Netherlands

How does experiencing a rare event, like a car accident or a lottery win, influence decision-makers consecutive decisions? Studying these so-called recency effects holds a long tradition in research on experience-based decision-making. Previous work suggests opposite behavioral patterns after experiencing a positive rare event: People have been found to be more likely to either repeat their choice (positive recency) or to avoid it (negative recency). The effect is thought to persevere for multiple choices and decrease over time. In this study, we provide new insights into recency effects by analyzing people’s repeated choices from an extensive database—consisting of 3 million choices by 8,000 participants across 12 different decision-from-experience paradigms collected from 139 studies. We provide a conceptual framework clarifying patterns of positive and negative recency, including how the direction and magnitude of impact change over time.

**Authors:**

Yujia Yang: Max Planck Institute for Human Develpoment; Anna Isabel Thoma: Max-Planck-Institute for Human Development; Ralph Hertwig: Max Planck Institute for Human Development; Dirk U Wulff: Max Planck Institute for Human Development

CogSci 2024

On the nature of recency after rare events in decisions from experience

poster

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.

Autonomous agents often need to decide between choosing actions that are familiar and have previously yielded positive results (exploitation) and seeking new information that could help uncover more effective actions (exploration). We present an “observe or bet” task that separates “pure exploration” from “pure exploitation”: 61 five-to-seven-year-old children, 60 adults and computational agents have to either observe an outcome without reward, or to bet on an action without immediate feedback at varying probability levels. Their performances were measured against solutions to the partially observable Markov decision process and meta-RL models. Children and adults tended to choose observation more than both algorithm classes would suggest. Children also modulated their betting policy based on the probability structure and amount of evidence, exhibiting “hedging behavior” a strategy not evident in standard bandit tasks. The results provide a benchmark for reasoning about reward and information in humans and neural network models.

**Authors:**

Eunice Yiu: UC Berkeley; Kai J Sandbrink: University of Oxford; Alison Gopnik: University of California at Berkeley

To observe or to bet? Investigating purely exploratory and purely exploitative actions in children, adults, and computational models.

We propose two novel benchmarks for assessing models of probability judgments: the impact of Cognitive Reflection Test (CRT) on probability judgment expressions and 16 "normality identities" expected to sum to 1 under classical probability theory. We compared three models on these benchmarks – the Probability Plus Noise Model (PPN), the Bayesian Sampler (BS), and the Quantum Sequential Sampler (QSS) – using the largest dataset to date for probability judgments. Our results reveal that higher CRT scores are associated with fewer probabilistic fallacies and identity violations, a trend most accurately captured by the QSS, although we also identified QSS limitations. Regarding the normality identities, the QSS outperformed the PPN and the BS, which had difficulty with both the average values of the normality identities and their dependence on CRT scores. Additionally, we uncovered a unique "1 crossing" effect for normality identities N8 and N11, an effect PPN and BS cannot capture.

**Authors:**

Jiaqi Huang: Indiana University; Jerome Busemeyer: Indiana University; Zo Ebelt: City, University of London; Emmanuel Pothos: City, University of London

Cognitive reflection and Normality Identities: two new benchmarks for models of probability judgments

Analogical transfer between source and target problems ought to be a major contributor to problem-solving and learning. Yet, data from laboratory studies show that, in the absence of explicit hints to analogize, lack of delay between source and target, or without extensive filler tasks, evidence for successful spontaneous analogical transfer is limited, a finding attributed to the complexity of analogy retrieval and mapping. Here, we show that participants solving variants of the Cards problem often failed to show transfer between source and target problems that shared both conceptual and superficial similarities. Frequency of re-inspecting the task instructions was a significant predictor of transfer, with participants successful at T2 requiring fewer re-inspections. The results suggest that analogical transfer may be limited, not just by the difficulty of mapping between source and target, but by a lack of conceptual understanding of the source and its solution, even when the source is solved.

**Authors:**

Wendy Ross: London Metropolitan; Thomas C Ormerod: University of Sussex

It’s only easy when you know why that’s the answer. Measuring conceptual understanding through instruction rechecking

Random generation studies have shown that people struggle to be unpredictable – they slowly and effortfully produce autocorrelated sequences instead. However, true random processes (such as radioactive decay) are also not instantaneous. In this project we explore how long it takes people in a random generation task to be random. We do so in two experiments asking people to draw samples from naturalistic domains (lifespans and heights), manipulating either the rate of generation or the requirement to be random (within participants). Irrespective of pace or instructions, we find that people can produce a random sample every four to five seconds. Additionally, the time a person needs to produce random samples is consistent across conditions, but varies widely between people. Following previous literature, we model random generation performance as an autocorrelated sampling algorithm, giving a process level account of how people do these tasks and why they need time to be random.

**Authors:**

Lucas Castillo: University of Warwick; Pablo Leon Villagra: Brown University; Johanna Falben: University of Amsterdam; Nick Chater: University of Warwick; Adam Sanborn: University of Warwick

People Need About Five Seconds to be Random: Autocorrelated Sampling Algorithms Can Explain Why

A detailed understanding of the cognitive process underlying diagnostic reasoning in medical experts is currently lacking. While high-level theories like hypothetico-deductive reasoning were proposed long ago, the inner workings of the step-by-step dynamics within the mind remain unknown. We present a fully automated approach to elicit, monitor, and record diagnostic reasoning processes at a fine-grained level. A web-based user interface enables physicians to carry out a full diagnosis process on a simulated patient, given as a pre-defined clinical vignette. By collecting the physician's information queries and hypothesis revisions, highly detailed diagnostic reasoning trajectories are captured leading to a diagnosis and its justification. Four expert epileptologists with a mean experience of 19 years were recruited to evaluate the system and share their impressions in semi-structured interviews. We find that the recorded trajectories validate proposed theories on broader diagnostic reasoning, while also providing valuable additional details extending previous findings.

**Authors:**

Dominik Battefeld: Research Center Cognitive Interaction Technology (CITEC), Bielefeld University; Sigrid Mues: Dpt. of Neurology, Ruhr-Epileptology; Tim Wehner: University Hospital Knappschaftskrankenhaus Bochum, Dept. of Neurology, Ruhr-Epileptology, Bochum; Patrick House: Epileptologicum Hamburg; Christoph Kellinghaus: Klinikum Osnabrück; Jörg Wellmer: University Hospital Knappschaftskrankenhaus Bochum, Dpt. of Neurology, Ruhr-Epileptology; Stefan Kopp: Bielefeld University

Revealing the Dynamics of Medical Diagnostic Reasoning as Step-by-Step Cognitive Process Trajectories

Causal inference plays a crucial role in humans' success in navigating the world. Fortunately then, numerous studies suggest that people are highly adept at making these inferences. Examining judgments of causal structure across numerous studies, Griffiths and Tenenbaum (2005) found that people’s average judgments tracked the predictions of an optimal Bayesian model of the task. However, Tauber et al. (2017) show that aggregate behavior may appear optimal even when few individuals exhibit optimal patterns of responses. Here, I applied hierarchical Bayesian cognitive modeling approaches to a new study of causal structure judgments (N = 80) to examine the optimality of causal structure judgments at the individual level. Expanding the findings of Griffiths and Tenenbaum (2005), I found that the majority of participants’ causal structure judgments were well-explained by an optimal Bayesian model (avg. r = .86). These findings suggest that human cognitive capacities are truly well-attuned to the causal inference task.

**Authors:**

Derek Powell: Arizona State University

Individually-optimal causal structure judgments in a descriptive Bayesian model

Underlying various routes to peer influence on risky choices is the assumption that individuals have beliefs around their peer’s preferences which are incorporated in their choices. However, much is unknown about the accuracy of these beliefs and how they weigh in individuals’ considerations. We tested these implicit assumptions by actually collecting real-life peers’ preference to contrast with people’s prediction, and quantifying what changes when individuals were asked to take the peer’s perspective as the decision-maker instead of themselves. Since perspective taking develops through late adolescence, adolescence makes an especially dynamic window for observation. With a sample of typically developing friend dyads (N=128, 11.98-22.82 years), we collected fully mutual data on decision preferences in an economic risky decision making task with safe (certain) and risky (more variable outcomes) options that vary in their expected values. Upon establishing individuals’ baseline risk preferences and their prediction of their peers’ risk preferences, they took their own and their peers’ perspective in choices where their unchosen option was assigned to the peer. We modified an economic expected utility model to include a new parameter representing the adjudication between one’s own and friend’s outcome, and analyzed age-related changes with Generalized Additive Models. We found although peer’s risk preferences were overestimated in decisions on average, participants aged 16-22 years weighed friend outcome more and earned less when taking their friend’s perspective compared to their own, indicating this is a heightened period for prosocial considerations.

**Authors:**

Yelina Yiyi Chen: Harvard University; Gail M Rosenbaum: New York University; Haoxue Fan: Harvard University; Tianxiang Li: Harvard University; John Flournoy: Harvard University; Patrick Mair: Harvard University; Leah Somerville PhD: Harvard University

How beliefs around peers’ risk preferences get incorporated into adolescents’ decision making

Humans possess knowledge of causal systems with deep compositional structures. For example, we know that a good soccer team needs players to fill different roles, with each role demanding a configuration of skills from the player. These causal systems operate on multiple object types (player roles) that are defined by features within objects (skills). This study explores how human learners perform on novel causal learning problems in which they need to infer multiple object types in a bottom-up manner, using causal information as a cue for their existence. We model subjects' learning process with Bayesian models, drawing hypotheses from different spaces of logical expressions. We find that, although subjects could sometimes learn object types, they failed at tasks that require learning of more than one object type. Our result identifies the number of object type as a major obstacle for human acquisition of complex causal systems.

**Authors:**

Feng Cheng: New York University; Bob Rehder: New York University

Learning Type-Based Compositional Causal Rules

Humans are remarkably adept at inferring the causes of events in our environment; doing so often requires incorporating information from multiple sensory modalities. For instance, if a car slows down in front of us, inferences about why they did so are rapidly revised if we also hear sirens in the distance. Here, we investigate the ability to reconstruct others' actions and events from the past by integrating multimodal information. Participants were asked to infer which of two agents performed an action in a household setting given either visual evidence, auditory evidence, or both. We evaluate our task on humans, a large language model (GPT-4), and a large multimodal model (GPT-4V). We find that humans are relatively accurate overall and perform best when given multimodal evidence, seeming to put more emphasis on visual evidence than on auditory evidence. GPT-4's overall accuracy closely matches that of humans in all modalities, but is only weakly correlated with human accuracy across trials, suggesting different reasoning mechanisms. Meanwhile, GPT-4V has lower accuracy and exhibits no evidence of incorporating multimodal information. People's ability to reconstruct the behavior of others relies on successfully integrating evidence across different senses. Such multimodal reasoning presents an intriguing challenge for multimodal AI systems.

**Authors:**

Sarah A Wu: Stanford University; Erik Brockbank: Stanford University; Hannah Cha: Stanford University; Jan-Philipp Fränken: University of Edinburgh; Emily Jin: Stanford University; Zhuoyi Huang: Stanford; Weiyu Liu: Stanford University; Ruohan Zhang: Stanford University; Jiajun Wu: Stanford University; Tobias Gerstenberg: Stanford University

Whodunnit? Inferring what happened from multimodal evidence

Mandarin speakers have different space-time mappings than English speakers, but how Mandarin-speaking children spatialize time is unknown. We explored the development of 3D time-space representations in Chinese children aged 4 to 6. 145 Mandarin-speaking children, divided into three conditions (Exp1: horizontal, vertical, and Exp2: sagittal axes), undertook a MTL task for ten picture stories. We analysed their choices in 3-step temporal events, intending to test their sequential and directional preference of time (e.g., order vs. disorder; left-to-right vs. right-to-left). The results showed that Chinese children acquired sequential temporal representations on the horizontal and vertical axes at age 4, similar to English-speaking children. However, their directional preferences appeared earlier than English children (Exp1). Furthermore, the sagittal axis had different patterns: sequentiality emerged only at age 5, with no significant directional preference observed in the 3-6 age group. These findings emphasize that language and culture impact children's conceptualization of time.

**Authors:**

Jiayu Jiang: Syracuse University; Yan Gu: UCL

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