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Body language and movement, verbal delivery.

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Effective presentation skills

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Robert Dolan, Effective presentation skills, FEMS Microbiology Letters , Volume 364, Issue 24, December 2017, fnx235, https://doi.org/10.1093/femsle/fnx235

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Most PhD's will have a presentation component during the interview process, as well as presenting their work at conferences. This article will provide guidance on how to develop relevant content and effectively deliver it to your audience.

Most organizations list communication skills as one of their most critical issues…and presentation skills are a large component of communications. Presentation skills are crucial to almost every aspect of academic/business life, from meetings, interviews and conferences to trade shows and job fairs. Often times, leadership and presentation skills go hand in hand. NACE Survey 2016 - Ability to communicate verbally (internally and externally) ranked 4.63/5.0 and was the #1 skill employers want. The information provided in this article is designed to provide tips and strategies for delivering an effective presentation, and one that aligns the speaker with the audience.

What type of speaker are you?

Facts and fears of public speaking.

Your blueprint for delivery.

Avoider —You do everything possible to escape from having to get in front of an audience.

Resister —You may have to speak, but you never encourage it.

Accepter —You’ll give presentations but don’t seek those opportunities. Sometimes you feel good about a presentation you gave.

Seeker —Looks for opportunities to speak. Finds the anxiety a stimulant that fuels enthusiasm during a presentation.

Public speaking can create anxiety and fear in many people. Dale Carnegie has a free e-book that provides tips and advice on how to minimize these fears www.dalecarnegie.com/Free-eBook

People are caught between their fear and the fact that many employers expect them to demonstrate good verbal communication skills.

Most interviews by PhD’s have a presentation component.

Academic interviews always have a presentation component.

If your job doesn’t demand presentation skills, odds are that you’ll need them in your next job

Develop your blueprint for delivery:

Information by itself can be boring, unless it's unique or unusual. Conveying it through stories, gestures and analogies make it interesting. A large portion of the impact of communications rests on how you look and sound, not only on what you say. Having good presentation skills allows you to make the most out of your first impression, especially at conferences and job interviews. As you plan your presentation put yourself in the shoes of the audience.

Values …What is important to them?

Needs …What information do they want?

Constraints …Understand their level of knowledge on the subject and target them appropriately.

Demographics …Size of audience and location may influence the presentation. For example, a large auditorium may be more formal and less personal than a presentation to your team or lab mates in a less formal setting.

Structure—Introduction, Content and Conclusion

Body Language and Movement

Verbal Delivery

Introduction

Build rapport with audience (easier in a smaller less formal setting).

State preference for questions—during or after?

Set stage: provide agenda, objective and intended outcomes

Introduce yourself providing your name, role and function. Let the audience know the agenda, your objectives and set their expectations. Give them a reason to listen and make an explicit benefit statement, essentially what's in it for them. Finally, let them know how you will accomplish your objective by setting the agenda and providing an outline of what will be covered.

Deliver your message logically and structured.

Use appropriate anecdotes and examples.

Illustrate and emphasize key points by using color schemes or animations.

Establish credibility, possibly citing references or publications.

Structure your presentation to maximize delivery. Deliver the main idea and communicate to the audience what your intended outcome will be. Transition well through the subject matter and move through your presentation by using phrases such as; ‘now we will review…’ or ‘if there are no more questions, we will now move onto…’ Be flexible and on course. If needed, use examples not in the presentation to emphasize a point, but don’t get side tracked. Stay on course by using phrases such as ‘let's get back to…’ Occasionally, reiterate the benefits of the content and the main idea of your presentation.

Restate the main objective and key supporting points

For Q&A: ‘Who wants more details?’ (Not, ‘any questions?’)

Prompting for questions: ‘A question I often hear is…’

Summarize the main elements of your presentation as they relate to the original objective. If applicable, highlight a key point or crucial element for the audience to take away. Signal the end is near…‘to wrap up’ or ‘to sum up’. Clearly articulate the next steps, actions or practical recommendations. Thank the audience and solicit final questions.

Your non-verbal communications are key elements of your presentation. They are composed of open body posture, eye contact, facial expressions, hand gestures, posture and space between you and the audience.

Stand firmly and move deliberately. Do not sway or shift.

Move at appropriate times during presentation (e.g. move during transitions or to emphasize a point).

Stand where you can see everyone and do not block the visuals/screen.

Decide on a resting position for hands (should feel and look comfortable).

Gestures should be natural and follow what you are saying.

Hand movement can emphasize your point.

Make gestures strong and crisp…ok to use both arms/hands.

Keep hands away from face.

When pointing to the screen, do so deliberately. Do not wave and face the audience to speak

Look at audience's faces, not above their heads.

If an interview or business meeting…look at the decision makers as well as everyone else.

Look at faces for 3–5 seconds and then move on to the next person.

Do not look away from the audience for more than 10 seconds.

Looking at a person keeps them engaged.

Looking at their faces tells you how your delivery and topic is being received by the audience. The audience's body language may show interest, acceptance, openness, boredom, hostility, disapproval and neutrality. Read the audience and adjust where and if appropriate to keep them engaged. For example, if they seem bored inject an interesting anecdote or story to trigger more interest. If they appear to disapprove, ask for questions or comments to better understand how you might adjust your delivery and content if applicable.

Use active rather than passive verbs.

Avoid technical terms, unless you know the audience is familiar with them.

Always use your own words and phrases.

Cut out jargon/slang words.

Look at your audience and use vocal techniques to catch their attention. Consider changing your pace or volume, use a longer than normal pause between key points, and change the pitch or inflection of your voice if needed. Consider taking a drink of water to force yourself to pause or slowdown. View the audience as a group of individual people, so address them as if they were a single person.

Tips for reducing anxiety

If you experience nervousness before your presentation, as most people do, consider the following.

Be Organized —Knowing that your presentation and thoughts are well organized will give you confidence.

Visualize —Imagine delivering your presentation with enthusiasm and leaving the room knowing that you did a good job.

Practice —All successful speakers rehearse their presentations. Either do it alone, with your team, or video tape yourself and review your performance after. Another tip is to make contact before your talk. If possible, speak with the audience before your presentation begins; however, not always possible with a large audience. Walk up to them and thank them in advance for inviting you to speak today.

Movement —Speakers who stand in one spot may experience tension. In order to relax, move in a purposeful manner and use upper body gestures to make points.

Eye Contact —Make your presentation a one-on-one conversation. Build rapport by making it personal and personable. Use words such as ‘ we ’ , ‘ our ’, ‘ us ’ . Eye contact helps you relax because you become less isolated from the audience.

Personal appearance

Clothes should fit well, not too tight. Consider wearing more professional business-like attire. Find two to three colors that work well for you. Conservative colors, such as black, blue, gray and brown, seem to be the safest bet when presenting or meeting someone for the first time in a professional setting. Depending upon the audience, a sport coat and well-matched dress slacks are fine. Generally, try to avoid bright reds, oranges and whites, since these tend to draw attention away from your face. Avoid jewelry that sparkles, dangles or makes noise. Use subtle accessories to compliment your outfit.

Other resources: www.toastmasters.org https://www.skillsyouneed.com/present/presentation-tips.html

https://www.ag.ndsu.edu/evaluation/documents/effective-presentations-a-toolkit-for-engaging-an-audience

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• J Adv Pract Oncol
• v.9(5); Jul-Aug 2018

Presenting With Confidence

Wendy h. vogel.

1 Wellmont Cancer Institute, Kingsport, Tennessee;

Pamela Hallquist Viale

2 University of California, San Francisco, San Francisco, California

Often, advanced practitioners must give clinical presentations. Public speaking, which is a major fear for most individuals, is a developed skill. Giving an oral presentation is a good way to demonstrate work, knowledge base, and expertise. Giving an effective presentation can help obtain recognition of skills and proficiency as an advanced practitioner or expert in the field. This paper will highlight skills and techniques that can help to improve presentation style and the ability to connect with an audience.

As an advanced practitioner, it is likely that you will be asked to deliver a lecture at some point in your career. Medical presentations can range from casual in-services to professional lectures given to audiences of thousands. Since public speaking is listed as one of the top fears of individuals living in the United States, it pays to develop skills as a speaker or presenter.

Giving an oral presentation is essential to demonstrating your work, knowledge base, and expertise. Giving an effective presentation can help you obtain recognition and acknowledgement of your skills and proficiency as an advanced practitioner or expert in the field. However, many presenters lack the skills to deliver a dynamic and persuasive lecture. Inadequate speaking skills can be detrimental to your ability to deliver an important message, or worse yet, bore your audience. This article will highlight skills and techniques that can help to improve your presentation style and ability to connect with your audience.

FEAR OF PUBLIC SPEAKING

If you are afraid of public speaking, you are not alone. Marinho, de Medeiros, Gama, and Teixeira ( 2016 ) studied college students to determine the prevalence of fear of public speaking. In a group of 1,135 undergraduate students (aged 17–58), over half of those surveyed (n = 63.9%) reported a fear of public speaking. Almost the entire group surveyed (89.3%) wanted classes to improve public speaking. Specific traits associated with a fear of speaking were reported as female gender, infrequent experience, and perception of poor voice quality.

Giving a bad presentation can alienate your audience from your lecture and the message you are trying to deliver. Table 1 lists ways to give a bad presentation. But, let us assume you do not want to give a bad presentation at all. In fact, you have an important message to share with your audience and you have been invited to give an hour-long lecture on the subject. How can you deliver that message in an effective and engaging manner?

Tips for Giving a Bad Presentation

HIGH-LEVEL TIPS

The first tip is to know your subject and know it well. In fact, should your audio-visual equipment malfunction (and if you speak often enough, this is likely to happen), you should have your presentation memorized. However, it is a good idea to make a hard copy of your slides and use them in case of equipment failure. Your audience might not be able to see a graph in detail, but you’ll be able to speak to a study and deliver the results without panicking about your lost slide deck or incompatible presentation equipment.

The second tip is to know your audience. If you are speaking to a group of nurses on a unit, your speaking style and delivery message will be more casual than when you speak to a room of 500 people. Nonetheless, you need to know who you are talking to and what they expect from your lecture. Table 2 lists some information you will want to know about your audience. Researching and knowing your audience will make your message more pertinent and personal.

What to Know About Your Audience

Understanding who your audience is will enable you to engage your audience. Look excited and enthusiastic. If you are motivated about your topic, then they will be too. Show your interest in your subject and your excitement about sharing the data with your audience.

Another tip is to develop your stage presence. Actors rehearse their roles until they can do it in their sleep, creating their best and most polished dramatic performances. You aren’t in a Broadway musical, but you need to have a stage presence. Recording your lecture and then examining ways to improve your delivery is a great way to develop your speaking skills. Utilize who you are and capitalize on that. Practice in front of a friend or mentor for feedback on your delivery

Your audience will develop an impression of you within the first 15 seconds. Develop an impactful opening to start off right. Table 3 gives some examples of impactful openings. For example, if you wanted to demonstrate the effect that tanning booths have had on the incidence of melanoma in young women, you could open with a photo of a tanning booth, followed by the daunting statistics in melanoma and an example of a case of melanoma. This slide becomes the "hook" that captures your audience’s interest.

Examples of Impactful Openings

When giving a medical presentation, advanced practitioners have a wonderful chance to share a patient story or vignette that will demonstrate the medical problem and its impact on practice ( Moffett, Berezowski, Spencer, & Lanning, 2014 ). You can do this easily by showing a patient radiological study or lab values, or a picture of a particularly challenging side effect. The net result is that your audience will be intrigued and relate to your story, especially if they take care of that patient population. Tell the story of the patient and describe the significance of the side effect or disease state. Clinical presentations often benefit from case studies that your audience may recognize from their own practices. Some of the most successful presentations use case studies followed by examples of right or wrong approaches to a patient problem, asking the audience to decide best practice and thereby engaging the audience fully. Tell your audience why this topic is important and why they need to know about it ( Moffett et al., 2014 ). Then, share the data supporting the importance of your story and how your audience can use the information to affect or change practice. You want to capture the attention of your audience at the very beginning of your presentation and then hold it. Humor may also be used for openings, but care must be taken with this and should be directed at yourself and not anyone else. Keep the attention of the audience by developing your delivery skills. Lastly, and perhaps the most important advice, is to "practice, practice, practice."

DEVELOPING SLIDES

Most medical speakers use PowerPoint to illustrate their talk and data. Using your slides effectively can make an important difference in your presentation and how your audience will respond. Develop your presentation and topic first, then create your slides. The 5/5/5 rule calls for no more than five words per line of text, five lines of text per slide, or five text or data-heavy slides in a row ( LearnFree.org, 2017 ). See Table 4 for tips for using PowerPoint.

PowerPoint Tips

Adding images to your slides can create visual interest. Pictures of patients with side effects or complications can immediately show the audience what you are trying to communicate. As with data slides, appropriate referencing of images must be added to each of your slides. If you are using clip art to add interest or humor to your presentation, be mindful of possible distractions to your main message. Use these kinds of imagery sparingly.

Using slides during your presentation can enhance the message you are giving, but it is vital that you use the slide and not let it use you. Know your slides well enough that you do not have to read them. The title of the slide should give the key message of that slide. You do not have to tell your audience everything on the slide; instead, give them an overview of what they are looking at. Never read a slide to an audience. Do not present to the slide; present to your audience.

KEEPING THE AUDIENCE’S INTEREST

If your presentation is longer than 20 minutes, you may have a "mid-talk slump." This is a great time to check in with your audience: Do they understand your message thus far? Pause for a moment and engage your audience with a question or anecdote, or perhaps a patient story. Ask your audience if they have something to share regarding the topic. Change the pace and change the inflection of your voice.

Taking questions from your audience can be daunting. Table 5 gives some tips on how to answer questions. Determining when to take questions will depend upon your audience size and makeup, and the setting of your presentation. The most important tip is to listen carefully to the question and be honest if you do not know the answer.

Handling Questions From Your Audience

Your delivery skills can determine how the audience perceives you and your message. Eye contact, voice, pace, inflection, gestures, and posture are all important aspects of your delivery. Eye contact establishes rapport and a feeling of being genuine. Although you shouldn’t stare someone down, making eye contact while making a statement, then moving to your next audience member and giving another statement fosters engagement. Scanning, which is running your eyes over the audience and not focusing on any one person, should be avoided.

Your voice should be loud and animated. Generally, however loud you think you should be, be louder. Convey your enthusiasm, and vary your pace and inflection.

Gestures can enhance or take away from your talk. Be natural with an open-body approach. Keep your hands at your sides if you’re not using them. Avoid pointing; instead, use open-handed gestures. Your posture should be good, with your shoulders back and weight equally balanced on both feet. When you move, move with purpose; do not sway, rock, or pace ( Butterfield, 2015 ).

It is very normal to feel anxious or nervous. But let that feeling work for you, not against you. When you are faced with a challenging situation, cortisol and adrenaline are released, causing dry mouth, difficulty getting words out, shallow breaths, tremors, sweating, and nervous behaviors like laughter or fidgeting. To combat this, take some deep breaths, which reduces adrenaline output. Slow down and look around. Take a moment, take a sip of water, and smile. Look confident even if you do not feel it. Utilize every resource you can find to further your skills (see Table 6 for further reading).

Resources for Presenters

Advanced practitioners have many opportunities to give medical presentations, both as part of their job and as a way to advance in their professional practice. The tools provided in this article can help you develop a presentation that will be meaningful and impactful to your audience. It is a great feeling when audience members come to you after your presentation to share with you how much they enjoyed and learned from your talk. With practice, your presentations can make a difference. And remember—your audience wants you to succeed.

The authors have no conflicts of interest to disclose.

Effective presentation skills

• PMID: 29106534
• DOI: 10.1093/femsle/fnx235

Most PhD's will have a presentation component during the interview process, as well as presenting their work at conferences. This article will provide guidance on how to develop relevant content and effectively deliver it to your audience.

Keywords: effective; presentation; skills.

© FEMS 2017. All rights reserved. For permissions, please e-mail: [email protected].

• Audiovisual Aids
• Professional Competence
• Research Personnel*

Princeton Correspondents on Undergraduate Research

How to Make a Successful Research Presentation

Turning a research paper into a visual presentation is difficult; there are pitfalls, and navigating the path to a brief, informative presentation takes time and practice. As a TA for  GEO/WRI 201: Methods in Data Analysis & Scientific Writing this past fall, I saw how this process works from an instructor’s standpoint. I’ve presented my own research before, but helping others present theirs taught me a bit more about the process. Here are some tips I learned that may help you with your next research presentation:

More is more

In general, your presentation will always benefit from more practice, more feedback, and more revision. By practicing in front of friends, you can get comfortable with presenting your work while receiving feedback. It is hard to know how to revise your presentation if you never practice. If you are presenting to a general audience, getting feedback from someone outside of your discipline is crucial. Terms and ideas that seem intuitive to you may be completely foreign to someone else, and your well-crafted presentation could fall flat.

Less is more

Limit the scope of your presentation, the number of slides, and the text on each slide. In my experience, text works well for organizing slides, orienting the audience to key terms, and annotating important figures–not for explaining complex ideas. Having fewer slides is usually better as well. In general, about one slide per minute of presentation is an appropriate budget. Too many slides is usually a sign that your topic is too broad.

Limit the scope of your presentation

Don’t present your paper. Presentations are usually around 10 min long. You will not have time to explain all of the research you did in a semester (or a year!) in such a short span of time. Instead, focus on the highlight(s). Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

You will not have time to explain all of the research you did. Instead, focus on the highlights. Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

Craft a compelling research narrative

After identifying the focused research question, walk your audience through your research as if it were a story. Presentations with strong narrative arcs are clear, captivating, and compelling.

• Introduction (exposition — rising action)

Orient the audience and draw them in by demonstrating the relevance and importance of your research story with strong global motive. Provide them with the necessary vocabulary and background knowledge to understand the plot of your story. Introduce the key studies (characters) relevant in your story and build tension and conflict with scholarly and data motive. By the end of your introduction, your audience should clearly understand your research question and be dying to know how you resolve the tension built through motive.

• Methods (rising action)

The methods section should transition smoothly and logically from the introduction. Beware of presenting your methods in a boring, arc-killing, ‘this is what I did.’ Focus on the details that set your story apart from the stories other people have already told. Keep the audience interested by clearly motivating your decisions based on your original research question or the tension built in your introduction.

• Results (climax)

Less is usually more here. Only present results which are clearly related to the focused research question you are presenting. Make sure you explain the results clearly so that your audience understands what your research found. This is the peak of tension in your narrative arc, so don’t undercut it by quickly clicking through to your discussion.

• Discussion (falling action)

By now your audience should be dying for a satisfying resolution. Here is where you contextualize your results and begin resolving the tension between past research. Be thorough. If you have too many conflicts left unresolved, or you don’t have enough time to present all of the resolutions, you probably need to further narrow the scope of your presentation.

• Conclusion (denouement)

Return back to your initial research question and motive, resolving any final conflicts and tying up loose ends. Leave the audience with a clear resolution of your focus research question, and use unresolved tension to set up potential sequels (i.e. further research).

Use your medium to enhance the narrative

Visual presentations should be dominated by clear, intentional graphics. Subtle animation in key moments (usually during the results or discussion) can add drama to the narrative arc and make conflict resolutions more satisfying. You are narrating a story written in images, videos, cartoons, and graphs. While your paper is mostly text, with graphics to highlight crucial points, your slides should be the opposite. Adapting to the new medium may require you to create or acquire far more graphics than you included in your paper, but it is necessary to create an engaging presentation.

The most important thing you can do for your presentation is to practice and revise. Bother your friends, your roommates, TAs–anybody who will sit down and listen to your work. Beyond that, think about presentations you have found compelling and try to incorporate some of those elements into your own. Remember you want your work to be comprehensible; you aren’t creating experts in 10 minutes. Above all, try to stay passionate about what you did and why. You put the time in, so show your audience that it’s worth it.

For more insight into research presentations, check out these past PCUR posts written by Emma and Ellie .

— Alec Getraer, Natural Sciences Correspondent

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• Open access
• Published: 25 October 2023

Human-like systematic generalization through a meta-learning neural network

• Brenden M. Lake   ORCID: orcid.org/0000-0001-8959-3401 1 &
• Marco Baroni 2 , 3  

Nature ( 2023 ) Cite this article

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• Computer science
• Human behaviour

The power of human language and thought arises from systematic compositionality—the algebraic ability to understand and produce novel combinations from known components. Fodor and Pylyshyn 1 famously argued that artificial neural networks lack this capacity and are therefore not viable models of the mind. Neural networks have advanced considerably in the years since, yet the systematicity challenge persists. Here we successfully address Fodor and Pylyshyn’s challenge by providing evidence that neural networks can achieve human-like systematicity when optimized for their compositional skills. To do so, we introduce the meta-learning for compositionality (MLC) approach for guiding training through a dynamic stream of compositional tasks. To compare humans and machines, we conducted human behavioural experiments using an instruction learning paradigm. After considering seven different models, we found that, in contrast to perfectly systematic but rigid probabilistic symbolic models, and perfectly flexible but unsystematic neural networks, only MLC achieves both the systematicity and flexibility needed for human-like generalization. MLC also advances the compositional skills of machine learning systems in several systematic generalization benchmarks. Our results show how a standard neural network architecture, optimized for its compositional skills, can mimic human systematic generalization in a head-to-head comparison.

People are adept at learning new concepts and systematically combining them with existing concepts. For example, once a child learns how to ‘skip’, they can understand how to ‘skip backwards’ or ‘skip around a cone twice’ due to their compositional skills. Fodor and Pylyshyn 1 argued that neural networks lack this type of systematicity and are therefore not plausible cognitive models, leading to a vigorous debate that spans 35 years 2 , 3 , 4 , 5 . Counterarguments to Fodor and Pylyshyn 1 have focused on two main points. The first is that human compositional skills, although important, may not be as systematic and rule-like as Fodor and Pylyshyn indicated 3 , 6 , 7 . The second is that neural networks, although limited in their most basic forms, can be more systematic when using sophisticated architectures 8 , 9 , 10 . In recent years, neural networks have advanced considerably and led to a number of breakthroughs, including in natural language processing. In light of these advances, we and other researchers have reformulated classic tests of systematicity and reevaluated Fodor and Pylyshyn’s arguments 1 . Notably, modern neural networks still struggle on tests of systematicity 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 —tests that even a minimally algebraic mind should pass 2 . As the technology marches on 19 , 20 , the systematicity debate continues.

In this Article, we provide evidence that neural networks can achieve human-like systematic generalization through MLC—an optimization procedure that we introduce for encouraging systematicity through a series of few-shot compositional tasks (Fig. 1 ). Our implementation of MLC uses only common neural networks without added symbolic machinery, and without hand-designed internal representations or inductive biases. Instead, MLC provides a means of specifying the desired behaviour through high-level guidance and/or direct human examples; a neural network is then asked to develop the right learning skills through meta-learning 21 .

a , During training, episode a presents a neural network with a set of study examples and a query instruction, all provided as a simultaneous input. The study examples demonstrate how to ‘jump twice’, ‘skip’ and so on with both instructions and corresponding outputs provided as words and text-based action symbols (solid arrows guiding the stick figures), respectively. The query instruction involves compositional use of a word (‘skip’) that is presented only in isolation in the study examples, and no intended output is provided. The network produces a query output that is compared (hollow arrows) with a behavioural target. b , Episode b introduces the next word (‘tiptoe’) and the network is asked to use it compositionally (‘tiptoe backwards around a cone’), and so on for many more training episodes. The colours highlight compositional reuse of words. Stick figures were adapted from art created by D. Chappard (OpenClipArt.org).

To demonstrate the abilities of MLC, we evaluated humans and machines side by side on the same tests of systematic generalization. Specifically, we used instruction-learning tasks in a pseudolanguage to examine human and machine learning of structured algebraic systems (details of the procedures are provided in the ‘Behavioural methods: few-shot learning task’ section of the Methods ). We also examined behaviour in response to highly ambiguous linguistic probes, designed to characterize human inductive biases and how these biases could either facilitate or hamper systematic generalization (see the ‘Behavioural methods: open-ended task’ section of the Methods ). Across these evaluations, MLC achieves (or even exceeds) human-level systematic generalization. MLC also produces human-like patterns of errors when human behaviour departs from purely algebraic reasoning, showing how neural networks are not only a capable but also a superior modelling tool for nuanced human compositional behaviour (see ‘Modelling results’). In a final set of simulations (see the ‘Machine learning benchmarks’ section of the Methods ), we show how MLC improves accuracy on popular benchmarks 11 , 16 for few-shot systematic generalization.

Behavioural results

First, we measured human systematic generalization, going beyond classic work that relied primarily on thought experiments to characterize human abilities 1 , 2 , 3 . Our experimental paradigm asks participants to process instructions in a pseudolanguage in order to generate abstract outputs (meanings), differing from artificial grammar learning 22 , statistical learning 23 and program learning 24 in that explicit or implicit judgments of grammaticality are not needed. Instead, the participants generate sequences of symbols in response to sequences of words, enabling computational systems to directly model the resulting data by building on the powerful sequence-to-sequence (seq2seq) toolkit from machine learning 25 , 26 . All experiments were run on Amazon Mechanical Turk, and detailed procedures are described in the ‘Behavioural methods: few-shot learning task’ and ‘Behavioural methods: open-ended task’ sections of the Methods . The complete set of human and machine responses is viewable online (Data availability).

Systematic generalization was evaluated through a few-shot learning paradigm. As illustrated in Fig. 2 , the participants ( n  = 25) were provided with a curriculum of 14 study instructions (input/output pairs) and asked to produce outputs for 10 query instructions (see the ‘Behavioural methods: few-shot learning task’ section of the Methods ). The study instructions were consistent with an underlying interpretation grammar, which derives outputs from inputs through a set of compositional rewrite rules (see the ‘Interpretation grammars’ section of the Methods ). To perform well, the participants must learn the meaning of words from just a few examples and generalize to more complex instructions. The participants were able to produce output sequences that exactly matched the algebraic standard in 80.7% of cases (indicated by an asterisk in Fig. 2b (i)). Chance performance is 2.8% for two-length output sequences if the length is known, and exponentially less for longer sequences. Notably, participants also generalized correctly in 72.5% of cases to longer output sequences than seen during training (an example is shown as the last instruction in Fig. 2b (i)), which is a type of generalization that neural networks often struggle with 11 . When deviating from this algebraic standard, the responses were still highly non-random and suggestive of strong inductive biases. Many errors involved ‘one-to-one’ translations that mapped each input word to exactly one output symbol, as if all words were primitives rather than functions (24.4% of all errors; marked with 1-to-1 in Fig. 2b (i)). Other errors involved applying a function but mixing up its arguments, often in ways that suggest an ‘iconic concatenation’ bias for maintaining the order of the input words in the order of the output symbols (23.3% of all errors involving function 3 followed this pattern; marked with IC in Fig. 2b (i)). These response patterns can be compared to biases in language acquisition more generally; indeed, forms of one-to-one 27 and iconic concatenation 28 , 29 are widely attested in natural language.

a , b , Based on the study instructions ( a ; headings were not provided to the participants), humans and MLC executed query instructions ( b ; 4 of 10 shown). The four most frequent responses are shown, marked in parentheses with response rates (counts for people and the percentage of samples for MLC). The superscript notes indicate the algebraic answer (asterisks), a one-to-one error (1-to-1) or an iconic concatenation error (IC). The words and colours were randomized for each participant and a canonical assignment is therefore shown here. A black circle indicates a colour that was unused in the study set.

These inductive biases were evaluated more directly through an open-ended instruction task in which the participants were not influenced by study examples and, therefore, their a priori preferences are more likely to shine through. Different human participants ( n  = 29) were asked to make plausible guesses regarding the outputs of seven unknown instructions and how they relate to one another (responding to ‘fep fep’ or ‘fep wif’ with a series of coloured circles), without seeing any input/output examples to influence their responses (see Fig. 3 for the full task and the ‘Behavioural methods: open-ended task’ section of the Methods for details). Despite the unconstrained nature of the test, people’s responses were highly structured and confirm the previous two inductive biases. People’s responses also followed a third bias related to mutual exclusivity that encourages assigning unique meanings to unique words 27 . Reflecting the strong influence of the biases, the majority of participants (17 out of 29; 58.6%) responded with a pattern analogous to that in Fig. 3a,b (left), which is perfectly consistent with all three inductive biases. Across all responses, 18 out of 29 participants followed one-to-one (62.1%), 23 out of 29 (79.3%) followed iconic concatenation and all but two followed mutual exclusivity in producing a unique response to each instruction (27 out of 29; 93.1%).

a , b , The participants produced responses (sequences of coloured circles) to the queries (linguistic strings) without seeing any study examples. Each column shows a different word assignment and a different response, either from a different participant ( a ) or MLC sample ( b ). The leftmost pattern (in both a and b ) was the most common output for both people and MLC, translating the queries in a one-to-one (1-to-1) and left-to-right manner consistent with iconic concatenation (IC). The rightmost patterns (in both a and b ) are less clearly structured but still generate a unique meaning for each instruction (mutual exclusivity (ME)).

Modelling results

We next evaluated MLC on its ability to produce human-level systematic generalization and human-like patterns of error on these challenging generalization tasks. A successful model must learn and use words in systematic ways from just a few examples, and prefer hypotheses that capture structured input/output relationships. MLC aims to guide a neural network to parameter values that, when faced with an unknown task, support exactly these kinds of generalizations and overcome previous limitations for systematicity. Importantly, this approach seeks to model adult compositional skills but not the process by which adults acquire those skills, which is an issue that is considered further in the general discussion. MLC source code and pretrained models are available online (Code availability).

As shown in Fig. 4 and detailed in the ‘Architecture and optimizer’ section of the Methods , MLC uses the standard transformer architecture 26 for memory-based meta-learning. MLC optimizes the transformer for responding to a novel instruction (query input) given a set of input/output pairs (study examples; also known as support examples 21 ), all of which are concatenated and passed together as the input. This amounts to meta-learning because optimization occurs over dynamically changing episodes (each with new study and query examples) rather than a static dataset; specifically, each episode constitutes a different seq2seq task 30 , 31 defined through a randomly generated latent grammar for interpreting inputs as outputs (see the ‘Meta-training procedures for MLC and MLC variants’ section of the Methods ). To succeed, the transformer must find parameter values that are capable of extracting meanings from the study words and composing them to answer queries, relying on meta-learning but also innovations in the transformer architecture that were not envisioned in Fodor and Pylyshyn’s arguments 1 (for example, variable length input, parameter sharing and self-attention). On test episodes, the model weights are frozen and no task-specific parameters are provided 32 . Finally, given the end goal of modelling human responses (including errors), we stochastically pair each query with either the algebraic output sequence (generated through the episode’s grammar) or a heuristic output sequence (sampled through one-to-one translations or misapplied rules), at approximately the same ratios as observed empirically (see the ‘Meta-training procedures for MLC and MLC variants’ section of the Methods ).

A standard transformer encoder (bottom) processes the query input along with a set of study examples (input/output pairs; examples are delimited by a vertical line ( ∣ ) token). The standard decoder (top) receives the encoder’s messages and produces an output sequence in response. After optimization on episodes generated from various grammars, the transformer performs novel tasks using frozen weights. Each box is an embedding (vector); input embeddings are light blue (latent are dark).

MLC is capable of optimizing models for highly systematic behaviour. The most systematic run produced a transformer that was perfectly systematic (100% exact match accuracy) when choosing the best responses on the same few-shot instruction-learning task given to people (Fig. 2 ; see the ‘Evaluation procedures’ section of the Methods for details and Supplementary Information  1 for model variability across 10 runs) and additionally capable of inferring novel rules that did not participate in meta-learning (Supplementary Information 1 ). An informal analysis of this run further shows that MLC is also capable of more subtle and bias-driven behaviours; when sampling from the distribution of model outputs (Fig. 2b ), the transformer produced systematic outputs at an average rate (82.4%) close to human performance (80.7%), and appropriately handled longer output sequences at a rate (77.8%) near human levels (72.5%). Moreover, like people, the MLC transformer made errors reflecting one-to-one translations (56.3% of errors; 24.4% for people) and iconic concatenations (13.8% of errors involving function 3; 23.3% for people). MLC can also predict which instructions are easier or harder for people on average (Pearson’s r  = 0.788, P  = 0.031, two-tailed permutation test, n  = 10 items; item-level performance is shown in Extended Data Fig. 1 ). Formally, in Table 1 (few-shot learning), we compare models through the log-likelihood of all the human responses (Fig. 2b (i)) given the model predictions 33 . In the rest of this paragraph, when we say that one model outperforms another, there is a difference of 8 natural log points or greater. The MLC transformer (Table 1 ; MLC) outperforms more rigidly systematic models at predicting human behaviour. This includes a probabilistic symbolic model that assumes that people infer the gold grammar but make occasional arbitrary lapses (symbolic (oracle); details of all of the symbolic and basic seq2seq models are provided in the ‘Alternative neural and symbolic models’ section of the Methods ) and a transformer optimized on the same training episodes as MLC although with strictly algebraic (rather than also bias-based) output responses (MLC (algebraic only); details of all of the MLC variants are provided in the ‘Meta-training procedures for MLC and MLC variants’ section of the Methods ). MLC also outperforms a basic seq2seq transformer fit to the patterns in Fig. 2 without meta-learning and an MLC model optimized for copying rather than systematic generalization (MLC (copy only); during training, the query examples always match one of the study examples). The MLC transformer performs comparably to a probabilistic symbolic model that assumes that people infer the gold grammar but respond stochastically with lapses based on the human inductive biases (symbolic (oracle/biases)). Indeed, MLC was similarly optimized to (implicitly) infer systematic rules and respond with the same biased-based patterns, and it is therefore natural that the two models would perform similarly. The top-performing MLC (joint) was jointly optimized on both the few-shot learning task and the open-ended human responses, as described in the next paragraph.

Although human few-shot learning behaviour can be well characterized by either MLC or a probabilistic symbolic model, a test of more open-ended behaviour emphasizes MLC’s relative strengths. The same transformer architecture was optimized on open-ended participant behaviour and then asked to fill in outputs for the seven instructions one by one (Fig. 3 ; see the ‘Evaluation procedures’ section of the Methods ). The MLC transformer responded exactly like the modal human participant in 65.0% of samples (Fig. 3b (left)), perfectly instantiating the three key inductive biases. An informal analysis further revealed that MLC captured more nuanced patterns of response that only partially use the inductive biases (Fig. 3b (right)). Across all model samples, 66.0% followed one-to-one (62.1% for people), 85.0% followed iconic concatenation (79.3% for people) and the vast majority (99.0%) chose a unique response for each unique command (93.1% for people). Model predictions were also evaluated through fivefold cross-validation 33 : MLC and other models were optimized on responses for either 23 or 24 participants (depending on the cross-validation split) and then predicted responses for held-out participants. Performance was scored by log-likelihood and is summarized in Table 1 (open-ended) (summed over five cross-validation splits, averaged over three runs). In the rest of this paragraph, when we say that one model outperforms another, there is a difference of 57 natural log points or greater. MLC outperforms all alternatives, including the same highly algebraic MLC model as described in the previous experiment (MLC (algebraic only)) and a probabilistic symbolic model that uses the three inductive biases to generate responses but, in contrast to MLC, is not capable of optimizing for other patterns in the human behaviour (Table 1 ; symbolic (oracle/biases)). Importantly, a single transformer can be optimized for both the few-shot learning and open-ended instruction tasks (MLC (joint)); in fact, this is the strongest overall model across experiments for predicting human behaviour (additional analysis is shown in Extended Data Fig. 5 and Supplementary Information 1 ).

Machine learning benchmarks

Beyond predicting human behaviour, MLC can achieve error rates of less than 1% on machine learning benchmarks for systematic generalization. Note that here the examples used for optimization were generated by the benchmark designers through algebraic rules, and there is therefore no direct imitation of human behavioural data. We experiment with two popular benchmarks, SCAN 11 and COGS 16 , focusing on their systematic lexical generalization tasks that probe the handling of new words and word combinations (as opposed to new sentence structures). MLC still used only standard transformer components but, to handle longer sequences, added modularity in how the study examples were processed, as described in the ‘Machine learning benchmarks’ section of the Methods . SCAN involves translating instructions (such as ‘walk twice’) into sequences of actions (‘WALK WALK’). In the ‘add jump’ split, the training set has just one example of how to ‘jump’ (mapping to ‘JUMP’) and the test set probes compositional uses of this verb (for example, ‘jump around right twice and walk thrice’), paralleling our human learning task (‘zup’ is the analogue of ‘jump’ in Fig. 2 ). COGS involves translating sentences (for example, ‘A balloon was drawn by Emma’) into logical forms that express their meanings (balloon( x 1 )  ∨  draw.theme( x 3 ,  x 1 )  ∨  draw.agent( x 3 , Emma)). COGS evaluates 21 different types of systematic generalization, with a majority examining one-shot learning of nouns and verbs. To encourage few-shot inference and composition of meaning, we rely on surface-level word-type permutations for both benchmarks, a simple variant of meta-learning that uses minimal structural knowledge, described in the ‘Machine learning benchmarks’ section of the Methods . These permutations induce changes in word meaning without expanding the benchmark’s vocabulary, to approximate the more naturalistic, continual introduction of new words (Fig. 1 ).

The benchmark error rates are summarized in Table 2 . On SCAN, MLC solves three systematic generalization splits with an error rate of 0.22% or lower (99.78% accuracy or above), including the already mentioned ‘add jump’ split and ‘around right’ and ‘opposite right’, which examine novel combinations of known words. On COGS, MLC achieves an error rate of 0.87% across the 18 types of lexical generalization. Without the benefit of meta-learning, basic seq2seq has error rates at least seven times as high across the benchmarks, despite using the same transformer architecture. However surface-level permutations were not enough for MLC to solve the structural generalization tasks in the benchmarks. MLC fails to handle longer output sequences (SCAN length split) as well as novel and more complex sentence structures (three types in COGS), with error rates at 100%. Such tasks require handling ‘productivity’ (page 33 of ref. 1 ), in ways that are largely distinct from systematicity. However, MLC did handle novel sentence structures in our few-shot instruction-learning task (77.8% correct on queries with both longer input and output sequences than seen during study; Fig. 2 ), suggesting that the right meta-training procedure can promote productivity—a challenge we leave to future work.

Over 35 years ago, when Fodor and Pylyshyn raised the issue of systematicity in neural networks 1 , today’s models 19 and their language skills were probably unimaginable. As a credit to Fodor and Pylyshyn’s prescience, the systematicity debate has endured. Systematicity continues to challenge models 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 and motivates new frameworks 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 . Preliminary experiments reported in Supplementary Information  3 suggest that systematicity is still a challenge, or at the very least an open question, even for recent large language models such as GPT-4. To resolve the debate, and to understand whether neural networks can capture human-like compositional skills, we must compare humans and machines side-by-side, as in this Article and other recent work 7 , 42 , 43 . In our experiments, we found that the most common human responses were algebraic and systematic in exactly the ways that Fodor and Pylyshyn 1 discuss. However, people also relied on inductive biases that sometimes support the algebraic solution and sometimes deviate from it; indeed, people are not purely algebraic machines 3 , 6 , 7 . We showed how MLC enables a standard neural network optimized for its compositional skills to mimic or exceed human systematic generalization in a side-by-side comparison. MLC shows much stronger systematicity than neural networks trained in standard ways, and shows more nuanced behaviour than pristine symbolic models. MLC also allows neural networks to tackle other existing challenges, including making systematic use of isolated primitives 11 , 16 and using mutual exclusivity to infer meanings 44 .

Our use of MLC for behavioural modelling relates to other approaches for reverse engineering human inductive biases. Bayesian approaches enable a modeller to evaluate different representational forms and parameter settings for capturing human behaviour, as specified through the model’s prior 45 . These priors can also be tuned with behavioural data through hierarchical Bayesian modelling 46 , although the resulting set-up can be restrictive. MLC shows how meta-learning can be used like hierarchical Bayesian models for reverse-engineering inductive biases (see ref. 47 for a formal connection), although with the aid of neural networks for greater expressive power. Our research adds to a growing literature, reviewed previously 48 , on using meta-learning for understanding human 49 , 50 , 51 or human-like behaviour 52 , 53 , 54 . In our experiments, only MLC closely reproduced human behaviour with respect to both systematicity and biases, with the MLC (joint) model best navigating the trade-off between these two blueprints of human linguistic behaviour. Furthermore, MLC derives its abilities through meta-learning, where both systematic generalization and the human biases are not inherent properties of the neural network architecture but, instead, are induced from data.

Despite its successes, MLC does not solve every challenge raised in Fodor and Pylyshyn 1 . MLC does not automatically handle unpractised forms of generalization or concepts outside the meta-learning distribution, reducing the scope of entirely novel structures it can correctly process (compare the encouraging results on learning novel rules reported in Supplementary Information  1 , with its failure on the SCAN and COGS productivity splits). Moreover, MLC is failing to generalize to nuances in inductive biases that it was not optimized for, as we explore further through an additional behavioural and modelling experiment in Supplementary Information 2 . In the language of machine learning, we conclude that the meta-learning strategy succeeds when generalization makes a new episode in-distribution with respect to the training episodes, even when the specific test items are out-of-distribution with respect to the study examples in the episode. However, meta-learning alone will not allow a standard network to generalize to episodes that are in turn out-of-distribution with respect to the ones presented during meta-learning. The current architecture also lacks a mechanism for emitting new symbols 2 , although new symbols introduced through the study examples could be emitted through an additional pointer mechanism 55 . Last, MLC is untested on the full complexity of natural language and on other modalities; therefore, whether it can achieve human-like systematicity, in all respects and from realistic training experience, remains to be determined. Nevertheless, our use of standard transformers will aid MLC in tackling a wider range of problems at scale. For example, a large language model could receive specialized meta-training 56 , optimizing its compositional skills by alternating between standard training (next word prediction) and MLC meta-training that continually introduces novel words and explicitly improve systematicity (Fig. 1 ). For vision problems, an image classifier or generator could similarly receive specialized meta-training (through current prompt-based procedures 57 ) to learn how to systematically combine object features or multiple objects with relations.

Our study raises natural developmental questions. The specific procedure of optimizing over many related grammar-based tasks is not developmentally plausible, but there are several ways in which the greater principle—that systematicity can be honed through incentive and practice—has developmental merit. First, children are not born with an adult-like ability to compose functions; in fact, there seem to be important changes between infancy 58 and pre-school 59 that could be tied to learning. Second, children become better word learners over the course of development 60 , similar to a meta-learner improving with training. It is possible that children use experience, like in MLC, to hone their skills for learning new words and systematically combining them with familiar words. Beyond natural language, people require a years-long process of education to master other forms of systematic generalization and symbolic reasoning 6 , 7 , including mathematics, logic and computer programming. Although applying the tools developed here to each domain is a long-term effort, we see genuine promise in meta-learning for understanding the origin of human compositional skills, as well as making the behaviour of modern AI systems more human-like.

Behavioural methods: few-shot learning task

The meaning of each word in the few-shot learning task (Fig. 2 ) is described as follows (see the ‘Interpretation grammars’ section for formal definitions, and note that the mapping of words to meanings was varied across participants). The four primitive words are direct mappings from one input word to one output symbol (for example, ‘dax’ is RED, ‘wif’ is GREEN, ‘lug’ is BLUE). Each output symbol is a circle of a particular colour. The other three words are functional terms that take arguments. Function 1 (‘fep’ in Fig. 2 ) takes the preceding primitive as an argument and repeats its output three times (‘dax fep’ is RED RED RED). Function 2 (‘blicket’) takes both the preceding primitive and following primitive as arguments, producing their outputs in a specific alternating sequence (‘wif blicket dax’ is GREEN RED GREEN). Last, function 3 (‘kiki’) takes both the preceding and following strings as input, processes them and concatenates their outputs in reverse order (‘dax kiki lug’ is BLUE RED). We also tested function 3 in cases in which its arguments were generated by the other functions, exploring function composition (‘wif blicket dax kiki lug’ is BLUE GREEN RED GREEN). During the study phase (see description below), participants saw examples that disambiguated the order of function application for the tested compositions (function 3 takes scope over the other functions).

Thirty participants in the United States were recruited using Amazon Mechanical Turk and the psiTurk platform 61 . All of the studies were approved by the NYU IRB, protocol FY2018-1728, and obtained informed consent. The participants were informed that the study investigated how people learn input–output associations, and that they would be asked to learn a set of commands and their corresponding outputs. Learning proceeded in a curriculum with four stages, with each stage featuring both a study phase and a test phase (see Extended Data Fig. 1 for the complete set of study and test instructions). In the first three stages, during the study phase, the participants learned individual functions from just two demonstrations each (functions 1 through 3; Fig. 2a ). In the final stage, participants learned to interpret complex instructions by combining these functions (function compositions; Fig. 2a ). After all stages, there was a short survey that asked about strategy and any technical problems. Participants spent an average of 23 min in the experiment (minimum 8 min and 41 s; maximum 41 min and 19 s).

Each study phase presented the participants with a set of example input–output mappings. For the first three stages, the study instructions always included the four primitives and two examples of the relevant function, presented together on the screen. For the last stage, the entire set of study instructions was provided together to probe composition. During the study phases, the output sequence for one of the study items was covered and the participants were asked to reproduce it, given their memory and the other items on the screen. Corrective feedback was provided, and the participants cycled through all non-primitive study items until all were produced correctly or three cycles were completed. The test phase asked participants to produce the outputs for novel instructions, with no feedback provided (Extended Data Fig. 1b ). The study items remained on the screen for reference, so that performance would reflect generalization in the absence of memory limitations. The study and test items always differed from one another by more than one primitive substitution (except in the function 1 stage, where a single primitive was presented as a novel argument to function 1). Some test items also required reasoning beyond substituting variables and, in particular, understanding longer compositions of functions than were seen in the study phase.

The response interface had a pool of possible output symbols that could be clicked or dragged to the response array. The circles could be rearranged within the array or cleared with a reset button. The study and test set only used four output symbols, but the pool provided six possibilities (that is, there were two extra colours that were not associated to words), to discourage reasoning by exclusion. The assignment of words to colours and functions was randomized for each participant (drawn from nine possible words and six colours), and the first three stages were presented in random order.

We used several strategies to ensure that our participants were paying attention. First, before the experiment, the participants practiced using the response interface and had to pass an instructions quiz; they cycled through the quiz until they passed it. Second, catch trials were included during the test phases, probing the study items rather than new items, with the answers clearly presented on the screen above. There was one catch trial per stage (except the last stage had two); participants were excluded if they missed two or more catch trials ( n  = 5). Finally, query responses were also excluded if the corresponding study phases were not completed correctly (for all items) within three attempts (13% of remaining data).

For statistical analyses of the data from this experiment and elsewhere, we tested for data normalcy and applied alternative nonparametric or permutation tests when the assumptions were not met.

Interpretation grammars

The few-shot learning task evaluated with humans and machines is defined through a set of compositional rewrite rules for translating linguistic instructions to output sequences (Extended Data Fig. 2 ). Inspired by formal semantics 62 , we denote a set of rules such as this as the ‘interpretation grammar’. We refer to the grammar in Extended Data Fig. 2 that defines the human learning task as the ‘gold interpretation grammar’, whereas a different interpretation grammar is shown in Extended Data Fig. 4 . The rules apply one by one, based on their conditions, until they produce an output sequence consisting of all terminal symbols (coloured circles). A worked example of interpreting a complex query is shown in Extended Data Fig. 3 . Four of the rules define how the primitive words (such as ‘dax’, ‘wif’) map to a single output symbol. The other rules define functions (‘fep’, ‘blicket’ and ‘kiki’) that apply when certain conditions are met through their arguments and, when applied, initiate recursive calls of the interpretation process on their intermediate outputs. Note that a different set of rules will define a different few-shot learning problem; this property is used to define many different few-shot learning problems for optimizing MLC. Although the situation does not arise for the study or query instructions in the few-shot task (see the ‘Behavioural methods: few-shot learning task’ section), it is possible that two rules satisfy their conditions at the same intermediate step. If so, the first rule in the interpretation grammar listing is used in order to resolve the ambiguity.

Behavioural methods: open-ended task

The instructions were as similar as possible to the few-shot learning task, although there were several important differences. First, because this experiment was designed to probe inductive biases and does not provide any examples to learn from, it was emphasized to the participants that there are multiple reasonable answers and they should provide a reasonable guess. Second, the participants responded to the query instructions all at once, on a single web page, allowing the participants to edit, go back and forth, and maintain consistency across responses. By contrast, the previous experiment collected the query responses one by one and had a curriculum of multiple distinct stages of learning.

Thirty participants in the United States were recruited using Mechanical Turk and psiTurk. The participants produced output sequences for seven novel instructions consisting of five possible words. The participants also approved a summary view of all of their responses before submitting. There were six pool options, and the assignment of words and item order were random. One participant was excluded because they reported using an external aid in a post-test survey. On average, the participants spent 5 min 5 s in the experiment (minimum 2 min 16 s; maximum 11 min 23 s).

Implementation of MLC

Architecture and optimizer.

As shown in Fig. 4 , our MLC implementation uses a standard seq2seq transformer 26 . This architecture involves two neural networks working together—an encoder transformer to process the query input and study examples, and a decoder transformer to generate the output sequence. Both the encoder and decoder have 3 layers, 8 attention heads per layer, input and hidden embeddings of size 128, and a feedforward hidden size of 512. Following GPT 63 , GELU 64 activation functions are used instead of ReLU. In total, the architecture has about 1.4 million parameters. Note that an earlier version of memory-based meta-learning for compositional generalization used a more limited and specialized architecture 30 , 65 .

The encoder network (Fig. 4 (bottom)) processes a concatenated source string that combines the query input sequence along with a set of study examples (input/output sequence pairs). The encoder vocabulary includes the eight words, six abstract outputs (coloured circles), and two special symbols for separating the study examples ( ∣ and →). The decoder network (Fig. 4 (top)) receives messages from the encoder and generates the output sequence. The decoder vocabulary includes the abstract outputs as well as special symbols for starting and ending sequences (<SOS> and <EOS>, respectively). Sinusoidal positional encodings are added to the input embeddings 26 .

MLC was trained to minimize the cross-entropy loss (averaged over tokens) with the Adam optimizer and a batch size of 25 episodes. Each episode contains many study examples and query examples (for example, up to 14 study examples and 10 queries in optimization for the few-shot learning task) and the effective sequence-level batch size was therefore larger (for example, (14 + 10)25 = 600). Training lasted for 50 epochs. The learning rate was 0.001, with a warm-up applied for the first epoch and then a linear decrease to 0.00005 across training. Dropout of 0.1 was applied to the input embeddings and transformers. For meta-training procedures with a validation set (for example, 200 held-out grammars for few-shot instruction learning), a variant of early stopping was used: although training was not actually truncated, the best parameter setting (across intervals of 100 steps) was saved according to the validation loss. All of the networks were trained using a NVIDIA Titan RTX GPU.

Meta-training procedures for MLC and MLC variants

MLC optimizes the transformers for systematic generalization through high-level behavioural guidance and/or direct human behavioural examples. To prepare MLC for the few-shot instruction task, optimization proceeds over a fixed set of 100,000 training episodes and 200 validation episodes. Extended Data Figure 4 illustrates an example training episode and additionally specifies how each MLC variant differs in terms of access to episode information (see right hand side of figure). Each episode constitutes a seq2seq task that is defined through a randomly generated interpretation grammar (see the ‘Interpretation grammars’ section). The grammars are not observed by the networks and must be inferred (implicitly) to successfully solve few-shot learning problems and make algebraic generalizations. The optimization procedures for the MLC variants in Table 1 are described below.

MLC (algebraic only). The interpretation grammars that define each episode were randomly generated from a simple meta-grammar. An example episode with input/output examples and corresponding interpretation grammar (see the ‘Interpretation grammars’ section) is shown in Extended Data Fig. 4 . Rewrite rules for primitives (first 4 rules in Extended Data Fig. 4 ) were generated by randomly pairing individual input and output symbols (without replacement). Rewrite rules for defining functions (next 3 rules in Extended Data Fig. 4 ) were generated by sampling the left-hand sides and right-hand sides for those rules. For the left-hand side (for example, ⟦ u 1  lug  x 1 ⟧ for the fifth rule in Extended Data Fig. 4 ), rules chose an input symbol as function name, whether the function has one or two arguments (with the function name appearing after the argument or in-between arguments, respectively), and whether each argument can take arbitrary non-empty strings ( x 1 or x 2 ) or just the primitive inputs ( u 1 or u 2 ). A rule’s right-hand side was generated as an arbitrary string (length ≤ 8) that mixes and matches the left-hand-side arguments, each of which are recursively evaluated and then concatenated together (for example, ⟦ x 1 ⟧   ⟦ u 1 ⟧   ⟦ x 1 ⟧   ⟦ u 1 ⟧   ⟦ u 1 ⟧ ). The last rule was the same for each episode and instantiated a form of iconic left-to-right concatenation (Extended Data Fig. 4 ). Study and query examples (set 1 and 2 in Extended Data Fig. 4 ) were produced by sampling arbitrary, unique input sequences (length ≤ 8) that can be parsed with the interpretation grammar to produce outputs (length ≤ 8). Output symbols were replaced uniformly at random with a small probability (0.01) to encourage some robustness in the trained decoder. For this variant of MLC training, episodes consisted of a latent grammar based on 4 rules for defining primitives and 3 rules defining functions, 8 possible input symbols, 6 possible output symbols, 14 study examples and 10 query examples. The study examples were presented in shuffled order on each episode.

The validation episodes were defined by new grammars that differ from the training grammars. Grammars were only considered new if they did not match any of the meta-training grammars, even under permutations of how the rules are ordered. The gold interpretation grammar that produced the few-shot instruction-learning task with humans and machines (Extended Data Fig. 2 ) was also reserved for testing in this way, with an additional structural requirement that grammars for producing the training and validation episodes should also not match the gold grammar through any permutation of the input and output symbol assignments.

For successful optimization, it is also important to pass each study example (input sequence only) as an additional query when training on a particular episode. This effectively introduces an auxiliary copy task—matching the query input sequence to an identical study input sequence, and then reproducing the corresponding study output sequence—that must be solved jointly with the more difficult generalization task.

MLC for the few-shot instruction-learning task. Optimization closely followed the procedure outlined above for the algebraic-only MLC variant. The key difference here is that full MLC model used a behaviourally informed meta-learning strategy aimed at capturing both human successes and patterns of error. Using the same meta-training episodes as the purely algebraic variant, each query example was passed through a bias-based transformation process (see Extended Data Fig. 4 for pseudocode) before MLC processed it during meta-training. Specifically, each query was paired with its algebraic output in 80% of cases and a bias-based heuristic in the other 20% of cases (chosen to approximately reflect the measured human accuracy of 80.7%). To create the heuristic query for meta-training, a fair coin was flipped to decide between a stochastic one-to-one translation and a noisy application of the underlying grammatical rules. For the one-to-one translations, first, the study examples in the episode are examined for any instances of isolated primitive mappings (for example, ‘tufa → PURPLE’). Second, each input symbol is mapped superficially to a single output symbol (in a left-to-right manner) using either the corresponding primitive mapping if observed as a study example, or using an arbitrary output symbol if a primitive mapping is not observed (for example, if the input symbol is a function name). For the noisy rule examples, each two-argument function in the interpretation grammar has a 50% chance of flipping the role of its two arguments. For example, as in Extended Data Fig. 4 , the rule ⟦ u 1  lug  x 1 ⟧  →  ⟦ x 1 ⟧   ⟦ u 1 ⟧   ⟦ x 1 ⟧   ⟦ u 1 ⟧   ⟦ u 1 ⟧ , when flipped, would be applied as ⟦ u 1  lug  x 1 ⟧  →  ⟦ u 1 ⟧   ⟦ x 1 ⟧   ⟦ u 1 ⟧   ⟦ x 1 ⟧   ⟦ x 1 ⟧ .

MLC for the open-ended task. An epoch of optimization consisted of 100,000 episode presentations based on the human behavioural data. To produce one episode, one human participant was randomly selected from the open-ended task, and their output responses were divided arbitrarily into study examples (between 0 and 5), with the remaining responses as query examples. Additional variety was produced by shuffling the order of the study examples, as well as randomly remapping the input and output symbols compared to those in the raw data, without altering the structure of the underlying mapping. The models were trained to completion (no validation set or early stopping).

MLC (joint). Optimization for the joint MLC model, tuned jointly for the few-shot instruction and open-ended tasks, proceeded as described in the two paragraphs above; each epoch combined 100,000 episodes of the few-shot instruction learning optimization and 100,000 episodes of the open-ended optimization. Finally, each epoch also included an additional 100,000 episodes as a unifying bridge between the two types of optimization. These bridge episodes revisit the same 100,000 few-shot instruction learning episodes, although with a smaller number of the study examples provided (sampled uniformly from 0 to 14). Thus, for episodes with a small number of study examples chosen (0 to 5, that is, the same range as in the open-ended trials), the model cannot definitively judge the episode type on the basis of the number of study examples. The models were trained to completion (no validation set or early stopping).

MLC (copy only). Optimization for the copy-only model closely followed the procedure for the algebraic-only variant. Critically, this model was trained only on the copy task of identifying which study example is the same as the query example, and then reproducing that study example’s output sequence (see specification in Extended Data Fig. 4 ; set 1 was used for both study and query examples). It was not trained to handle novel queries that generalize beyond the study set. Thus, the model was trained on the same study examples as MLC, using the same architecture and procedure, but it was not explicitly optimized for compositional generalization.

Evaluation procedures

Few-shot instruction-learning task. MLC was evaluated on this task in several ways; in each case, MLC responded to this novel task through learned memory-based strategies, as its weights were frozen and not updated further. MLC predicted the best response for each query using greedy decoding, which was compared to the algebraic responses prescribed by the gold interpretation grammar (Extended Data Fig. 2 ). MLC also predicted a distribution of possible responses; this distribution was evaluated by scoring the log-likelihood of human responses and by comparing samples to human responses. Although the few-shot task was illustrated with a canonical assignment of words and colours (Fig. 2 ), the assignments of words and colours were randomized for each human participant. Thus, to evaluate MLC comparably, these factors were also randomized. For comparison with the gold grammar or with human behaviour via log-likelihood, performance was averaged over 100 random word/colour assignments. Samples from the model (for example, as shown in Fig. 2 and reported in Extended Data Fig. 1 ) were based on an arbitrary random assignment that varied for each query instruction, with the number of samples scaled to 10× the number of human participants.

Open-ended task. MLC was evaluated on sampling human-like responses and predicting human responses through log-likelihood scores. Human participants made plausible guesses for how to respond to 7 query instructions (see the ‘Behavioural methods: open-ended task’ section). They responded jointly to all 7 queries on the same web page; as analysed in the main text, people’s predicted word meanings followed strong consistency constraints across the responses. Thus, to model these data, MLC cannot simply answer the queries independently. Instead, MLC factorizes the problem of responding jointly to 7 query inputs x 1 , …,  x 7 with 7 query outputs y 1 , …,  y 7 as

using ( x 1 ,  y 1 ), …, ( x i −1 ,  y i −1 ) as study examples for responding to query x i with output y i . Thus, sampling a response for the open-ended task proceeded as follows. First, MLC samples P ( y 1 ∣ x 1 ) with no study examples. Second, when sampling y 2 in response to query x 2 , the previously sampled ( x 1 ,  y 1 ) is now a study example, and so on. The query ordering was chosen arbitrarily (this was also randomized for human participants).

For scoring a particular human response y 1 , …,  y 7 by log-likelihood, MLC uses the same factorization as in equation ( 1 ). Performance was averaged over 200 passes through the dataset, each episode with different random query orderings as well as word and colour assignments.

Alternative neural and symbolic models

In addition to the range of MLC variants specified above, the following additional neural and symbolic models were evaluated.

Lapse model. All MLC, symbolic and neural models were fit to the human behavioural responses (Table 1 ) with a lapse parameter λ . With this parameter, the probability of a participant producing any given output symbol s   ∈   S is \(P(s)=(1-\lambda ){P}_{M}(s)+\lambda \frac{1}{| S| }\) , where S (with cardinality ∣ S ∣ ) is the set of abstract outputs (colour circles) plus the end-of-sequence token ( ) and P M is the model prediction before the lapse mechanism. If the model has no prediction for a particular symbol (for example, this symbol extends beyond the model’s predicted output sequence), \(P(s)=\frac{1}{| S| }\) .

Symbolic (oracle). This probabilistic symbolic model assumes that people can infer the gold grammar from the study examples (Extended Data Fig. 2 ) and translate query instructions accordingly. Non-algebraic responses must be explained through the generic lapse model (see above), with a fit lapse parameter. Note that all of the models compared in Table 1 have the same opportunity to fit a lapse parameter.

Symbolic (oracle/biases). For the few-shot instruction-learning task, this probabilistic symbolic model augments the oracle, described above, by passing the algebraic input/output pairs through the same bias-based transformation process used when optimizing MLC (see pseudocode in Extended Data Fig. 4 and see the ‘MLC few-shot instruction-learning task’ section for more description). Thus, using the gold grammar in Extended Data Fig. 2 , this model predicts a mixture of algebraic outputs, one-to-one translations and noisy rule applications to account for human behaviour.

For the open-ended task, this probabilistic symbolic model operationalizes the three key inductive biases. Using the same factorization as MLC does for the open-ended task (equation ( 1 )), the response sequence y i to query sequence x i is modelled based on previous participant responses, P ( y i ∣ x i ,  x < i ,  y < i ). Each input token within the sequence x i is stochastically translated as a single output token in y i using a left-to-right (iconic concatenation), one-to-one strategy. For example, if x i is ‘dax wug’, a coloured circle for ‘dax’ is sampled in proportion to the number of times ‘dax’ aligned with each coloured circle in the previous x < i and y < i pairs. After handling ‘dax’, a coloured circle for ‘wug’ is sampled in the same manner. If a word is new (and does not appear previously in x < i ), its coloured circle is sampled from the set of unused output symbols (that do not appear in y < i ), implementing mutual exclusivity. As with all models, a fit lapse parameter is also used.

Neural (basic seq2seq). A basic seq2seq transformer can be obtained through a straightforward modification of the MLC diagram (Fig. 4 ): the study examples were excluded from the input sequence, leaving the transformer to process only the query input before producing the query output. Given that only the architecture’s use has changed (not the architecture itself), the model has approximately the same number of learnable parameters as in MLC (except for the smaller input vocabulary). Without access to study examples, the model is poorly equipped for learning words with changing meanings; it has no in-context memory and, therefore, all of its knowledge must be stored in the learned weights. To perform the few-shot instruction-learning task, the basic seq2seq model was trained in the typical way for seq2seq modelling: training iterates over the input/output sequence pairs with the aim of learning the target mapping. In this case, the training set is the 14 study instructions and the test set is the 10 query instructions (Extended Data Fig. 1 ). Otherwise, the same architecture and optimizer was used as described in the ‘Architecture and optimizer’ section. The network was trained for 1,000 epochs over the batched set of study instructions. It was not clear how much training would be optimal and we wanted to examine this model under favourable conditions. To this end, we gave it an additional advantage not offered to any other model class: we tracked each step of the optimizer and selected the best parameter values on the basis of the test loss. Typically, this point was reached within a few dozen steps. Nevertheless, all 10 runs failed to generalize systematically on the few-shot instruction task (0% exact-match accuracy).

We informally examined a couple of other basic seq2seq variants. First, we evaluated lower-capacity transformers but found that they did not perform better. Second, we tried pretraining the basic seq2seq model on the entire meta-training set that MLC had access to, including the study examples, although without the in-context information to track the changing meanings. Then model was then fine-tuned as described above. On the few-shot instruction task, this improves the test loss marginally, but not accuracy.

Handling long in-context sequences

The tasks from the machine-learning literature that we experimented with, SCAN 11 , 66 and COGS 16 , feature long sequences as (in-context) study examples. This raises issues for the previous architecture (see the ‘Architecture and optimizer’ section). Specifically, it is intractable to process a single source sequence that consists of the concatenated query input sequence and multiple study example sequences, which could have a worst-case source sequence of length S  ≈ 1,500 on COGS and potentially longer in other applications (for each individual study example, the maximum length in SCAN is 9 for inputs and 49 for outputs; the maximum length in COGS is 22 for inputs and 154 for outputs). The bottlenecks are the encoder self-attention layers, which are \({\mathcal{O}}({S}^{2})\) . A more scalable procedure for applying a standard transformer (Extended Data Fig. 6 ) was therefore developed for optimizing MLC on machine learning benchmarks. We copy each query input sequence m times and concatenate the copies separately with each of the m study examples. This creates m smaller source sequences to be processed separately by the standard transformer encoder. Each of the resulting contextual embeddings are then marked according to their origin in one of the m study examples, which is done by adding an index embedding vector that enables the decoder to see which embedding came from which study example (one for each index 1, …,  m ). Finally, the set of contextual embeddings is passed to the standard transformer decoder. The decoder cross-attention layers are less expensive, \({\mathcal{O}}(ST)\) , because the target sequence length T , which does not include any study examples, is typically much shorter ( T   ≪   S ).

Optimization

For each SCAN split, both MLC and basic seq2seq models were optimized for 200 epochs without any early stopping. For COGS, both models were optimized for 300 epochs (also without early stopping), which is slightly more training than the extended amount prescribed in ref. 67 for their strong seq2seq baseline. The batch size was 200 episodes for SCAN and 40 episodes for COGS. This more scalable MLC variant, the original MLC architecture (see the ‘Architecture and optimizer’ section) and basic seq2seq all have approximately the same number of learnable parameters (except for the fact that basic seq2seq has a smaller input vocabulary).

Each SCAN episode contained 10 study examples and 2 query examples (COGS used 8 study and 2 query), such that one query example was a randomly chosen study example (as an auxiliary copy task; see the ‘Meta-training procedures for MLC and MLC variants’ section) and the other query was distinct from the study examples and required generalization. All of the query and study examples were drawn from the training corpus. Each episode was scrambled (with probability 0.95) using a simple word type permutation procedure 30 , 65 , and otherwise was not scrambled (with probability 0.05), meaning that the original training corpus text was used instead. Occasionally skipping the permutations in this way helps to break symmetries that can slow optimization; that is, the association between the input and output primitives is no longer perfectly balanced. Otherwise, all model and optimizer hyperparameters were as described in the ‘Architecture and optimizer’ section.

SCAN: meta-training and testing

During SCAN meta-training (an example episode is shown in Extended Data Fig. 7 ), each episode is formed by sampling a set of study and query examples from the training corpus of a particular SCAN split (‘add jump’, ‘around right’ and so on). Given these examples, a simple permutation procedure remaps the full set of output actions (‘JUMP’, ‘RUN’, ‘WALK’, ‘LOOK’, ‘TURN LEFT’, ‘TURN RIGHT’) through a random permutation of these same set of actions, and remaps the input primitives (‘jump’, ‘run’, ‘walk’, ‘look’, ‘left’, ‘right’) through another random permutation to the same set of words. Note that several other input words (the mostly ‘functional’ words ‘turn’, ‘twice’, ‘thrice’, ‘around’, ‘opposite’, ‘and’, ‘after’) have stable meanings that can be stored in the model weights. To make sense of an episode, MLC must become adept at inferring, from just a few study examples, how words map to meanings. MLC must also become adept at composition: it must systematically compose the inferred word meanings to correctly answer the queries.

During SCAN testing (an example episode is shown in Extended Data Fig. 7 ), MLC is evaluated on each query in the test corpus. For each query, 10 study examples are again sampled uniformly from the training corpus (using the test corpus for study examples would inadvertently leak test information). Neither the study nor query examples are remapped; in other words, the model is asked to infer the original meanings. Finally, for the ‘add jump’ split, one study example is fixed to be ‘jump → JUMP’, ensuring that MLC has access to the basic meaning before attempting compositional uses of ‘jump’.

COGS: meta-training and testing

The COGS output expressions were converted to uppercase to remove any incidental overlap between input and output token indices (which MLC, but not basic seq2seq, could exploit). As in SCAN meta-training, an episode of COGS meta-training involves sampling a set of study and query examples from the training corpus (see the example episode in Extended Data Fig. 8 ). The vocabulary in COGS is much larger than in SCAN; thus, the study examples cannot be sampled arbitrarily with any reasonable hope that they would inform the query of interest. Instead, for each vocabulary word that takes a permuted meaning in an episode, the meta-training procedure chooses one arbitrary study example that also uses that word, providing the network an opportunity to infer its meaning. Any remaining study examples needed to reach a total of 8 are sampled arbitrarily from the training corpus.

COGS is a multi-faceted benchmark that evaluates many forms of systematic generalization. To master the lexical generalization splits, the meta-training procedure targets several lexical classes that participate in particularly challenging compositional generalizations. As in SCAN, the main tool used for meta-learning is a surface-level token permutation that induces changing word meaning across episodes. These permutations are applied within several lexical classes; for examples, 406 input word types categorized as common nouns (‘baby’, ‘backpack’ and so on) are remapped to the same set of 406 types. The other remapped lexical classes include proper nouns (103 input word types; ‘Abigail’, ‘Addison’ and so on), dative verbs (22 input word types; ‘given’, ‘lended’ and so on) and verbs in their infinitive form (21 input word types; such as ‘walk’, ‘run’). Surface-level word type permutations are also applied to the same classes of output word types. Other verbs, punctuation and logical symbols have stable meanings that can be stored in the model weights. Importantly, although the broad classes are assumed and could plausibly arise through simple distributional learning 68 , 69 , the correspondence between input and output word types is unknown and not used.

In one case, COGS meta-learning goes beyond surface-level remapping to use a minimal amount of semantic structure. To guide the networks toward flexible substitution of common nouns with proper nouns, any common noun input token has an independent chance of replacement (probability 0.01) with an arbitrary proper noun input token, while also removing the preceding determiner token. Independently, any common noun output token can also be arbitrarily remapped (again with probability 0.01) to a proper noun output token, with the corresponding minimal change to the structural form to remove the determiner (if remapping the output token ‘cookie’ to ‘John’, the cookie( x i ) predicate is removed, occurrences of variable x i are replaced with ‘John’ and variables j  >  i are decremented by 1). As before, the correspondence between input and output tokens is unknown, both at the levels of a sentence and the whole dataset. Thus, during an episode of meta-training, a common noun (phrase) might correspond to a logical form expressing a proper noun or vice versa. At test, MLC must sort this out and recover how proper and common nouns work on the basis of the study examples.

During the COGS test (an example episode is shown in Extended Data Fig. 8 ), MLC is evaluated on each query in the test corpus. For each query, eight study examples are sampled from the training corpus, using the same procedure as above for picking study examples that facilitate word overlap (note that picking study examples from the generalization corpus would inadvertently leak test information). Neither the study nor query examples are remapped to probe how models infer the original meanings.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

Human behavioural data are available at Zenodo ( https://doi.org/10.5281/zenodo.8274609 ). The complete set of human and machine responses is also illustrated and viewable in HTML at the previous link. The human behavioural data also appeared in a previous non-archival conference paper 70 .

Code availability

MLC source code and pretrained models are available online 71 , including MLC models of human behaviour ( https://doi.org/10.5281/zenodo.8274609 ) and MLC models applied to machine learning benchmarks ( https://doi.org/10.5281/zenodo.8274617 ). Any additional code is available on request.

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Radford, A., Narasimhan, K. R., Salimans, T. & Sutskever, I. Improving language understanding by generative pre-training. Preprint at https://openai.com/research/language-unsupervised (2018).

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Mitchell, E., Finn, C. & Manning, C. Challenges of acquiring compositional inductive biases via meta-learning. In Proc. AAAI Workshop on Meta-Learning and MetaDL Challenge 138–148 (2021).

Loula, J., Baroni, M. & Lake, B. M. Rearranging the familiar: testing compositional generalization in recurrent networks. Preprint at http://arxiv.org/abs/1807.07545 (2018).

Csordás, R., Irie, K. & Schmidhuber, J. The devil is in the detail: simple tricks improve systematic generalization of transformers. In Proc. EMNLP 2021—2021 Conference on Empirical Methods in Natural Language Processing 619–634 (Association for Computational Linguistics, 2021).

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Lake, B. M. brendenlake/MLC: meta-learning for compositionality (v1.0.0). Zenodo https://doi.org/10.5281/zenodo.8274609 (2023).

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Acknowledgements

We thank T. Linzen for involvement in the design of the behavioural studies; Y. Boureau, T. Brochhagen, B. Karrer, T. Kwan, G. Murphy and J. Russin for feedback on earlier versions of this Article; the members of the NYU ConCats group, M. Frank, K. Gulordava, G. Kruszewski, R. Levy and A. Williams for suggestions; and N. Kim for guidance on using COGS.

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Contributions

B.M.L. and M.B. designed the research and edited the Article. B.M.L. collected and analysed the behavioural data, designed and implemented the models, and wrote the initial draft of the Article.

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Correspondence to Brenden M. Lake .

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Extended data figures and tables

Extended data fig. 1 few-shot instruction learning task with full set of queries..

Based on the study instructions (A; headings were not provided to participants), humans and the MLC model executed 10 query instructions by generating coloured circles from a fixed inventory (B; headings were not provided to participants). The percent of participants who produced each sequence exactly as prescribed algebraically is shown. Similarly, the percent of samples from MLC that match the prescribed sequence is shown in parentheses, which correlates with the human values (Pearson’s r  = 0.788, p  = 0.031 via permutation test, two-tailed, n  = 10 items). The words and colours were randomized for each participant.

Extended Data Fig. 2 The gold interpretation grammar that defines the human instruction learning task.

The double brackets ( ⟦ ⟧ ) denote the interpretation function for translating linguistic instructions into sequences of abstract outputs (colour circles). Each human participant received a different permutation of words and colours. Symbols x i and u i denote variables: x i applies to arbitrary non-empty strings, while u i applies only to ‘dax’, ‘wif’, ‘lug’, and ‘zup’.

Extended Data Fig. 3 Using the gold interpretation grammar for processing ‘zup blicket wif kiki dax fep’.

Each step is annotated with the next re-write rules to be applied, and how many times (e.g., 3 × , since some steps have multiple parallel applications). A rule’s condition is met if and only if it matches the entire string inside the brackets ( ⟦ ⟧ ); for instance, only the ‘kiki’ rule applies on the first step because its condition matches two arbitrary non-empty sequences on either side of ‘kiki,’ thus being able to encompass the entire input.

Extended Data Fig. 4 Example meta-learning episode and how it is processed by different MLC variants.

The interpretation grammar defines the episode but is not observed directly and must be inferred implicitly. Set 1 has 14 input/output examples consistent with the grammar, used as Study examples for all MLC variants. Set 2 has 10 examples, used as Query examples for most MLC variants (except copy only). Pseudocode for the bias-based transformation process is shown here for the instruction ‘tufa lug fep’. This transformation is applied to the query outputs before MLC and MLC (joint) process them. Here, flip ( p ) is a coin flip that returns True with probability p .

Extended Data Fig. 5 Human responses for the (A) few-shot learning task and (B) open-ended task that most favour MLC (joint) compared to a MLC model optimized for individual tasks only.

Panel (A) shows the average log-likelihood advantage for MLC (joint) across five patterns (that is, ll(MLC (joint)) - ll(MLC)), with the algebraic target shown here only as a reference. A black circle indicates a colour that was unused in the study set. Panel (B) shows three participant responses.

Extended Data Fig. 6 Handling long in-context sequences with a MLC transformer.

The query input sequence (shown as ‘jump twice after run twice’) is copied and concatenated to each of the m study examples, leading to m separate source sequences (3 shown here). A shared standard transformer encoder (bottom) processes each source sequence to produce latent (contextual) embeddings. The contextual embeddings are marked with the index of their study example, combined with a set union to form a single set of source messages, and passed to the decoder. The standard decoder (top) receives this message from the encoder, and then produces the output sequence for the query. Each box is an embedding (vector); input embeddings are light blue and latent embeddings are dark blue.

Extended Data Fig. 7 Example SCAN meta-training (top) and test (bottom) episodes for the ‘add jump’ split.

The word and action meanings are changing across the meta-training episodes (‘look’, ‘walk’, etc.) and must be inferred from the study examples. During the test episode, the meanings are fixed to the original SCAN forms. Here, the latter probes a compositional use of ‘jump’.

Extended Data Fig. 8 Example COGS meta-training (top) and test (bottom) episodes.

Word meanings are changing across the meta-training episodes (here, ‘driver’ means ‘PILLOW’, ‘shoebox’ means ‘SPEAKER’ etc.) and must be inferred from the study examples. The meanings are fixed to the original forms during the test episode. This test episode probes the understanding of ‘Paula’ (proper noun), which just occurs in one of COGS’s original training patterns.

Supplementary information

Supplementary information.

Supplementary 1–3 (additional modelling results, experiment probing additional nuances in inductive biases, and few-shot instruction learning with OpenAI models), Supplementary Figs. 1–7 and Supplementary References.

Reporting Summary

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

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Lake, B.M., Baroni, M. Human-like systematic generalization through a meta-learning neural network. Nature (2023). https://doi.org/10.1038/s41586-023-06668-3

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Published : 25 October 2023

DOI : https://doi.org/10.1038/s41586-023-06668-3

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What Are Effective Presentation Skills (and How to Improve Them)

Presentation skills are essential for your personal and professional life. Learn about effective presentations and how to boost your presenting techniques.

At least seven out of 10 Americans agree that presentation skills are essential for a successful career [ 1 ]. Although it might be tempting to think that these are skills reserved for people interested in public speaking roles, they're critical in a diverse range of jobs. For example, you might need to brief your supervisor on research results.

Presentation skills are also essential in other scenarios, including working with a team and explaining your thought process, walking clients through project ideas and timelines, and highlighting your strengths and achievements to your manager during performance reviews.

Whatever the scenario, you have very little time to capture your audience’s attention and get your point across when presenting information—about three seconds, according to research [ 2 ]. Effective presentation skills help you get your point across and connect with the people you’re communicating with, which is why nearly every employer requires them.

Understanding what presentation skills are is only half the battle. Honing your presenting techniques is essential for mastering presentations of all kinds and in all settings.

What are presentation skills?

Presentation skills are the abilities and qualities necessary for creating and delivering a compelling presentation that effectively communicates information and ideas. They encompass what you say, how you structure it, and the materials you include to support what you say, such as slides, videos, or images.

You'll make presentations at various times in your life. Examples include:

Making speeches at a wedding, conference, or another event

Making a toast at a dinner or event

Explaining projects to a team 

Delivering results and findings to management teams

Teaching people specific methods or information

Proposing a vote at community group meetings

Pitching a new idea or business to potential partners or investors

Why are presentation skills important? 

Delivering effective presentations is critical in your professional and personal life. You’ll need to hone your presentation skills in various areas, such as when giving a speech, convincing your partner to make a substantial purchase, and talking to friends and family about an important situation.

No matter if you’re using them in a personal or professional setting, these are the skills that make it easier and more effective to convey your ideas, convince or persuade others, and experience success. A few of the benefits that often accompany improving your presentation skills include:

Enriched written and verbal communication skills

Enhanced confidence and self-image

Boosted critical thinking and problem-solving capabilities

Better motivational techniques

Increased leadership skills

Expanded time management, negotiation, and creativity

The better your presenting techniques, the more engaging your presentations will be. You could also have greater opportunities to make positive impacts in business and other areas of your life.

Effective presentation skills

Imagine yourself in the audience at a TED Talk or sitting with your coworkers at a big meeting held by your employer. What would you be looking for in how they deliver their message? What would make you feel engaged?

These are a few questions to ask yourself as you review this list of some of the most effective presentation skills.

Verbal communication

How you use language and deliver messages play essential roles in how your audience will receive your presentation. Speak clearly and confidently, projecting your voice enough to ensure everyone can hear. Think before you speak, pausing when necessary and tailoring the way you talk to resonate with your particular audience.

Body language

Body language combines various critical elements, including posture, gestures, eye contact, expressions, and position in front of the audience. Body language is one of the elements that can instantly transform a presentation that would otherwise be dull into one that's dynamic and interesting.

Voice projection

The ability to project your voice improves your presentation by allowing your audience to hear what you're saying. It also increases your confidence to help settle any lingering nerves while also making your message more engaging. To project your voice, stand comfortably with your shoulders back. Take deep breaths to power your speaking voice and ensure you enunciate every syllable you speak.

How you present yourself plays a role in your body language and ability to project your voice. It also sets the tone for the presentation. Avoid slouching or looking overly tense. Instead, remain open, upright, and adaptable while taking the formality of the occasion into account.

Storytelling

Incorporating storytelling into a presentation is an effective strategy used by many powerful public speakers. It has the power to bring your subject to life and pique the audience’s curiosity. Don’t be afraid to tell a personal story, slowly building up suspense or adding a dramatic moment. And, of course, be sure to end with a positive takeaway to drive your point home.

Active listening

Active listening is a valuable skill all on its own. When you understand and thoughtfully respond to what you hear—whether it's in a conversation or during a presentation—you’ll likely deepen your personal relationships and actively engage audiences during a presentation. As part of your presentation skill set, it helps catch and maintain the audience’s attention, helping them remain focused while minimizing passive response, ensuring the message is delivered correctly, and encouraging a call to action.

Stage presence

During a presentation, projecting confidence can help keep your audience engaged. Stage presence can help you connect with your audience and encourage them to want to watch you. To improve your presence, try amping up your normal demeanor by infusing it with a bit of enthusiasm. Project confidence and keep your information interesting.

Watch your audience as you’re presenting. If you’re holding their attention, it likely means you’re connecting well with them.

Self-awareness

Monitoring your own emotions and reactions will allow you to react well in various situations. It helps you remain personable throughout your presentation and handle feedback well. Self-awareness can help soothe nervousness during presentations, allowing you to perform more effectively.

Writing skills

Writing is a form of presentation. Sharp writing skills can help you master your presentation’s outline to ensure you stay on message and remain clear about your objectives from the beginning until the end. It’s also helpful to have strong writing abilities for creating compelling slides and other visual aids.

Understanding an audience

When you understand your audience's needs and interests, you can design your presentation around them. In turn, you'll deliver maximum value to them and enhance your ability to make your message easy to understand.

Learn more about presentation skills from industry experts at SAP:

How to improve presentation skills

There’s an art to public speaking. Just like any other type of art, this is one that requires practice. Improving your presentation skills will help reduce miscommunications, enhance your time management capabilities, and boost your leadership skills. Here are some ways you can improve these skills:

Work on self-confidence.

When you’re confident, you naturally speak more clearly and with more authority. Taking the time to prepare your presentation with a strong opening and compelling visual aids can help you feel more confident. Other ways to improve your self-confidence include practicing positive self-talk, surrounding yourself with positive people, and avoiding comparing yourself (or your presentation) to others.

Develop strategies for overcoming fear.

Many people are nervous or fearful before giving a presentation. A bad memory of a past performance or insufficient self-confidence can contribute to fear and anxiety. Having a few go-to strategies like deep breathing, practicing your presentation, and grounding can help you transform that fear into extra energy to put into your stage presence.

Learn grounding techniques.

Grounding is any type of technique that helps you steer your focus away from distressing thoughts and keeps you connected with your present self. To ground yourself, stand with your feet shoulder-width apart and imagine you’re a large, mature tree with roots extending deep into the earth—like the tree, you can become unshakable.

Learn how to use presentation tools.

Visual aids and other technical support can transform an otherwise good presentation into a wow-worthy one. A few popular presentation tools include:

Canva: Provides easy-to-design templates you can customize

Powtoon: Animation software that makes video creation fast and easy

PowerPoint: Microsoft's iconic program popular for dynamic marketing and sales presentations

Practice breathing techniques.

Breathing techniques can help quell anxiety, making it easier to shake off pre-presentation jitters and nerves. It also helps relax your muscles and get more oxygen to your brain.  For some pre-presentation calmness, you can take deep breaths, slowly inhaling through your nose and exhaling through your mouth.

While presenting, breathe in through your mouth with the back of your tongue relaxed so your audience doesn't hear a gasping sound. Speak on your exhalation, maintaining a smooth voice.

Gain experience.

The more you practice, the better you’ll become. The more you doanything, the more comfortable you’ll feel engaging in that activity. Presentations are no different. Repeatedly practicing your own presentation also offers the opportunity to get feedback from other people and tweak your style and content as needed.

Tips to help you ace your presentation

Your presentation isn’t about you; it’s about the material you’re presenting. Sometimes, reminding yourself of this ahead of taking center stage can help take you out of your head, allowing you to connect effectively with your audience. The following are some of the many actions you can take on the day of your presentation.

Arrive early.

Since you may have a bit of presentation-related anxiety, it’s important to avoid adding travel stress. Give yourself an abundance of time to arrive at your destination, and take into account heavy traffic and other unforeseen events. By arriving early, you also give yourself time to meet with any on-site technicians, test your equipment, and connect with people ahead of the presentation.

Become familiar with the layout of the room.

Arriving early also gives you time to assess the room and figure out where you want to stand. Experiment with the acoustics to determine how loudly you need to project your voice, and test your equipment to make sure everything connects and appears properly with the available setup. This is an excellent opportunity to work out any last-minute concerns and move around to familiarize yourself with the setting for improved stage presence.

Listen to presenters ahead of you.

When you watch others present, you'll get a feel for the room's acoustics and lighting. You can also listen for any data that’s relevant to your presentation and revisit it during your presentation—this can make the presentation more interactive and engaging.

Use note cards.

Writing yourself a script could provide you with more comfort. To prevent sounding too robotic or disengaged, only include talking points in your note cards in case you get off track. Using note cards can help keep your presentation organized while sounding more authentic to your audience.

Learn to deliver clear and confident presentations with Dynamic Public Speaking from the University of Washington. Build confidence, develop new delivery techniques, and practice strategies for crafting compelling presentations for different purposes, occasions, and audiences.

Article sources

Forbes. “ New Survey: 70% Say Presentation Skills are Critical for Career Success , https://www.forbes.com/sites/carminegallo/2014/09/25/new-survey-70-percent-say-presentation-skills-critical-for-career-success/?sh=619f3ff78890.” Accessed December 7, 2022.

Beautiful.ai. “ 15 Presentation and Public Speaking Stats You Need to Know , https://www.beautiful.ai/blog/15-presentation-and-public-speaking-stats-you-need-to-know. Accessed December 7, 2022.

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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How to Make a PowerPoint Presentation of Your Research Paper

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Table of Contents

A research paper presentation is often used at conferences and in other settings where you have an opportunity to share your research, and get feedback from your colleagues. Although it may seem as simple as summarizing your research and sharing your knowledge, successful research paper PowerPoint presentation examples show us that there’s a little bit more than that involved.

In this article, we’ll highlight how to make a PowerPoint presentation from a research paper, and what to include (as well as what NOT to include). We’ll also touch on how to present a research paper at a conference.

Purpose of a Research Paper Presentation

The purpose of presenting your paper at a conference or forum is different from the purpose of conducting your research and writing up your paper. In this setting, you want to highlight your work instead of including every detail of your research. Likewise, a presentation is an excellent opportunity to get direct feedback from your colleagues in the field. But, perhaps the main reason for presenting your research is to spark interest in your work, and entice the audience to read your research paper.

So, yes, your presentation should summarize your work, but it needs to do so in a way that encourages your audience to seek out your work, and share their interest in your work with others. It’s not enough just to present your research dryly, to get information out there. More important is to encourage engagement with you, your research, and your work.

Tips for Creating Your Research Paper Presentation

In addition to basic PowerPoint presentation recommendations, which we’ll cover later in this article, think about the following when you’re putting together your research paper presentation:

• Know your audience : First and foremost, who are you presenting to? Students? Experts in your field? Potential funders? Non-experts? The truth is that your audience will probably have a bit of a mix of all of the above. So, make sure you keep that in mind as you prepare your presentation.

Know more about: Discover the Target Audience .

• Your audience is human : In other words, they may be tired, they might be wondering why they’re there, and they will, at some point, be tuning out. So, take steps to help them stay interested in your presentation. You can do that by utilizing effective visuals, summarize your conclusions early, and keep your research easy to understand.
• Running outline : It’s not IF your audience will drift off, or get lost…it’s WHEN. Keep a running outline, either within the presentation or via a handout. Use visual and verbal clues to highlight where you are in the presentation.
• Where does your research fit in? You should know of work related to your research, but you don’t have to cite every example. In addition, keep references in your presentation to the end, or in the handout. Your audience is there to hear about your work.
• Plan B : Anticipate possible questions for your presentation, and prepare slides that answer those specific questions in more detail, but have them at the END of your presentation. You can then jump to them, IF needed.

What Makes a PowerPoint Presentation Effective?

You’ve probably attended a presentation where the presenter reads off of their PowerPoint outline, word for word. Or where the presentation is busy, disorganized, or includes too much information. Here are some simple tips for creating an effective PowerPoint Presentation.

• Less is more: You want to give enough information to make your audience want to read your paper. So include details, but not too many, and avoid too many formulas and technical jargon.
• Clean and professional : Avoid excessive colors, distracting backgrounds, font changes, animations, and too many words. Instead of whole paragraphs, bullet points with just a few words to summarize and highlight are best.
• Know your real-estate : Each slide has a limited amount of space. Use it wisely. Typically one, no more than two points per slide. Balance each slide visually. Utilize illustrations when needed; not extraneously.
• Keep things visual : Remember, a PowerPoint presentation is a powerful tool to present things visually. Use visual graphs over tables and scientific illustrations over long text. Keep your visuals clean and professional, just like any text you include in your presentation.

Know more about our Scientific Illustrations Services .

Another key to an effective presentation is to practice, practice, and then practice some more. When you’re done with your PowerPoint, go through it with friends and colleagues to see if you need to add (or delete excessive) information. Double and triple check for typos and errors. Know the presentation inside and out, so when you’re in front of your audience, you’ll feel confident and comfortable.

How to Present a Research Paper

If your PowerPoint presentation is solid, and you’ve practiced your presentation, that’s half the battle. Follow the basic advice to keep your audience engaged and interested by making eye contact, encouraging questions, and presenting your information with enthusiasm.

We encourage you to read our articles on how to present a scientific journal article and tips on giving good scientific presentations .

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STEM Students Recognized for their Research and Presentation Skills at 2023 National Diversity in STEM Conference

Portland, OR — Society for the Advancement of Chicanos/Hispanics and Native Americans in Science (SACNAS) awarded over one hundred graduate and undergraduate students from historically excluded communities for their research and presentation skills at their premier event, the National Diversity in STEM Conference held in Portland from October 26 through October 28. S tudent research presentations help equip young researchers with the skills and mentoring they need to be successful on their STEM journey. This experience helps them refine presentation skills, receive one-on-one mentoring and feedback on research, and connect with a supportive community of peers, mentors, and role models.

The 2023 National Diversity in STEM Conference Presentation Awardees are as follows:

ORAL PRESENTATIONS

Animal Sciences/Zoology, Marine Sciences & Plant Sciences/Botany Miguel Angel Rosas, Washington State University

Astronomy & Astrophysics Guadalupe Tovar Mendoza, University of Washington, Seattle

Biochemistry & Genetics Natalie Sahabandu, University of California, Davis

Cancer Biology Yvonne Vasquez, University of California, Santa Cruz

Cell/Molecular Biology Manette Tanelus, Virginia Polytechnic Institute and State University

Cell/Molecular Biology & Developmental Biology Evan Morrison, University of Colorado Denver | Anschutz Medical Campus

Chemical Engineering Gabrielle Balistreri, University of Washington

Computer & Information Sciences, Statistics & Engineering Juan Florez-Coronel, University of Puerto Rico, Mayaguez

Ecology/Evolutionary Biology I Aracely Martinez, Louisiana State University Health Sciences Center, New Orleans

Ecology/Evolutionary Biology II Nicole Doran, University of Washington

Electrical & Mechanical Engineering Luis Rafael Miranda Rodriguez, Rutgers University-New Brunswick

Environmental Science, Earth Science, and Other Geoscience Allison Chartrand, The Ohio State University

Inorganic Chemistry Jenna Bustos, Oregon State University

Materials Research Kimberly Lopez-Zepeda, University of Minnesota Twin Cities

Microbiology Luis Valentin, University of California, Berkeley

Neurosciences and Genetics Jace Kuske, University of California, Davis

Neurosciences, Psychology (general), and Other Psychology & Social Sciences Mia Pacheco, Texas A&M University

Organic Chemistry & Other Chemistry Luis Cervantes, University of Michigan - Ann Arbor

Physics Christopher Gonzalez, University of California Irvine

Physiology/Pathology & Public Health Thien Phan, Texas A&M University School of Medicine

STEM Education & Learning Janet Mansaray, Louisiana State University

Traditional Knowledge Z Zenobia, Cal Poly Humboldt

GRADUATE STUDENT POSTER PRESENTATIONS

Chemistry  Emmanuel Rivera-Iglesias, University of Massachusetts, Amherst

Engineering Esai Lopez, Worcester Polytechnic Institute

Life Sciences Jaila Lewis, University of Houston

Life Sciences Gabriel Escobedo, Baylor College of Medicine

Traditional Knowledge Shania Tamagyongfal, University of Hawaii at Hilo

UNDERGRADUATE POSTER PRESENTATIONS

Chemistry Samuel Mussetter, University of California Santa Cruz Tan Nguyen, Pasadena City College Ethan Chavarin, California State Polytechnic University, Pomona Keyara Piri, California State University, Fresno Isaac Garcia, University of California, Berkeley Brianna Brooks Medina, University of the Incarnate Word Idsa Gonzale, InterAmerican University of Puerto Rico Ponce campus Mila Cordero, St. Mary's University Alejandro Chavarri, Emory University

Computer & Information Sciences Analiese Gonzale, Cypress College Ethan Tam, University of California, Berkeley Haley Lepe, MiraCosta College Mirakle Wright, University of Colorado Denver

Engineering Juan Castano, Universidad CES, Medellin, Colombia. Wei Wang, Berkeley City College Dominique Gooden, University of Wisconsin, Madison Mabel Espinoz, University of California, Merced Darian Rosales, Mesa Community College Elderson Mercado Rivera, University of Puerto Rico, Mayaguez Polina Popova, San Diego State University

Geoscience Alexis Lee, Augustana College Madison Lin, Colby College Frida Garcia, University of Texas at El Paso Kimberly Maisonet Gonzalez, University of Puerto Rico, Mayaguez

Health Rachel Pham, University of California, Berkeley Tricia Wan, New York University Cheylah Bitsui, Northwest Nazarene University, Nampa, Idaho Gustavo Nativio, University of North Carolina Lyza Lash, Wayne State University, Detroit, MI

Life Sciences, Animal Sciences/Zoology Darlene Villalobos Cazares, California Polytechnic State University, Humboldt

Life Sciences, Biochemistry Ángel Garza Reyna, Broad Institute of MIT and Harvard Isaac Melendrez, New Mexico State University Adam Murra, University of California, Davis

Life Sciences, Biology Mudita Goyal, Massachusetts Institute of Technology Sophie Lopez, Columbia University Kimberly Rice, Northeastern Illinois University Kayla Rendon-Torres, Eckerd College, St. Petersburg, FL Allie Vann, Sonoma State University

Life Sciences, Cancer Biology Maria Parrilla, Emory University Alines Lebron-Torres, Davidson College

Life Sciences, Cell/Molecular Biology Amy Rios, University of California, Los Angeles Emily Chase, University of California, Berkeley Prashant Jha, University of North Carolina at Charlotte Alena Albertson, University of Hawai'i Manoa Elizabeth Abedi, UNC Charlotte Maribel Solano Alcantara, Western Washington University Vincent Lotesto, Northeastern Illinois University

Life Sciences, Developmental Biology Elena Pearson, New Mexico State University Hamelynn Harzman, Iowa State University

Life Sciences, Environmental Science Ivory Bacy, Northern Arizona University Evelyn Martinez, University of Houston

Life Sciences, Ecology/Evolutionary Biology Patricia Sanchez, University of California, Riverside Derick Gil, University of Puerto Rico, Mayagüez Campus. Ricardo Hernandez, Georgia State University Olivia Maule, University of Florida Alizee Gamber, University of San Francisco Yichao Ma, Boston College

Life Sciences, Genetics Payton Compton, The Ohio State University Aydin Karata, University of California, Los Angeles Angelina Betie Dulay,  University of Portland

Life Sciences, Marine Sciences Alexandra Larson, The University of Tampa Ally Wilkins, Texas A&M University-Corpus Christi Angie Stefannia Tasayco, University of Central Florida Anya Isabelle Ocampos, Texas A&M University-Corpus Christi Keenan Larriviere, University of Texas A&M - Corpus Christi

Life Sciences, Microbiology Elizabeth Negron, University of Texas at San Antonio Celine Lopez Padilla, University of Oregon Carmina Chavez, University of Nevada, Las Vegas Alejandro Rivera-Madera, University of Puerto Rico at Cayey Marianna Velasco, San Jose State University Andrea Laboy-Figueroa, University of Puerto Rico Humacao Campus

Life Sciences, Neurosciences Rojan Javaheri, University of Southern California Ella Wasel, University of Houston Downtown Ella Say, Salk Institute for Biological Studies Maria Tavares, University of San Diego Ana Armenta Vega, University of California, Davis Ashley Yeh, University of California, Santa Barbara

Life Sciences, Other Life Sciences Brent Riley, Lovelace Biomedical Research Institution

Life Sciences, Pharmacology Joaquin Parrilla-Garcia, University of Puerto Rico - Mayaguez

Life Sciences, Physiology/Pathology DNatasha Castañeda, University of Oregon, Eugene

Life Sciences, Plant Sciences/Botany Radha Srinivasan, University of California, Berkeley Cecilia Regalado Diaz, California State University, Monterey Bay

Materials Research Layla James, Navajo Technical University

Mathematics Emrys King, Pomona College EunSang (Patrick) Park, University of California, Merced Ethan Bassingthwait, The College of Idaho Yuan-Jen Kuo, University of Connecticut Esteban Ayala, Pomona College Oscar Murillo-Espinoza, California State University Monterey Bay Cabrini Aguon, University of Guam

Physics & Astronomy Yoshi Levey, Fort Lewis College Isabella Macias, University of Florida Benjamin Awad,    California State University, Fresno Katie Barajas, Harvard University

Psychology & Social Science Pansée ElGhayati, University of California, San Diego Tristain Lewis, Southern Nazarene University Olivia Ortiz, California Polytechnic State University, Humboldt Mariana Gonzalez-Torres, Marquette University Yesika Menera, University of San Diego

STEM Education & Learning Brandon Patrick Respicio, University of Guam, Guam Amy Wu, University of California, Berkeley Kely Juan, University of North Texas

Traditional Knowledge Taecia Akana, University of Hawaii at Hilo

TCU STUDENT RESEARCH REWARD

Health Mariah Lee, Diné College

NATIVE AMERICAN/INDIGENOUS STUDENT RESEARCH ABSTRACT AWARD 

Health Brianson Yazzie, Northern Arizona University

Life Sciences Olivia Anderson, University of Washington

Psychology & Social Sciences Mariah Brigman, Washington State University

Click here to download the full 2023 SACNAS conference Student Research Presentation Abstract Book.

For 50 years, SACNAS has served as an inclusive organization dedicated to fostering the success of Chicano/Hispanics & Native Americans, from college students to professionals, in attaining advanced degrees, careers, and positions of leadership within STEM. Today, the organization serves a growing community of over 20,000 supporters including 10,000+ members, and more than 140 student and professional chapters throughout the United States and Puerto Rico. SACNAS influences the STEM diversity movement through STEM outreach & advocacy, the promotion of STEM leaders, and The SACNAS National Diversity in STEM Conference.   Learn more about SACNAS at  sacnas.org , Instagram , TikTok , YouTube , Facebook , or  X/Twitter .

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Presenting paper research using PowerPoint: best practices

How to write a research paper ppt.

To create your research thesis presentation, you have to write it first in the doc version. We recommend starting slides after you’ve collected and structured enough data and findings. You will get tired of changing slides all the time. If you lack time or skills, you can order PowerPoint presentation services .

Text features of the research paper presentation ppt:

• You have to focus on salient points to deliver valuable data while being on time.
• You should express gratitude to the organizers and audience at the end.
• The presentation states the importance/impact of your study (answers ‘so what’ question).
• It includes not only your findings but also related and credible quotes.
• It must show your results instead of talking about the literature.
• It should not include more than two objectives.

Design features of the research paper presentation:

• A typical research presentation takes up to 15 minutes (better 10).
• A presentation has to include no more than 25 slides.
• Do not throw paragraphs on slides and add less than 50 words per slide.
• Do not display more than two images per slide; add image titles and animation.
• Do not use fancy font styles less than 30pt: use Times New Roman, Arial, or Calibri 48pt.
• Use consistent and neutral colors and avoid text overlaying images.
• Do not use multiple-style bullets.
• Use keywords rather than paragraphs.
• Be consistent in animation and use the ‘one by one’ animation option.
• Do not clutter text and organize it according to its relativity.

Research Paper Presentation Outline or Slides to Include

Students and teachers use research papers to share facts, disclose evidence, and present findings interpreted in their own manner. To keep peers’ interest, you have to include only relevant slides and stick to the point.

Examples of slides to include in your research paper ppt:

• Self-introduction (name, affiliation, country);
• Study title;
• Purpose statement;
• Scope and limitation;
• Research design and methodology;
• Research gap;
• Study significance;
• Literature review;
• Theoretical framework;
• Study objectivity;
• Research questions;
• Hypothesis;
• Questionnaire detail;
• Conclusion;
• Recommendations.

It is not a strict outline, and you can omit some slides or add them in another sequence. The point is to add concise slides that reveal your study and deliver its significance due to each slide supplementing the next one and vice versa. And it depends on you how many slides you need to demonstrate it.

How to Present a Research Paper?

Presentation skills can be your superpower in the research argument delivery. Despite the audience type and size, your task is to defend your thesis and fill your peers with confidence. Indeed, people remember how you make them feel, not your words. Review these paper presentation tips and techniques to deliver a killer research presentation.

Know Your Audience

Sometimes, you will change for your audience, and it’s OK. If you want to be heard and accepted, you have to consider people’s backgrounds, interests, or even the average age. Therefore, discover and analyze who your audience is and the exact information they need before the presentation. Using the right terminology and knowledge level will set a connection between you and people because the worst thing you can do is to present to people who hear your subject first. Nobody likes to feel stupid, so always think about who will be in the room.

Tell a Story

After you’ve defined your audience, the next task is to make your research a story with structure. People will not remember figures or words as strong as they can remember the story with a typical story structure. If you can adjust your research paper slides to a necessary outline, you will attract the audience and make them remember you. If you doubt slides design, never hesitate to get a professional look or PowerPoint redesign from the agency.

Follow the next simple story structure:

• AND: include background with statements so that people understand what you’re saying.
• BUT: add the biggest possible and emotional problem statement your research is solving.
• THEREFORE: provide relief and present a solution to the problem you’ve set up.

Those are elementary steps to telling a story that flows. Besides, they will help you not to get lost or muddled up about your presentation with plenty of research data.

Energy Structure

If you want to capture their attention immediately, you have to practice and find a balance between a noisy man that erases any boundaries and a static person without emotions. You have to demonstrate your interest in the research and, thus, engage people till the presentation ends. An emotional and impactful statement is what people will remember, not the table you’ve designed for 5 hours for the research paper writing ppt part.

As we’ve mentioned, people will remember not what you’ve said but how you’ve made them feel. Therefore, you have to both develop your slides and inject the necessary mind-thinking into your process of creating the research presentation. A creative story and attractive slides always increase the impactfulness of what you’re saying.

If you need help with slide design, get in touch with our agency and receive custom and unique research paper slides for any subject.

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Research Paper PowerPoint Presentation Examples

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Once you have mastered the basics of PowerPoint, it is time to start creating more complex slides. But first, you should have a clear understanding of all the different types of presentations with different purposes. Most people probably have a few clear examples of the types of presentations they need to make, but in this post, we compare collected great research paper PowerPoint presentations .

Research papers come from a complicated place, and they often need a presenter who has not just experience in the subject but also brings a variety of content and educational materials as evidence. The presenter must be able to demonstrate that they are qualified to present the paper and what they will be talking about. A good and attractive research paper PowerPoint presentation can be developed by using well-designed templates. Let's find a good one:

1. Fresh Refined Thesis Defense Presentation

This slideshow is a great example of a PowerPoint presentation format that a student could use to visually introduce to the audience their research paper's thesis. This is a great example of a presentation done properly with a thesis defense, which should be something that is well argued and built upon.

2. Red Thesis Defense Presentation

PowerPoint is a good tool that allows educators to use it to provide plenty of designs and presentations of their own literature. These examples show how to use PowerPoint effectively to present the paper.

3. Landscape Thesis Defense Presentation

In this example, you'll learn the basics of how to plan a research paper. These are the basics the presenter might have used in their final thesis defense presentation. They are a framework for presenting your research paper, and the essential part of your paper is your thesis, which is the central argument in your paper.

4. Dark Blue Graduation Thesis Defenses Presentation

There are three different ways to present a research paper: a visual PowerPoint presentation, a PowerPoint slideshow presentation, or a paper. These presentation formats can inspire or help to guide you in creating your own presentation by providing a variety of examples to help you adapt.

5. Drawbridge Universal Thesis Defense Presentation

No matter what kind of essay or presentation you're writing, you can always follow the same steps. Begin by brainstorming out all of the content that you'd like to throw down on the slide. After that, outline the main points on individual slides. The final step is to create a timeline for the project and put it all together in PowerPoint.

6. Blue Simple Graduation Thesis Defense Presentation

These slides are for a research presentation about the question, What steps in the current paradigm are best for business? as made by experts in their field. The first slide is an introduction to the paper, and the other slides are the body of the paper. These slides are to present the content in a structured fashion for any audience, whether fact-oriented or opinion-based.

7. Fresh General Graduation Thesis Defense Presentation

At the end of a research project, you will have a myriad of examples of research papers, dissertations, and presentations. PowerPoint is one powerful tool that helps you present all of your ideas. In order to create a good PowerPoint presentation, it is best to first have a clear thesis that you want to argue. This thesis should be relevant to your particular project and what you are trying to convey.

8. Minimalist Graduation Thesis Defense Presentation

To help you craft your own presentation, you can use our very own video to watch research paper PowerPoint presentations from a variety of universities and colleges. By using one of these examples, you might discover what would be the best PowerPoint talk for your upcoming event.

9. Blue Doctoral Hat Graduation Thesis Defense Presentation

If you're writing a research paper or presenting your findings as a student, you're going to want to use PowerPoint as a way to enhance your thesis defense presentation. Before you get started, be sure you've created an outline that includes the introduction, body, and conclusion, and one that includes a visual you want to include in the presentation.

10. Dark Blue Sky Thesis Defense Presentation

Research paper PowerPoint presentations can be used to demonstrate that a written research paper has been thoughtfully constructed while still being read. Use examples to show the potential of how to design these PowerPoint.

The point is to keep all your main talking points on the same slide, but you don't need to do everything on one slide. After doing this presentation, it's important to be able to analyze your report and change it accordingly to make it a perfect thesis essay. With those skills, you are well on your way to making a great thesis defense presentation that you will be able to use for years to come.

These research paper PowerPoint presentation examples can be looked at as the ideal presentation format for your presentation. These are the most common types of PowerPoint presentations that may be able to be used as a template and inspiration, though you may find a variety of ideas and approaches that are more suited to your specific topic. Download the WPS office and explore more professional example templates for free of cost.

• 1. Example of PowerPoint Presentation for Research
• 2. Free examples of PPT template for research proposal
• 3. Free examples of a research PowerPoint presentation
• 4. Best Example of Research Presentation PowerPoint Templates
• 5. Research Presentation PowerPoint Example
• 6. 10 Professional Research Presentation PowerPoint Template: Best Designs for 2022

Research Paper Presentation

Research paper presentation is a procedure of public presenting the results of some scientific investigation with the purpose of getting recognition for the achievements which may be presented in various ways.

As the most common type of research paper presentation is an oral one, this article proposes you useful research paper makin’ tips for preparing to such a presentation.

First of all we suggest that you should be morally ready for the presentation. Sleepless nights won’t help, shivering in front of the committee won’t contribute to your success.

You should realize that spending so many hours preparing your research and the fact that you have approached to the day of your research paper presentation make you an expert in the field you are going to talk about.

The next aspect of your preparation should be setting an outline of your preparation. This should include introduction where you give a general overview of the work done. Mind that your introduction of the paper will give the first and, sometimes, the most important impression of your paper. Make sure it is the most appropriate one.

Next comes problem statement. After outlining your paper you should present precisely the problem of your research paper. Do it in such a way so that the audience clearly understood your message.

Then you should talk about existing solutions and their criticism. Inform the listeners about the research in your field which were made by your predecessors. State how your research paper is connected with them.

Then propose your own original solution to the problem, explain why you consider it to be more effective than others. Advertise your research, give concrete arguments proving its significance.

Then comes the most important part of your research paper presentation – you suggest the audience a thorough analysis of your work. Speak here about the contribution your research paper has made to the development of the problem’s study. Say who will benefit from its results and analyze newly open problems.

Don’t forget to give references to all sources that you used while conducting the research.

Research Papers Presentation: Points To Consider

• your presentation shouldn’t exceed 12 minutes in length
• use slides, overheads, graphs, charts to make your research paper presentation more convincing
• have copies of a printed version of your paper and the necessary material to distribute them to the audience.

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How to Create a Research Paper Presentation through AI Presentation Maker

• Introduction

What is Research Paper Presentation?

Purpose of research paper presentation, how weslides can help you, how ai presentation maker helps in research paper presentation, how to create a research paper presentation outline, how to make an effective research paper presentation, benefits of presenting your research paper presentation, how to implement effective elements in your research paper presentation, how to explain your research.

• Final Thoughts

So, have you got a chance to make a research paper presentation through AI Presentation Maker? Yes, you heard right, earlier we all are aware to make a presentation simply through PowerPoint but now as technology has advanced and grown so rapidly where a lot of AI

presentation Maker has come where you just have to select your desired template and start it right away.

A Research Paper Presentation is a visual representation of an individual’s or organisation’s systematic investigation of a subject. It assists the speaker to obtain feedback on their proposed research.

For instance, Educational establishments require Higher Degree Research students to present their research papers in research presentations.

Basically, the purpose of a research proposal presentation is to help students to develop their presentation skills. Delivering the research paper presentation also communicates the subject matter in powerful ways.

An appealing research paper presentation must include:

• Explain the significance of the topic
• All the information should be relevant
• Clearly state your findings and the method of analysis
• Make the audience learn from your presentation

Furthermore, it helps you to build professional relationships. Research paper presentation allows researchers to connect with individuals, establish collaborations, and explore potential opportunities.

Additionally, presenting a research paper presentation requires effective communication skills to convey complex ideas, methodologies, and results in a concise and understandable manner.

Through this, you can hone your presentation skills as well as communication skills

A research paper presentation plays a vital role in the research process by facilitating the exchange of knowledge, fostering collaboration, and promoting the advancement of scientific understanding.

WeSlides assists you to save your time. So stop wasting your valuable time spending hours in designing slides and creating content but instead, we are here for you. With our AI slideshow maker, you can just easily create your research paper presentation at its best.

With WeSlides you will get what you want and it gives you a feels for your presentation. So what are you waiting for?

Now let’s discuss how you can make a research paper presentation through AI Presentation Maker. Since AI Presentation Maker tools are a relatively new addition to the world of presentation software.

The appealing feature of this tool is to create engaging and dynamic presentations that are both visually stunning and informative. Though it has many advantages but one of the most significant advantages of using the AI Presentation Maker tool is that it can automate many of the time-consuming tasks associated with creating a presentation.

According to the survey, most people believe that presentations are boring if not made with effort and that is so much time-consuming. But through the AI presentation maker tool, you can easily select your desired templates and make effective presentations that will be interesting for the audience.

Let’s have a look at AI presentation-maker tools.

• Presentation AI
• Motionit.ai
• Powerpresent.ai
• Beautiful.AI

So you can easily create a stunning and powerful presentation through presentation AI maker.

Before crafting your presentation, it is crucial to create an outline. Your outline will act as your guide to put your information in order and ensure you touch on all your major points.

Hence, an outline assists guide you as you prepare your presentation as follows:

• It enables you to organize your ideas.
• Presents your research in a logical format.
• Constructs an order overview of your presentation.
• Groups ideas into main points.

For instance, your research paper presentation should look like this,

• Introduction and purpose
• Background and Context
• Data and Methodology
• Descriptive data
• Quantitative and Qualitative Analysis
• Future Research

So this is how you can create an outline for your research paper presentation. Make sure to include all the relevant points related to the topic.

Basically, the purpose of the presentation is to tell your audience a story so clearly and explicitly and it all depends on you. Telling a great story is more important than any embellishments you use to do it.

Now let’s discuss how you can make your presentation effective by just following a few simple steps.

• Decide what your most Important Messages are:

Thus, simply what you have to do is try to narrow down your findings to two to three of the most important takeaways that would resonate with people. These takeaways are the messages of your presentation.

• Start at the beginning and keep it Simple:

Now think about what question did you ask that led you to do this research. And why did you ask it? Tell your audience all information that you gathered. Use simple language that is tailored to the level of understanding of your audience.

Do not make it complex by using perplexing terminologies in your presentation. Instead, you need to understand the level of your audience as well.

• Tell them how you Addressed your Question:

Tell your audience how you address your question, but do not overwhelm them with detail as they do not need to have every detail. Just simply tell them what they need to know to get a basic idea of how you got your results.

• Tell them your Most Important Findings:

Give your audience a streamlined version of your results and tell them how you got it. You must know the qualitative research findings presentation.

• Don’t overwhelm your Presentation with too many Texts, Images, tables, or charts:

Having too much text on a slide or eligible images is a major fault of many research presentations. Just keep it simple and explain clearly about your topic or you may say what efforts you put into that topic.

• Engage with your audience:

This is the most important step which you must keep in mind while preparing your research paper presentation. You can present your research in a way that invites audience engagement.

Ask questions as you go that anticipate a slide you are about to show. While delivering your presentation you must know well how you can engage with your audience as they are the listeners. And one more thing is not to make your presentation boring.

• Explain your work:

Explain why your work is interesting. Place the study in context and how it relates to your topic. Use some pretty visuals to get the audience excited about the issue and questions you are addressing.

• Don’t just read the text:

If you are using some form of presentation that involves slides or words on a screen so do not just read those words. Instead, your best approach is to use short phrases in the slides and then add your own expansion as you talk.

Thus, listening to someone read a slide packed with text while reading along with them is mind-numbing. This is what your audience will feel.

• Do not go over your Time:

This must be kept in your mind. No one wants to listen to anyone talk longer than they are supposed to talk. If you have been given a ten-minute limit for your presentation, then do not take more than ten minutes.

Make sure that you have the right pace to stay within limits. Moreover, you do not need to be rushed while presenting.

• Implications and Conclusions:

Correctively interpret your results. Always put the endings in your conclusions so that it would be more clear for your audience to gulp the topic.

There are several advantages to presenting your research both for the presenter and the audience. Most importantly, participants are able to learn from other researchers as well. When you share your work with the audience then they will also get you better.

Since, when you are presenting your research paper presentation so at first you will share your knowledge and research findings with the audience.

Secondly, it will benefit you in the way that it can lead to valuable networking opportunities and collaborations. Moreover, presenting your research paper presentation allows you to receive feedback from experts in your field.

Most importantly it enhances your communication and presentation skills. So it’s like your skills are honing. It further assists you to organise your thoughts and speak in front of an audience.

Thus, it mainly focuses on enhancing your professional growth. So these are the benefits of presenting a Research Paper Presentation.

Following are some tips to consider when attempting to deliver an effective presentation

• Determine your Audience needs

It is important to learn who your audience is before your presentation so you can tailor your information to their interests and desires. This fosters a more engaging and effective atmosphere.

• Learn from Experience

Note the most effective elements you used and why they worked. Consider reflecting upon each of your research presentations.

• Be Creative

It is also significant to consider elements that engage your audience and make your presentation more clearly understandable.

• Clear Objective

A clear objective help you effectively outline your research paper presentation and avoid any unnecessary information. So definitely if you are having clear objective then it would be easy for you to follow all the guidelines according to it.

In the end, after you have completed your presentation, you must do a question-and-answer session with your audience. It all depends on you which method you choose, but keep in mind to follow the below tips.

• Repeat the questions
• Involve the audience by asking for their opinions on certain questions
• Spend time prior to questions preparing answers to commonly asked questions
• Remain updated on current issues related to your topic

So these above are some tips to follow when asking questions to the audience. This will help you a lot in making outstanding presentations.

Since you are preparing a research paper presentation, use more slides to explain the research papers you directly contributed to. Sometimes, people spend nearly all of the presentation going over the existing research and giving background information on that particular case.

Bear in your mind that your audience is there to learn about your new and exciting research, not to hear a summary of old work. However, do not try to include the words in the slides that you will present.

So if you are creating 20 slides for the presentation, spend at least 15 slides explaining your research.

On the other hand, you need to include visuals that will help in explaining your research topic at best. Moreover, this may include setting out all of the elements which will enhance your presentation and make it appealing.

So now say goodbye to the boring presentations and let’s welcome the outstanding one.

Final Thoughts:

So in this blog, you will get to know how to make a perfect research paper presentation. As it is essential that your presentation must be appealing and stunning and also make others interested in what you are heading towards.

Thus, using AI technology tools in making your research paper presentation would be the best decision ever. It will save your time and make you work faster. With AI tools for creating presentations, anyone can become a masterful presenter. We have discussed above every essential step that is used in delivering the best presentation.

As AI technology continues to develop and improve, the possibilities of what it can do in terms of presentation design are endless. WeSlides is providing the best presentation designs through which you can save time. So do not just think too much, but leave all your worries to us.

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• MSc graduate Mr. Zhaojin Guo wins the Outstanding Graduate Student Paper and Presentation Award at the 2023 International Symposim on Animal Environment and Welfare

Mr. Zhaojin Guo, the graduate of MSc in Public Health and Epidemiology and the Part-time Research Assistant for Prof. LIU Kai, wins the Outstanding Graduate Student Paper and Presentation Award at the 2023 International Symposim on Animal Environment and Welfare ( ISAEW ) in Chongqing, China. The title of his paper and presentation is “DeMVpp-YOLO: A lightweight pig behavior detection model for improved pig health management in farrowing pens”.