Netherlands

The mechanisms of learning stimulus-stimulus relationships are a longstanding research subject in psychology and neuroscience. Although traditional computational models provide valuable insights into learning processes, they often focus on the average behavior of a population. Individual learning trajectories, however, exhibit a diverse range of behaviors not captured by these models. In this paper, we compare sampling-based process-level models (i.e., particle filters) to representative associative and causal models (i.e., augmented Rescorla-Wagner and PowerPC) in their ability to capture individual learning behavior. We use likelihood-free inference incorporating machine-learned summary statistics for model estimation. We conduct a simulation study to demonstrate high model identifiability and test the models on an existing dataset and a newly conducted experiment which replicates and extends previous studies. We find that most participants are best explained by a particle filtering account, but more targeted experimental designs are required to estimate the best-fitting sub-type of these particle filter models.

**Authors:**

Simon Valentin: University of Edinburgh; Lucas Castillo: University of Warwick; Adam Sanborn: University of Warwick; Chris Lucas: University of Edinburgh

CogSci 2024

Distinguishing Between Process Models of Causal Learning

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**
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**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.

poster

Humans are resistant to changing their beliefs even in the face of disconfirming evidence. The Bayesian brain theory suggests that we should update our beliefs optimally in light of new ev- idence, but recent research indicates that belief formation is far from the Bayesian ideal. Individuals can exhibit “stronger- than-rational” updating or be resistant to revising their beliefs. The present study proposes a novel paradigm to explore per- ceptions and preferences for belief updating patterns in stable and dynamic stochastic environments, using an advice-taking paradigm. In an experiment (N=567) based on a fishing task, we introduce three advisor characters representing formal up- dating models: Bayesian, Volatile and Rigid. We find that par- ticipants exhibit higher trust for the Bayesian advisor than the Rigid advisor, in the stable but not changeable environment conditions. In the changeable environment, participants ex- hibit higher trust for the Volatile advisor, compared to both the Bayesian and Rigid advisors. The findings also suggest that participants’ own learning closely mimics the pattern of the Volatile model. This study illustrates that people can dif- ferentiate between Bayesian updating, and its “stronger-than” and “weaker-than” variations, and exhibit preferences for these updating patterns, in different environment structures.

**Authors:**

Trisevgeni Papakonstantinou: University College London; Nichola J Raihani: University College London; David Lagnado: University College London

Belief updating patterns and social learning in stable and dynamic environments

We compare two perspectives on base-rate neglect (Kahneman
& Tversky, 1973) in probabilistic judgment. The evidential
impact perspective derives it from humans’ focus on the impact
of evidence on belief, rather than conditional probabilities.
The Causal Models perspective derives it from humans’
inability to integrate information that is causally opaque,
as base-rates often are in such experiments. Because causal
and evidential-impact relations are often concomitant and confounded,
we designed an experiment that specifically teases
apart their respective influence on probabilistic judgment. Our
results support a combination of the two perspectives, with
causal transparency influencing the degree to which one engages
in evidential impact reasoning strategies.

**Authors:**

Can Konuk: Institut Jean Nicod; Nicolas Navarre: University of Edinburgh; Salvador Mascarenhas: Ecole Normale Supérieure

Effects of causal structure and evidential impact on probabilistic reasoning

Although well-intentioned, diversity, equity, and inclusion (DEI) initiatives aim to increase minority representation in elite groups, they can sometimes backfire by lowering successful candidates’ beliefs about their competence. Prior work in social psychology suggests that this effect is driven by stereotype threat. Here, we propose a novel cognitive framework: DEI initiatives backfire due to causal inference. When candidates hear that they were selected based on a DEI initiative and/or enter a group where they are a minority, they may hypothesize that their selection was based more on their identity and less on their merit. Across two pre-registered experiments manipulating selection messages (DEI vs. merit) and statistical gender representation (represented or underrepresented in the selected group), we find evidence in favor of our hypothesis. Effects replicated across different selection task difficulties and did not differ by gender. However, selection messages and under-representation did not impact people's assessment of their ability at the selected task. Taken together, our work shows that people form different causal hypotheses about the role of their merit and identity in their admission to an elite group based on DEI selection messages and group composition. Importantly, this work paves the way for designing DEI-based initiatives that license more helpful causal inferences about success to ensure that minority candidates thrive in their positions.

**Authors:**

Aarthi Popat: Yale University; Julia Anne Leonard: Yale University

“I’m here for my gender, not my skill”: Causal reasoning shapes beliefs about merit in response to DEI initiatives

Suppose you are asked to choose randomly between left or right 100 times, would you expect the average of your choices to be roughly even or to have a bias? In the literature, human randomness falls on a spectrum from being close to unbiased to very biased in random choices. To create a model with a neural implementation of human randomness, unsupervised artificial neural networks were used to generate a random representation of binary numbers. These random representations were tested with both orthogonal and correlated stimuli as inputs and the properties of all outputs are discussed. An example of how to bias this generated randomness to model different cognitive processes is shown under two conditions, where random decisions are biased for desired outcomes and for list exhaustion (random sampling without replacement). Other possible uses for this method of generating randomness in cognitive modelling are discussed.

**Authors:**

Kinsey A Church: University of Ottawa; Marija Bolic: University of Ottawa; Sylvain Chartier: University of Ottawa

Generating Distributed Randomness using Artificial Neural Networks

While a number of disciplines have empirically investigated self-control (e.g., psychology, cognitive science, and sociology, along with philosophy), they have offered differing (although overlapping) perspectives. A process-based, mechanistic theory explaining empirical self-control data can help integrate these perspectives. A mechanistic (computational) approach through a computational cognitive architecture where simulations can be performed may unify the interpretations of empirical studies based on various (e.g., implicit-explicit) conflicts as well as utility calculation (e.g., from motivational considerations). Such a framework may capture notions of self-control capacity and self-control decrease (or failure), facilitating detailed explanations.

**Authors:**

Adan Alberto Gomez: Rensselaer Polytechnic Institute; Ron Sun: RPI

A “Rational” Framework for Self-Control

How can we explain that people are capable of performing new tasks with no or little instruction? Earlier work has proposed that new tasks can be acquired by a rapid composition of cognitive skills, and implemented this in the ACT-R and PRIMs cognitive architectures. Here we discuss a possible application of rapid composition in building tutoring systems. The goal is to identify underlying skills through unsupervised machine learning from a dataset of arithmetic learning for students in a Dutch vocational program. The resulting skill graph is used as a basis for a tutoring system. The results show evidence for predictive power of the system, and tentative evidence of a learning benefit compared to control groups.

**Authors:**

Niels Taatgen: University of Groningen; Corné Hoekstra: University of Groningen; Jori Blankestijn: MemoryLab

Data-driven cognitive skills with an application in personalized education

It takes children considerable learning and development to accurately predict whether an object is safely balanced or will fall --- something that happens if its center of mass is not supported from below. In the meantime, children go through a characteristic set of mistaken beliefs. Here we use an adapted version of the classical balance task to evaluate whether different models go through the same stages. Preliminary results show that convolutional neural networks (CNNs) do learn the task but do not necessarily go through the same stages. We are also testing several simulation-based accounts. We anticipate completing this work in time for the conference. The findings will help clarify the space of possible accounts of children’s acquisition of intuitions about gravity and balance.

**Authors:**

Tianyi Li: Boston College; Mengguo Jing: The Ohio State University; Zakir Makhani: Northeastern University; Iris Oved: Boston College; Nikhil Krishnaswamy: Colorado State University; James Pustejovsky: Brandeis University; Joshua K Hartshorne: Boston College

Modeling the development of intuitive mechanics

One of the core properties of human language is compositionality: the meaning of a sentence can be understood by the meaning of individual words and the rules for combining them (Szabó, 2020). We investigate the development of conceptual compositionality (the combination of concepts). In our study, 6- to 9-year-old children (N = 40) were shown a card with two objects (e.g., a car and a star). Participants were introduced to two characters (a robot and a wizard) that used their powers to change the objects in different ways (e.g., turning one object pink). In the test trials, participants were asked to predict what a card would look like after both characters used their powers on the same card. All participants successfully learned the character's powers, but only participants 7.5 years and older succeeded in the compositionality test trials. Our findings suggest that by age 7.5 children can successfully compose functions.

**Authors:**

Stephanie Alderete: University of California, Berkeley; Wenqing Cao: University of California, Berkeley; Steven Piantadosi: UC Berkeley; Fei Xu: UC Berkeley

The Development of Conceptual Compositionality in Children

This study investigated the effect of encoding context on the formation of false memories, using Category Associates Procedure with pictorial stimuli. In the literature, distinctiveness is suggested to decrease false memories; however, the mechanisms of this effect are still of debate. Participants studied objects from several categories, each category list either presented on congruent or incongruent backgrounds. Later, they performed a recognition test for three item types: studied, critical lure and unrelated items. We expected that incongruent condition should require more distinctive encoding, which may lead to decreased false recognition of critical lures compared to congruent condition. The results revealed a false memory effect consistent with existing literature; however, there was no difference between congruent and incongruent conditions in terms of false memory rates. The results were discussed in the light of encoding-based and retrieval-based theories. Additionally, visual imagery measures and reported strategies were also considered.

**Authors:**

İrem Küsmüş: Sabancı University; Olesya Blazhenkova: Sabanci University

The effect of encoding context on false memory formation in a picture-based category associates procedure

What is the developmental trajectory of probability learning in early childhood, and how do changes in choice behavior relate to changes in executive functions? We conducted a two-year longitudinal study with children during the ages from 3.5 to 6.5 years (N = 74 at T1) and complemented behavioral analyses with computational modeling to illuminate underlying cognitive processes. On average, children became more likely to choose the high-probability option as they grew older and increasingly diversified choices in line with probability matching by T3. Moreover, younger children in the cohort were more likely to maximize probability than older children. Our analyses suggest that increasing choice diversification across childhood may relate to improving executive functions and value-based learning, whereas probability maximizing may serve as an easily implementable satisficing strategy. Finally, our findings emphasize how children’s variability in choice behavior may affect the estimated direction of change and highlight the need for longitudinal research.

**Authors:**

Anna Isabel Thoma: Max-Planck-Institute for Human Development; Christin Schulze: UNSW

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Belief updating patterns and social learning in stable and dynamic environments

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