages: (1) Individual organisms interact with the environment in such a way as to produce nonhereditary adaptations; (2) genetic factors producing similar traits occur in the population; and (3) these factors increase in frequency under natural selection (taken from Waddington, 1953 ). Later, Piaget revised his own theory and updated Baldwin’s effect under the influence of Waddington ( Burman, 2013 ). Recently, epigenetic theories have emerged in biology, and the importance of development is generally accepted. On the developmental side, it has been proposed that Piaget’s theory might be replaced as a metatheory for cognitive development by evolutionary psychology ( Bjorklund, 2018 ).

The debate between Chomsky and Piaget is interesting because it is a clear example of the impossibility of dialogue between one of the fathers of cognitive science and the scholar who, at that moment, personified developmental psychology. Piaget was unable to justify his position that grammar rules could also be accounted for by sensorimotor schemata, and Chomsky appeared to have won the debate. At the same time, Chomsky presented the emergence of syntactic rules in the child’s mind, excluding in principle any possible form of learning. However, in hindsight, we know how the task of establishing abstract principles of universal grammar proved to be arduous, underwent many substantial changes and is not yet realized.

Another controversial aspect of Piaget’s position was his adherence to the recapitulation theory, i.e., the idea originally proposed by Haeckel, that ontogeny recapitulates phylogeny. It is this principle that motivated Piaget’s study of development as a way of contributing to the study of the evolution of human thought ( Koops, 2015 ). However, this position has as its consequence the idea that primitive populations would exist wherein we might find adult thought processes that in modern civilizations are typical of young children.

What is striking in this debate is that the specific biological model that Piaget adopted was not the only point of disagreement. What was questioned was in general the relevance of development for the study of a basic human ability such as language. Certainly, in the work of the first figures of developmental psychology, we find a baffling mix of very interesting ideas regarding the place of humans as biological entities in evolution and a difficulty in taking into account the complexities of actual biological theories and of social aspects such as cultural variation. At the same time, these scholars were confronted with objections from cognitive scientists who did not admit the relevance of investigating development for the study of the human mind.

The Interactionist Perspective

Piaget’s perspective was, in a sense, paradoxical. This perspective considered children’s development as the product of their action on the environment, but at the same time postulated a rather rigid succession of stages that led to adult thought and excluded the importance of the social aspects of this environment in the first years. In fact, infants and young children were considered closed in their egocentrism and unable to take advantage of their interactions with adults and peers.

These aspects have been criticized within developmental psychology, where a cultural turn, fathered by Vygotsky (1962/1986) and mainly interpreted in the United States by Bruner (1990) , has arisen. For both these authors, biological factors are considered an endowment of potentialities that develop in a society of co-specifics and are submitted to variability and to cultural variation.

Bruner was, at the outset, an enthusiastic supporter of cognitive science and in particular of the mentalist theory of language proposed by Chomsky ( Bruner, 1983b ). Later, however, the primacy that Chomsky assigns to syntax turned out to be unsatisfactory to Bruner, according to whom language is fundamentally a communicative device. The problem of language acquisition is thus redefined as the development of a communicative capacity that appears in the prelinguistic stage. This position was the result of Bruner’s work on preverbal communication carried out at the Center for Cognitive Studies at Harvard University starting in 1966.

For Bruner, language requires the maturation of cognitive structures, which underlie intentional action in general. His debt to Piaget with regard to the importance of action is evident. Language is “a specialized and conventionalized extension of cooperative action” ( Bruner, 1975 ). In this, he rejoins the communication theories proposed within the philosophy of language by Austin (1962) and Grice (1989) .

Bruner’s studies are part of a revolution in developmental studies in which more careful scrutiny and more sophisticated experimentation led to the discovery that children begin to engage in rather complex cognitive activity very early on. Prior to these studies, many of the aspects relating to infant cognition were not taken into consideration. The prejudice that saw human development as the slow acquisition of rationality prevented researchers from seeking elements of complexity in the cognition of a new-born.

In brief, since its origin, developmental psychology has undergone an important change. At the outset, the idea was that what characterized human cognition was adult rational thought, and studying development meant understanding the stages that led to this achievement. Later, the goal became understanding the development of the different faculties that characterize cognition starting from birth. This goal has also opened the door to comparative studies.

The Problems of Method

Developmental psychologists have always struggled with problems of method.

Piaget frequently discussed his observations of his three children. Studies on language acquisition have often benefited from researchers’ observations of their own children (see, for instance, Stern and Stern, 1928 ). These procedures, which have been considered as barely scientific by other psychologists, have provided useful inspiration for further research. Note that Darwin’s observations of his children were a fundamental source for his work on emotions ( Darwin, 1872/1965 ).

Ethical reasons forbid experiments, which may perturb children. Moreover, conceiving experiments that have ecological validity is even more difficult to do with young children than with adults. Hence, the necessity of using different methods in order to produce data that cannot be collected using classic experimental procedures. Without using observational methods, for instance, it is not possible to assess the spontaneous appearance of a given phenomenon ( Airenti, 2016 ). Furthermore, some behaviors may appear only in specific situations and would go unnoticed if they were not observed by caregivers who may see children at different moments of the day and in different situations. Thus, developmental psychologists have used different methodologies, classic experiments but also fieldwork, ethological observation, and parent reports.

A fundamental advancement was the development of techniques permitting to assess infants’ and young children’s abilities in experiments. A key element was the elaboration of the habituation paradigm ( Fantz, 1964 ; Bornstein, 1985 ). After repeated exposure to a stimulus, infants’ looking time decreases due to habituation and increases when a novel stimulus is presented. Habituation allows us to understand if infants discriminate among different stimuli.

In particular for language studies, nonnutritive sucking ( Siqueland and De Lucia, 1969 ) has been used. This is an experimental method based on operant conditioning allowing one to test infants’ discrimination of and preference for different stimuli. This technique has been used to show, for instance, that infants already acquire in the mother’s womb the ability to recognize and prefer the prosody of a language and of familiar voices ( DeCasper and Fifer, 1980 ).

Currently, the most utilized technique with infants is preferential looking or reaching. In this technique, two stimuli are presented together and what is measured is the infant’s preference. Specific types of this technique are used to claim surprise, anticipation, and preferences for novel or familiar stimuli and to evaluate preference over and above novelty or familiarity ( Hamlin, 2014 ) 5 .

Another technique presently used to investigate infant cognitive development is EEG recordings, even if creating infant-friendly laboratory environments, age-appropriate stimuli, and infant- friendly paradigms requires special care ( Hoehl and Wahl, 2012 ).

The development of these experimental techniques has vastly enlarged the scope of infant studies. In particular, a new research trend has emerged aimed at discovering what has been called the core knowledge ( Spelke, 2000 ; Spelke and Kinzler, 2007 ). The idea is that at the basis of human cognition, there is a set of competencies, such as representing objects, action, number and space, which are already present in infants and which underlie and constrain later acquisitions. Researchers have also been working on other possible basic competencies such as social cognition ( Baillargeon et al., 2016 ) and morality ( Wynn and Bloom, 2014 ).

In the literature, debate continues surrounding the replicability and robustness of findings obtained within these experimental paradigms, in particular with respect to infants’ and toddlers’ implicit false belief and morality ( Hamlin, 2014 ; Tafreshi et al., 2014 ; Baillargeon et al., 2018 ; Sabbagh and Paulus, 2018 ).

This debate also involves the relation between development and evolution. For Tafreshi and colleagues, for instance, the idea of core knowledge would involve a consideration of high-level cognitive capacities as biologically predetermined instead of constructed in interaction with the environment. This is not the perspective of those who consider that development does exist in the social environment but is constrained by a number of basic competencies ( Hamlin, 2014 ). An important element of this perspective is comparing human and animal capacities. In fact, research has shown that such basic competencies also exist in some form in animals. For instance, numerous studies have shown that adult nonhuman primates have the core systems of object, number, agent representations, etc. ( Spelke and Kinzler, 2007 ).

These preoccupations have also informed work by Tomasello and the Leipzig group. “All we can claim to have done so far–writes Tomasello–is to establish some comparative facts–organized by some theoretical speculations–that hopefully get us started in the right direction toward an evolutionary informed account of the ontogeny of uniquely human psychology” ( Tomasello, 2018 ). Comparing experimental work on great apes and young children has led him to formulate the hypothesis that the factors marking the difference between these two groups are different aspects of social cognition. Nonhuman primates have some basic capacities in these areas. In humans, the evolved capacity for shared intentionality transforms them in the species-unique human cognition and sociality ( Tomasello and Herrmann, 2010 ).

Tomasello’s work has also aroused criticism. In this case, the criticism is because his research, both with young children and primates, uses experimental methods and is carried out in a laboratory. Fieldwork primatologists have claimed that primates in captivity, tested by someone of another species, cannot display the abilities that their conspecifics display in their natural environment ( Boesch, 2007 ; De Waal et al., 2008 ). Tomasello answered this criticism by maintaining that the fact of being raised in a human environment enhances primates’ capacities ( Tomasello et al., 1993 ; Tomasello and Call, 2008 ).

In conclusion, in developmental psychology, a multiplicity of methods has been applied, and the debate over their respective validity and correct application continues. However, what is not in question is that development is a complex and multifaceted phenomenon that must be analyzed as such and from different points of view.

A paradigmatic case in the present research is the study of the theory of mind. Discovering how subjects represent their own mind and other minds was proposed in 1978 by Premack and Woodruff as a problem of research on primates, and in a short time, it has become one of the main topics in developmental research ( Premack and Woodruff, 1978 ). It is currently being studied in groups of different ages, from infants to the elderly, both in typical and clinical subjects and using different methodologies, from classic experiments to clinical observation. Moreover, a number of studies investigate individual and cross-cultural variation and its role in human-robots interactions. Philosophers have contributed to the definition of this phenomenon, and neuroscientists are working to discover its neural basis.

Computational Models of Development

Some researchers have pursued the goal of constructing computational models of cognitive development using different computational approaches (for a review, see Mareschal, 2010 ). However, as the author of this review remarks, all the models have explored cognition “as an isolated phenomenon”, i.e., they did not consider the physical and social context in which development unfolds.

Karmiloff-Smith, a developmental psychologist who proposed the most interesting theory about developmental change as an alternative to Piaget’s, considered that a number of features of her RR ( representational redescription ) model happened to map onto features of connectionist models ( Karmiloff-Smith, 1992 ; for a review of these models, see Plunkett et al., 1997 ). However, she also remarks that connectionist models have modeled tasks, while development is not simply task-specific learning, as it involves deriving and using previously acquired knowledge 6 .

One result of the dissatisfaction with the results deriving from the relation between cognitive psychology and artificial intelligence and the concomitant increase in interest in embodied cognition has been the growth of developmental robotics ( Lungarella et al., 2003 ). The aim of this field is to produce baby robots endowed with sensorimotor and cognitive abilities inspired by child psychology and to model developmental changes ( Cangelosi and Schlesinger, 2018 ). This approach has led to the comparison of results in experiments with robots and children. This is a promising field, even if it does not overcome the problems described above regarding the specificity of tasks that does not allow to account for infants’ ability to utilize previously differently acquired knowledge in the performance of a given task.

In conclusion, some approaches within cognitive science have acknowledged the usefulness of studying children in order to understand the mechanisms of development. Especially in the case of developmental robotics, this has allowed for studying the interaction of different capacities such as sensorimotor abilities, perception, and language. At the same time, the computational constraints do not allow for overcoming task specificity.

Concluding Remarks

I have argued that since their beginning, general psychology and developmental psychology have followed parallel paths that have only occasionally converged. The reason is due essentially to methodological problems that have involved not only research methods but also the very object of inquiry.

Psychology was founded with the ambition of becoming a science performed in laboratories and based on experimental work. However, as early as in 1934, Vygotsky had already deplored the attempt to achieve scientific standards by limiting the importance of general issues. “As long as we lack a generally accepted system incorporating all available psychological knowledge, any important factual discovery inevitably leads to the creation of a new theory to fit the newly observed facts” ( Vygotsky, 1962/1986 , p. 13).

The birth of cognitive science has taken important steps toward constructing links with other disciplines and also other ways to study cognition. However, this opening was soon transformed in the search for a unifying methodology, namely computer modeling, as a guarantee of scientific results. Many interesting ideas have been generated. However, after four decades of work in this direction, it has become impossible to ignore that too many important aspects of the human mind and activity have been eluded.

The relative isolation of developmental psychology came from the prejudice, also shared by eminent developmental psychologists like Piaget, that what characterizes human cognition are adult cognitive abilities.

However, from the start, developmental psychology was not limited to investiganting the specificity of children’s cognition. It devoted attention to what makes development possible, including biological endowment and cultural transmission; whether an infant should be considered a blank slate or if one can define some pre-existent basic abilities; what makes humans different from animals and nonhuman primates; and how specific human abilities such as language have evolved.

At present, a rapprochement between adult and child studies is made possible by different factors. The possibility of using experimental methods to study infancy has allowed us to realize the complexity of young humans. Moreover, development is increasingly being considered as a phenomenon not only characterizing childhood but also present over the life span, including both the acquisition and the decay of mental abilities ( Bialystok and Craik, 2006 ). Studying the human mind means studying how the human mind changes in interaction with the external environment all life long. In this sense, the study of human mind is inherently connected with the study of its development.

An important question of method emerges here. We have observed that over the years, developmental psychologists have sought to construct methods that can be reliable and at the same time can adequately address the topics under discussion here. The achievement of finding ways to carry out experiments with infants and nonhuman primates has been an important advancement in this perspective. This advancement has garnered both praise and criticism. To be reliable, experiments with infants require very rigorous procedures. Frequently, a detailed analysis of procedures is necessary to explain divergent results. However, it can be noted that reproducibility is an open problem for psychological science in general ( Open Science Collaboration, 2015 ). For nonhuman primates, the ecological validity of laboratory experiments has been questioned. More generally, it has been shown that in the field of developmental psychology, experimental studies do not completely replace other methodologies, but rather should coexist with them.

The human mind is complex, and all the methods that have been proposed in different disciplines may be useful in advancing our knowledge of it. The explanation of this complexity was the main goal underlying the proposal of cognitive science and is the perspective we must pursue in the future.

On this ground, the paths of psychology and developmental psychology may reconverge.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of Interest Statement

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

The reviewer MT declared a shared affiliation, with no collaboration, with the author to the handling editor at time of review.

1 The very earliest date was 1875 and that same year William James’ laboratory at Harvard in the United States was established ( Harper, 1950 ).

2 William James was influenced by Darwin and this appears in particular in his conceiving the mind as a function and not as a thing ( Bredo, 1998 ). However, his book The Principles of Psychology , first published in 1890 and later revised several times, ignored child development. In the chapter devoted to methods and snares in psychology, he adds to introspective observation and experimental method the comparative method. “So it has come to pass that instincts of animals are ransacked to throw light on our own; and that the reasoning faculties of bees and ants, the minds of savages, infants, madmen, idiots, the deaf and blind, criminals, and eccentrics, are invoked in support of this or that special theory about some part of our own mental life” ( James, 1983 , p. 193). If he admits that “information grows and results emerge”, he also cautions that “there are great sources of error in the comparative method” and that “comparative observation, to be definite, must usually be made to test some pre-existing hypothesis” ( James, 1983 ).

3 Putnam was actually the first to employ the term functionalism , and his aim in doing so was anti-reductionist. In his 1975 work he used the comparison with a computer program to show that psychological properties do not have a physical and chemical nature, even though they are realized by physical and chemical properties ( Putnam, 1975 ).

4 , Hewitt (1991) highlights the difficulties inherent in constructing artificial systems, which, like social systems, are founded on concepts such as commitment, cooperation, conflict, negotiation, and so forth.

5 Gaze and eye-tracking techniques are normally used in psychological research with adults ( Mele and Federici, 2012 ) but it is in developmental studies that they have had a dramatic impact on the possibilities of inquiry.

6 A different approach that has given origin to formal models and simulations is the paradigm that views the developmental process as a change within a complex dynamic system. Cognition in this perspective is embodied in the processes of perception and action ( Smith and Thelen, 2003 ).

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  • Published: 20 October 2023

Developmental psychology: The expanding reach of emotions

  • Jennifer A. Bellingtier 1  

Communications Psychology volume  1 , Article number:  29 ( 2023 ) Cite this article

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For some individuals, daily changes in positive and negative emotions corresponds to fluctuations in overall life satisfaction. A new study in Psychology and Aging suggests that the expanding reach of negative emotions is greater for younger than older adults.

research studies on developmental psychology

Emotions are typically transient feelings that fluctuate from day to day whereas evaluations of life-satisfaction have traditionally been thought to be relatively more stable. Emerging research suggests that for some individuals emotion fluctuations are also associated with daily changes in global life satisfaction, a phenomenon known as emotion globalizing.

Meaghan A. Barlow at Wilfrid Laurier University and colleagues investigated if the tendency to expand the reach of daily emotions was more pronounced in younger versus older adults 1 . Across two studies, they tracked positive and negative emotions either at the end of the day or following the day’s most stressful event as well as either global life satisfaction or satisfaction with the current day. They found that younger adults (ages 23–42 and 18–34), as compared to older adults (ages 51–79 and 64–95), were more likely to link their current-day negative emotions with more negative overall life evaluations. There were no age-related differences in the tendency to globalize positive emotions to either life or day satisfaction or negative emotions to day satisfaction. Interestingly, overall life satisfaction did not differ on average based on age, although average day satisfaction was higher for older adults.

These findings align with lifespan developmental theories suggesting that older age is associated with accrued wisdom and an improved ability to manage daily negative events. The ability to keep negative emotions from expanding may be one way older adults maintain life satisfaction into their later years.

Barlow, M. A., Willroth, E. C., Wrosch, C., John, O. P., & Mauss, I. B. When daily emotions spill into life satisfaction: age differences in emotion globalizing. Psychol. Aging https://doi.org/10.1037/pag0000771 (2023).

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Bellingtier, J.A. Developmental psychology: The expanding reach of emotions. Commun Psychol 1 , 29 (2023). https://doi.org/10.1038/s44271-023-00029-6

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research studies on developmental psychology

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Advancing Human Assessment pp 453–486 Cite as

Research on Developmental Psychology

  • Nathan Kogan 5 ,
  • Lawrence J. Stricker 5 ,
  • Michael Lewis 6 &
  • Jeanne Brooks-Gunn 7  
  • Open Access
  • First Online: 18 October 2017

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Part of the Methodology of Educational Measurement and Assessment book series (MEMA)

Developmental psychology was a major area of research at ETS from the late 1960s to the early 1990s. This work was a natural extension of the programs in cognitive, personality, and social psychology that had begun shortly after the organization’s founding in 1947, consistent with Henry Chauncey’s vision of investigating intellectual and personal qualities. This chapter covers research on representational competence; parental influences, migration, and measurement; cognitive, personality, and social development of infants and young children; and cognitive, personality, and social development from infancy to adolescence.

  • Representational Competence
  • Paper Folding Task
  • Teaching Parents Behavior
  • infantsInfants
  • Young childrenYoung Children

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Developmental psychology was a major area of research at ETS from the late 1960s to the early 1990s, a natural extension of the work in cognitive, personality, and social psychology that had begun shortly after the organization’s founding in 1947, consistent with Henry Chauncey’s vision of investigating intellectual and personal qualities (see Stricker, Chap. 13 , this volume). For a full understanding of these qualities, it is essential to know how they emerge and evolve. Hence the work in developmental psychology complemented the efforts already under way in other fields of psychology.

A great deal of the research in developmental psychology was conducted at ETS’s Turnbull Hall in the Infant Laboratory , equipped with physiological recording equipment and observation rooms (e.g., Lewis 1974 ), and in a full-fledged Montessori school outfitted with video cameras (e.g., Copple et al. 1984 ). Hence, as Lewis ( n.d .) recalled, the building “had sounds of infants crying and preschool children laughing” (p. 4). Other research was done in homes, schools, and hospitals, including a multisite longitudinal study of Head Start participants (e.g., Brooks-Gunn et al. 1989 ; Laosa 1984 ; Shipman 1972 ).

A handful of investigators directed most of the research, each carrying out a distinct program of extensive and influential work. This chapter covers research by Irving Sigel , on representational competence ; Luis Laosa, on parental influences , migration , and measurement ; Michael Lewis, on cognitive, personality, and social development of infants and young children ; and Jeanne Brooks-Gunn, on cognitive, personality, and social development from infancy to adolescence. Other important research was conducted by Gordon Hale (e.g., Hale and Alderman 1978 ), on attention; Walter Emmerich (e.g., Emmerich 1968 , 1982 ), on sex roles and personality development ; and Nathan Kogan (e.g., Wallach and Kogan 1965 ) and William Ward (e.g., Ward 1968 ), on creativity . (The Kogan and Ward research is included in Kogan, Chap. 14 , this volume.) In the present chapter, Kogan describes Sigel’s research, and Stricker takes up Laosa’s ; Lewis and Brooks-Gunn discuss their own work.

1 Representational Competence and Psychological Distance

Representational competence was the focus of Sigel’s research program. Roughly defined by Sigel and Saunders ( 1983 ), representational competence is the ability to transcend immediate stimulation and to remember relevant past events and project future possibilities. Also indicative of representational competence in preschoolers was the understanding of equivalence in symbol systems, whereby an object could be rendered three-dimensionally in pictorial form and in words.

The level of a child’s representational competence was attributed in large part to parental beliefs and communicative behaviors and to family constellation (number of children and their birth order and spacing). Earlier research by Sigel and collaborators emphasized ethnicity and socioeconomic status (SES ; see Kogan 1976 ). SES was retained in many of the ETS studies in addition to a contrast between typical children and those with communicative–language disabilities .

A conceptual model of the Sigel team’s research approach is presented in a chapter by McGillicuddy-DeLisi et al. ( 1979 ): Mothers’ and fathers’ backgrounds determined their parental belief systems. Belief systems, in turn, influenced parental communication strategies, which then accounted for the child’s level of cognitive development . It was a nonrecursive model, the child’s developmental progress (relative to his or her age) feeding back to alter the parental belief systems. In terms of research design, then, parental background was the independent variable, parental belief systems and child-directed communicative behavior were mediating variables, and children’s representational competence was the dependent variable. The full model was not implemented in every study, and other relevant variables were not included in the model. In most studies, family constellation (e.g., spacing and number of children), SES , the nature of the parent–child interaction task, the child’s communicative status (with or without language disability ), and the gender of the parent and child were shown to yield main or interaction effects on the child’s representational competence.

In the view of Sigel and his associates, the critical component of parental teaching behavior was distancing (Sigel 1993 ). Parental teachings could reflect high- or low-level distancing. Thus, in a teaching context, asking the child to label an object was an example of low-level distancing, for the child’s response was constrained to a single option with no higher-thinking processes invoked in the answer. By contrast, asking the child to consider possible uses of an object was an example of high-level distancing, for the child was forced to go beyond the overt stimulus properties of the object to adopt new perspectives toward it. In brief, the concept of distancing, as reflected in parental teaching behavior, referred to the degree of constraint versus openness that the parent imposed on the child. Sigel’s principal hypothesis was that higher-level distancing in child-directed communication by an adult would be associated with greater representational competence for that child. Correspondingly, low-level distancing by an adult would inhibit the development of a child’s representational competence.

An additional feature of Sigel’s research program concerned the nature of the task in the parent–child interaction. Two tasks were selected of a distinctively different character. For the storytelling task, age-appropriate edited versions of children’s books were used, with parents instructed to go through a story as they typically would do at home. The other task required paper folding, with the parent required to teach the child to make a boat or a plane.

1.1 Influence of Parental Beliefs and Behavior on Representational Competence

Having outlined the conceptual underpinning of Sigel’s research program along with the nature of the variables selected and the research designs employed, we can now proceed to describe specific studies in greater detail. We begin with a study of 120 families in which the target child was 4 years of age (McGillicuddy-DeLisi 1982 ; Sigel 1982 ). Family variables included SES (middle vs. working class) and single-child versus three-child families. For the three-child families, there was variation in the age discrepancy between the first and second sibling (more than 3 years apart vs. less than 3 years apart), with the restriction that siblings be of the same sex. Each mother and father performed the storytelling and paper-folding tasks with their 4-year-old child. Proper controls were employed for order of task presentations. A total of 800 parent and child observations were coded by six raters with satisfactory interrater reliability .

The presentation of the research was divided into two parts, corresponding to the portion of the analytic model under investigation. In the first part (McGillicuddy-DeLisi 1982 ), the influence of the demographic variables, SES and family constellation, on parental beliefs was examined, and in turn the influence of parental beliefs for their prediction of overt parental behaviors in a teaching situation was explored. The second part, the child’s representational competence, was treated separately in the Sigel ( 1982 ) chapter. Note that the assessment of beliefs was focused exclusively on the parents’ views of how a preschool child acquired concepts and abilities, hence making such beliefs relevant to the parental strategies employed in facilitating the child’s performance in a teaching context.

Parental beliefs were assessed in an interview based on 12 vignettes involving a 4-year-old and a mother or father. The interviewer asked the parent whether the child in the vignette had the necessary concepts or abilities to handle the problem being posed. Further inquiry focused more generally on parents’ views of how children acquire concepts and abilities. Analysis of these data yielded 26 parental belief variables that were reliably scored by three coders. ANOVA was then employed to determine the influence of SES, family constellation, gender of child, and gender of parent on each of the 26 belief variables. Beliefs were found to vary more as a function of SES and family constellation than of gender of parent or child. More specifically, parents of three children had views of child development that differed substantially from those of single-child parents. For the parents of three children, development involved attributes more internal to the child (e.g., reference to self-regulation and impulsivity) as opposed to greater emphasis on external attributes (e.g., direct instruction) in single-child parents. The results as a whole constituted an intriguing mosaic, but they were post hoc in the absence of predictions derived from a theoretical framework . Of course, the exploratory nature of such research reflected the dearth at that time of theoretical development in the study of child-directed parental beliefs and behaviors.

Consider next the observed relationships between parental beliefs and teaching behaviors. Having shown that SES and family constellation influenced parental beliefs, the question of interest was whether such beliefs provided useful information about parents’ teaching behaviors beyond what might be predicted from SES and family constellation. To answer the question, stepwise regressions were carried out with SES and family constellation entered into the analysis first, followed by the belief variables. Separate regressions—four in all—were conducted for mothers’ and fathers’ performance on the storytelling and paper-folding tasks, the dependent variables.

Demonstration of belief effects on teaching behaviors would require that multiple correlations show significant increments in magnitude when beliefs were entered into the regression analysis. Such increments were observed in all four regressions, indicating that parents’ beliefs about their children’s competencies were predictive of the way they went about teaching their children on selected tasks. Noteworthy is the evidence that the significant beliefs varied across the two tasks and that this variation was greater for mothers than for fathers. In other words, mothers appeared to be more sensitive to the properties of the task facing the child, whereas fathers appeared to have internalized a set of beliefs generally applied to different kinds of tasks. Mothers would seem to have a more differentiated view of their children’s competencies and hence were more attuned to the nature of the task than were fathers.

Thus far, we have considered the relations among family demographics, parental beliefs, and teaching strategies. The missing link, the child’s cognitive performance, was examined in the Sigel ( 1982 ) chapter, where it was specifically related to parental teaching behaviors. The child’s responses to the storytelling and paper-folding tasks were considered (e.g., extent of engagement and problem solutions), as was the child’s performance on tasks independent of parental instructions. These latter tasks included Piagetian conservation and imagery assessments and the Sigel Object Categorization Task (Sigel and Olmsted 1970 ). The major hypothesis was that the parents’ uses of distancing strategies in their teaching behaviors would be associated with enhanced cognitive performances in their children—representational competence.

To address this hypothesis, stepwise regressions were analyzed. The results confirmed the basic hypothesis linking parental child-directed distancing to the child’s representational competence. This general observation, however, conceals the specificity of the effects. Thus mothers and fathers employed different teaching strategies, and these strategies, in turn, varied across the storytelling and paper-folding tasks. Of special interest are those analyses in which the mothers’ and fathers’ teaching behaviors were entered into the same regression equation. Doing so in sequence often pointed to the complementarity of parental influences . In concrete terms, the multiple correlations sometimes demonstrated significant enhancements when both parents’ teaching strategies entered into the analysis compared to the outcome for the parents considered separately. This result implied that the children could intellectually profit from the different, but complementary, teaching strategies of mothers and fathers.

1.2 Impact of a Communicative Disability

Sigel and McGillicuddy-DeLisi ( 1984 ) were able to recruit families who had a child with a communicative disability (CD), making it possible to compare such families with those where the child was not communicatively disabled (non-CD). It was possible to match the CD and non-CD children on SES , family size, gender, age, and birth order. Again, mothers’ and fathers’ distancing behaviors were examined in the task context of storytelling and paper folding.

In the case of the child’s intellectual ability, assessed by the Wechsler Preschool and Primary School Scale of Intelligence (WPPSI; Wechsler 1949b ), parental effects were largely confined to the CD sample. Low parental distancing strategies were tightly associated with lower WPPSI scores. Of course, we must allow for the possibility that the parent was adjusting his or her distancing level to the perceived cognitive ability of the child. In contrast, the child’s representational competence , as defined by the assessments previously described in Sigel ( 1982 ), was linked with parental distancing behaviors in both CD and non-CD samples, with the magnitude of the relationship somewhat higher in the CD sample.

Of course, these associations could not address the causality question: The parent might be affecting the child or reacting to the child or, more likely, the influence was proceeding in both directions. Sigel and McGillicuddy-DeLisi ( 1984 ) argued that low-level distancing strategies by parents discouraged active thinking in the child; hence it was no surprise that such children did not perform well on representational tasks that required such thinking. They were optimistic about CD children, for high-level parental distancing seemed to encourage the kind of representational thinking that could partially compensate for their communicative disabilities (Sigel 1986 ).

1.3 Belief-Behavior Connection

Working with a subsample of the non-CD families described in the previous section, Sigel ( 1992 ) plunged into the controversial issue of the linkage between an individual’s beliefs and actual behavior instantiating those beliefs. He also developed a measure of behavioral intentions—a possible mediator of the belief–behavior connection. Although the focus was naturally on parental beliefs and behaviors, similar work in social psychology on the belief and behavior connection (e.g., Ajzen and Fishbein 1977 ), where major advances in theory and research had occurred, was not considered.

Three categories of variables were involved: (a) parents’ beliefs about how children acquired knowledge in four distinct domains (physical, social, moral, and self); (b) the strategies that parents claimed they would use to facilitate the children’s acquisition of knowledge in those domains; and (c) the behavioral strategies employed by the parents in a teaching context with their children. The first two categories were assessed with a series of vignettes. Thus, in the vignette for the physical domain, the child asks the parent how to use a yardstick to measure the capacity of their bathtub. The parents’ view about how children learn about measurement constituted the belief measure; the parents’ statements about how they would help their child learn about measurement constituted the self-referent strategy measure. For the third category, the parents taught their child how to tie knots, and the strategies employed in doing so were observed. Note that the knots task involved different content than was used in the vignettes.

Parental beliefs regarding children’s learning were categorized as emphasizing cognitive processing (e.g., children figuring out things on their own) or direct instruction (e.g., children learning from being told things by adults). Parental intended teaching strategies were classified as distancing, rational authoritative (e.g., parent gives reasons with commands), or direct authoritative (e.g., parent offers statement or rule without rationale). Parental behavioral strategies were scored for high-level versus low-level distancing.

The three variable classes—parental beliefs, parental intended teaching strategies, and parental behavioral strategies—were intercorrelated. Substantial relationships were observed between parental beliefs about learning (cognitive processing vs. direct instruction) and the strategies the parent intended to employ. As anticipated, cognitive processing was associated with distancing strategies, and direct instruction was linked to authoritative strategies. Of course, both the beliefs and self-referent strategies were derived from the same vignettes used in the parental interview, suggesting the likely influence of method variance on the correlational outcomes. When the foregoing variables were related to the parents’ behavioral strategies in teaching the knots task, the magnitude of the correlations dropped precipitously, though the marginally significant correlations were in the predicted direction. Sigel ( 1992 ) attributed the correlational decline to variation across domains. Thus the belief–strategy linkages were not constant across physical, social, and moral problems. Aggregation across these domains could not be justified. Obviously, the shifting task content and context were also responsible for the absence of anticipated linkages. Conceivably, an analytic procedure in which parents’ intended strategies were cast as mediators between their beliefs and their behavioral strategies would have yielded further enlightenment.

1.4 Collaborative Research

The large majority of Sigel’s publications were either solely authored by him or coauthored with former or present members of his staff at ETS. A small number of papers, however, were coauthored with two other investigators, Anthony Pellegrini and Gene Brody, at the University of Georgia. These publications are of particular interest because they cast Sigel’s research paradigm within a different theoretical framework , that of Vygotsky ( 1978 ), and they introduced a new independent variable into the paradigm, marital quality.

In the case of marital quality, Brody et al. ( 1986 ) raised the possibility that the quality of the marital relationship would influence mothers’ and fathers’ interactions with their elementary-school age children. More specifically, Brody et al., leaning on clinical reports, examined the assumption that marital distress would lead to compensatory behaviors by the parents when they interact with their children in a teaching context. Also under examination was the possibility that mothers and fathers would employ different teaching strategies when interacting with the children, with the nature of such differences possibly contingent on the levels of marital distress.

Again, storytelling and paper-folding tasks were used with the mothers and fathers. Level of marital distress was assessed by the Scale of Marriage Problems (Swenson and Fiore 1975 ), and a median split was used to divide the sample into distressed and nondistressed subgroups. Observation of parental teaching strategies and the child’s responsiveness was accomplished with an event-recording procedure (Sigel et al. 1977 ) that yielded interrater reliability coefficients exceeding .75 for each of the eight behaviors coded. ANOVAs produced significant Marital Problems × Parent interactions for seven of the eight behavioral indices. Nondistressed mothers and fathers did not differ on any of the behavioral indices. By contrast, distressed mothers and fathers differed in their teaching strategies, the mothers’ strategies being more effective: more questions, feedback, and suggestions and fewer attempts to take over the child’s problem-solving efforts.

Fathers in the distressed group “behave in a more intrusive manner with their school-aged children, doing tasks for them rather than allowing them to discover their own solutions and displaying fewer positive emotions in response to their children’s learning attempts” (p. 295). Mothers in distressed marriages, by contrast, responded with more effective teaching behaviors, inducing more responsive behavior from their children. Hence the hypothesis of compensatory maternal behaviors in a distressed marriage was supported. The psychological basis for such compensation, however, remained conjectural, with the strong likelihood that mothers were compensating for perceived less-than-satisfactory parenting by their husbands. Finally, Brody et al. ( 1986 ) offered the caveat that the outcomes could not be generalized to parents with more meager educational and economic resources than characterized the well-educated parents employed in their study.

In two additional studies (Pellegrini et al. 1985 , 1986 ), the Sigel research paradigm was applied, but interpretation of the results leaned heavily on Vygotsky’s ( 1978 ) theory of the zone of proximal development. Pellegrini et al. ( 1985 ) studied parents’ book-reading behaviors with 4- and 5-year-old children. Families differed in whether their children were communicatively disabled. MANOVA was applied, with the parental interaction behavior as the dependent variable and age, CD vs. non-CD status, and parent (mother vs. father) as the independent variables. Only CD vs. non-CD status yielded a significant main effect. Parental behaviors were more directive and less demanding with CD children. Furthermore, stepwise regression analysis examined the link between the parental interaction variables and WPPSI verbal IQ. For non-CD children, high cognitive demand was significantly associated with higher IQ levels; for CD children, the strongest positive predictor of IQ was the less demanding strategy of verbal/emotional support.

In general, parents seemed to adjust the cognitive demands of their teaching strategies to the level of the children’s communicative competences. In Vygotskyan terms, parents operated within the child’s zone of proximal development. Other evidence indicated that parents engaged in scaffolding to enhance their children’s cognitive–linguistic performances. Thus parents of non-CD children manifested more conversational turns in a presumed effort to elicit more language from their children. Similarly, more parental paraphrasing with non-CD children encouraged departures from the literal text, thereby fostering greater depth of interaction between parent and child. In sum, parental scaffolding of their children’s task-oriented behavior activated the potential for children to advance toward more independent problem solving as outlined in Vygotsky’s theory.

We turn, finally, to the second study (Pellegrini et al. 1986 ) influenced by Vygotsky’s theory . The research paradigm was similar to studies previously described. Again, gender of parent, children’s CD vs. non-CD status, and the tasks of book reading and paper folding constituted the independent variables, and the teaching strategies of the parents comprised the dependent variables. In addition, the extent of task engagement by the child was also examined. MANOVA was employed, and it yielded a significant main effect for the child’s communicative status and for its interaction with the task variable. ANOVAs applied to the separate teaching variables indicated that (a) parents were more directive and less demanding with CD children than with non-CD children; (b) parents were more demanding, gave less emotional support, and asked fewer questions with the paper-folding task than with the book-reading task; and (c) communicative status and task variable interacted: A CD versus non-CD difference occurred only for the book-reading task, with parents of CD children asking more questions and making lower cognitive demands.

The teaching strategy measures were factor analyzed, and the resulting four orthogonal factors became the predictor variables in a regression analysis with children’s rated task engagement as the criterion variable. For the paper-folding task, parents of both CD and non-CD children used high-demand strategies to keep their children engaged. For the book-reading task, parents of CD and non-CD children differed, with the CD parents using less demanding strategies and the non-CD parents using more demanding ones.

Pellegrini et al. ( 1986 ) had shown how ultimate problem-solving outcomes are of less significance than the processes by which such outcomes are achieved. Adult guidance is the key, with non-CD children requiring considerably less of it to remain engaged with the task than was the case for CD children. Hence the children’s competence levels alert the parents to how demanding their teaching strategies should be. Pellegrini et al. further recommended the exploration of the sequence of parental teaching strategies, as parents found it necessary on occasion to switch from more demanding to less demanding strategies when the child encountered difficulty (see Wertsch et al. 1980 ). In sum, the findings strongly support the Vygotsky model of parents teaching children through the zone of proximal development and the adjustment of parental teaching consistent with the competence level of their children.

1.5 Practice

An important feature of Sigel’s research program was linking research to practice (Renninger 2007 ). As Sigel ( 2006 ) noted,

efforts to apply research to practice require acknowledging the inherent tensions of trying to validate theory and research in practical settings. They require stretching and/or adapting the root metaphors in which we have been trained so that collaborations between researchers and practitioners are the basis of research and any application of research to practice. (p. 1022)

The research on representational competence and psychological distance has had widespread impact, notably for early childhood education (Hyson et al. 2006 ) and cognitive behavior therapy (Beck 1967 ).

2 Parental Influences , Migration, and Measurement

Laosa’s empirical work and his position papers spanned the psychological development of children, particularly Hispanics. His methodological contributions included test theory, especially as it relates to the assessment of minority children , and a standardized measure of parental teaching strategies. The major foci of Laosa’s work to be considered here are parental influences on children’s development, the consequences of migration for their adjustment and growth, and the measurement of their ability.

2.1 Parental Influences

Parental influence on children’s intellectual development has been a topic of long-standing interest to developmental psychologists (e.g., Clarke-Stewart 1977 ). A particular concern in Laosa’s work was Hispanic children , given the gap in their academic achievement. His early research concerned maternal teaching. Unlike much of the previous work in that area, Laosa made direct observations of the mothers teaching their children, instead of relying on mothers’ self-reports about interactions with their children, and distinguished between two likely SES determinants of their teaching: education and occupation. In a study of Hispanic mother–child dyads (Laosa 1978 ), mother’s education correlated positively with praising and asking questions during the teaching and correlated negatively with modeling (i.e., the mother working on the learning task herself while the child observes). However, mother’s occupation did not correlate with any of the teaching variables, and neither did father’s occupation. Laosa speculated that the education-linked differences in teaching strategies account for the relationship between mothers’ education and their children’s intellectual development found in other research (e.g., Bradley et al. 1977 ). Subsequently, Laosa ( 1980b ) also suggested that the more highly educated mothers imitate how they were taught in school.

In a follow-up study of Hispanic and non-Hispanic White mother–child dyads (Laosa 1980b ), the two groups differed on most of the teaching variables. Non-Hispanic White mothers praised and asked questions more, and Hispanic mothers modeled, gave visual cues, directed, and punished or restrained more. However, when mothers’ education was statistically controlled, the differences between the groups disappeared; controlling for mothers’ or fathers’ occupation did not reduce the differences.

In a third study, with the Hispanic mother–child dyads (Laosa 1980a ), mother’s field independence, assessed by the Embedded Figure Test (Witkin et al. 1971 ) and WAIS Block Design (Wechsler 1955 ), correlated positively with mother’s asking questions and praising, and correlated negatively with mother’s modeling. The correlations were reduced, but their pattern was similar when mother’s education was statistically controlled. Laosa suggested that asking questions and praising are self-discovery approaches to learning that reflect field independence, whereas modeling is a concrete approach that reflects field dependence; hence mothers were using strategies that foster their own cognitive style in their children. Mother’s teaching strategies, in fact, correlated modestly but inconsistently with the children’s field independence, as measured by the Children’s Embedded Figures Test (Witkin et al. 1971 ), WISC Block Design (Wechsler 1949a ), and Human Figure Drawing (Harris 1963 ), another measure of field independence. Most of the teaching strategies had scattered correlations with the Children’s Embedded Figures Test and Block Design: positive correlations with asking questions and praising (field-independent strategies) and negative correlations with modeling, punishing or restraining, and giving visual cues (field-dependent strategies).

In Laosa’s later research, a recurring topic was the impact of parents’ education on their children’s intellectual development; this line of work was presumably motivated by the influence of education in his maternal-teaching studies. Laosa ( 1982b ) viewed parental education as impacting the parent–child interaction and presented a conceptual model of this interaction as the mediator between parent education and the child’s development. He reported further analyses of the samples of Hispanic and non-Hispanic White mother–child dyads.

In one analysis, non-Hispanic White mothers and fathers read to their children more than did Hispanic parents. When parents’ education was statistically controlled, the group difference disappeared, but controlling for parents’ occupation did not reduce it. In addition, non-Hispanic mothers had higher realistic educational aspirations for their children (“ realistically , how much education do you think your child will receive?”); this difference also disappeared when mothers’ education was controlled but not when their occupation was controlled.

In another analysis, mother’s education correlated positively in both the Hispanic and non-Hispanic White groups with mother’s reading to the child, but father’s education was uncorrelated with father’s reading to the child in either group. Parent’s occupation did not correlate with reading in the two groups. In both groups, mother’s education also correlated positively with mother’s educational aspirations for the child, but mother’s occupation was uncorrelated.

Also, in an analysis of the Hispanic group, mother’s education correlated positively with the child’s ability to read or write before kindergarten, though father’s education was uncorrelated. Parent’s occupation was also uncorrelated with literacy . In addition, parent’s education correlated positively with their use of English with the child; parent’s occupation also correlated positively but weakly with English use.

Laosa argued that the set of findings, in total, suggests that the lower educational level of Hispanic parents produced a discontinuity between their children’s home and school environments that adversely affected academic achievement.

He explored the consequences of these parental influences on the test performance of 3-year-olds in two studies. In the first study (Laosa 1982a ), which targeted non-Hispanic White children , a path analysis was employed to assess the relationships, direct and indirect, between a host of family influences (e.g., mother’s education and occupation, mother’s reading to the child, nonparents in the household reading to the child, mother’s teaching strategies) and performance on the Preschool Inventory (Caldwell 1970 ), a test of verbal, quantitative, and perceptual-motor skills for kindergarten children. A Mother’s Socioeducational Values factor (defined by mother’s education and occupation and mother’s reading to child) was the strongest determinant of test performance. Less powerful determinants included nonparents in the household (probably older siblings) reading to the child and mother’s use of modeling in teaching. Laosa highlighted two important and unanticipated findings: the apparent influence of siblings and the substantial and positive influence of modeling, contrary to the conventional wisdom that verbal teaching strategies, such as asking questions, are superior to nonverbal ones, such as modeling.

In the second study (Laosa 1984 ) of Hispanic and non-Hispanic White children , the groups differed in their means on three of the five scales of the McCarthy Scales of Children’s Abilities (McCarthy 1972 ): Verbal, Quantitative, and Memory. When a Sib Structure/Size factor (later-born child, many siblings) was statistically controlled, the group differences were unaffected. But when either a Language factor (mother uses English with child, child uses English with mother) or an SES factor (parents’ education, father’s occupation, household income) was controlled, the differences were reduced; when both factors were controlled, the differences were eliminated. The findings led Laosa to conclude that these early ethnic-group differences in ability were explainable by differences in SES and English-language usage.

2.2 Migration

In a series of white papers, Laosa reviewed and synthesized the extant research literature on the consequences of migration for children’s adjustment and development, particularly Hispanic children , and laid out the salient issues (Laosa 1990 , 1997 , 1999 ). One theme was the need for—and the absence of—a developmental perspective in studying migration: “what develops, and when, how, and why it develops” (Laosa 1999 , p. 370). The pioneering nature of this effort is underscored by the observation almost two decades later that migration is neglected by developmental psychology (Suárez-Orozco and Carhill 2008 ; Suárez-Orozco et al. 2008 ).

In a 1990 paper, Laosa proposed a multivariate, conceptual model that described the determinants of the adaptation of Hispanic immigrant children to the new society. Key features of the model were the inclusion of variables antedating immigration (e.g., sending community), moderator variables (e.g., receiving community), and mediating variables (e.g., child’s perceptions and expectations) between the stresses of immigration and the outcomes.

In a complex, longitudinal survey of Puerto Rican migrants in New Jersey schools, Laosa ( 2001 ) found that the majority of the student body were Hispanic in 46% of the schools and were native speakers of Spanish in 31%. Additionally, the majority of the student body was eligible for free lunch in 77% of the schools and was from families on public assistance in 46%. Laosa concluded that the migrants faced considerable segregation by ethnicity or race as well as considerable isolation by language in high-poverty schools, factors with adverse consequences for the students’ social and academic development.

2.3 Measurement

The measurement and evaluation of children’s ability and achievement, particularly the unbiased assessment of minority children, has long been beset by controversies (see Laosa 1977 ; Oakland and Laosa 1977 ). These controversies were sparked in the 1960s and 1970s by the Coleman report (Coleman et al. 1966 ), which suggested that average differences in the academic performance of Black and White students are more affected by their home background than by their schools’ resources, and by Jensen’s ( 1969 ) review of research bearing on genetic and environmental influences on intelligence. He concluded that genetics is a stronger influence, which many observers interpreted as suggesting that the well-established disparity between Black and White children in their average scores on intelligence tests is largely genetic in origin. The upshot was widespread concerns that these tests are biased and calls for banning their use in schools. These arguments were reignited by The Bell Curve (Herrnstein and Murray 1994 ), which concluded that intelligence is mainly heritable. As Laosa ( 1996 ) noted, “thus, like a refractory strain of retrovirus, the issues tend to remain latent and from time to time resurge brusquely onto the fore of public consciousness” (p. 155).

In a 1977 paper, Laosa summarized the earlier controversies and other criticisms of testing and discussed alternatives to current testing practices that had been developed in response. The alternatives included constructing “culture-fair” tests “whose content is equally ‘fair’ or ‘unfair’ to different cultural groups” (p. 14), translating tests from English, using norms for subgroups, adjusting scores for test takers with deprived backgrounds, devising tests for subgroups (e.g., the BITCH, a vocabulary test based on Black culture; Williams 1972 ), using criterion-based interpretations of scores (i.e., how well a student achieves a specific objective) instead of norm-based interpretations (i.e., how well he or she does on the test relative to others), employing tests of specific abilities rather than global measures like IQ, and making observations of actual behavior. Laosa cautioned that these alternatives may also be problematic and would need to be carefully evaluated.

In a companion piece, Laosa, joined by Thomas Oakland of the University of Texas, Austin (Oakland and Laosa 1977 ), provided a comprehensive account of standards for minority-group testing that had been formulated by professional organizations, the government, and the courts. They argued for the need to consider these standards in testing minority-group children.

Laosa ( 1982c ), in a subsequent paper on measurement issues in the evaluation of educational programs , specifically Head Start , delineated the concept of population validity and its applicability to program evaluation . Population validity deals with the question, “Do the results yielded by a given assessment technique have the same meaning when administered to persons of different sociocultural backgrounds?” (p. 512). Laosa discussed threats to population validity: familiarity (performance is influenced by familiarity with the task), communication, role relations (performance is influenced by the test taker’s relationship with the tester), and situational (e.g., physical setting, people involved).

In another paper, Laosa ( 1991 ) explicated the links between population validity, cultural diversity, and professional ethics. As an illustration, he described a study by Bradley et al. ( 1989 ) of children in three ethnic groups, Black, Hispanic, and non-Hispanic White, matched on their HOME inventory (Caldwell and Bradley 1985 ) scores, a measure of the home environment. The HOME inventory scores correlated appreciably with performance on the Bayley Scales of Infant Development (Bayley 1969 ) and the Stanford–Binet Intelligence Test (Terman and Merrill 1960 ) for the Black and non-Hispanic White children but not for the Hispanic children . Laosa suggested that this finding highlights the importance of evaluating test results separately for different ethnic groups.

Laosa pointed out that population validity is a scientific concern in basic research and an ethical issue in applied work, given the inability to predict the results in different populations from the one studied. He also noted that when population differences are observed, two questions need to be answered. One, relevant to applied work, is, Which populations react differently? The other question, pertinent to scientific research, but rarely asked, is, Why do they differ?

In his last paper on measurement issues, Laosa ( 1996 ), responding to The Bell Curve controversy, made several general points about test bias. One was that bias reflects the absence of population validity. He noted that this view accords with the Cole and Moss ( 1989 ) definition of bias: “Bias is differential validity of a given interpretation of a test score for any definable, relevant group of test takers” (p. 205).

Another point was that the definition of predictive bias in the then current third edition of the Standards for Educational and Psychological Testing (American Educational Research Association, American Psychological Association, & National Council on Measurement in Education 1985 ) is insufficient. According to the Standards , predictive bias is absent if “the predictive relationship of two groups being compared can be adequately described by a common algorithm (e.g., regression line)” (p. 12). Laosa took up the argument that intelligence tests cannot be considered to be unbiased simply because their predictions are equally accurate for different races or social classes, noting Campbell’s rejoinder that the same result would occur if the tests simply measured opportunity to learn (D. T. Campbell, personal communication, May 18, 1995).

The last point was that the consequences of test use also need to be considered (Messick 1989 ). Laosa cited Linn’s ( 1989 ) example that requiring minimum high school grades and admissions test scores for college athletes to play during their freshman year can affect what courses minority athletes take in high school, whether they will attend college if they cannot play in their freshman year, and, ultimately, their education and employment.

3 Cognitive, Personality, and Social Development of Infants and Young Children

Lewis studied infant’s cognitive attention and language ability, infants’ and young children’s physiological responses during attention, and infants’ social and emotional development . He also formulated theories of development as well as theories about the integration of children’s various competencies.

3.1 Social Development

Social development was a major interest, in particular, the mother–child interaction and the role this interaction played in the child’s development. This work on social development revolved around four themes: (a) the mother–child relationship, (b) the growth of the child’s social knowledge, (c) social cognition or the nature of the social world, and (d) the social network of children.

For example, in a 1979 volume (Lewis and Rosenblum 1979 ), The Child and Its Family , Lewis challenged the idea that the child’s mother was the only important figure in the infant’s early life and showed that fathers and siblings, as well as grandparents and teachers, were also key influences. And in a 1975 volume, on peer friendship in the opening years of life (Lewis and Rosenblum 1975 ), Lewis disputed the Piagetian idea that children could not form and maintain friendships before the age of 4 years. The finding that infants are attracted to and enjoy the company of other infants and young children , and that they can learn from them through observation and imitation, helped open the field of infant daycare (Goldman and Lewis 1976 ; Lewis 1982b ; Lewis and Schaeffer 1981 ; Lewis et al. 1975 ). Because the infant’s ability to form and maintain friends is important for the daycare context, where groups of infants are required to play together, this work also showed that the learning experience of young children and infants involved both direct and indirect interactions, such as modeling and imitation with their social world of peers, siblings, and teachers (Feiring and Lewis 1978 ; Lewis and Feiring 1982 ). This information also had an important consequence on hospital care; until this time, infants were kept far apart from each other in the belief that they could not appreciate or profit from the company of other children.

Another major theme of the research on social development involved infants’ social knowledge. In a series of papers, Lewis was able to show that infants could discriminate among human faces (Lewis 1969 ); that they were learning about their gender (Lewis and Brooks 1975 ); that they were showing sex-role-appropriate behaviors (Feiring and Lewis 1979 ; Goldberg and Lewis 1969 ; Lewis 1975a ); that they were learning about how people look, for example, showing surprise at the appearance of a midget—a child’s height but an adult’s face (Brooks and Lewis 1976 ); and that they were detecting the correspondence between particular faces and voices (McGurk and Lewis 1974 ). All of these results indicated that in the first 2 years, children were learning a great deal about their social worlds (Brooks-Gunn and Lewis 1981 ; Lewis 1981b ; Lewis et al. 1971 ).

The most important aspect of this work on social development was the child’s development of a sense of itself, something now called consciousness, which occurs in the second and third years of life. In the Lewis and Brooks-Gunn ( 1979a ) book on self-recognition, Social Cognition and the Acquisition of Self, Lewis described his mirror self-recognition test, a technique that has now been used across the world. Results with this test revealed that between 15 and 24 months of age, typically developing children come to recognize themselves in mirrors. He subsequently showed that this ability, the development of the idea of “me,” along with other cognitive abilities gives rise to the complex emotions of empathy, embarrassment, and envy as well as the later self-conscious emotions of shame, guilt, and pride (Lewis and Brooks 1975 ; Lewis and Brooks-Gunn 1981b ; Lewis and Michalson 1982b ; Lewis and Rosenblum 1974b ).

These ideas, an outgrowth of the work on self-recognition, led to Lewis’s interest in emotional development . They also resulted in a reinterpretation of the child’s need for others. While children’s attachment to their mothers was thought to be the most important relationship for the children, satisfying all of their needs, it became clear that others played an important role in children’s social and emotional lives. His empirical work on fathers (Lewis and Weinraub 1974 ) and peers (Lewis et al. 1975 ) led to the formulation of a social network theory (Feiring and Lewis 1978 ; Lewis 1980 ; Lewis and Ban 1977 ; Lewis and Feiring 1979 ; Weinraub et al. 1977 ).

3.2 Emotional Development

Lewis’s interest in social development and in consciousness led quite naturally to his research on emotional development, as already noted (Lewis 1973 , 1977b , 1980 ; Lewis and Brooks 1974 ; Lewis et al. 1978 ; Lewis and Michalson 1982a , b ; Lewis and Rosenblum 1978a , b ). Two volumes framed this work on the development of emotional life (Lewis and Rosenblum 1974b , 1978b ) and were the first published studies of emotional development. These early efforts were focused on the emotions of infants in the first year of life, including fear, anger, sadness, joy, and interest. To study emotional life, Lewis created experimental paradigms and devised a measurement system. So, for example, paradigms were developed for peer play (Lewis et al. 1975 ), social referencing (Feinman and Lewis 1983 ; Lewis and Feiring 1981 ), stranger approach (Lewis and Brooks-Gunn 1979a ), mirror recognition (Lewis and Brooks-Gunn 1979a ), and contingent learning (Freedle and Lewis 1970 ; Lewis and Starr 1979 ). A measurement system was created for observing infants’ and young children’s emotional behavior in a daycare situation that provided scales of emotional development (Lewis and Michalson 1983 ). These scales have been used by both American and Italian researchers interested in the effects of daycare on emotional life (Goldman and Lewis 1976 ).

3.3 Cognitive Development

Lewis’s interests in development also extended to the study of infants’ and children’s cognitive development, including attentional processes, intelligence, and language development (Dodd and Lewis 1969 ; Freedle and Lewis 1977 ; Hale and Lewis 1979 ; Lewis 1971b , 1973 , 1975b , 1976a , b , 1977a , 1978a , 1981a , 1982a ; Lewis and Baldini 1979 ; Lewis and Baumel 1970 ; Lewis and Cherry 1977 ; Lewis and Freedle 1977 ; Lewis and Rosenblum 1977 ; Lewis et al. 1969a, 1971 ; McGurk and Lewis 1974 ).

Lewis first demonstrated that the Bayley Scales of Infant Development (Bayley 1969 ), which were—and still are—the most widely used test of infant intelligence, had no predictive ability up to 18 months of age (Lewis and McGurk 1973 ). In an effort to find an alternative, Lewis turned to research on infants’ attentional ability, which he had begun at the Fels Research Institute, and developed it further at ETS. This work used a habituation–dishabituation paradigm where the infant was presented with the same visual stimulus repeatedly and then, after some time, presented with a variation of that stimulus. Infants show boredom to the repeated event, or habituation, and when the new event is presented, the infants show recovery of their interest, or dishabituation (Kagan and Lewis 1965 ; Lewis et al. 1967a , b ). Infants’ interest was measured both by observing their looking behavior and by assessing changes in their heart rate (Lewis 1974 ; Lewis et al. 1966a , b ; Lewis and Spaulding 1967 ). He discovered that the infants’ rate of habituation and degree of dishabituation were both related to their subsequent cognitive competence, in particular to their IQ. In fact, this test was more accurate than the Bayley in predicting subsequent IQ (Lewis and Brooks-Gunn 1981a , c ; Lewis et al. 1969 ; Lewis and McGurk 1973 ).

This research on attentional processes convinced Lewis of the usefulness of physiological measures, such as heart rate changes, in augmenting behavior observation, work which he also began at the Fels Research Institute and continued and expanded at ETS (Lewis 1971a , b , 1974 ; Lewis et al. 1969 , 1970 , 1978 ; Lewis and Taft 1982 ; Lewis and Wilson 1970 ; Sontag et al. 1969 ; Steele and Lewis 1968 ).

3.4 Atypical Development

Lewis’s research on normal development, especially on attentional processes as a marker of central nervous system functioning, led to an interest in atypical developmental processes and a program of research on children with disabilities (Brinker and Lewis 1982a , b ; Brooks-Gunn and Lewis 1981 , 1982a , b , c ; Fox and Lewis 1982a , b ; Lewis 1971c ; Lewis and Fox 1980 ; Lewis and Rosenblum 1981 ; Lewis and Taft 1982 ; Lewis and Wehren 1982 ; Lewis and Zarin-Ackerman 1977 ; Thurman and Lewis 1979 ; Zarin-Ackerman et al. 1977 ). Perhaps of most importance was the development of an intervention strategy based on Lewis’s work with typically developing children, the Learning to Learn Curriculum. Infants with disabilities were given home- and clinic-based interventions where their simple motor responses resulted in complex outcomes and where they had to learn to produce these outcomes, which served as operants—in effect, an applied-behavior-analysis intervention strategy (Brinker and Lewis 1982a , b ; Lewis 1978a , b ; Thurman and Lewis 1979 ).

3.5 Theories

Lewis formulated several influential theories about infant development . These included (a) a reconsideration of attachment theory (Weinraub and Lewis 1977 ) and (b) the infant as part of a social network (Weinraub et al. 1977 ). He also began work on a theory of emotional development (Lewis 1971b ; Lewis and Michalson 1983 ).

3.6 The Origin of Behavior Series

Lewis and Leonard Rosenblum of SUNY Downstate Medical Center organized yearly conferences on important topics in child development for research scientists in both child and animal (primate) development to bring together biological, cultural, and educational points of view. These conferences resulted in a book series, The Origins of Behavior (later titled Genesis of Behavior ), under their editorship, with seven highly cited volumes (Lewis and Rosenblum 1974a , b , 1975 , 1977 , 1978a , 1979 , 1981 ). The initial volume, The Effect of the Infant on the Caregiver (Lewis and Rosenblum 1974a ), was so influential that the term caregiver became the preferred term, replacing the old term caretaker. The book became the major reference on the interactive nature of social development —that the social development of the child involves an interaction between the mother’s effect on the infant and the effect of the infant on the mother. It was translated into several languages, and 15 years after publication, a meeting sponsored by the National Institutes of Health reviewed the effects of this volume on the field.

4 Cognitive, Personality, and Social Development From Infancy to Adolescence

Brooks-Gunn’s work encompassed research on the cognitive, personality, and social development of infants , toddlers, and adolescents , primarily within the framework of social-cognitive theory . Major foci were the acquisition of social knowledge in young children , reproductive processes in adolescence, and perinatal influences on children’s development. These issues were attacked in laboratory experiments, other cross-sectional and longitudinal studies, and experimental interventions . (A fuller account appears in Brooks-Gunn 2013 .)

4.1 Social Knowledge in Infants and Toddlers

In collaboration with Lewis, Brooks-Gunn carried out a series of studies on the development of early knowledge about the self and others in infancy and toddlerhood. They investigated how and when young children began to use social categories, such as gender, age, and relationship, to organize their world and to guide interactions (Brooks and Lewis 1976 ; Brooks-Gunn and Lewis 1979a , b , 1981 ) as well as the development of self-recognition as a specific aspect of social cognition (Lewis and Brooks-Gunn 1979b , c ; Lewis et al. 1985 ). This developmental work was embedded in genetic epistemology theory as well as social-cognitive theory , with a strong focus on the idea that the self (or person) only develops in relation to others and that the self continues to evolve over time, as does the relation to others.

The studies demonstrated that social knowledge develops very early. Infants shown pictures of their parents, strange adults, and 5-year olds and asked, Who is that? were able to label their parents’ pictures as mommy and poppy, labeling their fathers’ pictures more accurately and earlier than their mothers’ (Brooks-Gunn and Lewis 1979b ). Shown pictures of their parents and strange adults, infants smiled more often and looked longer at their parents’ pictures (Brooks-Gunn and Lewis 1981 ). And when infants were approached by strangers—5-year-old boys and girls, adult men and women, and a midget woman—the children discriminated among them on the basis of age and height, smiling and moving toward the children but frowning and moving away from the adults and, compared to the other adults, watching the midget more intently and averting their gaze less (Brooks and Lewis 1976 ).

4.2 Reproductive Events

4.2.1 menstruation and menarche.

Brooks-Gunn’s interest in the emergence of social cognition broadened to its role in the development of perceptions about reproductive events, at first menstruation and menarche. Her focus was on how social cognitions about menstruation and menarche emerge in adolescence and how males’ and females’ cognitions differ. Brooks-Gunn and Diane Ruble, then at Princeton University, began a research program on the salience and meaning of menarche and menstruation, especially in terms of definition of self and other in the context of these universal reproductive events (Brooks-Gunn 1984 , 1987 ; Brooks-Gunn and Ruble 1982a , b , 1983 ; Ruble and Brooks-Gunn 1979b ). They found that menstruation was perceived as more physiologically and psychologically debilitating and more bothersome by men than by women (Ruble et al. 1982 ). In addition, their research debunked a number of myths about reproductive changes (Ruble and Brooks-Gunn 1979a ), including the one that menarche is a normative crisis experienced very negatively by all girls. In fact, most girls reported mixed emotional reactions to menarche that were quite moderate. These reactions depended on the context the girls experienced: Those who were unprepared for menarche or reached it early reported more negative reactions as well as more symptoms (Ruble and Brooks-Gunn 1982 ).

4.2.2 Pubertal Processes

Brooks-Gunn’s research further broadened to include pubertal processes. With Michelle Warren , a reproductive endocrinologist at Columbia University, she initiated a research program on pubertal processes and the transition from childhood to early adolescence. Brooks-Gunn and Warren conducted longitudinal studies of girls to chart their emotional experiences associated with pubertal changes and the socialization practices of families . The work included measurement of hormones to better understand pubertal changes and possible emotional reactions. The investigations followed girls who were likely to have delayed puberty because of exercise and food restriction (dancers training in national ballet companies as well as elite swimmers and gymnasts) and girls attending private schools—many of the girls were followed from middle school through college (Brooks-Gunn and Warren 1985 , 1988a , b ; Warren et al. 1986 , 1991 ).

The private-school girls commonly compared their pubertal development and had no difficulty categorizing their classmates’ development (Brooks-Gunn et al. 1986 ). Relatedly, the onset of breast development for these girls correlated positively with scores on measures of peer relationships, adjustment, and body image, but pubic hair was uncorrelated, suggesting that breast growth may be a visible sign of adulthood, conferring enhanced status (Brooks-Gunn and Warren 1988b ).

The context in which the girls were situated influenced their reactions. In a context where delayed onset of puberty is valued (the dance world—most professional ballerinas are late maturers), dancers with delayed puberty had higher scores (relative to on-time dancers) on a body-image measure (Brooks-Gunn, Attie, Burrow , Rosso , & Warren , Brooks-Gunn et al. 1989 ; Brooks-Gunn and Warren 1985 ). (They also had lower scores on measures of psychopathology and bulimia; Brooks-Gunn and Warren 1985 .) In contrast, in contexts where delayed onset is not valued (swimmers, private-school students/nonathletes), delayed and on-time girls did not differ in their body images (Brooks-Gunn, Attie et al., Brooks-Gunn et al. 1989 ; Brooks-Gunn and Warren 1985 ).

Two publications in this program, in particular, were very widely cited, according to the Social Science Citation Index: Attie and Brooks-Gunn ( 1989 ), on eating problems, and Brooks-Gunn et al. ( 1987 ), on measuring pubertal status, with 389 and 323 citations through 2015, respectively.

4.2.3 Adolescent Parenthood

Given Brooks-Gunn’s research interest in menarche and other pubertal processes , it is not surprising that she moved on to research on pregnancy and parenthood, events that presage changes in self-definition as well as social comparisons with others. Brooks-Gunn joined Frank Furstenberg, a family sociologist at the University of Pennsylvania, in a 17-year follow-up of a group of teenage mothers who gave birth in Baltimore in the early 1970s (Furstenberg et al. 1987a , b , 1990 ). They charted the trajectories of these mothers as well as their children, who were about the age that their mothers had been when they gave birth to them. The interest was in both how well the mothers were doing and how the mothers’ life course had influenced their children.

In brief, the teenage mothers differed widely in their life chances: About one third were on welfare and three quarters had jobs, usually full-time ones. Characteristics of the mothers’ family of origin and of their own families (e.g., higher levels of education) and their attendance at a school for pregnant teenagers predicted the mothers’ economic success.

The outcomes for their teenage children were “strikingly poor” (Brooks-Gunn 1996 , p. 168). About one third were living with their biological father or stepfather. Half had repeated at least one grade in school, and most were sexually active. Maternal characteristics were linked to the children’s behavior. Children of mothers who had not graduated from high school were 2.4 times as likely as other children, and children of unmarried mothers were 2.2 times as likely, to have repeated a grade. And children of unmarried mothers were 2.4 times as likely to have been stopped by the police, according to their mothers.

The Furstenberg et al. ( 1987b ) monograph chronicling this study, Adolescent Mothers in Later Life, won the William J. Goode Book Award from the American Sociological Association’s Sociology of the Family Section and is considered one of the classic longitudinal studies in developmental psychology.

Brooks-Gunn and Lindsay Chase-Lansdale, then at George Washington University, also began a study of low-income, Black multigenerational families (grandmother/grandmother figure–young mother–toddler) in Baltimore to investigate family relationships, via home visits (Chase-Lansdale et al. 1994 ). One issue was the parenting by the grandmother and mother, as observed separately in videotaped interactions of them aiding the child in working on a puzzle. The quality of parenting depended on whether they resided together and on the mother’s age. Mothers’ parenting was lower in quality when they lived with grandmothers. (Residing with the grandmothers and sharing child caring may be stressful for the mothers, interfering with their parenting.) Grandmothers’ parenting was higher in quality when they lived with younger mothers than when they lived apart, but it was lower in quality when they lived with older mothers rather than apart. (Coresiding grandmothers may be more willing to help younger mothers, whom they view as needing assistance in parenting, than older mothers, whom they see as capable of parenting on their own.)

4.3 Perinatal Influences

Another line of research expanded beyond teenage parents to look at perinatal conditions, such as low birth weight and pregnancy behavior (e.g., smoking, no prenatal care), that influence parenting and children’s development. Poor families and mothers with low education were often the focus of this research, given the differential rates of both early parenthood and adverse perinatal conditions as a function of social class.

In a joint venture between ETS, St. Luke’s–Roosevelt Hospital, and Columbia University’s College of Physicians and Surgeons, Brooks-Gunn studied low-birth-weight children and their parents, many from disadvantaged families because of the greater incidence of low-birth-weight children in these families. The work led to her thinking about how to ameliorate cognitive, emotional, and academic problems in these vulnerable children (Brooks-Gunn and Hearn 1982 ).

Brooks-Gunn joined Marie McCormick , a pediatrician then at the University of Pennsylvania, in a 9-year follow-up of low-birth-weight infants from previous multisite studies (Klebanov et al. 1994 ; McCormick et al. 1992 ). The focus was on very low birth weight infants, for more of them were surviving because of advances in neonatal intensive care.

At age 9, the low-birth-weight children did not differ from normal-birth-weight children on most aspects of classroom behavior, as reported by their teachers, but they had lower attention/ language skills and scholastic competence and higher daydreaming and hyperactivity; these differences were most pronounced for extremely low birth weight children. This pattern of differences resembles attention deficit disorder (Klebanov et al. 1994 ). The low-birth-weight children also had lower mean IQs and, at home, more behavioral problems, as reported by their mothers. The adverse health status of these children underscores the importance of efforts to reduce the incidence of premature births (McCormick et al. 1992 ).

4.4 Interventions With Vulnerable Children

4.4.1 low-birth-weight children.

Brooks-Gunn and McCormick also collaborated on two other research programs involving interventions with biologically vulnerable children, the majority of whom were poor. One program focused on reducing the incidence of low-birth-weight deliveries by providing pregnant women with child-rearing and health information. This program used a public health outreach model to locate pregnant women who were not enrolled in prenatal care; the intervention was located at Harlem Hospital. This effort was a logical extension of Brooks-Gunn’s work on adolescent mothers in Baltimore (Brooks-Gunn et al. 1989 ; McCormick et al. 1987 , 1989a , b ).

The women in the program were very disadvantaged: One fifth were adolescents , three quarters were single, and half had not graduated from high school. The birth weight of their infants was unrelated to traditional risk factors: mother’s demographic (e.g., education) and psychosocial characteristics (e.g., social support). This outcome suggests that low birth weight in poor populations is largely due to poverty per se. Birth weight was associated with the adequacy of prenatal care (Brooks-Gunn et al. 1988 ; McCormick et al. 1987 ).

The outreach program was extensive—four local people searching for eligible women over the course of a year, each making roughly 20 to 25 contacts daily—but recruited only 52 additional women, at a cost of about $850 each. The labor-intensive and expensive nature of this outreach effort indicates that more cost-effective alternatives are needed (Brooks-Gunn et al. 1988 , 1989 ).

The other program involved the design and implementation of an early intervention for premature, low-birth-weight infants : enrollment in a child development education center and family support sessions. This program was initiated in eight sites and included almost 1000 children and their families; randomization was used to construct treatment and control groups. These children were followed through their 18th year of life, with the intervention from birth to 3 years of age being evaluated by Brooks-Gunn (Infant Health and Development Program 1990 ). The 3-year-olds in the intervention group had higher mean IQs and fewer maternally reported behavior problems, suggesting that early intervention may decrease low-birth-weight infants’ risk of later developmental disability .

4.4.2 Head Start

Brooks-Gunn also carried out a notable evaluation of Head Start, based on data from an earlier longitudinal study conducted at ETS in the 1970s. The ETS–Head Start Longitudinal Study, directed by Shipman ( 1972 , 1973 ), had canvassed poor school districts in three communities in an effort to identify and recruit for the study all children who were 3 ½ to 4 ½ years old, the Head Start population. The children were then assessed and information about their families was obtained. They were reassessed annually for the next 3 years. After the initial assessment, some children had entered Head Start, some had gone to other preschool programs, and some had not enrolled in any program. Clearly families chose whether to enroll their children in Head Start, some other program, or none at all (by processes that are difficult if not impossible to measure). But, by having the children’s assessments and familial and demographic measures at age 3, it was possible to document and control statistically for initial differences among children and families in the three groups. Children’s gains in ability in these groups could then be compared.

In several studies of two communities (Lee et al. 1988 , 1990 ; Schnur et al. 1992 ), Brooks-Gunn and her collaborators investigated differences in the children’s gains in the Head Start and other groups as well as preexisting group differences in the children’s demographic and cognitive characteristics. Black children enrolled in Head Start made greater gains on a variety of cognitive tests than their Black peers in the other groups by the end of the program (Lee et al. 1988 ) and diminished gains after 2 years (Lee et al. 1990 ). (The gains for the small samples of White children did not differ between the Head Start and other groups in the initial study; these children were not included in the follow-up study.) These findings imply that Head Start may have some efficacy in improving participants’ intellectual status. The Head Start children were the most disadvantaged (Schnur et al. 1992 ), seemingly allaying concerns that Head Start does not take the neediest children (Datta 1979 ).

5 Conclusion

As this review documents, ETS was a major center for basic and applied research in developmental psychology for decades. The number and quality of investigators (and their prodigious output) made for a developmental psychology program that rivaled the best in the academic community.

The research was wide ranging and influential, spanning the cognitive, personality, and social development of infants , children, and adolescents , with an emphasis on minority, working-class, and disabled individuals; addressing key theoretical, substantive, and methodological issues; using research methods that ran the gamut: laboratory and field experiments, correlational studies, surveys, and structured observations; and impacting theory, research, and practice across developmental psychology.

In common with ETS’s research in cognitive, personality, and social psychology (Stricker, Chap. 13 , and Kogan, Chap. 14 , this volume), this achievement was probably attributable to the confluence of ample institutional and financial support, doubtless due to the vision of Chauncey, who saw the value of a broad program of psychological research.

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Developmental Psychology 101: Theories, Stages, & Research

Developmental psychology stages

You can imagine how vast this field of psychology is if it has to cover the whole of life, from birth through death.

Just like any other area of psychology, it has created exciting debates and given rise to fascinating case studies.

In recent years, developmental psychology has shifted to incorporate positive psychology paradigms to create a holistic lifespan approach. As an example, the knowledge gained from positive psychology can enhance the development of children in education.

In this article, you will learn a lot about different aspects of developmental psychology, including how it first emerged in history and famous theories and models.

Before you continue, we thought you might like to download our three Positive Psychology Exercises for free . These science-based exercises explore fundamental aspects of positive psychology, including strengths, values, and self-compassion, and will give you the tools to enhance the wellbeing of your clients, students, or employees.

This Article Contains:

What is developmental psychology, 4 popular theories, stages, & models, 2 questions and research topics, fascinating case studies & research findings, a look at positive developmental psychology, applying developmental psychology in education, resources from positivepsychology.com, a take-home message.

Human beings change drastically over our lifetime.

The American Psychological Association (2020) defines developmental psychology as the study of physical, mental, and behavioral changes, from conception through old age.

Developmental psychology investigates biological, genetic, neurological, psychosocial, cultural, and environmental factors of human growth (Burman, 2017).

Over the years, developmental psychology has been influenced by numerous theories and models in varied branches of psychology (Burman, 2017).

History of developmental psychology

Developmental psychology first appeared as an area of study in the late 19th century (Baltes, Lindenberger, & Staudinger, 2007). Developmental psychology focused initially on child and adolescent development, and was concerned about children’s minds and learning (Hall, 1883).

There are several key figures in developmental psychology. In 1877, the famous evolutionary biologist Charles Darwin undertook the first study of developmental psychology on innate communication forms. Not long after, physiologist William Preyer (1888) published a book on the abilities of an infant.

The 1900s saw many significant people dominating the developmental psychology field with their detailed theories of development: Sigmund Freud (1923, 1961), Jean Piaget (1928), Erik Erikson (1959), Lev Vygotsky (1978), John Bowlby (1958), and Albert Bandura (1977).

By the 1920s, the scope of developmental psychology had begun to include adult development and the aging process (Thompson, 2016).

In more recent years, it has broadened further to include prenatal development (Brandon et al., 2009). Developmental psychology is now understood to encompass the complete lifespan (Baltes et al., 2007).

Developmental Psychology Theories

Each of these models has contributed to the understanding of the process of human development and growth.

Furthermore, each theory and model focuses on different aspects of development: social, emotional, psychosexual, behavioral, attachment, social learning, and many more.

Here are some of the most popular models of development that have heavily contributed to the field of developmental psychology.

1. Bowlby’s attachment styles

The seminal work of psychologist John Bowlby (1958) showcased his interest in children’s social development. Bowlby (1969, 1973, 1980) developed the most famous theory of social development, known as attachment theory .

Bowlby (1969) hypothesized that the need to form attachments is innate, embedded in all humans for survival and essential for children’s development. This instinctive bond helps ensure that children are cared for by their parent or caregiver (Bowlby, 1969, 1973, 1980).

Bowlby’s original attachment work was developed further by one of his students, Mary Ainsworth. She proposed several attachment styles between the child and the caregiver (Ainsworth & Bell, 1970).

This theory clearly illustrates the importance of attachment styles to a child’s future development. Consistent and stable caregiving results in a secure attachment style (Ainsworth, Blehar, Waters, & Wall, 1978). In contrast, unstable and insecure caregiving results in several negative attachment styles: ambivalent, avoidant, or disorganized (Ainsworth & Bell, 1970; Main & Solomon, 1986).

Bowlby’s theory does not consider peer group influence or how it can shape children’s personality and development (Harris, 1998).

2. Piaget’s stage theory

Jean Piaget was a French psychologist highly interested in child development. He was interested in children’s thinking and how they acquire, construct, and use their knowledge (Piaget, 1951).

Piaget’s (1951) four-stage theory of cognitive development sequences a child’s intellectual development. According to this theory, all children move through these four stages of development in the same order (Simatwa, 2010).

The sensorimotor stage is from birth to two years old. Behaviors are triggered by sensory stimuli and limited to simple motor responses. If an object is removed from the child’s vision, they think it no longer exists (Piaget, 1936).

The pre-operational stage occurs between two and six years old. The child learns language but cannot mentally manipulate information or understand concrete logic (Wadsworth, 1971).

The concrete operational stage takes place from 7 to 11 years old. Children begin to think more logically about factual events. Abstract or hypothetical concepts are still difficult to understand in this stage (Wadsworth, 1971).

In the formal operational stage from 12 years to adulthood, abstract thought and skills arise (Piaget, 1936).

Piaget did not consider other factors that might affect these stages or a child’s progress through them. Biological maturation and interaction with the environment can determine the rate of cognitive development in children (Papalia & Feldman, 2011). Individual differences can also dictate a child’s progress (Berger, 2014).

3. Freud’s psychosexual development theory

One of the most influential developmental theories, which encompassed psychosexual stages of development, was developed by Austrian psychiatrist Sigmund Freud (Fisher & Greenberg, 1996).

Freud concluded that childhood experiences and unconscious desires influence behavior after witnessing his female patients experiencing physical symptoms and distress with no physical cause (Breuer & Freud, 1957).

According to Freud’s psychosexual theory, child development occurs in a series of stages, each focused on different pleasure areas of the body. During each stage, the child encounters conflicts, which play a significant role in development (Silverman, 2017).

Freud’s theory of psychosexual development includes the oral, anal, phallic, latent, and genital stages. His theory suggests that the energy of the libido is focused on these different erogenous zones at each specific stage (Silverman, 2017).

Freud concluded that the successful completion of each stage leads to healthy adult development. He also suggested that a failure to progress through a stage causes fixation and developmental difficulties, such as nail biting (oral fixation) or obsessive tidiness (anal fixation; Silverman, 2017).

Freud considered personality to be formed in childhood as a child passes through these stages. Criticisms of Freud’s theory of psychosexual development include its failure to consider that personality can change and grow over an entire lifetime. Freud believed that early experiences played the most significant role in shaping development (Silverman, 2017).

4. Bandura’s social learning theory

American psychologist Albert Bandura proposed the social learning theory (Bandura, Ross, & Ross, 1961). Bandura did not believe that classical or operant conditioning was enough to explain learned behavior because some behaviors of children are never reinforced (Bandura, 1986). He believed that children observe, imitate, and model the behaviors and reactions of others (Bandura, 1977).

Bandura suggested that observation is critical in learning. Further, the observation does not have to be of a live actor, such as in the Bobo doll experiment (Bandura, 1986). Bandura et al. (1961) considered that learning and modeling can also occur from listening to verbal instructions on behavior performance.

Bandura’s (1977) social theory posits that both environmental and cognitive factors interact to influence development.

Bandura’s developmental theory has been criticized for not considering biological factors or children’s autonomic nervous system responses (Kevin, 1995).

Overview of theories of development – Khan Academy

Developmental psychology has given rise to many debatable questions and research topics. Here are two of the most commonly discussed.

1. Nature vs nurture debate

One of the oldest debates in the field of developmental psychology has been between nature and nurture (Levitt, 2013).

Is human development a result of hereditary factors (genes), or is it influenced by the environment (school, family, relationships, peers, community, culture)?

The polarized position of developmental psychologists of the past has now changed. The nature/nurture question now concerns the relationship between the innateness of an attribute and the environmental effects on that attribute (Nesterak, 2015).

The field of epigenetics  describes how behavioral and environmental influences affect the expression of genes (Kubota, Miyake, & Hirasawa, 2012).

Many severe mental health disorders have a hereditary component. Yet, the environment and behavior, such as improved diet, reduced stress, physical activity, and a positive mindset, can determine whether this health condition is ever expressed (Śmigielski, Jagannath, Rössler, Walitza, & Grünblatt, 2020).

When considering classic models of developmental psychology, such as Piaget’s schema theory and Freud’s psychosexual theory, you’ll see that they both perceive development to be set in stone and unchangeable by the environment.

Contemporary developmental psychology theories take a different approach. They stress the importance of multiple levels of organization over the course of human development (Lomas, Hefferon, & Ivtzan, 2016).

2. Theory of mind

Theory of mind allows us to understand that others have different intentions, beliefs, desires, perceptions, behaviors, and emotions (American Psychological Association, 2020).

It was first identified by research by Premack and Woodruff (1978) and considered to be a natural developmental stage of progression for all children. Starting around the ages of four or five, children begin to think about the thoughts and feelings of others. This shows an emergence of the theory of mind (Wellman & Liu, 2004).

However, the ability of all individuals to achieve and maintain this critical skill at the same level is debatable.

Children diagnosed with autism exhibit a deficit in the theory of mind (Baron-Cohen, Leslie, & Frith, 1985).

Individuals with depression (psychotic and non-psychotic) are significantly impaired in theory of mind tasks (Wang, Wang, Chen, Zhu, & Wang, 2008).

People with social anxiety disorder have also been found to show less accuracy in decoding the mental states of others (Washburn, Wilson, Roes, Rnic, & Harkness, 2016).

Further research has shown that the theory of mind changes with aging. This suggests a developmental lifespan process for this concept (Meinhardt-Injac, Daum, & Meinhardt, 2020).

research studies on developmental psychology

1. Little Albert

The small child who was the focus of the experiments of behavioral psychologists Watson and Rayner (1920) was referred to as ‘Little Albert.’ These experiments were essential landmarks in developmental psychology and showed how an emotionally stable child can be conditioned to develop a phobia.

Albert was exposed to several neutral stimuli including cotton wool, masks, a white rat, rabbit, monkey, and dog. Albert showed no initial fear to these stimuli.

When a loud noise was coupled with the initially neutral stimulus, Albert became very distressed and developed a phobia of the object, which extended to any similar object as well.

This experiment highlights the importance of environmental factors in the development of behaviors in children.

2. David Reimer

At the age of eight months, David Reimer lost his penis in a circumcision operation that went wrong. His worried parents consulted a psychologist, who advised them to raise David as a girl.

David’s young age meant he knew nothing about this. He went through the process of hormonal treatment and gender reassignment. At the age of 14, David found out the truth and wanted to reverse the gender reassignment process to become a boy again. He had always felt like a boy until this time, even though he was socialized and brought up as a girl (Colapinto, 2006).

3 positive psychology exercises

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Contemporary theories of developmental psychology often encompass a holistic approach and a more positive approach to development.

Positive psychology has intersected with developmental disciplines in areas such as parenting, education, youth, and aging (Lomas et al., 2016).

These paradigms can all be grouped together under the umbrella of positive developmental psychology. This fresh approach to development focuses on the wellbeing aspects of development, while systematically bringing them together (Lomas, et al., 2016).

  • Positive parenting is the approach to children’s wellbeing by focusing on the role of parents and caregivers (Latham, 1994).
  • Positive education looks at flourishing in the context of school (Seligman, Ernst, Gillham, Reivich, & Linkins, 2009).
  • Positive youth development is the productive and constructive focus on adolescence and early adulthood to enhance young people’s strengths and promote positive outcomes (Larson, 2000).
  • Positive aging , also known as healthy aging, focuses on the positivity of aging as a healthy, normal stage of life (Vaillant, 2004).

Much of the empirical and theoretical work connected to positive developmental psychology has been going on for years, even before the emergence of positive psychology itself (Lomas et al., 2016).

We recommend this related article Applying Positive Psychology in Schools & Education: Your Ultimate Guide for further reading.

Developmental Psychology in Education

In the classroom, developmental psychology considers children’s psychological, emotional, and intellectual characteristics according to their developmental stage.

A report on the top 20 principles of psychology in the classroom, from pre-kindergarten to high school, was published by the American Psychological Association in 2015. The report also advised how teachers can respond to these principles in the classroom setting.

The top 5 principles and teacher responses are outlined in the table below.

There are many valuable resources to help you foster positive development no matter whether you’re working with young children, teenagers, or adults.

To help get you started, check out the following free resources from around our blog.

  • Adopt A Growth Mindset This exercise helps clients recognize instances of fixed mindset in their thinking and actions and replace them with thoughts and behaviors more supportive of a growth mindset.
  • Childhood Frustrations This worksheet provides a space for clients to document key challenges experienced during childhood, together with their emotional and behavioral responses.
  • What I Want to Be This worksheet helps children identify behaviors and emotions they would like to display and select an opportunity in the future to behave in this ideal way.
  • 17 Positive Psychology Exercises If you’re looking for more science-based ways to help others enhance their wellbeing, this signature collection contains 17 validated positive psychology tools for practitioners . Use them to help others flourish and thrive.
  • Developmental Psychology Courses If you are interested in a career in Developmental Psychology , we suggest 15 of the best courses in this article.

Earlier developmental psychology models and theories were focused on specific areas, such as attachment, psychosexual, cognitive, and social learning. Although informative, they did not take in differing perspectives and were fixed paradigms.

We’ve now come to understand that development is not fixed. Individual differences take place in development, and the factors that can affect development are many. It is ever changing throughout life.

The modern-day approach to developmental psychology includes sub-fields of positive psychology. It brings these differing disciplines together to form an overarching positive developmental psychology paradigm.

Developmental psychology has helped us gain a considerable understanding of children’s motivations, social and emotional contexts, and their strengths and weaknesses.

This knowledge is essential for educators to create rich learning environments for students to help them develop positively and ultimately flourish to their full potential.

We hope you enjoyed reading this article. Don’t forget to download our three Positive Psychology Exercises for free .

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  • Ainsworth, M. D. S., Blehar, M. C., Waters, E., & Wall, S. (1978). Patterns of attachment: A psychological study of the strange situation . Lawrence Erlbaum Associates.
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Developmental Psychology Research Methods

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

research studies on developmental psychology

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

research studies on developmental psychology

Jose Luis Pelaez Inc/Getty Images 

Cross-Sectional Research Methods

Longitudinal research methods, correlational research methods, experimental research methods.

There are many different developmental psychology research methods, including cross-sectional, longitudinal, correlational, and experimental. Each has its own specific advantages and disadvantages. The one that a scientist chooses depends largely on the aim of the study and the nature of the phenomenon being studied.

Research design provides a standardized framework to test a hypothesis and evaluate whether the hypothesis is correct, incorrect, or inconclusive. Even if the hypothesis is untrue, the research can often provide insights that may prove valuable or move research in an entirely new direction.

At a Glance

In order to study developmental psychology, researchers utilize a number of different research methods. Some involve looking at different cross-sections of a population, while others look at how participants change over time. In other cases, researchers look at how whether certain variables appear to have a relationship with one another. In order to determine if there is a cause-and-effect relationship, however, psychologists much conduct experimental research.

Learn more about each of these different types of developmental psychology research methods, including when they are used and what they can reveal about human development.

Cross-sectional research involves looking at different groups of people with specific characteristics.

For example, a researcher might evaluate a group of young adults and compare the corresponding data from a group of older adults.

The benefit of this type of research is that it can be done relatively quickly; the research data is gathered at the same point in time. The disadvantage is that the research aims to make a direct association between a cause and an effect. This is not always so easy. In some cases, there may be confounding factors that contribute to the effect.

To this end, a cross-sectional study can suggest the odds of an effect occurring both in terms of the absolute risk (the odds of something happening over a period of time) and the relative risk (the odds of something happening in one group compared to another).  

Longitudinal research involves studying the same group of individuals over an extended period of time.

Data is collected at the outset of the study and gathered repeatedly through the course of study. In some cases, longitudinal studies can last for several decades or be open-ended. One such example is the Terman Study of the Gifted , which began in the 1920s and followed 1528 children for over 80 years.

The benefit of this longitudinal research is that it allows researchers to look at changes over time. By contrast, one of the obvious disadvantages is cost. Because of the expense of a long-term study, they tend to be confined to a smaller group of subjects or a narrower field of observation.

Challenges of Longitudinal Research

While revealing, longitudinal studies present a few challenges that make them more difficult to use when studying developmental psychology and other topics.

  • Longitudinal studies are difficult to apply to a larger population.
  • Another problem is that the participants can often drop out mid-study, shrinking the sample size and relative conclusions.
  • Moreover, if certain outside forces change during the course of the study (including economics, politics, and science), they can influence the outcomes in a way that significantly skews the results.

For example, in Lewis Terman's longitudinal study, the correlation between IQ and achievement was blunted by such confounding forces as the Great Depression and World War II (which limited educational attainment) and gender politics of the 1940s and 1950s (which limited a woman's professional prospects).

Correlational research aims to determine if one variable has a measurable association with another.

In this type of non-experimental study, researchers look at relationships between the two variables but do not introduce the variables themselves. Instead, they gather and evaluate the available data and offer a statistical conclusion.

For example, the researchers may look at whether academic success in elementary school leads to better-paying jobs in the future. While the researchers can collect and evaluate the data, they do not manipulate any of the variables in question.

A correlational study can be appropriate and helpful if you cannot manipulate a variable because it is impossible, impractical, or unethical.

For example, imagine that a researcher wants to determine if living in a noisy environment makes people less efficient in the workplace. It would be impractical and unreasonable to artificially inflate the noise level in a working environment. Instead, researchers might collect data and then look for correlations between the variables of interest.

Limitations of Correlational Research

Correlational research has its limitations. While it can identify an association, it does not necessarily suggest a cause for the effect. Just because two variables have a relationship does not mean that changes in one will affect a change in the other.

Unlike correlational research, experimentation involves both the manipulation and measurement of variables . This model of research is the most scientifically conclusive and commonly used in medicine, chemistry, psychology, biology, and sociology.

Experimental research uses manipulation to understand cause and effect in a sampling of subjects. The sample is comprised of two groups: an experimental group in whom the variable (such as a drug or treatment) is introduced and a control group in whom the variable is not introduced.

Deciding the sample groups can be done in a number of ways:

  • Population sampling, in which the subjects represent a specific population
  • Random selection , in which subjects are chosen randomly to see if the effects of the variable are consistently achieved

Challenges in Experimental Resarch

While the statistical value of an experimental study is robust, it may be affected by confirmation bias . This is when the investigator's desire to publish or achieve an unambiguous result can skew the interpretations, leading to a false-positive conclusion.

One way to avoid this is to conduct a double-blind study in which neither the participants nor researchers are aware of which group is the control. A double-blind randomized controlled trial (RCT) is considered the gold standard of research.

What This Means For You

There are many different types of research methods that scientists use to study developmental psychology and other areas. Knowing more about how each of these methods works can give you a better understanding of what the findings of psychological research might mean for you.

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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

A Paradigm Shift in How Scientists Study Kids

Developmental psychology is notoriously reliant on certain demographics of children. A new tool is changing that.

A baby stacks digital blocks

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There is an open secret in the study of child development: Most of what we think we know about how babies develop is actually based on a specific subset of kids—those born to families from Western, educated, industrialized, rich, and democratic (a.k.a. WEIRD) nations. The acronym was first coined in an influential 2010 paper to describe the wildly unrepresentative populations that many psychology studies have long relied on. This is an issue in the field generally, and certainly a thorny problem in developmental psychology, which primarily studies children: According to one paper , WEIRD subjects make up 96 percent of the data used in published developmental-science studies but represent only 12 percent of the world’s population.

As a result, it’s hard to be certain whether many things we think we know about babies’ development are truly universal elements of human nature. It means that we tell an incomplete story about the process of our own becoming. Yet the problem has remained hard to fix. Even within the U.S., similar demographic biases have arisen: The families that most often participate in research studies tend to be white, affluent, and highly educated. The type of parent who brings their baby to a study typically lives near a university, many of which are located in cities, and has the resources and free time to travel to a lab and wait. “Some labs can book a single baby for a day” to collect one data point, Elizabeth Bonawitz, a cognitive scientist at Harvard, told me.

The upheaval of the coronavirus pandemic, however, provided an unexpected opportunity. In spring 2020, Laura Schulz, a cognitive scientist at MIT, and collaborators released a tool called Lookit. At the time, in-person studies were hard to do and research groups had started running online ones instead. Lookit recruited families, connected them with institutions that needed subjects, and hosted virtual studies—including game-based experiments, surveys, and video interviews. Last year, the site merged with Children Helping Science, or CHS, a virtual bulletin board (also co-founded by Schulz) where researchers can advertise studies they need online participants for. Today, CHS has enrolled more than 8,000 children for studies spanning more than 200 labs in all 50 U.S. states and on multiple continents.

The basic technology that underlies CHS is straightforward: a combination of video capture, messaging, and gaming interfaces. In a typical experiment, a child might play a computer game devised by a researcher, the subject can be recorded during play, and both in-game responses and the video are reviewed by scholars later. The platform started in 2013 as a side project for Kim Scott, then a graduate student in Schulz’s lab, but it wasn’t an easy sell to many academics. “Some people still have attachments to doing developmental science in front of a child in their lab, controlling the environment,” Schulz told me. The pandemic meant that scholars had no choice but to relinquish some of that control.

Online experiments might have grown out of necessity, but they help address two of modern developmental psychology’s core problems. First, not enough children participate in experiments in general, so researchers are less likely to identify rarer or more subtle behaviors in them. Second, the WEIRD issue: When experiments consider just a slice of the world’s children, can they really claim that their conclusions are universal?

Take the famed marshmallow study of the 1970s, which offered preschoolers either one marshmallow immediately or two of them if they could wait. The study ultimately suggested that children who delayed short-term gratification in favor of a bigger reward had better outcomes later in life. But the original study was both small (32 children) and demographically specific (all were students of Bing Nursery School at Stanford University). Subsequent attempts to replicate the experiment found the effect diminished or absent altogether. In 2020, researchers even demonstrated that for children from low-socioeconomic-status backgrounds, snapping up that treat immediately could predict future success. In unstable environments, “it may be more effective for you to just go ahead, when you have an opportunity, to take advantage,” Candice Mills, a developmental psychologist at the University of Texas at Dallas, told me.

Read: Why rich kids are so good at the marshmallow test

Other developmental processes that scientists long thought were universal, such as language acquisition, can be affected by one’s environment too. For years, scientists believed that children gained language through one-on-one interactions with adults , but in an island community in Oceania, children largely learn from one another.

Scientists have tried various methods to tackle the field’s biases. The Stanford psychologist Anne Fernald, for example, traveled in an RV to a low-income community in Northern California in order to collect data on how children learn language. But this took time and money that not every experimenter has. In recent years, broader movements within academia at large—including Open Science and Big Team Science —have embraced sharing data among research groups and collaborating on studies. And in the developmental-science world, tools such as Databrary (a video and audio library) and CHILDES (primarily a repository of language transcripts) help scientists use existing data for new studies.

CHS is an extension of these efforts. Elena Tenenbaum, a clinical psychologist at Duke University, is studying younger siblings of autistic children, who are up to 17 times more likely to receive a diagnosis of autism compared with the general public. Yet this population is a particularly difficult one to bring into the lab. “These families that are already stretched thin from their appointments for their older child—if they need to come into the lab, it gets really challenging, really quickly,” Tenenbaum told me. With CHS, researchers can test this group—for example, measuring how many words they know or whether they can pay attention to and remember faces—to see if early hints precede more obvious symptoms of autism without needing them to come to a lab.

But the implications are bigger than any one study: Online testing tools have the potential to use technology to understand the whole child. For instance, one kid could participate in different studies at different labs—for example, to do with language, or motor skills, or causal reasoning—all connected through CHS. “How does change in one ability relate to changes in another ability?” Schulz said. “We’re going to get a much, much better window into a developing child.”

Crucially, the platform also has the potential to broaden geographic and ethnic sample diversity within the U.S. For instance, data supplied by CHS show that 13.1 percent of its subjects are Latino and 5.5 percent are Black. Many researchers don’t record the demographics of their in-person study subjects, but CHS’s figures are a striking increase from numbers reported by a survey of top journals, which estimated that more than 90 percent of subjects are so-called convenience samples—in other words, people who live near a university or research center, who tend to be white and affluent.

Even with a tool like CHS, developmental psychology still needs to reach more international, rural, and low-socioeconomic-status communities. Most of the world’s children are growing up in Africa and Asia, some of them “in rural settings, very often with some access to electricity, but not necessarily a tablet or easy access to internet,” Alejandrina Cristia, a linguist at the École Normale Supérieure in Paris, told me. And domestically, for CHS to expand further, researchers may need to bring laptops to recreational centers and libraries, Lisa Oakes, a developmental psychologist at UC Davis and an early CHS adopter, told me. Melissa Kline Struhl, the executive director of CHS, hopes that improving the platform’s functionality on smartphones will widen its reach too.

Indeed, forming truly universal theories of how children develop was never going to be an easy task, and still has a long way to go. Yet a shift to online studies is helping provide one thing that the smaller, less representative samples of the past couldn’t: kids who don’t typically come to university labs. For developmental psychology, that alone is a vital step.

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Research Methods in Developmental Psychology

University of Calfornia, Irvine

What do infants know about the world in which they live – and how do they grow and change with age? These are the kinds of questions answered by developmental scientists. This module describes different research techniques that are used to study psychological phenomena in infants and children, research designs that are used to examine age-related changes in development, and unique challenges and special issues associated with conducting research with infants and children. Child development is a fascinating field of study, and many interesting questions remain to be examined by future generations of developmental scientists – maybe you will be among them!

  • Child development
  • Developmental psychology
  • Infant development
  • Research designs
  • Research methods
  • Learning Objectives
  • Describe different research methods used to study infant and child development
  • Discuss different research designs, as well as their strengths and limitations
  • Report on the unique challenges associated with conducting developmental research

Introduction

A group of children were playing hide-and-seek in the yard. Pilar raced to her hiding spot as her six-year-old cousin, Lucas, loudly counted, “… six, seven, eight, nine, ten! Ready or not, here I come!”. Pilar let out a small giggle as Lucas ran over to find her – in the exact location where he had found his sister a short time before. At first glance, this behavior is puzzling: why would Pilar hide in exactly the same location where someone else was just found? Whereas older children and adults realize that it is likely best to hide in locations that have not been searched previously, young children do not have the same cognitive sophistication. But why not… and when do these abilities first develop?

A young girl smiles as she peeks out from a hiding place.

Developmental psychologists investigate questions like these using research methods that are tailored to the particular capabilities of the infants and children being studied. Importantly, research in developmental psychology is more than simply examining how children behave during games of hide-and-seek – the results obtained from developmental research have been used to inform best practices in parenting, education, and policy.

This module describes different research techniques that are used to study psychological phenomena in infants and children, research designs that are used to examine age-related changes in developmental processes and changes over time, and unique challenges and special issues associated with conducting research with infants and children.

Research Methods

Infants and children—especially younger children—cannot be studied using the same research methods used in studies with adults. Researchers, therefore, have developed many creative ways to collect information about infant and child development. In this section, we highlight some of the methods that have been used by researchers who study infants and older children, separating them into three distinct categories: involuntary or obligatory responses , voluntary responses , and psychophysiological responses . We will also discuss other methods such as the use of surveys and questionnaires. At the end of this section, we give an example of how interview techniques can be used to study the beliefs and perceptions of older children and adults – a method that cannot be used with infants or very young children.

Involuntary or obligatory responses

One of the primary challenges in studying very young infants is that they have limited motor control – they cannot hold their heads up for short amounts of time, much less grab an interesting toy, play the piano, or turn a door knob. As a result, infants cannot actively engage with the environment in the same way as older children and adults. For this reason, developmental scientists have designed research methods that assess involuntary or obligatory responses. These are behaviors in which people engage without much conscious thought or effort. For example, think about the last time you heard your name at a party – you likely turned your head to see who was talking without even thinking about it. Infants and young children also demonstrate involuntary responses to stimuli in the environment. When infants hear the voice of their mother, for instance, their heart rate increases – whereas if they hear the voice of a stranger, their heart rate decreases (Kisilevsky et al., 2003). Researchers study involuntary behaviors to better understand what infants know about the world around them.

An infant lies on its back with its eyes fixed on a nearby object.

One research method that capitalizes on involuntary or obligatory responses is a procedure known as habituation . In habituation studies, infants are presented with a stimulus such as a photograph of a face over and over again until they become bored with it. When infants become bored, they look away from the picture. If infants are then shown a new picture--such as a photograph of a different face-- their interest returns and they look at the new picture. This is a phenomenon known as dishabituation . Habituation procedures work because infants generally look longer at novel stimuli relative to items that are familiar to them. This research technique takes advantage of involuntary or obligatory responses because infants are constantly looking around and observing their environments; they do not have to be taught to engage with the world in this way.

One classic habituation study was conducted by Baillargeon and colleagues ( 1985 ). These researchers were interested in the concept of object permanence , or the understanding that objects exist even when they cannot be seen or heard. For example, you know your toothbrush exists even though you are probably not able to see it right this second. To investigate object permanence in 5-month-old infants, the researchers used a violation of expectation paradigm . The researchers first habituated infants to an opaque screen that moved back and forth like a drawbridge (using the same procedure you just learned about in the previous paragraph). Once the infants were bored with the moving screen, they were shown two different scenarios to test their understanding of physical events. In both of these test scenarios, an opaque box was placed behind the moving screen. What differed between these two scenarios, however, was whether they confirmed or violated the solidity principle – the idea that two solid objects cannot occupy the same space at the same time. In the possible scenario, infants watched as the moving drawbridge stopped when it hit the opaque box (as would be expected based on the solidity principle). In the impossible scenario, the drawbridge appeared to move right through the space that was occupied by the opaque box! This impossible scenario violates the solidity principle in the same way as if you got out of your chair and walked through a wall, reappearing on the other side.

The results of this study revealed that infants looked longer at the impossible test event than at the possible test event. The authors suggested that the infants reacted in this way because they were surprised – the demonstration went against their expectation that two solids cannot move through one another. The findings indicated that 5-month-old infants understood that the box continued to exist even when they could not see it. Subsequent studies indicated that 3½- and 4½-month-old infants also demonstrate object permanence under similar test conditions ( Baillargeon, 1987 ). These findings are notable because they suggest that infants understand object permanence much earlier than had been reported previously in research examining voluntary responses (although see more recent research by Cashon & Cohen, 2000 ).

Voluntary responses 

A woman inspects tomatoes as she puts them into a shopping bag.

As infants and children age, researchers are increasingly able to study their understanding of the world through their voluntary responses. Voluntary responses are behaviors that a person completes by choice. For example, think about how you act when you go to the grocery store: you select whether to use a shopping cart or a basket, you decide which sections of the store to walk through, and you choose whether to stick to your grocery list or splurge on a treat. Importantly, these behaviors are completely up to you (and are under your control). Although they do not do a lot of grocery shopping, infants and children also have voluntary control over their actions. Children, for instance, choose which toys to play with.

Researchers study the voluntary responses of infants and young children in many ways. For example, developmental scientists study recall memory in infants and young children by looking at voluntary responses. Recall memory is memory of past events or episodes, such as what you did yesterday afternoon or on your last birthday. Whereas older children and adults are simply asked to talk about their past experiences, recall memory has to be studied in a different way in infants and very young children who cannot discuss the past using language. To study memory in these subjects researchers use a behavioral method known as elicited imitation ( Lukowski & Milojevich, in press ).

In the elicited imitation procedure, infants play with toys that are designed in the lab to be unlike the kinds of things infants usually have at home. These toys (or event sequences, as researchers call them) can be put together in a certain way to produce an outcome that infants commonly enjoy. One of these events is called Find the Surprise. As shown in Figure 1, this toy has a door on the front that is held in place by a latch – and a small plastic figure is hidden on the inside. During the first part of the study, infants play with the toy in whichever way they want for a few minutes. The researcher then shows the infant how make the toy work by (1) flipping the latch out of the way and (2) opening the door, revealing the plastic toy inside. The infant is allowed to play with the toy again either immediately after the demonstration or after a longer delay. As the infant plays, the researcher records whether the infant finds the surprise using the same procedure that was demonstrated.

The two-step event sequence Find the Surprise. The picture on the left shows all of the toys needed to complete the event. The picture in the middle shows a hand flipping the latch out of the way so the door can be opened (step 1). The picture on the right shows a hand opening the door, ultimately revealing a plastic figurine hidden inside (step 2).

Use of the elicited imitation procedure has taught developmental scientists a lot about how recall memory develops. For example, we now know that 6-month-old infants remember one step of a 3-step sequence for 24 hours ( Barr, Dowden, & Hayne, 1996 ; Collie & Hayne, 1999 ). Nine-month-olds remember the individual steps that make up a 2-step event sequence for 1 month, but only 50% of infants remember to do the first step of the sequence before the second ( Bauer, Wiebe, Carver, Waters, & Nelson, 2003 ; Bauer, Wiebe, Waters, & Bangston, 2001 ; Carver & Bauer, 1999 ). When children are 20 months old, they remember the individual steps and temporal order of 4-step events for at least 12 months – the longest delay that has been tested to date ( Bauer, Wenner, Dropik, & Wewerka, 2000 ).

Psychophysiology

Behavioral studies have taught us important information about what infants and children know about the world. Research on behavior alone, however, cannot tell scientists how brain development or biological changes impact (or are impacted by) behavior. For this reason, researchers may also record psychophysiological data, such as measures of heart rate, hormone levels, or brain activity. These measures may be recorded by themselves or in combination with behavioral data to better understand the bidirectional relations between biology and behavior.

An infant wears an EEG cap.

One manner of understanding associations between brain development and behavioral advances is through the recording of event-related potentials , or ERPs. ERPs are recorded by fitting a research participant with a stretchy cap that contains many small sensors or electrodes. These electrodes record tiny electrical currents on the scalp of the participant in response to the presentation of particular stimuli, such as a picture or a sound (for additional information on recording ERPs from infants and children, see DeBoer, Scott, & Nelson, 2005 ). The recorded responses are then amplified thousands of times using specialized equipment so that they look like squiggly lines with peaks and valleys. Some of these brain responses have been linked to psychological phenomena. For example, researchers have identified a negative peak in the recorded waveform that they have called the N170 ( Bentin, Allison, Puce, Perez, & McCarthy, 2010 ). The peak is named in this way because it is negative (hence the N) and because it occurs about 140ms to 170ms after a stimulus is presented (hence the 170). This peak is particularly sensitive to the presentation of faces, as it is commonly more negative when participants are presented with photographs of faces rather than with photographs of objects. In this way, researchers are able to identify brain activity associated with real world thinking and behavior.

 The use of ERPs has provided important insight as to how infants and children understand the world around them. In one study ( Webb, Dawson, Bernier, & Panagiotides, 2006 ), researchers examined face and object processing in children with autism spectrum disorders, those with developmental delays, and those who were typically developing. The children wore electrode caps and had their brain activity recorded as they watched still photographs of faces (of their mother or of a stranger) and objects (including those that were familiar or unfamiliar to them). The researchers examined differences in face and object processing by group by observing a component of the brainwave they called the prN170 (because it was believed to be a precursor to the adult N170). Their results showed that the height of the prN170 peak (commonly called the amplitude ) did not differ when faces or objects were presented to typically developing children. When considering children with autism, however, the peaks were higher when objects were presented relative to when faces were shown. Differences were also found in how long it took the brain to reach the negative peak (commonly called the latency of the response). Whereas the peak was reached more quickly when typically developing children were presented with faces relative to objects, the opposite was true for children with autism. These findings suggest that children with autism are in some way processing faces differently than typically developing children (and, as reported in the manuscript, children with more general developmental delays).

Parent-report questionnaires 

A mother and infant lie together on the grass.

Developmental science has come a long way in assessing various aspects of infant and child development through behavior and psychophysiology – and new advances are happening every day. In many ways, however, the very youngest of research participants are still quite limited in the information they can provide about their own development. As such, researchers often ask the people who know infants and children best – commonly, their parents or guardians – to complete surveys or questionnaires about various aspects of their lives. These parent-report data can be analyzed by themselves or in combination with any collected behavioral or psychophysiological data.

One commonly used parent-report questionnaire is the Child Behavior Checklist (CBCL; Achenbach & Rescorla, 2000 ). Parents complete the preschooler version of this questionnaire by answering questions about child strengths, behavior problems, and disabilities, among other things. The responses provided by parents are used to identify whether the child has any behavioral issues, such as sleep difficulties, aggressive behaviors, depression, or attention deficit/hyperactivity problems.

A recent study used the CBCL-Preschool questionnaire ( Achenbach & Rescorla, 2000 ) to examine preschooler functioning in relation to levels of stress experienced by their mothers while they were pregnant ( Ronald, Pennell, & Whitehouse, 2011 ). Almost 3,000 pregnant women were recruited into the study during their pregnancy and were interviewed about their stressful life experiences. Later, when their children were 2 years old, mothers completed the CBCL-Preschool questionnaire. The results of the study showed that higher levels of maternal stress during pregnancy (such as a divorce or moving to a new house) were associated with increased attention deficit/hyperactivity problems in children over 2 years later. These findings suggest that stressful events experienced during prenatal development may be associated with problematic child behavioral functioning years later – although additional research is needed.

Interview techniques 

Whereas infants and very young children are unable to talk about their own thoughts and behaviors, older children and adults are commonly asked to use language to discuss their thoughts and knowledge about the world. In fact, these verbal report paradigms are among the most widely used in psychological research. For instance, a researcher might present a child with a vignette or short story describing a moral dilemma, and the child would be asked to give their own thoughts and beliefs ( Walrath, 2011 ). For example, children might react to the following:

“Mr. Kohut’s wife is sick and only one medication can save her life. The medicine is extremely expensive and Mr. Kohut cannot afford it. The druggist will not lower the price. What should Mr. Kohut do, and why?”

Children can provide written or verbal answers to these types of scenarios. They can also offer their perspectives on issues ranging from attitudes towards drug use to the experience of fear while falling asleep to their memories of getting lost in public places – the possibilities are endless. Verbal reports such as interviews and surveys allow children to describe their own experience of the world.

Research Design

Now you know about some tools used to conduct research with infants and young children. Remember, research methods are the tools that are used to collect information. But it is easy to confuse research methods and research design . Research design is the strategy or blueprint for deciding how to collect and analyze information. Research design dictates which methods are used and how.

Researchers typically focus on two distinct types of comparisons when conducting research with infants and children. The first kind of comparison examines change within individuals . As the name suggests, this type of analysis measures the ways in which a specific person changes (or remains the same) over time. For example, a developmental scientist might be interested in studying the same group of infants at 12 months, 18 months, and 24 months to examine how vocabulary and grammar change over time. This kind of question would be best answered using a longitudinal research design. Another sort of comparison focuses on changes between groups . In this type of analysis, researchers study average changes in behavior between groups of different ages. Returning to the language example, a scientist might study the vocabulary and grammar used by 12-month-olds, 18-month-olds, and 24-month-olds to examine how language abilities change with age. This kind of question would be best answered using a cross-sectional research design. 

Longitudinal research designs

Longitudinal research designs are used to examine behavior in the same infants and children over time. For example, when considering our example of hide-and-seek behaviors in preschoolers, a researcher might conduct a longitudinal study to examine whether 2-year-olds develop into better hiders over time. To this end, a researcher might observe a group of 2-year-old children playing hide-and-seek with plans to observe them again when they are 4 years old – and again when they are 6 years old. This study is longitudinal in nature because the researcher plans to study the same children as they age. Based on her data, the researcher might conclude that 2-year-olds develop more mature hiding abilities with age. Remember, researchers examine games such as hide-and-seek not because they are interested in the games themselves, but because they offer clues to how children think, feel and behave at various ages.

Chart of a longitudinal research design. Child "A" is first observed in 2004 at the age of two. Child "A' is next observed in 2006 at age four. The next observation is in 2008 when Child "A" is six. Finally, in 2010 at the age of eight Child "A" is observed again.

Longitudinal studies may be conducted over the short term (over a span of months, as in Wiebe, Lukowski, & Bauer, 2010 ) or over much longer durations (years or decades, as in Lukowski et al., 2010) . For these reasons, longitudinal research designs are optimal for studying stability and change over time. Longitudinal research also has limitations, however. For one, longitudinal studies are expensive: they require that researchers maintain continued contact with participants over time, and they necessitate that scientists have funding to conduct their work over extended durations (from infancy to when participants were 19 years old in Lukowski et al., 2010 ). An additional risk is attrition . Attrition occurs when participants fail to complete all portions of a study. Participants may move, change their phone numbers, or simply become disinterested in participating over time. Researchers should account for the possibility of attrition by enrolling a larger sample into their study initially, as some participants will likely drop out over time.

The results from longitudinal studies may also be impacted by repeated assessments. Consider how well you would do on a math test if you were given the exact same exam every day for a week. Your performance would likely improve over time not necessarily because you developed better math abilities, but because you were continuously practicing the same math problems. This phenomenon is known as a practice effect . Practice effects occur when participants become better at a task over time because they have done it again and again; not due to natural psychological development. A final limitation of longitudinal research is that the results may be impacted by cohort effects . Cohort effects occur when the results of the study are affected by the particular point in historical time during which participants are tested. As an example, think about how peer relationships in childhood have likely changed since February 2004 – the month and year Facebook was founded. Cohort effects can be problematic in longitudinal research because only one group of participants are tested at one point in time – different findings might be expected if participants of the same ages were tested at different points in historical time.

Cross-sectional designs 

Cross-sectional research designs are used to examine behavior in participants of different ages who are tested at the same point in time. When considering our example of hide-and-seek behaviors in children, for example, a researcher might want to examine whether older children more often hide in novel locations (those in which another child in the same game has never hidden before) when compared to younger children. In this case, the researcher might observe 2-, 4-, and 6-year-old children as they play the game (the various age groups represent the “cross sections”). This research is cross-sectional in nature because the researcher plans to examine the behavior of children of different ages within the same study at the same time. Based on her data, the researcher might conclude that 2-year-olds more commonly hide in previously-searched locations relative to 6-year-olds.

A chart shows an example of a cross-sectional design. The year is 2004 and three separate cohorts are included in a study. Participants in Cohort

Cross-sectional designs are useful for many reasons. Because participants of different ages are tested at the same point in time, data collection can proceed at a rapid pace. In addition, because participants are only tested at one point in time, practice effects are not an issue – children do not have the opportunity to become better at the task over time. Cross-sectional designs are also more cost-effective than longitudinal research designs because there is no need to maintain contact with and follow-up on participants over time.

One of the primary limitations of cross-sectional research, however, is that the results yield information on age-related change, not development per se . That is, although the study described above can show that 6-year-olds are more advanced in their hiding behavior than 2-year-olds, the data used to come up with this conclusion were collected from different children. It could be, for instance, that this specific sample of 6-year-olds just happened to be particularly clever at hide-and-seek. As such, the researcher cannot conclude that 2-year-olds develop into better hiders with age; she can only state that 6-year-olds, on average, are more sophisticated hiders relative to children 4 years younger.

Sequential research designs

Sequential research designs include elements of both longitudinal and cross-sectional research designs. Similar to longitudinal designs, sequential research features participants who are followed over time; similar to cross-sectional designs, sequential work includes participants of different ages. This research design is also distinct from those that have been discussed previously in that children of different ages are enrolled into a study at various points in time to examine age-related changes, development within the same individuals as they age, and account for the possibility of cohort effects.

Consider, once again, our example of hide-and-seek behaviors. In a study with a sequential design, a researcher might enroll three separate groups of children (Groups A, B, and C). Children in Group A would be enrolled when they are 2 years old and would be tested again when they are 4 and 6 years old (similar in design to the longitudinal study described previously). Children in Group B would be enrolled when they are 4 years old and would be tested again when they are 6 and 8 years old. Finally, children in Group C would be enrolled when they are 6 years old and would be tested again when they are 8 and 10 years old.

A chart of a sequential design: The study begins in 2002 with Cohort

Studies with sequential designs are powerful because they allow for both longitudinal and cross-sectional comparisons. This research design also allows for the examination of cohort effects. For example, the researcher could examine the hide-and-seek behavior of 6-year-olds in Groups A, B, and C to determine whether performance differed by group when participants were the same age. If performance differences were found, there would be evidence for a cohort effect. In the hide-and-seek example, this might mean that children from different time periods varied in the amount they giggled or how patient they are when waiting to be found. Sequential designs are also appealing because they allow researchers to learn a lot about development in a relatively short amount of time. In the previous example, a four-year research study would provide information about 8 years of developmental time by enrolling children ranging in age from two to ten years old.

Because they include elements of longitudinal and cross-sectional designs, sequential research has many of the same strengths and limitations as these other approaches. For example, sequential work may require less time and effort than longitudinal research, but more time and effort than cross-sectional research. Although practice effects may be an issue if participants are asked to complete the same tasks or assessments over time, attrition may be less problematic than what is commonly experienced in longitudinal research since participants may not have to remain involved in the study for such a long period of time.

When considering the best research design to use in their research, scientists think about their main research question and the best way to come up with an answer. A table of advantages and disadvantages for each of the described research designs is provided here to help you as you consider what sorts of studies would be best conducted using each of these different approaches.

Advantages and disadvantages of different research designs are summarized from the text

Challenges Associated with Conducting Developmental Research

The previous sections describe research tools to assess development in infancy and early childhood, as well as the ways that research designs can be used to track age-related changes and development over time. Before you begin conducting developmental research, however, you must also be aware that testing infants and children comes with its own unique set of challenges. In the final section of this module, we review some of the main issues that are encountered when conducting research with the youngest of human participants. In particular, we focus our discussion on ethical concerns, recruitment issues, and participant attrition.

Ethical concerns 

As a student of psychological science, you may already know that  Institutional Review Boards (IRBs) review and approve of all research projects that are conducted at universities, hospitals, and other institutions. An IRB is typically a panel of experts who read and evaluate proposals for research. IRB members want to ensure that the proposed research will be carried out ethically and that the potential benefits of the research outweigh the risks and harm for participants. What you may not know though, is that the IRB considers some groups of participants to be more vulnerable or at-risk than others. Whereas university students are generally not viewed as vulnerable or at-risk, infants and young children commonly fall into this category. What makes infants and young children more vulnerable during research than young adults? One reason infants and young children are perceived as being at increased risk is due to their limited cognitive capabilities, which makes them unable to state their willingness to participate in research or tell researchers when they would like to drop out of a study. For these reasons, infants and young children require special accommodations as they participate in the research process.

When thinking about special accommodations in developmental research, consider the informed consent process. If you have ever participated in psychological research, you may know through your own experience that adults commonly sign an informed consent statement (a contract stating that they agree to participate in research) after learning about a study. As part of this process, participants are informed of the procedures to be used in the research, along with any expected risks or benefits. Infants and young children cannot verbally indicate their willingness to participate, much less understand the balance of potential risks and benefits. As such, researchers are oftentimes required to obtain written informed consent from the parent or legal guardian of the child participant, an adult who is almost always present as the study is conducted. In fact, children are not asked to indicate whether they would like to be involved in a study at all (a process known as assent ) until they are approximately seven years old. Because infants and young children also cannot easily indicate if they would like to discontinue their participation in a study, researchers must be sensitive to changes in the state of the participant (determining whether a child is too tired or upset to continue) as well as to parent desires (in some cases, parents might want to discontinue their involvement in the research). As in adult studies, researchers must always strive to protect the rights and well-being of the minor participants and their parents when conducting developmental science.

Recruitment 

An additional challenge in developmental science is participant recruitment. Recruiting university students to participate in adult studies is typically easy. Many colleges and universities offer extra credit for participation in research and have locations such as bulletin boards and school newspapers where research can be advertised. Unfortunately, young children cannot be recruited by making announcements in Introduction to Psychology courses, by posting ads on campuses, or through online platforms such as  Amazon Mechanical Turk . Given these limitations, how do researchers go about finding infants and young children to be in their studies?

The answer to this question varies along multiple dimensions. Researchers must consider the number of participants they need and the financial resources available to them, among other things. Location may also be an important consideration. Researchers who need large numbers of infants and children may attempt to do so by obtaining infant birth records from the state, county, or province in which they reside. Some areas make this information publicly available for free, whereas birth records must be purchased in other areas (and in some locations birth records may be entirely unavailable as a recruitment tool). If birth records are available, researchers can use the obtained information to call families by phone or mail them letters describing possible research opportunities. All is not lost if this recruitment strategy is unavailable, however. Researchers can choose to pay a recruitment agency to contact and recruit families for them. Although these methods tend to be quick and effective, they can also be quite expensive. More economical recruitment options include posting advertisements and fliers in locations frequented by families, such as mommy-and-me classes, local malls, and preschools or day care centers. Researchers can also utilize online social media outlets like Facebook, which allows users to post recruitment advertisements for a small fee. Of course, each of these different recruitment techniques requires IRB approval.

A tired looking mother closes her eyes and rubs her forehead as her baby cries.

Another important consideration when conducting research with infants and young children is attrition . Although attrition is quite common in longitudinal research in particular, it is also problematic in developmental science more generally, as studies with infants and young children tend to have higher attrition rates than studies with adults. For example, high attrition rates in ERP studies oftentimes result from the demands of the task: infants are required to sit still and have a tight, wet cap placed on their heads before watching still photographs on a computer screen in a dark, quiet room. In other cases, attrition may be due to motivation (or a lack thereof). Whereas adults may be motivated to participate in research in order to receive money or extra course credit, infants and young children are not as easily enticed. In addition, infants and young children are more likely to tire easily, become fussy, and lose interest in the study procedures than are adults. For these reasons, research studies should be designed to be as short as possible – it is likely better to break up a large study into multiple short sessions rather than cram all of the tasks into one long visit to the lab. Researchers should also allow time for breaks in their study protocols so that infants can rest or have snacks as needed. Happy, comfortable participants provide the best data.

Conclusions

Child development is a fascinating field of study – but care must be taken to ensure that researchers use appropriate methods to examine infant and child behavior, use the correct experimental design to answer their questions, and be aware of the special challenges that are part-and-parcel of developmental research. After reading this module, you should have a solid understanding of these various issues and be ready to think more critically about research questions that interest you. For example, when considering our initial example of hide-and-seek behaviors in preschoolers, you might ask questions about what other factors might contribute to hiding behaviors in children. Do children with older siblings hide in locations that were previously searched less often than children without siblings? What other abilities are associated with the development of hiding skills? Do children who use more sophisticated hiding strategies as preschoolers do better on other tests of cognitive functioning in high school? Many interesting questions remain to be examined by future generations of developmental scientists – maybe you will make one of the next big discoveries!

  • Outside Resources

  • Discussion Questions
  • Why is it important to conduct research on infants and children?
  • What are some possible benefits and limitations of the various research methods discussed in this module?
  • Why is it important to examine cohort effects in developmental research?
  • Think about additional challenges or unique issues that might be experienced by developmental scientists. How would they handle the challenges and issues you’ve addressed?
  • Work with your peers to design a study to identify whether children who were good hiders as preschoolers are more cognitively advanced in high school. What research design would you use and why? What are the advantages and limitations of the design you selected?
  • Achenbach, T. M., & Rescorla, L. A. (2000). Manual for the ASEBA preschool forms and profiles: An integrated system of multi-informant assessment. Burlington, VT: University of Vermont Department of Psychiatry.
  • Baillargeon, R. (1987). Object permanence in 3½- and 4½-month-old infants. Developmental Psychology, 23, 655-664. doi: 10.1037/0012-1649.23.5.655
  • Baillargeon, R., Spelke, E., & Wasserman, S. (1985). Object permanence in five-month-old infants. Cognition, 20, 191-208. doi: 10.1016/0010-0277(85)90008-3
  • Barr, R., Dowden, A., & Hayne, H. (1996). Developmental changes in deferred imitation by 6- to 24-month-old infants. Infant Behavior and Development, 19 , 159-170. doi: 10.1016/s0163-6383(96)90015-6
  • Bauer, P. J., Wenner, J. A., Dropik, P. L., & Wewerka, S. S. (2000). Parameters of remembering and forgetting in the transition from infancy to early childhood. Monographs of the Society for Research in Child Development, 65 , 1-204. doi: 10.1016/j.imlet.2014.04.001
  • Bauer, P. J., Wiebe, S. A., Carver, L. J., Waters, J. M., & Nelson, C. A. (2003). Developments in long-term explicit memory late in the first year of life: Behavioral and electrophysiological indices. Psychological Science, 14 , 629-635. doi: 10.1046/j.0956-7976.2003.psci_1476.x
  • Bauer, P. J., Wiebe, S. A., Waters, J. M., & Bangston, S. K. (2001). Reexposure breeds recall: Effects of experience on 9-month-olds’ ordered recall. Journal of Experimental Child Psychology, 80 , 174-200. doi: 10.1006/jecp.2000.2628
  • Bentin, S., Allison, T., Puce, A., Perez, E., & McCarthy, G. (2010). Electrophysiological studies of face perception in humans. Journal of Cognitive Neuroscience , 8, 551-565. doi: 10.1162/jocn.1996.8.6.551
  • Carver, L. J., & Bauer, P. J. (1999). When the event is more than the sum of its parts: 9-month-olds’ long-term ordered recall. Memory, 7 , 147-174. doi: 10.1080/741944070
  • Cashon, C. H., & Cohen, L. B. (2000). Eight-month-old infants’ perception of possible and impossible events. Infancy, 1 , 429-446. doi: 10.1016/s0163-6383(98)91561-2
  • Collie, R., & Hayne, H. (1999). Deferred imitation by 6- and 9-month-old infants: More evidence for declarative memory. Developmental Psychobiology, 35 , 83-90. doi: 10.1002/(sici)1098-2302(199909)35:2 3.0.co;2-s
  • DeBoer, T., Scott, L. S., & Nelson, C. A. (2005). ERPs in developmental populations. In T. C. Handy (Ed.), Event-related potentials: A methods handbook (pp. 263-297) . Cambridge, MA: The MIT Press.
  • Lukowski, A. F., & Milojevich, H. M. (2016). Examining recall memory in infancy and early childhood using the elicited imitation paradigm. Journal of Visualized Experiments, 110 , e53347.
  • Lukowski, A. F., Koss, M., Burden, M. J., Jonides, J., Nelson, C. A., Kaciroti, N., … Lozoff, B. (2010). Iron deficiency in infancy and neurocognitive functioning at 19 years: Evidence of long-term deficits in executive function and recognition memory. Nutritional Neuroscience, 13 , 54-70. doi: 10.1179/147683010x12611460763689
  • Ronald, A., Pennell, C. E., & Whitehouse, A. J. O. (2011). Prenatal maternal stress associated with ADHD and autistic traits in early childhood. Frontiers in Psychology, 1 , 1-8. doi: 10.3389/fpsyg.2010.00223
  • Walrath, R. (2011). Kohlberg’s theory of moral development. In Encyclopedia of Child Behavior and Development (pp. 859–860).
  • Webb, S. J., Dawson, G., Bernier, R., & Panagiotides, H. (2006). ERP evidence of atypical face processing in young children with autism. Journal of Autism and Developmental Disorders, 36 , 884-890. doi: 10.1007/s10803-006-0126-x
  • Wiebe, S. A., Lukowski, A. F., & Bauer, P. J. (2010). Sequence imitation and reaching measures of executive control: A longitudinal examination in the second year of life. Developmental Neuropsychology, 35 , 522-538. doi: 10.1080/87565641.2010.494751

research studies on developmental psychology

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Developmental Psychology Studies: 10 Examples

Discover ten classic developmental psychology experiments that study how children’s self, memory, language, learning and more emerge.

developmental psychology

Once upon a time, although it seems barely credible to us now, we were all children.

We gurgled, we cried, we laughed, we explored, we fell down, and we had very little idea about the journey on which we had just embarked.

Barring mishap, over the first few years of our lives we developed memory, language, self-concept, cognitive, social and emotional abilities.

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We took our first steps towards our future selves.

Child psychology — or, more broadly, developmental psychology — is not just the study of children, it is the study of you and me and how we came to be this way.

Just as discovering your history can teach you about the future, so developmental psychology shows us what we once were and even what we will become.

Here are 10 classic developmental psychology studies that have illuminated crucial areas of childhood development.

Each one is a piece in the jigsaw puzzle that is ourselves, and each one reminds us, through examining just one piece, how aspects of experience we now take for granted were once so complex.

Click the links for a more extensive description of each developmental psychology experiment.

1. Infant memory develops very early on

Some argue it’s impossible for us to remember anything much from before around two to four years of age.

Others think our memories can go way back – perhaps even to before birth.

The question of infant memory is thorny because it’s hard to test whether adults’ earliest memories are real or imagined.

What psychologists have done, though, is examine the emergence of memory in our first few years with a series of now classic experiments in developmental psychology.

These have found that our memory systems actually work quite well from very early on.

Infants’ memories also seems to work in much the same way as adult memories – it’s just that infant memories are much more fragile.

2. Developmental psychology: when the self emerges

To this day the ‘mirror test’ remains the best developmental psychology experiment yet developed for examining the emergence of self-concept in infants using the mirror test .

Most people look out for number one, themselves, which makes it strange to think that there was ever a time when we had no concept of ‘me’.

A simple study dating from the early 70s suggests that before the age of around two years old we can’t recognise ourselves in the mirror.

Because of this study, and the many variations in developmental psychology that have followed, some claim that it isn’t until our second birthday that our self-concept emerges.

3. How children learn

A classic study of childhood learning suggests true understanding comes from letting go of established preconceptions.

How children revise their understanding of the world is one of the most fascinating areas of developmental psychology.

But it is not just relevant to children; we all have to take on new concepts from time-to-time – even though they may not be as profound as the origin of the species.

It’s tempting to think that learning is largely about memory – especially since in the bad old days of education learning was largely accomplished by rote.

However, the idea of ‘mental models’ suggests children create, and then test, mental models of the way the world works in order to build up our understanding, and that is how children learn.

4. Attachment styles in developmental psychology

Attachment styles analyse how people respond to threats and problems in their personal relationships.

People who find relationships difficult often become unable to participate in the ordinary give-and-take of everyday life.

They may become hostile towards others, have problems in education as well as a greater chance of developing psychiatric disorders later in life.

These difficulties sometimes have their roots in the most important early relationships, evidenced in attachment styles.

It’s no wonder that developmental psychologists are so interested in the first relationships we build with our primary caregivers.

These attachment styles are likely to prove a vital influence on all our future relationships, including those with our spouse, our workmates and our own children.

While you can’t blame everything on your parents, early relationship attachment styles are like a template that we take forward with us in life.

5. Infants imitate others when only weeks old

One of the most basic forms of social behaviour is copying another person.

Although imitation is something we adults take for granted, it’s actually a pretty demanding process for a young infant.

At the heart of imitation is understanding the difference between yourself and others – something that famous Swiss child psychologist Jean Piaget thought didn’t emerge immediately in infants.

Consequently, he argued that infants could not imitate others until they were 8 to 12 months of age.

However, now some researchers think tiny infants who are between 12- and 21-days-old can imitate others.

6. When children can simulate other minds

Theory of mind is when we can put ourselves in other people’s shoes to try and imagine their thoughts, intentions and possible actions.

Without the ability to simulate what other people are thinking we would be lost in the social world.

The emergence of theory of mind in children is a vital developmental milestone; some psychologists think that a failure to develop a theory of mind is a central component of autism.

Some developmental psychology experiments suggest that at about 4- to 6-years old a range of remarkable skills start to emerge in young children that are vital for their successful functioning in society.

They begin to understand that others can hold false beliefs, they themselves can lie, and that others can lie to them — they have a theory of mind.

7. Object permanence in developmental psychology

Object permanence , or object constancy, in developmental psychology is understanding that things continue to exist, even if you cannot seem them.

Research in developmental psychology has found that infants as young as 3.5 months seem to have a basic grasp of object permanence.

It appears that young infants are not necessarily trapped in a world of shapes which have little meaning for them.

Instead, they seem to be intuitive physicists who can carry out rudimentary reasoning about physical concepts like gravity, inertia and object permanence.

8. How infants learn their first word

An infant’s very first step in their year-long developmental journey to their first word is perhaps their most impressive.

This first step is discriminating and categorising the basic sound components of the language they are hearing.

To get an idea how hard this might be think about listening to someone speaking a language you don’t understand.

Foreign languages can sound like continuous streams of noise in which it’s very hard to pick up where one word starts and another word begins.

Research in developmental psychology finds that until about 11 months of age infants are masters of discriminating phonemes used in all different types of languages.

But after 11 months infants settle down with one set of phonemes for their first language, and lose the ability to discriminate the phonemes from other languages.

9. Play and developmental psychology

The pioneering developmental psychologist Lev Vygotsky thought that, in the preschool years, play is the leading source of development.

Through play children learn and practice many basic social skills.

They develop a sense of self, learn to interact with other children, how to make friends, how to lie and how to role-play.

The classic developmental psychology study of how play develops in children was carried out by Mildred Parten in the late 1920s at the Institute of Child Development in Minnesota ( Parten, 1933 ).

She closely observed children between the ages of 2 and 5 years and categorised the types of play.

She found six different types of play , ranging from solitary, through associative to cooperative

10. Piaget’s developmental psychology theory

Jean Piaget was a developmental psychologist whose four-stage theory, published in 1936, has proved extremely influential.

Piaget’s four stages of development theory  has the dubious claim to fame of being one of the most criticised psychological theories ever.

From the sensorimotor stage, through the pre-operational stage, the concrete operational stage and the formal operational stage, his theory attempts to describe how childhood development progresses.

However, Piaget’s experiments and theories about how children build up their knowledge of the world have faced endless challenges, many of them justified.

Read on about them here .

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research studies on developmental psychology

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Psychologist, Jeremy Dean, PhD is the founder and author of PsyBlog. He holds a doctorate in psychology from University College London and two other advanced degrees in psychology. He has been writing about scientific research on PsyBlog since 2004. He is also the author of the book "Making Habits, Breaking Habits" (Da Capo, 2013) and several ebooks. View all posts by Jeremy Dean

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A finalist in the Science and Sustainability category for Greece, Dr Antonios Christou (PhD Psychology, 2016) is an Assistant Professor at the University of Thessaly and Co-ordinator of the Section of Neuropsychology at the Hellenic Psychological Society. He primarily researches emotion development, sensitivity and emotional resilience in typically and atypically developing children. He also advocates for child and disability rights in Greece. Antonios believes that by choosing to study in the UK he had the unique opportunity to be educated in an academic environment with state-of-the-art infrastructure that enabled him to excel in his academic career. With his science advocacy, Antonios strives to make impactful changes in public attitudes towards disability and child rights in Greece, which are captured and catalysed by public policies.

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ORIGINAL RESEARCH article

Joint developmental trajectories and temporal precedence of physical function decline and cognitive deterioration: a longitudinal population-based study.

\r\nXiao Wei&#x;

  • School of Nursing, Qingdao University, Qingdao, China

Objectives: Previous studies primarily explored the unidirectional impact of cognition on physical function. However, the interplay between physical function and cognition and the temporal precedence in their predictive relationships have not been elucidated. We explored the bidirectional mechanism between physical function and cognition in a longitudinal dataset.

Materials and methods: A total of 1,365 participants in the Chinese Longitudinal Healthy Longevity Survey assessed physical function and cognition in 2011 (T1), 2014 (T2), and 2018 (T3) by the Katz scale and the Chinese version of the Mini-Mental State Examination scale, respectively. Changes in the trajectories of physical function and cognition were examined using the latent growth model. The correlational and reciprocal relationships between physical function and cognition were examined using the parallel process latent growth model and autoregressive cross-lagged (ARCL) models.

Results: Cognition and physical function decreased by an average of 0.096 and 0.017 points per year, respectively. Higher physical function was associated with better cognition at baseline ( r = 0.237, p < 0.05), and longitudinal changes in physical function and cognition were positively correlated ( r = 0.756, p < 0.05). ARCL analysis indicated that physical function at T1 positively predicted T2 cognitive function. However, this predictive relationship reversed between T2 and T3, whereby cognitive function at T2 predicted physical function at T3.

Conclusion: Both physical function and cognition declined over time. Early identification and intervention in physical dysfunction among older adults could be critical to prevent further cognitive impairment and maintain functional independence. Hence, regular functional assessment and individualized care plans are required to achieve healthy aging.

Introduction

The number of adults aged ≥ 60 years is expected to reach 2 billion by 2050, representing 22% of the entire population, almost double the 12% reported in 2015 ( World Health Organization, 2018 ). Meeting the health needs of an aging population of this size poses a serious challenge to global health systems. It is accepted that prevention is more important than treatment in achieving healthy aging ( Nivestam et al., 2020 ). Functional decline is a largely preventable feature of aging and is defined as a progressive weakening of functional autonomy, comprising mainly cognitive deterioration and decline in physical function (PF), impairing the ability of older adults to live safely and independently ( Grimmer et al., 2013 ). Recognizing the characteristics of cognitive function (CF) and PF decline is crucial for supporting healthy aging and maintaining the quality of life of older adults.

Cognitive deterioration is placed on a continuum, including normal aging, mild cognitive impairment, and severe cognitive impairment (dementia) ( Chertkow et al., 2007 ) that are accompanied by different PF states, especially in the areas of feeding, dressing, toileting, and bathing ( Ebly et al., 1995 ). Physical function in the dementia group was lower than that in the mild cognitive impairment group, specifically, regarding grooming, bathing, and bowel control domains ( Lee et al., 2019 ). Cognitive impairment has been proven to be a risk factor for PF decline among older adults ( Arias-Merino et al., 2012 ; Ha and Kim, 2014 ). Several studies on the trajectories of functional capacity decline have revealed that participants with worse CF at baseline exhibited larger PF decline ( Dodge et al., 2005 ; Helvik et al., 2015 ), and older adults with lower levels of cognition are more likely to decline in functional capacity ( Menezes et al., 2021 ).

Relatively few studies have focused on the effects of PF on cognition. However, available research has begun to propose that cognitive deterioration and PF decline may mutually influence and reinforce each other’s development ( Black and Rush, 2002 ). One study reported that the onset of functional disability accelerated CF decline that progressed faster in individuals with more severe functional disabilities ( Rajan et al., 2013 ). This echoes the findings of another study that the prevalence of mild cognitive impairment in individuals with disabilities is higher than that in persons without disabilities ( Chang et al., 2017 ).

In summary, many of the existing studies have focused on the unidirectional relationship between CF and PF. Meanwhile, evidence from cross-sectional studies has limited utility when considering temporal associations between variables. Moreover, existing longitudinal studies have focused more on the contributions of longitudinal changes in cognition to longitudinal changes in PF ( Dodge et al., 2006 ; Cahn-Weiner et al., 2007 ), leading to the possibility that existing studies overlooked the impact on cognitive function of PF decline due to various causes, such as falls and fractures, when cognitive function is not impaired. Clarifying whether PF or cognitive ability changes first during the aging process and the subsequent impact on each other would better guide functional screening and interventions for older adults. Hence, longitudinal studies exploring the reciprocal relationship between PF and cognition are required.

In addition, previous studies have shown that changes in cognitive function varied significantly by sex, age, educational level, income, living arrangements, psychological status, social participation, and age-related health conditions such as hearing function and the number of chronic diseases ( Zhang et al., 2019 ; Chen and Zhou, 2020 ). However, the association between lifestyle factors, such as smoking and alcohol consumption, and cognitive impairment is inconsistent across studies. Some studies reported that smoking status and excessive alcohol consumption are associated with cognitive decline in older adults ( Hagger-Johnson et al., 2013 ), whereas other studies have disproven this association ( Hou et al., 2018 ; Zhang et al., 2019 ). Similarly, studies demonstrated that sex, age, education level, depressive symptoms, and age-related health conditions such as hearing function and the number of chronic conditions are associated with PF decline in older adults ( Diaz-Venegas et al., 2016 ; Chen and Zhou, 2020 ). Therefore, the current study includes all these variables as a covariate to identify protective or risk factors for CF and PF in older adults.

Hence, this study aimed to explore the longitudinal correlational and reciprocal relationships between PF and CF by utilizing large-scale data from the older adult Chinese population and advanced statistical methods, including the latent growth model (LGM) and an autoregressive cross-lagged (ARCL) model.

Materials and methods

Participants and settings.

The data were obtained from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), a longitudinal study investigating the health status of older adults in China, conducted by the Center for Research on Healthy Aging and Development at Peking University, which obtained data from eight face-to-face interviews with the cohort since 1998, utilizing internationally compatible questionnaires. Survivors were reinterviewed at each follow-up visit, and those deceased were substituted with new participants ( Yang et al., 2016 ). Of 31 provinces in China, 23 were randomly selected, accounting for approximately half of the urban areas in each province ( Qiu et al., 2020 ). The age of the respondents who voluntarily agreed to participate in the study covered the middle age group (34–64 years), the lower age group (65–80 years), and the higher age group (80 years and above). Individuals were excluded if they died, were lost to follow-up, or failed to respond to the items comprising the outcome variable ( Ni et al., 2020 ). More detailed data information is available at https://doi.org/10.18170/DVN/WBO7LK .

We used data from three waves of the CLHLS: 2011 (T1), 2014 (T2), and 2018 (T3) as a result of follow-up loss and natural death of subjects, and the first measurement of health information including hearing function in 2011. The two inclusion criteria were (1) completion of three surveys and (2) no missing PF and CF data. There were 9,765 subjects in 2011. By 2018, 6,862 subjects were lost to follow-up or had died during the follow-up period. After excluding a further 1,538 subjects with missing PF and CF data, we ultimately analyzed 1,365 individuals who responded to the survey from 2011 to 2018.

We compared the baseline characteristics of included and excluded participants (see Supplementary Table 1 ). The results revealed no differences between included and excluded participants regarding income, living arrangements, and number of chronic conditions. Predictably, and consistent with previous studies ( Yu et al., 2020 ; Li et al., 2021 ), these excluded participants tended to be older, female, with lower levels of education, who smoked less, drank less alcohol, and performed less well in terms of mental status, social engagement, and hearing function, but did not differ from included participants in terms of income, living arrangements, and number of chronic conditions.

Cognitive function

Cognitive function in the CLHLS was tested using the Chinese version of the Mini-Mental State Examination (C-MMSE), which comprises six subscales with 24 items: five orienting, three registering, one naming, five noticing and counting, three recalling, and seven verbal items. The total C-MMSE rating ranged from 0 to 30 points, with larger scores indicating stronger cognition. The C-MMSE is a well-established instrument for CF assessment that has been verified in previous studies ( Yang et al., 2016 ; Zhang et al., 2019 ; Qiu et al., 2020 ).

Physical function

Physical function was evaluated by sum scores (ranging from 6 to 18 points) in six components of daily living: bathing, dressing, grooming, indoor transferring, toileting, and feeding according to the Katz scale ( Katz et al., 1963 ). Older adults were scored as 1 (totally dependent on others), 2 (partially independent), or 3 (completely independent) points, depending on their ability to complete these actions without assistance, with larger scores indicating better daily living ability.

The following covariates were evaluated in this study: (1) demographic characteristics, including age, sex (1 = man, 2 = woman), years of education, living arrangement (1 = living with family members, 0 = not living with family members), and income; (2) lifestyle factors, including smoking (1 = yes, 0 = no) and drinking (1 = yes, 0 = no); (3) social engagements, including doing housework, working in the garden, reading newspapers/books, raising domestic poultry/pets, playing poker/mahjong, watching television or listening to the radio, and participating in social activities. Respondents were asked about the frequency of participation in each activity: 5 = almost every day, 4 = at least once a week, 3 = at least once a month, 2 = sometimes, and 1 = never; (4) mental status, representing the extent of mental wellbeing, rated by the sum of seven questions scored on a scale from 7 to 35 points, including being optimistic, maintaining tidiness, not feeling afraid or restless, not feeling alone, making own decisions, not feeling useless with age, and being happier than in youth, with better scores indicating a more positive mental state; (5) health status, including hearing function and the numbers of chronic diseases, where the hearing function was assessed based on the interviewee’s ability to hear the interviewer’s question, with four possible responses: 4 = yes, without hearing aid; 3 = yes, but needs hearing aid; 2 = partly, despite hearing aid; 1 = no. Chronic diseases included hypertension, diabetes, heart disease, stroke, bronchitis, tuberculosis, cataracts, glaucoma, cancer, gastric or duodenal ulcer, Parkinson’s disease, bedsore, arthritis, and dementia. Sex, education, and income levels were time-invariant covariates, while the other variables were time-varying covariates.

Data analysis

The participants’ characteristics, including CF scores, PF scores, and covariate distributions or scores, were presented as descriptive statistics and analyzed using SPSS 25.0 (IBM Corp., Armonk, NY, United States). Missing covariate data were substituted using multiple imputations based on Bayesian methods in SPSS 25.0.

Latent growth model

The LGM allows analysis of repeatedly measured temporal trajectories of a variable by establishing random intercepts and slopes ( Sha et al., 2018 ), thereby describing individual trajectories and capturing individual differences in trajectory variation over time ( Wang et al., 2017 ). See the Supplementary Data Sheet 1 for the specific rationale. Therefore, LGM analysis was performed to examine the changing trajectories of PF and CF. The change in trajectory was represented by two latent variables: a potential intercept growth factor, which reflects the initial state of variables, and a potential slope growth factor, which reflects the changing rate of variables. By considering the three waves of data, the trajectories of the variables were modeled using the specified linear LGM. The loading of the intercept factor was set for each measurement at a constant value of 1.0. For the slope factor, we fixed loadings sequentially at 0, 3.0, and 7.0, based on the survey time point.

Two unconditional LGMs were first modeled to mirror the growing trajectories of PF and CF without predictor variables ( Figures 1A,B ). The parallel process LGM (PP-LGM) enabled the estimation of interrelationships between growth factors (intercept and slope) by building a hypothetical model of two variables in parallel processes ( Ni et al., 2020 ). Unconditional (without covariates or predictors, Figure 1C ) and conditional (with covariates or predictors added) PP-LGMs were established to examine whether the growth parameters of one trajectory were related to those of the other.

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Figure 1. Latent growth model. (A) Measurement of latent growth model for cognitive function. (B) Measurement of latent growth model for physical function. (C) Structural latent growth model to assess the relationships between changes in physical function and cognitive function. PF, physical function; CF, cognitive function; I, intercept; S, linear slope.

Autoregressive cross-lagged model

The ARCL model, comprising an autoregressive component and a cross-lagged component, adjusts for the stability of all variables over time and simultaneously explores the interaction of the two variables over time ( Han and Kim, 2020 ). Thus, it is often applied to deduce temporal precedence of predictive relationships among variables in longitudinal studies. The cross-lagged parameters are typically interpreted as the between-person effect of X time1 on Y time2 , controlling for Y time1 and vice versa ( Gueron-Sela and Gordon-Hacker, 2020 ). Therefore, the ARCL model helped to distinguish between the effects of PF on CF and the effects of CF on PF in the current study, reflecting the reciprocal relationship between the two variables over an extended period.

All LGM and ARCL model analyses were conducted using Mplus version 7.0 (Muthén & Muthén, Los Angeles, CA, United States). The following metrics were used to evaluate the model fit: chi-square (χ 2 ) statistic, 1 < χ 2 /degrees of freedom (df) < 3, comparative fit index (CFI) of > 0.90, preferably of > 0.95; root mean square error of approximation (RMSEA) of < 0.08; 90% confidence interval (CI) ≤ 0.08, preferably of < 0.06 ( Melka et al., 2011 ); and standardized root mean square residual (SRMR) of ≤ 0.08 ( Brown and Weisman de Mamani, 2018 ).

The respondents’ characteristics are presented in Table 1 . In the baseline survey, 54.9% of the participants were men, and 81.8% lived with their families. Most participants were non-smokers and non-drinkers; the corresponding proportions of participants increased during the follow-up period. During the 7-year period, the scores of covariates reflecting healthy status, including mental status, social engagements, and hearing function, declined with increasing age and the number of chronic diseases. The mean cognitive and physical functioning scores at each time point also decreased over time.

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Table 1. Characteristics of the study participants.

Measurement model

In the LGM, the two trajectories of CF and PF change were described by a linear model with satisfactory fit indices ( Table 2 ). Based on the mean intercept representing the baseline level, the initial scores for PF and CF were estimated to be 17.942 and 28.323 points, respectively. Based on the mean slope indicating the direction and magnitude of the trajectory change, the mean annual reductions in PF and CF scores were separately estimated at 0.017 and 0.096 points, respectively. Roughly, these results are consistent with the descriptive statistics on CF and PF as shown in Table 1 , indicating that this LGM fits well to the data.

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Table 2. Results of univariate latent growth model analyses.

Structural model

Unconditional parallel process-latent growth model modeling.

The unconditional PP-LGM used to test the correlation between PF decline trajectory with the trajectory of CF deterioration showed a better model fit ( Table 3 ). The PF intercept was significantly associated with the CF intercept in 2011 ( r = 0.237, p < 0.05), which suggested that participants with better PF had higher cognitive scores at baseline. The PF slope was significantly associated with the CF slope ( r = 0.756, p < 0.05), indicating that subjects with a greater rate of PF decline experienced greater decreases in CF over time.

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Table 3. Standardized coefficients for parallel process LGM.

Conditional parallel process-latent growth model modeling

Compared with the unconditional PP-LGM, the conditional PP-LGM that controlled for predictors yielded better fit indices, but the results differed ( Table 3 ). The correlation between initial PF levels and baseline CF scores was non-significant ( r = 0.169, p > 0.05). Only the correlation between PF slope and CF slope remained significant, and the standardized coefficients were attenuated ( r = 0.464, p < 0.05).

The variances of intercepts and slopes for PF and CF scores were significant ( p < 0.05, Table 2 ), indicating strong individual differences between the trajectories of PF and CF. Therefore, time-invariant and time-varying covariates were included in the PP-LGM to determine whether individual characteristics were predictive of bivariate trajectories of CF and PF.

Sex, living arrangement, drinking status, and the number of chronic diseases were negatively associated with initial CF scores. Participants with higher education or lower incomes had a greater CF decline rate. Meanwhile, mental status, social engagement, and hearing function were positively related to CF scores at all three time points. However, the hearing function was positively correlated with PF in the last two waves. Living with family members and more chronic diseases was associated with lower PF scores in the third wave ( Table 4 ).

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Table 4. Standardized coefficients for covariates in the conditional parallel process latent growth model.

Bidirectional associations between physical function and cognition

We inspected the bidirectional association between PF and CF at three time points using the ARCL model. The model fits the data well [χ 2 /(4) = 33.78, p < 0.001, CFI = 0.937, RMSEA = 0.074, RMSEA 90% CI (0.052, 0.098), and SRMR = 0.026]. The significant autoregressive paths indicate the stability of PF and CF over time. PF in the first wave had a statistically significant effect on cognition in the second wave (β = 0.061, p = 0.019) and cognition in the second wave on PF in the third wave (β = 0.097, p < 0.001) ( Figure 2 ). Compared with the result which controls for covariates (see Supplementary Figure 1 ), the results show the same predictive chain that between T1 and T2, PF positively predicted CF. However, from T2 to T3, the opposite effect was observed, whereby CF positively predicted PF.

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Figure 2. Autoregressive cross-lagged model assessing bidirectional relationships between physical function and cognition over 3 years. Solid lines indicate significant associations, and dashed lines indicate non-significant associations. PF, physical function; CF, cognitive function, * p < 0.05.

The present study investigated the trajectories of PF performance and CF scores and the longitudinal relationships between PF decline and cognitive deterioration in the Chinese older population based on longitudinal data collected over 7 years. The LGM revealed a declining trend in both CF and PF scores with a low change rate, consistent with a previous study that reported a minor decline in PF and cognition, with average annual decline rates of 0.03 ( Diaz-Venegas and Wong, 2016 ) and 0.07 ( Ku et al., 2012 ), respectively. The finding can be explained by the “benefits of success” concept ( Fries, 1980 ); in other words, persons are living longer (success) and in better health at older ages than previously (benefits). The cognition and PF of older adults have benefited from these advances, with lower rates of functional disability, more effective disease treatment and healthier lifestyles, reduced disability rates for some major chronic diseases, such as stroke and cardiovascular disease, and improved living standards due to China’s rapid socio-economic development ( Zeng et al., 2017 ). Overall, the decrease in PF and CF is insidious. Regular assessments of CF and PF are essential to achieving good outcomes and should ideally be included in routine medical examinations of middle-aged and older individuals.

The correlational relationship between CF and PF was explored using PP-LGM. Individuals with lower PF levels at baseline showed lower CF scores. However, the weak correlation ( Cohen, 1992 ) between PF and CF at baseline was weakened to insignificance by the inclusion of control variables as competing explanatory variables ( Lee Ray, 2016 ) during the initial CF, and PF of the study subjects was less impaired. Both initial cognitive functioning and PF performance were shown to be primarily influenced by age and social engagements in this study. Consistent with previous research, social engagement helps protect against functional decline in PF and CF among older adults, namely more social engagement is associated with better cognitive performance and less PF decline. The cognitive reserve, stress, and vascular hypothesis proposes that social activity can increase cognitive reserve to selectively improve CF and delay CF decline by increasing social contact with people to maintain a positive emotional state or reduce stress levels and consequently reduce the risk of cardiovascular disease ( Fratiglioni et al., 2004 ). Other studies have reported the protective effect of social participation on PF ( Ma et al., 2020 ; Dos Santos and Alberto Gobbo, 2021 ), even if older adults only do chores around the house that require strength and limb exercises in certain situations, such as carrying a bucket of water or sweeping, sufficiently to contribute to the maintenance of PF. Health policy initiatives should therefore encourage older adults to engage in social activities to delay their functional decline, including the provision of community activity centers for older adults.

However, the decreased rate of PF is positively correlated with the rate of CF deterioration, independently of adjustments for covariates, indicating that accelerated decline in function on either side triggers accelerated functional decline on the other side. It means the accelerated PF decline could trigger an accelerated decline in cognitive function and vice versa, which corroborates that PF and CF reciprocally influence each other’s development ( Black and Rush, 2002 ) and may form a self-perpetuating malignant cycle in the absence of appropriate interventions. This finding deserves more attention from clinicians. Focusing on disability prevention and PF exercise could reduce the occurrence of cognitive decline. Equally, attention to the prevention and treatment of cognitive impairment will potentially prevent functional disability. Thus, attention to risk factors for cognitive or physical decline, such as specific chronic health conditions and mental status, would be helpful in preventing functional decline in older adults.

We further used the ARCL model to detect the sequence of predicted relationships between PF decline and CF deterioration. Consistent with a previous study ( Farias et al., 2013 ) showing a gradual decline of daily functioning in individuals with normal CF and an accelerated decline as MCI progresses toward dementia, our results support that PF decline precedes CF decline and subsequently induces more PF decline. It could be explained by two potential mechanisms. First, neurofibrillary tangles in the substantia nigra related to gait may represent preclinical cognitive impairment in the brain pathology ( Schneider et al., 2006 ). Second, stroke or diabetes can induce an initial reduction in PF, and disease-related vascular problems may subsequently exacerbate CF decline ( Fauth et al., 2013 ).

The specific predictive chronology of functional decline identified in this study suggests that the health goals of older adults at different levels of functioning are distinct and that health management programs should focus on effectively preventing and delaying PF decline. Focusing on the etiology of unintentional injuries, such as falls, fractures, and cardiovascular disease, may help prevent or enable early intervention in PF decline, thereby helping to maintain a satisfactory quality of life.

This longitudinal study was based on a large national sample from the CLHLS database. Data quality was ensured by implementing strict quality control measures at each stage of the CLHLS survey. The use of LGM and ARCL analyses helped to identify the joint trajectory characteristics and sequential relationships between PF and CF decline, helping to determine the stage of functional decline among older adults, to select the timing of intervention, and to develop targeted health management programs that aim to delay functional decline and achieve healthy aging. Moreover, this study identified factors affecting both PF and cognition, which helped elucidate the association between PF and CF.

The present study has some limitations. Although the data quality was assured by stringent quality control at each stage of the CLHLS, the long duration of follow-up yielded missing data. The study performed multiple interpolations of data with missing confounding variables to achieve the best possible use of the available data. Furthermore, retained cohort members were younger and had better mental status and social engagement in general than excluded individuals, which may lead to the study being underpowered and biased toward the healthier segment of the aging population. Since we used a nationally representative database, confirmation bias could be limited to an extent. We are reminded to expand the sample in future studies by adding older individuals with different mental statuses and social engagement levels as subjects to improve the generalizability of this study’s findings. In addition, the research items, including PF assessment, social engagement, and mental status, were based on self-reported data, which may have been affected by recall and measurement biases. Nevertheless, the face-to-face interview method allowed investigators to get a more accurate picture of overall respondent profiles, which ensured the response quality to some extent. Furthermore, although this study revealed declines in cognitive and physical function among older adults with three measurements over a 7-year period, the two intervals may not be long enough to detect significant functional decline and to examine the non-linear relationship between PF and cognition. Therefore, future studies should consider selecting objective measurement instruments and extending the follow-up period to explore the long-term dynamic relationship between PF and cognition by constructing non-linear LGM.

This longitudinal study examined the temporal relationship between PF and CF in Chinese older adults, using survey data collected at three time points over 7 years. The results revealed that a decrease in PF and CF was characterized as implicit, with accelerated decline in one variable causing a faster decrease in the other variable. There is a clear prediction priority in the time between PF decrement and cognitive deterioration, whereby initial PF scores predicted CF, and CF predicted subsequent PF. Early identification and intervention in physical dysfunction among older adults would be critical to prevent further cognitive impairment and maintain functional independence. Regular functional assessment and individualized care plans are required to achieve healthy aging.

Data availability statement

The data used were obtained from the public database of the Chinese Longitudinal Healthy Longevity Survey: https://opendata.pku.edu.cn/dataset.xhtml?persistentId=doi:10.18170/DVN/WBO7LK .

Author contributions

XW and HL were responsible for the study conception, design, and drafting of the manuscript. ZG, JK, KZ, and MX collated data and conducted data analyses. LY was responsible for supervision. All authors made critical revisions to the manuscript.

This research was funded by the Shandong Provincial Natural Science Foundation (No. ZR2021MG031).

Acknowledgments

We would like to thank the Center for Healthy Aging and Development Research at Peking University for access to the CLHLS data.

Conflict of interest

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.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2022.933886/full#supplementary-material

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Keywords : cognitive function, physical function, aging, latent growth model, autoregressive cross-lagged model

Citation: Wei X, Liu H, Yang L, Gao Z, Kuang J, Zhou K and Xu M (2022) Joint developmental trajectories and temporal precedence of physical function decline and cognitive deterioration: A longitudinal population-based study. Front. Psychol. 13:933886. doi: 10.3389/fpsyg.2022.933886

Received: 16 May 2022; Accepted: 09 September 2022; Published: 12 October 2022.

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Copyright © 2022 Wei, Liu, Yang, Gao, Kuang, Zhou and Xu. 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(s) 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: Li Yang, [email protected]

† These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Parents Are Highly Involved in Their Adult Children’s Lives, and Fine With It

New surveys show that today’s intensive parenting has benefits, not just risks, and most young adults seem happy with it, too.

research studies on developmental psychology

By Claire Cain Miller

American parenting has become more involved — requiring more time, money and mental energy — not just when children are young, but well into adulthood.

The popular conception has been that this must be detrimental to children — with snowplow parents clearing obstacles and ending up with adult children who have failed to launch , still dependent upon them.

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But two new Pew Research Center surveys — of young adults 18 to 34 and of parents of children that age — tell a more nuanced story. Most parents are in fact highly involved in their grown children’s lives, it found, texting several times a week and offering advice and financial support. Yet in many ways, their relationships seem healthy and fulfilling.

Nine in 10 parents rate their relationships with their young adult children as good or excellent, and so do eight in 10 young adults, and this is consistent across income. Rather than feeling worried or disappointed about how things are going in their children’s lives, eight in 10 parents say they feel proud and hopeful.

“These parents, who are Gen X, are more willing to say, ‘Hey, this is good, I like these people, they’re interesting, they’re fun to be with,’” said Karen L. Fingerman, a professor at the University of Texas at Austin who studies adults’ relationships with their families.

As for the adult children, she said, “You get advice from a 50-year-old with life experience who is incredibly invested in you and your success.”

Also, these close relationships don’t seem to be holding back young people from reaching certain milestones of independence. Compared with their parents as young adults in the early 1990s, they are much more likely to be in college or have a college degree, Pew found. They are somewhat more likely to have a full-time job, and their inflation-adjusted incomes are higher. (They are much less likely, though, to be married or have children.)

Experts say contemporary hyper-intensive parenting can go too far — and has only gotten more hands-on since the young adults in the survey were children. Young people say their mental health is suffering , and recent data shows they are much more likely to say this than those before them. Some researchers have sounded alarms that one driver of this is children’s lack of independence, and that overparenting can deprive children of developing skills to handle adversity.

The new data suggests that, indeed, young adults are more reliant on their parents — texting them for life advice when older generations may have figured out their problems on their own. But the effects do not seem to be wholly negative.

Professor Fingerman and her colleagues have found that close relationships between parents and grown children protected children from unhealthy behaviors, and young adults who received significant parental support were better able to cope with change and had higher satisfaction with their lives. It was a finding “we just couldn’t believe the first time,” she said, because of the assumptions about over-involved parents.

Both things can be true, said Eli Lebowitz, director of the Program for Anxiety Disorders at the Yale Child Study Center — “that they do rely a lot on their parents, and they do get a lot of positive support from them.”

In previous research , parents often expressed ambivalence about their continued involvement in their adult children’s lives. But the Pew study suggests that has changed, Professor Fingerman said, perhaps a sign they have come to embrace it.

Among parents, seven in 10 say they are satisfied with their level of involvement in their grown child’s life. Just 7 percent say they’re too involved, and one-quarter would like even more involvement. Young adults say the same.

Adriana Goericke, from Santa Cruz, Calif., texts with her daughter, Mia, a college sophomore in Colorado, a few times a day. They share pictures of their food, workouts or funny selfies.

When her daughter asks for advice, mostly about navigating friendships and dating, her mother said she sees her role as a sounding board: “She knows I’m not going to try and run her life, but I’m always there if she needs me.”

Mia Goericke has seen friends who can’t solve problems or make small decisions on their own, but she said that’s different from asking her mother for help. “She will usually ask me what my goals are and try to understand my thinking rather than just tell me what to do,” she said. “It’s like an incredible resource I have at my fingertips.”

When baby boomers were growing up, there was a belief, rooted in the American ideal of self-sufficiency, that children should be independent after age 18. But that was in some ways an aberration, social scientists said. Before then, and again now, it has been common for members of different generations to be more interdependent.

Parents’ involvement in young adults’ lives began to grow in the 1970s. The transition to adulthood became longer , and less clear-cut: It was no longer necessarily the case that at 18 children left home for college, marriage or jobs. Parenting gradually became more intensive , as people had fewer children and invested more in their upbringings.

Cathy Perry, 66, said she has a very different relationship with her sons, 32 and 36, than she had with her parents when she was that age. They all live in the St. Louis area, and text on a family group chat several times a week. Her older son shares updates on his children, and asks for advice on his career, finances and home remodeling.

As a young adult, she lived an 11-hour drive from her parents, and calls were charged by the minute. “I feel that I have a much closer and more open relationship with my kids, where they are more free to express their opinions on things I might not agree with,” she said.

Open, emotional conversations have become more of a priority for parents, research shows : “They may be the first generation of adults who have parents who actually grew up with the mind-set of talking about this kind of stuff,” Professor Lebowitz said.

In the survey, six in 10 young adults said they still relied on their parents for emotional support, and a quarter of young adults said their parents relied on them for the same, including 44 percent of daughters who said their mothers did.

About seven in 10 parents of young adults said their children ask them for advice, especially about finances, careers, physical health and parenting (among those with children). That’s a change from when they were young — half said they rarely or never asked their parents for advice.

There were gender differences: Young adults were somewhat more likely to say they had a good relationship with their mother than their father. Young women communicated with their parents more frequently than young men.

Cultural and policy factors play a role in parents’ involvement in their grown children’s lives. In the United States , parents and children often rely on one another for child care and elder care . In many immigrant families, it is common for multiple generations to live together or support one another. And technology has made it easier to stay in regular touch.

There is also an increasing understanding that children have different needs, and decreasing stigma around helping them, said Mark McConville, a clinical psychologist in Cleveland. Consider a bright teenager with ADHD, he said. A generation ago, his potential might have been written off. Now, it’s much more likely that his parents identify the issue and find programs to support him — and as a result, that he attends college.

He said a small subset of young adults struggle with starting independent lives (the subject of his book, “ Failure to Launch : When Your Twentysomething Hasn’t Grown Up … and What to Do About It”). But overall, “this new prioritization of their relationship with their kids and attending to their kids’ needs” helps children succeed, he said.

Economic factors have changed, too. Young people are more likely than in their parents’ generation to have student debt — 43 percent do in their late 20s, compared with 28 percent when their parents were that age, Pew found — and are buying homes later, if at all .

Partly as a consequence, parents support their children financially for longer periods — one-third of young adults told Pew they were not financially independent from their parents. They are a bit more likely to live with their parents than the previous generation.

But for many families, support in the form of money or housing can be beneficial to parents, too. Of young adults living at home, three-quarters helped with expenses. One-third of young adults gave their parents financial help in the last year, particularly in low-income families.

And a majority of adult children living at home and parents in that situation said it had a positive effect on their relationship.

“There’s a two-way street going on that I think we need to acknowledge,” Professor Fingerman said. “They’re not all kids living in the basement being pampered. They’re kids having relationships with their parents that are good ones.”

Audio produced by Tally Abecassis .

Claire Cain Miller writes about gender, families and the future of work for The Upshot. She joined The Times in 2008 and was part of a team that won a Pulitzer Prize in 2018 for public service for reporting on workplace sexual harassment issues. More about Claire Cain Miller

How to Communicate Better With the Teens in Your Life

Simple strategies can go a long way toward building a stronger, more open relationship..

Active listening is an essential skill when seeking to engage any family member in conversation — teens included. Here is how to get better at it .

There are many reasons why a teen might not be opening up to you. These are the most common explanations for their silence .

Is your kid dismissing your solutions to their problems as irritating or irrelevant? It is usually because they’re not looking for you solve their problems.

Developing a healthy relationship with social media can be tricky. Here is how to talk to teens about it .

If your teen is surly or standoffish, these strategies can help you reconnect .

Are you worried that your kid might be struggling with his or her mental health? Understand the warning signs  and make sure to approach the issue with the utmost sensitivity.

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    Developmental psychology is the branch of psychology that focuses on how people grow and change over the course of a lifetime. Those who specialize in this field are not just concerned with the physical changes that occur as people grow; they also look at the social, emotional, and cognitive development that occurs throughout life.

  9. Developmental Psychology

    Developmental Psychology: A Definition. Psychology (from Greek psyche = breath, spirit, soul and logos = science, study, research) is a relatively young scientific discipline.Among the first to define Psychology was James who defined it as "the science of mental life, both of its phenomena and their conditions."Today, Psychology is usually defined as the science of mind and behavior ...

  10. Volume 5, 2023

    The Annual Review of Developmental Psychology covers the significant advances in the developmental sciences, including cognitive, linguistic, social, cultural, and biological processes across the lifespan. The invited reviews will synthesize the theoretical, methodological, and technological developments made over the past several decades that have led to important new discoveries relevant ...

  11. 25320 PDFs

    Developmental psychology is the scientific study of systematic psychological changes, emotional changes, and perception changes that occur in... | Explore the latest full-text research PDFs,...

  12. The Place of Development in the History of Psychology and Cognitive

    In this article, I analyze how the relationship of developmental psychology with general psychology and cognitive science has unfolded. This historical analysis will provide a background for a critical examination of the present state of the art. I shall argue that the study of human mind is inherently connected with the study of its development.

  13. Developmental Psychology

    Developmental Psychology. At Harvard's Laboratory for Developmental Studies , faculty and students seek to shed light on the human mind and human nature by studying their origins and development in infants and children, in relation to the mental capacities of non-human animals and of human adults in diverse cultures. Current research interests ...

  14. Developmental psychology: The expanding reach of emotions

    A new study in Psychology and Aging suggests that the expanding reach of negative emotions is greater for younger than older adults. Emotions are typically transient feelings that fluctuate from ...

  15. Research on Developmental Psychology

    Developmental psychology was a major area of research at ETS from the late 1960s to the early 1990s, a natural extension of the work in cognitive, personality, and social psychology that had begun shortly after the organization's founding in 1947, consistent with Henry Chauncey's vision of investigating intellectual and personal qualities (see S...

  16. British Journal of Developmental Psychology

    Dawn is Professor of Psychology at Royal Holloway, University of London. During her early career, Dawn's work explored children's social-emotional understanding, with a particular focus on development of an understanding of impression management, of emotion recognition skills, and of expression of emotion within drawings.

  17. Developmental Psychology 101: Theories, Stages, & Research

    A Take-Home Message References What Is Developmental Psychology? Human beings change drastically over our lifetime. The American Psychological Association (2020) defines developmental psychology as the study of physical, mental, and behavioral changes, from conception through old age.

  18. Developmental Psychology Topics

    Developmental psychology is a broad topic that involves studying how people grow and change throughout their whole lifetime. Topics don't just include physical growth but also the emotional, cognitive, and social development that people experience at different stages of their lives. At a Glance

  19. Developmental Psychology Research Methods

    There are many different developmental psychology research methods, including cross-sectional, longitudinal, correlational, and experimental. Each has its own specific advantages and disadvantages. The one that a scientist chooses depends largely on the aim of the study and the nature of the phenomenon being studied.

  20. Can the Remote-Work Era Fix How Scientists Study Kids?

    This is an issue in the field generally, and certainly a thorny problem in developmental psychology, which primarily studies children: According to one paper, WEIRD subjects make up 96 percent of ...

  21. Research Methods in Developmental Psychology

    This module describes different research techniques that are used to study psychological phenomena in infants and children, research designs that are used to examine age-related changes in developmental processes and changes over time, and unique challenges and special issues associated with conducting research with infants and children.

  22. (PDF) Research on Developmental Psychology

    Research on Developmental Psychology Authors: Nathan Kogan Lawrence J. Stricker Educational Testing Service Michael Lewis Rutgers, The State University of New Jersey Jeanne Brooks-Gunn Abstract...

  23. Developmental Psychology Studies: 10 Examples

    The classic developmental psychology study of how play develops in children was carried out by Mildred Parten in the late 1920s at the Institute of Child Development in Minnesota (Parten, 1933). She closely observed children between the ages of 2 and 5 years and categorised the types of play.

  24. Researching emotional development in children

    A finalist in the Study UK Alumni Awards 2023 Science and Sustainability category for Greece, Dr Antonios Christou (PhD Psychology, 2016) researches children's emotional development, sensitivity and emotional resilience. ... the path toward establishing an academic career in the field of special educational needs and developmental psychology ...

  25. Frontiers

    The data were obtained from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), a longitudinal study investigating the health status of older adults in China, conducted by the Center for Research on Healthy Aging and Development at Peking University, which obtained data from eight face-to-face interviews with the cohort since 1998 ...

  26. Graduate Program • UCLA Department of Psychology

    Areas of Study Behavioral Neuroscience Clinical Psychology Prospective Clinical Area Applicants Student Admissions, Outcomes, and Other Data Cognitive Neuroscience Cognitive Psychology Computational Cognition Developmental Psychology Health Psychology Learning & Behavior Quantitative Psychology Departmental Statistical Consulting Social Psychology Social and Affective Neuroscience Prospective ...

  27. Clinical Psychology • UCLA Department of Psychology

    Areas of Study Behavioral Neuroscience Clinical Psychology Prospective Clinical Area Applicants Student Admissions, Outcomes, and Other Data Cognitive Neuroscience Cognitive Psychology Computational Cognition Developmental Psychology Health Psychology Learning & Behavior Quantitative Psychology Departmental Statistical Consulting Social Psychology Social and Affective Neuroscience Prospective ...

  28. Parents Are Highly Involved in Their Adult Children's Lives, and Fine

    In previous research, parents often expressed ambivalence about their continued involvement in their adult children's lives. But the Pew study suggests that has changed, Professor Fingerman said ...

  29. Psychology Undergraduate Research Conference • UCLA Department of

    Psychology Undergraduate Advising Major & Minor Requirements Psychology Major Psychobiology Major Cognitive Science Major Applied Developmental Psychology Minor ADP Minor Course Requirements The ADP Minor Internship ADP Minor Admissions Enrollment Information Pre-Major & Major Declaration Transfer & AP/IB/A-level Course Credit Studying Abroad Graduate School & Careers Grading and Instruction ...