Netherlands

Deep Learning is a neural network approach where a network of multiple layers is trained to process complex data. Applications for deep learning include recognizing complex patterns in pictures, text, and sounds to, in some cases, produce insights into how human process information. Deep learning has garnered widescale interest, fostered by advances in, for instance, natural language processing and the release of innovative tools like Chat GPT. But does deep learning have implications for theory in the cognitive sciences; that is, is deep learning the answer for understanding human cognitive dynamics?

**Speakers:**

John Spencer: University of East Anglia; Brenden Lake: NYU; Raul Grieben: Ruhr-Universität Bochum; Gregor Schöner: Ruhr-Universität Bochum; Mariya Toneva: Max Planck Institute for Software Systems; Gina Kuperberg: Tufts University

CogSci 2024

Is Deep Learning the Answer for Understanding Human Cognitive Dynamics?

The 46th Annual Meeting of the Cognitive Science Society was an in-person meeting held in Rotterdam, The Netherlands at the Postillion Hotel & Conference Centre.

**ON-DEMAND PROGRAM ACCESS AFTER THE CONFERENCE** 
Recordings of the invited program are now available. You can access them by clicking on the 'Schedule' icon on the left. Select view by week and navigate to the day and time slot of the recording you wish to view. Click on the time slot to access the recording.

Recordings available (click on each recording to view):

**Thursday, July 25** 
0900: [Keynote Speaker Morgan Barense on Enhancing real-world event memory](https://underline.io/events/465/sessions/17997/lecture/99084-dynamics-between-minds-and-the-environment) 
1000: [Gleitman Award Winner Isabelle Dautriche on Language Foundations: Insights from acquisition, communication, cognition, and more](https://underline.io/events/465/sessions/18000/lecture/99085-gleitman-talk) 
1415: [Invited Symposium: Dynamics between minds and the environment](https://underline.io/events/465/sessions?eventSessionId=18016&searchGroup=lecture) 
1700: [Rumelhart Prize Presentation Speaker Alison Gopnik on Exploit, explore, empower: Three ages and three intelligences](https://underline.io/events/465/sessions/18028/lecture/99161-rp1-rumelhart-prize-presentation) 

**Friday, July 26** 
0900: [C.L. de Carvalho-Heineken Prize Keynote Speaker Kia Nobre on Focusing in memory](https://underline.io/events/465/sessions/18035/lecture/99162-hp1-c-l-de-carvalho-heineken-prize-keynote-address) 
1030: [Rumelhart Symposium: Childhood as exploration](https://underline.io/events/465/sessions/18038/lecture/100346-childhood-as-exploration) 
1415: [Elman Prize Symposium](https://underline.io/events/465/sessions?eventSessionId=18053&searchGroup=lecture) 
1600: [Invited Symposium: Dynamics between minds](https://underline.io/events/465/sessions?eventSessionId=18065&searchGroup=lecture) 
1745: [Keynote Speaker Andrea E. Martin on Neural dynamics encode the structure and statistics of language](https://underline.io/events/465/sessions/18077/lecture/99271-neural-dynamics-encode-the-structure-and-statistics-of-language) 

**Saturday, July 27** 
0900: [Keynote Speaker Gregor Schöner on How higher cognition emerges from the dynamics of strongly interacting neural populations](https://underline.io/events/465/sessions/18083/lecture/99273-dynamics-within-the-mind) 
1000: [Glushko Talks](https://underline.io/events/465/sessions?eventSessionId=18086&searchGroup=lecture) 
1500: [Invited Symposium: Dynamics within minds](https://underline.io/events/465/sessions?eventSessionId=18100&searchGroup=lecture) 


#### **CogSci 2024 Program Book**
<div style="position:relative;padding-top:0;width:900px;height:500px;"><iframe style="position:absolute;border:none;width:100%;height:100%;left:0;top:0;" src="https://online.fliphtml5.com/ebtyf/cble/" seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe></div>


**The Program Book can be downloaded [here](https://drive.google.com/file/d/1yRVp1cBqRnAbdYi4GXWV_pZeyWNsRquv/view?usp=sharing).**

#### **Letter from the President**

Welcome to the 2024 Meeting of the Cognitive Science Society in Rotterdam! CogSci 2024 brings together a large community of cognitive scientists who have traveled here from around the world, as well as a smaller group of remote presenters. I would like to extend an especially warm welcome to our first-time CogSci meeting attendees, and hope that 
their experience inspires them to join our future meetings for many years to come.

I want to thank this year’s conference co-chairs, Larissa K Samuelson, Stefan Frank, Mariya Toneva, Allyson Mackey, and Eliot Hazeltine, for putting together an amazing series of invited talks and symposia around the theme of Dynamics of Cognition. The co-chairs also deserve credit for handling a record-breaking number of submissions and producing a truly exciting conference program 

Beyond the invited talks and symposia dedicated to this year’s conference theme, I would like to highlight the presentations and events honoring the 
Rumelhart, Elman, Gleitman and C .L . de Carvalho Heineken prize winners, including the Rumelhart reception on Thursday. As you may know, we are recording all of the invited talks and symposia, and all prize-winners’ talks, and will be making this content available after the conference. 

The Cognitive Science Society relies on the volunteer efforts of our Governing Board members who work on matters related to our membership; conference policies; diversity and inclusion, international, and outreach initiatives; prizes and much more. We welcome interest from Society members who would like to become more involved in what we do. The 
Society is deeply grateful to Rick Dale and Andrea Bender, who serve as editors of the two Society journals, Cognitive Science and Topics in Cognitive Science respectively. We especially acknowledge our editors’ generosity in donating their stipends each year to D&I and outreach efforts. 

This year’s Annual Meeting depends critically on the support of Marischal De Armond and his team at Podium Conference Specialists: Sharon Zwack, Cendrine De Vis, Sarah-Kate Burke, and Rachel Waller worked tirelessly to secure the venue and handled a multitude of logistical and organizational issues, from arranging coffee breaks and receptions to making sure that a huge number of posters fits comfortably into the available space. Finally, and importantly, I would like to acknowledge the Cognitive Science Society Executive Officer, Erica Wojcik, for managing the complex and ever expanding sphere of our Society’s activities.

I hope you enjoy this year’s meeting and the many different opportunities to engage with the vibrant community of cognitive scientists gathered here. I also hope you get a chance to explore the great city of Rotterdam with its many attractions, museums, restaurants and scenic views. If you’d like to talk more about our Society, or simply want to say hello, please stop by!

![](https://assets.underline.io/markdown_image/1/image/3c66d0e21fff30e204df1138ffd15173.jpeg)

**Anna Papafragou** 
President, Cognitive Science 
Society 2023-2024

**CODE OF CONDUCT** 
By attending the CogSci 2024 Conference, you are required to adhere to the society’s [Code of Conduct](https://drive.google.com/file/d/16-6KkptF0Gn3ZYGJDlpqpwTdImw45Ng0/view?usp=drive_link).

**ABOUT THE COGNITIVE SCIENCE SOCIETY** 
The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education.

The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

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The 46th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

symposium

Reducing inequality, mitigating climate change, and responding to public health crises are large-scale goals that require the cooperation and coordination of many individuals. These goals cannot be achieved by one individual alone, and contributing is not always beneficial to each individual. And yet, individuals must contribute in order to make a difference. How do we hold individuals and groups responsible for collective action?

**Speakers:**

Casey Lewry: Princeton University; Tania Lombrozo: Princeton University; Shannon Wing: Massachusetts Institute of Technology; Sydney Levine: Allen Institute for AI; Josh Tenenbaum: MIT; Lionel Wong: Massachusetts Institute of Technology; Sofia Bonicalzi: Roma Tre University; Tobias Gerstenberg: Stanford University

Who is responsible for collective action?

We are able to learn from others through a combination of trust and vigilance: we trust and believe people who are reliable and have our interests at heart; we ignore those who are incompetent or self-interested. While past work has studied how others' competence influences social learning, relatively little attention has been paid to how others' motivations influence such processes. To address this gap, we develop a Bayesian model of vigilance that considers the speaker's instrumental self-interest, and test predictions of this model through an experiment. In accordance with our model, participants become more vigilant when informants stand to benefit from influencing their actions. When perceived self-interest is maximal, testimony can be discounted wholesale, rendering middle ground increasingly difficult, if not impossible, to find. Our results have implications for research on polarization, misinformation, and disagreement.

**Authors:**

Kerem Oktar: Princeton University; Ted Sumers: Princeton University; Tom Griffiths: Princeton University

A Rational Model of Vigilance in Motivated Communication

We offer a computational framework for modeling explanation as cooperative rational communication. Under our framework, when an explainer is faced with a ``why?'' question, they reason about the question-asker's current mental model, and intervene on that mental model in order to maximize the listener's future utility. We instantiate our framework in a planning domain, and show that our framework can model human explanations about plans across a wide variety of scenarios.

**Authors:**

Kartik Chandra: MIT; Tony Chen: MIT; Tzu-Mao Li: UCSD; Jonathan Ragan-Kelley: MIT; Josh Tenenbaum: MIT

Cooperative Explanation as Rational Communication

Theory of Mind enables us to represent and reason about other people's mental states like beliefs and knowledge. By considering what other people know, this allows us to strategically construct believable lies. Previous work has shown that people construct lies to be consistent with others' beliefs even when those beliefs differ from their own. However, in most real world cases, we don't know everything that the other person knows. We propose that to produce believable lies, the sender considers what private information the receiver may have. Here, we develop our theory into a computational model and test it in a novel paradigm that allows us to distinguish between knowledge shared between the lie sender and receiver and knowledge private to the receiver. Our main model successfully captures how people lie in this paradigm over alternative models. Overall, our work furthers our understanding of human social cognition in adversarial situations.

**Authors:**

Zhi Yi Tan: Yale NUS College; Julian Jara-Ettinger: Yale University; Marlene Berke: Yale University

Reasoning about knowledge in lie production

Social rewards shape human behavior. During development, a caregiver guides a learner’s behavior towards culturally aligned goals and values. How do these behaviors persist and generalize when the caregiver is no longer present, and the learner must continue autonomously? Here, we propose a model of value internalization where social feedback trains an internal social reward (ISR) model that generates internal rewards when social rewards are unavailable. Through empirical simulations, we show that an ISR model prevents agents from unlearning socialized behaviors and enables generalization in out-of-distribution tasks. Incomplete internalization, akin to "reward hacking" on the ISR, is observed when the model is undertrained.  Finally, we show that our model internalizes prosocial behavior in a multi-agent environment. Our work provides a framework for understanding how humans acquire and generalize values and offers insights for aligning AI with human values.

**Authors:**

Frieda Rong: Stanford University; Max Kleiman-Weiner: University of Washington

Value Internalization: Learning and Generalizing from Social Reward

A key aspect in child language development involves inducing
the rules that determine the relations of the arguments to
their verbal predicate, i.e., semantic roles. Here, we investigate
whether child-directed speech facilitates learning ‘who
does what to whom’ in English and Russian, two languages
that strongly differ in their amount of case-marking and word
order variation. We ask whether a contextual, distributional
learner can more easily learn to assign semantic roles to arguments
based on child-directed speech versus adult-directed
speech. To this end, we represent the arguments of a verb
with contextualised word embeddings extracted from neural
language models. We compare the classification accuracy
of semantic roles based on these representations between utterances
extracted from corpora of child-directed speech and
adult-directed speech. We further study to what extent semantic
roles can be predicted based on arguments represented
by different levels of information, such as non-contextualised
representations, the position in the sentence, and case marking.
We find that child-directed speech facilitates the learning
of semantic roles, an important cornerstone for learning the
morphosyntactic features of a language. However, the effect
of child-directed speech is more pronounced in Russian than
in English, indicating that child-directed speech may be optimised
more strongly in a language where arguments are expressed
in more varied forms and positions, as is the case in
Russian.

**Authors:**

Eva Huber: University of Zurich; Sabine Stoll: University of Zurich; Balthasar Bickel: University of Zurich

Readily grasping ‘who’ and ‘whom’: child-directed speech facilitates semantic role learning

Across three experiments, we show that 15-month-old infants understand that arbitrary objects can be used as symbols. Experiment 1 shows that infants map geometric shapes (e.g., a triangle) onto familiar discourse referents (e.g., a duck) based on labeling (e.g., “Look, a duck!”). Experiment 2 shows that infants do not generalize these mappings to a new speaker. This rules out the alternative hypothesis that infants interpret the labeling events literally. Experiment 3 shows that infants are sensitive to the conceptual identity of the discourse referent. After being told that one shape represents an agent (e.g., a duck) and another shape represents a patient (e.g., a cup), infants attend differentially when the agent symbol moves towards the patient symbol than the opposite. This rules out the alternative hypothesis that infants interpret the labeling events as referential pacts. The findings jointly indicate that symbolic relations are easily activated and available early in human development.

**Authors:**

Barbu Revencu: Central European University; Barbara Pomiechowska: University of Birmingham; Gabor Brody: Brown University; Gergely Csibra: Central European University

Fifteen-month-olds accept arbitrary shapes as symbols of familiar kind tokens

The control-of-variables strategy is often considered to be the superior strategy when children learn from experiments. However, by simulating Bayesian likelihoods of outcomes from a water displacement task, we show that certain confounded comparisons may support belief revision better than controlled comparisons. We tested this assumption by experimentally varying the types of comparisons that participants observed in a learning task involving balls of different sizes and materials (N = 90, age range 6- to 9-yrs). In the Size, Material, and Mixed conditions we presented controlled comparisons. In the Confounded Incongruent Condition, we presented confounded comparisons in which the larger ball was made of the heavier material. In line with our hypotheses, children in the Confounded Incongruent Condition revised their beliefs more than children in the other conditions, as indicated by higher transfer test scores. These findings suggest that confounded comparisons may in fact sometimes provide more optimal information for learning.

**Authors:**

Lucas Lörch: DIPF Leibniz Institute for Research and Information in Education; Elizabeth Bonawitz: Harvard; Garvin Brod: DIPF Leibniz Institute for Research and Information in Education

Challenging the control-of-variables strategy: How confounded comparisons can support children’s science learning

Previous research has shown that our subjective sense of certainty doesn't always accurately reflect the strength of the evidence that has been presented to us. We investigate several key factors that drive children's certainty using a Boolean concept learning task. We created an idealized learning model to predict children's accuracy and certainty during the experiment, given past evidence that they have seen in the task, and we compared its predictions with our behavioral results. Our results suggest that while predictors from the idealized learning model capture children's accuracy, behavioral predictors generated by the behavioral data can better predict children's certainty. We also show that younger children's certainty can be explained by the idealized learning model, while older children's certainty is primarily predicted by how well they observed themselves doing in the experiment.

**Authors:**

Huiwen Alex Yang: University of California, Berkeley; Louis Marti: UC Berkeley; Carolyn Baer: University of British Columbia; Aaraam Granera: University of California, Berkeley; Holly Palmeri: University of California, Berkeley; Celeste Kidd: University of California, Berkeley

Emergence of certainty representations for guiding concept learning

We test whether the support one holds about an event is influenced by other hypotheses. We addressed this by examining context effects in subjective probabilities (SPs) when forecasting NCAA men's basketball team rankings. A challenge in investigating context effects with naturalistic stimuli is the need to model the different representations of the options. To do so, we adapted the Spatial Arrangement method to capture individual representations and developed an algorithm to select stimuli. We asked participants steeped in basketball knowledge to create spatial maps for 50 teams. They were then presented with customized triplets of teams and asked to estimate their SP that one team would outrank the others. The study uncovered context effects in SPs, and moderators of the effects. Our findings suggest that similar cognitive processes may govern the construction of belief and preference and highlight the importance of modeling mental representations to understand forecasting scenarios.

**Authors:**

Jun Fang: Indiana University; Xiaohong Cai: Indiana University; James Adaryukov: Indiana University; Tim Pleskac: Indiana University

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