Netherlands

Understanding and predicting the relevant risky choices of modern life is a key goal of the behavioral sciences and cognitive decision research specifically. However, do researchers study those choices that people actually face in their lives, or choices that at least capture the same cognitive processes? To address these open questions, we compare 214 risky choices from three perspectives (research, layperson, life outcomes) and use semantic embeddings extracted from a LLM to assess the similarity of choices between perspectives. Furthermore, by means of a Bayesian mixed effects model we examine the potential overlaps and gaps between the three perspectives regarding which cognitive mechanisms may be at play when people make the various choices. Our research informs theories of risk taking by revealing discrepancies of behavioral research with real-life choices, both regarding the choices that are considered timely as well as the cognitive underpinnings that influence these choices.

**Authors:**

Olivia Fischer: University of Zurich; Aaron Benjamin Lob: University of Zurich; Renato Frey: University of Zurich

CogSci 2024

Three perspectives on decisions under risk and uncertainty: A comparative analysis of potential discrepancies and their explanations

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

Past research examining developmental differences in exploration behavior has shown that children are more likely than adults to seek out uncertainty. However, children’s exploration behavior may be shaped by their distinct prior experiences and assumptions, differing from those of adults. We investigate these differences and their potential impact on exploration, using the game “Little Alchemy”, in which players can create new elements (e.g. clay) by combining previously discovered elements (e.g. stone and mud). Previous work found that adults use an empowerment strategy: They combine elements with the goal of creating new elements with the potential for many successful combinations. We observed that children were less likely to use an empowerment strategy, but relied more on their uncertainty compared to adults. This discrepancy decreased over age. In a follow-up experiment, we showed that this difference was indeed due to children using different strategies rather than the influence of different semantic priors.

**Authors:**

Franziska Brändle: Max Planck Institute for Biological Cybernetics; Silja Kessler: Max Planck Institute for Biologial Cybernetics; Azzurra Ruggeri: Technical University Munich; Eric Schulz: Max Planck Institute for Biological Cybernetics

Uncertainty-driven little alchemists: Differences in exploration strategies between adults and children in an online game

People’s mental representations of complex stimuli, such as images of facial affect, are difficult to elicit. To address this challenge, methods such as Markov Chain Monte Carlo with People (MCMCP), integrate human agents into computer-based sampling algorithms. However, such methods suffer from slow convergence, making them impractical for recovering the representations of individuals. Here, we extended MCMCP by introducing an adapted Variational Auto-Encoder (VAE) with domain knowledge as an auxiliary agent, guiding the sampling process away from less useful experimental trials. To test this approach, we ran a new experiment comparing such a VAE-guided MCMCP against baseline MCMCP in terms of convergence speed and quality of recovering human representations of facial affect. Preliminary results demonstrated that most guided chains converged on an individual’s facial affect representation within a single experimental session, faster than the baseline methods, and results showed the extent of individual differences in facial affect representations. Thus, VAE-guided MCMCP provides a promising framework for interfacing machine intelligence with psychological experiments to enhance our understanding of human cognition.

**Authors:**

Haijiang Yan: University of Warwick; Nick Chater: University of Warwick; Christian Tsvetkov: University of Warwick; Adam Sanborn: University of Warwick

Recovering individual mental representations of facial affect using Variational Auto-Encoder Guided Markov Chain Monte Carlo with People

Others not only influence our behavior, but also our metacognitive evaluations of those behavior (i.e. decision confidence), even when feedback is random and uninformative. Here we ask if metacognitive ability to monitor reliability of one’s decisions predicts social susceptibility. We also ask if mood (anxiety and depression) further modulates this effect. We gave 46 healthy participants a perceptual task and presented them with random social feedback (positive, negative, neutral). Participants rated their confidence in their decisions before and after feedback, and lastly had an opportunity to change their initial decisions. In a separate task we also measured their metacognitive abilities, as well as their anxiety and depression scores. Results showed metacognitive ability to increase susceptibility to random social feedback. Surprisingly for those with high levels of metacognitive ability anxiety exacerbates this effect, whereas depression suppresses it.

**Authors:**

Cem Karbuz: Koç University; Terry Eskenazi: Koc University

Mood and social influence: the role of metacognitive ability

In optimal-stopping problems, people encounter options sequentially with the goal of finding the best one; once it is rejected, it is no longer available. Previous research indicates that people often do not make optimal choices in these tasks. We examined whether additional information about the task's environment enhances choices, aligning people's behaviour closer to the optimal policy. Our study implemented two additional-information conditions: (1) a transparent presentation of the underlying distribution and (2) a provision of the optimal policy. Our results indicated that while choice patterns varied weakly with additional information when providing the optimal policy, it did not significantly enhance participants' performance. This finding suggests that the challenge in following the optimal strategy is not only due to its computational complexity; even with access to the optimal policy, participants often chose suboptimal options. These results align with other studies showing people's reluctance to rely on algorithmic or AI-generated advice.

**Authors:**

Henrik Singmann: UCL; Yunxi Xiong: UCL; Yue SONG: UCL; Mia Breen: University College London; Christiane Baumann: Harvard Kennedy School

Full-Information Optimal-Stopping Problems: Providing People with the Optimal Policy Does not Improve Performance

Since undetected fake news influence opinions and behaviors, it is important to understand under which conditions people can detect fake news, and how aware they are of their detection performance. Adapting a study about phishing mails (Canfield et al., 2019), we investigated metacognition for fake and legitimate news separately as well as related individual and task factors. In a single-factor within-subjects design, 175 participants read 19 legitimate and 19 automatically generated fake news in random order and answered a detection task and a metacognitive confidence judgment for each. Overall, participants showed overconfidence with 68% correct detection and 73% confidence. The results also showed better calibration and resolution for fake news than for legitimate news. Notably, there was a tendency for participants to misjudge legitimate news at high confidence levels. Prior knowledge positively impacted performance, whereas agreement with fake and disagreement with legitimate news resulted in performance falling below random.

**Authors:**

Nora Cremille: Technische Universität Darmstadt; Björn Mattes: Technische Universität Darmstadt; Stephanie Pieschl: Technische Universität Darmstadt

A Question of Beliefs. Metacognitive Judgments about Fake News Detection

In situations demanding loss avoidance or gain maximization, individuals must possess a profound understanding of the rules and regularities in their environment. However, exploration behavior varies across such scenarios. Past research has been inconclusive regarding the impact of a loss compared to a gain domain, particularly when exploration involves potential costs. In contrast, the current project centers on scenarios where subjects receive positive or negative rewards while exploring the environment or exploiting their knowledge. Participants engaged in a Multi-Armed Bandit task in three conditions: only gains in the environment, only losses in the environment, and a mixed condition involving both gains and losses. Notably, participants exhibited reduced exploration in the gain domain compared to the loss domain, with the mixed domain falling in between. Interestingly, participants performed best in the mixed domain. Computational modeling of participants' choice behavior revealed that individuals tend to underestimate outcomes of unchosen options in the gain domain and overestimate them in the loss domain. This pattern of findings could be attributed to the effects of absolute gains and losses or the effects of outcomes being relatively better or worse than the initial expectations.

**Authors:**

Ludwig Danwitz: University of Bremen; Ann-Katrin Hosch: University of Bremen; Bettina von Helversen: University of Bremen

Framing the Exploration-Exploitation Trade-Off: Distinguishing Between Minimizing Losses and Maximizing Gains

Here we present results from three experiments demonstrating that, in human learning and decision-making, the way in which options are organized during learning (i.e., the choice architecture) significantly affects the resulting memory representations of their economic values. That is, options that are optimal in the learning contexts tend to be significantly overvalued in the follow-up memory tests. By changing the choice architecture of the learning phase across experiments, we were able to show that this irrational bias is a direct consequence of the learning choice architecture, because presenting options in all possible combinations during learning eradicates this effect. Critically, all the results stand irrespective of the memory elicitation used.

**Authors:**

Magdalena Soukupova: Ecole Normale Superieure; Stefano Palminteri: École normale supérieure

Choice Architecture Induces Distortions in Economic Values: a Test across Two Memory Elicitations

Salient features of a task play an important role in how people create task representations which then influence strategy selection for accomplishing the task. We examined two theories, Represent-Construct-Choose-Learn (RCCL) and Rational Metareasoning (RM), both of which incorporate task features into their models of strategy selection. RCCL theory posits that when a strategy’s success rate is low, it indicates that the task representation is not useful and those represented features are irrelevant in this case so people tend to drop these features from the task representation. Conversely, RM theory posits that strategy selection is based on consideration of all available features, with no discrete changes in the features incorporated into the task representation. A study was conducted to examine how participants changed their strategy choices based on the success rate of using a specific task feature. The results showed that neither theory aligned closely with empirical data.

**Authors:**

Xinyu Xie: Mississippi State University; Jarrod Moss: Mississippi State University

Comparing Theories that Posit a Role for Task Features in Strategy Selection

What does a social learner learn? Research has explored imitation-based social learning strategies as well as inverse reinforcement algorithms that estimate others' true reward function. In the current study, we propose that social learning may be more elaborate and develop a model of social learning using Bayesian inference that seeks to understand both the task an observed demonstrator is performing and the demonstrator itself. Using simulations we show that the model is able to learn about the demonstrator when provided with full and partial information. We strengthen this point by asking the model to make inferences about missing choice and reward information. Last, we show that the model is able to represent one set of beliefs about the environment while attributing a distinct set of beliefs to the demonstrator. Thus, we move away from simple models of social learning, investigating inference-making as a core mechanism of social learning.

**Authors:**

Alexandra Ortmann: Stony Brook University; Anurag Dutt: Stony Brook University; Christian Luhmann: Stony Brook University

Social Learning via Bayesian Inverse Reinforcement Learning: Learning from and about a Learner

Humans are efficient social learners who can leverage social information to rapidly adapt to new environments, but the computational process by which we combine social information with our own prior knowledge is poorly understood. We study social learning within the context of multi-armed bandits using a novel "asteroid mining" video game where participants learn through a mix of active play and passive observation of expert and novice players. We simulate the combination of human exploration and social learning using both a naive Thompson sampling algorithm and a hybrid algorithm that uses Thompson sampling for direct learning and a multi-layer perceptron to estimate what should be learned from other players. Two variants of the hybrid model provide a good, parameter-free fit to human rewards across a range of learning conditions. Our work shows a route for integrating social learning into reinforcement learning models and suggests that human social learning conforms to the predictions of such models.

**Authors:**

Julio Martinez: Stanford University; Michael C. Frank: Stanford University; Nick Haber: Stanford

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