Netherlands

The Locally Bayesian Learning (LBL) approach bridges the gap between optimal Bayesian learning and suboptimal performance that arises from human behavior. Although this learning model has considerable potential, it has been underdeveloped and has remained in original form for several decades. In this paper, we extend the original LBL model to an exemplar approach, which we refer to as the exemplar-LBL model. Two notable features of this extension are that (a) the model can take continuous features as inputs and (b) can conduct exemplar-based categorization. We report various simulations, which show that the model can generate various important predictions about category learning. Moreover, we introduce the extra-learning hypothesis, which can account for how classification and observation training can produce differential learning. Our results showcase scenarios under which classification training is superior to observation training and other instances in which the opposite holds.

**Authors:**

Yu-Wei Chang: Syracuse University; Sinem Aytac: Syracuse University; Cindy G Mendoza Gonzalez: Syracuse University ; Michael Kalish: Syracuse University; Daniel Corral: Syracuse University

CogSci 2024

Extending the Locally Bayesian Learning Model to Exemplar-Based Categorization with Continuous Features

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

Category learning, a fundamental cognitive ability, is significantly influenced by variability. In this research, we propose a model describing how people adjust information search in self-regulated category learning to the level of category variability. Participants in the self-regulated category learning task sampled from two categories until they felt confident in categorizing novel objects. Our model assumes an influence of the variability of the focal and counter category on sampling by considering a within-category and between-category processes. In both processes, variability is quantified using an information-theoretic measure. Within this model, we test if a between-category process can be better conceptualized as either a contrasting or an assimilation process.
The comparison of both processes support a between-category assimilation process, where the sample size adjusts to the counter category's variability. This novel focus sheds light on between-category dynamics, providing valuable insights into the mechanisms of category learning.

**Authors:**

Ann-Katrin Hosch: University of Bremen; Janina A Hoffmann: University of Bath; Bettina von Helversen: University of Bremen

Category Learning in Context: Modelling an Assimilation Process in Self-regulated Category Learning

We examined whether selective attention, which is mainly theorized as the ability to focus on the category-relevant dimension, is a sole construct in understanding category learning. As the attention literature dissociates selective attention into focusing and filtering, we argue that filtering is another component that should be considered to fully understanding category learning. In the study, we provide an experimental paradigm that can dissociate filtering from focusing. By utilizing the paradigm along with collecting individual attention control measures, we show that filtering is related to the ability to inhibit irrelevant information. We also present that the current computational models that incorporate selective attention only as an ability to focus can not explain the results from the current study.

**Authors:**

Hyungwook Yim: Hanyang University; Sejin Yoon: Hanyang University

Is focusing enough in category learning?

When people solve problems, they may try multiple invalid solutions before finally having an insight about the correct solution. Insight problem-solving is an example of the flexibility of the human mind which remains unmatched by machines. In this paper, we present a novel experimental paradigm for studying insight problem-solving behavior in a physical reasoning domain. Using this paradigm, we seek to quantify precisely what it means to have an insight during physical problem-solving and identify behavioral traces that predict subjective insight ratings collected from human participants. The project provides the first steps towards a computationally informed theory of insight problems solving.

**Authors:**

Solim LeGris: NYU; Brenden Lake: NYU; Todd M Gureckis: New York University

Predicting Insight during Physical Reasoning

Social norms as an interactive process: An agent-based cognitive modelling study

When people make decisions, these often do not stand alone but are made in a sequence of decisions. For instance, a doctor will first decide on a patient's treatment and then about the duration of the treatment, such that later decisions frequently depend on the outcome of the first decision. While there is research on how humans discover inter-relations between sequentially presented information (e.g., grammar), little is known about learning complex inter-category relations in decision sequences. Hence, we present an experiment in which we embedded an Exclusive-Or (Type-II) structure, as known in category learning, in a sequence of three categorization tasks. In a Type-II condition, task 1 and 2 outcomes predicted those of task 3. We hypothesized that embedded structure would facilitate learning and generalization compared to sequence without regularity. Instead, the evidence favored the Null hypothesis in both cases, contrasting to the findings in the visual categorization domain.

**Authors:**

Vedant Biren Shah: University of Bremen; René Schlegelmilch: University of Bremen; Bettina von Helversen: University of Bremen

XOR in Order: Category Learning of Exclusive-Or in a Temporal Sequence

In decision-making, the Levy flights model (LFM), an extension of the diffusion decision model, adopts a heavy-tailed distribution with the pivotal 'alpha' parameter controlling the shape of the tail. This study critically examines the theoretical foundations of alpha, emphasizing that its test-retest reliability is essential to classify it as a cognitive style measure. Our analysis confirms the alpha parameter's test-retest reliability across various occasions and tasks, supporting its role as a trait-like characteristic. The study also explores LFM parameter interrelations, despite low correlation among the other parameters (so representing distinct aspects of data), there is a pattern of strong correlation between alpha and threshold. Investigating the practice effect, our analyses indicate a consistent decrease in non-decision time, threshold, and often alpha across sessions, alongside the drift-rate increase. We also employ Bayesflow for parameter estimation, evaluating its precision with different trial counts. These findings provide valuable guidelines for future LFM research.

**Authors:**

Mehdi Ebrahimi Mehr: Shahid Beheshti University; Jamal Amani Rad: Shahid Beheshti University

Examining the Psychological Significance of the Jumps in the Decision Process through Test-Retest Reliability Analysis

Regret Theory (Loomes & Sugden, 1982) is a theory of decision making based on the idea that people not only consider outcome utility, but also future regret or rejoicing, which depends on both the chosen option and foregone options. Regret theory was originally proposed as a theory of choice under uncertainty. Here, we demonstrate that Regret Theory also predicts the widely studied attraction, compromise, and similarity effects. First, we show that it predicts attraction effects in choice among gamble triples. Second, we apply Regret Theory to non-gamble multi-attribute choice settings and show that it not only predicts these effects individually, but also predicts a within-subject dissociation between the compromise and similarity effects previously observed in empirical studies. Regret Theory provides a foundation for a unified account of risky and multi-attribute choice, and we believe the form we present here provides the simplest account to date that explains phenomena in both domains.

**Authors:**

Logan A Walls: University of Michigan; Andrew Howes: University of Exeter; Rick Lewis: University of Michigan

Regret Theory predicts decoy effects in risky and multiattribute choice

Childhood adversity (e.g., poverty, violence exposure) has been associated with broad cognitive deficits as well as with cognitive adaptations in specific abilities. Integrating these perspectives requires a process-level understanding of how deficit and adaptation processes operate. We investigated how adversity was associated with inhibition, attention shifting, and mental rotation in the Adolescent Brain Cognitive Development (ABCD) study (N ≈ 10,500). Using Hierarchical Bayesian Drift Diffusion Modeling, we distinguished between speed of information uptake, response caution, and stimulus encoding/response execution. We further used structural equation modeling to isolate task-general and task-specific variances in each of these processing stages. Youth with more exposure to household threat showed slower task-general processing speed, but showed intact task-specific abilities. In addition, youth with more exposure to household threat and material deprivation tended to respond more cautiously in general. These findings suggests that traditional assessments might overestimate the extent to which childhood adversity reduces specific abilities.

**Authors:**

Stefan Vermeent: Utrecht University

Cognitive deficits and enhancements in youth from adverse conditions: An integrative assessment using Drift Diffusion Modeling in the ABCD study

Sensation-seeking (SS) is characterised by a proclivity for intense experiences with a disregard for potential aversive consequences. While SS is implicated as a vulnerability factor in various mental disorders, the underlying mechanisms remain elusive. Recent approaches propose an alternative perspective, suggesting that SS may be linked to highly explorative, and therefore risky, behaviours driven by a preference for informative environments. To probe this hypothesis, we reanalysed a dataset where participants chose to self-administer or avoid mild electric stimulation (MES) in an economic decision-making task. Contrary to previous interpretations associating higher sensation-seeking with the positive economic value of experiencing MES, Bayesian models of learning reveal an alternative account: sensation-seekers are more attuned to information about stimuli-shock contingencies. Specifically, high sensation-seeking individuals are less avoidant of information about the possibility of a shock, supporting the idea that sensation-seeking is linked to a preference for informative environments.

**Authors:**

Ern Wong: IMT School for Advanced Studies Lucca; Tobias Hauser: University of Tübingen; Pietro Pietrini: IMT School for Advanced Studies Lucca; Charley M Wu: University of Tubingen

Shock to Thrill: Linking Sensation and Information Seeking

Scatterplots are commonly used data visualizations to depict relationships between variables. There are inconsistent findings in the literature regarding how outliers in scatterplots affect trendline estimates. Correll & Heer (2017) found no difference for trendline estimations between the no-outlier and the outlier conditions consisting of a separate group of items creating an outlier cluster. However, Ciccione et al. (2022) showed that single outlier points might be included in trendline estimations. To investigate whether an outlier cluster was perceived as a salient and separate unit and thus excluded from the remaining data points, we directly compared the effects of single and multiple outliers on trendline estimations, controlling for correlation strength, outlier position and trend direction. Participants drew trendlines. We found that participants included single outliers more than they’ve included outlier clusters into the trendlines; this pattern was similar across all other control variables; suggesting grouping might play a role in this process.

**Authors:**

Özgür Saydır: Koç University; Aysecan Boduroglu: Koc University

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Category Learning in Context: Modelling an Assimilation Process in Self-regulated Category Learning

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