Netherlands

The present research evaluates a coherence-based network approach to moral judgement. Under this view, judgement is an outcome of achieving coherence between a network of causally interacting beliefs. Consistent with this, despite similar initial views, participants re-evaluated their beliefs and attitudes in support of their judgement, driving polarisation between individuals reporting competing judgements. Different properties of the dynamic network structure determined metacognitive properties of judgement such as confidence and perceived task difficulty. Whilst the judgement formation process involves revising beliefs and values to achieve a coherent arrangement, the nature of the judgement reached depends on the aggregate weight of these beliefs once the revision process is completed.0

CogSci 2024

A coherence-based approach to moral trade-offs

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CogSci 2022

The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





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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.

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**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** 
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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** 
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**ABOUT THE COGNITIVE SCIENCE SOCIETY** 
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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.

In this paper, we explored the attribution of causal responsibility in a causal chain of events, where an agent A instructs an intermediate agent B to execute some harmful action which leads to a bad outcome. In Study 1, participants judged B to be more causally responsible, more blameworthy, and more deserving of punishment than A. In Study 2, we explored the effect of proximity on judgments of the two agents by adding a third, subsequent contributing cause, such that B’s action no longer directly caused the final outcome. Participants judged both agents A and B to be less causally responsible and deserving of punishment (but not less blameworthy) when they were less proximal to the outcome, and there were no differences in judgments between the two agents. In Study 3, we varied whether each of the two agents (A and B) intended for the final outcome to occur. We find an interaction between role and intent, where participants only mitigated judgments for A when A did not intend for the outcome to occur – regardless of B’s intent. We discuss possible explanations for our findings and its implications for moral and legal decision-making.0

Attribution of Responsibility Between Agents in a Causal Chain of Events

Decisions about extinction risks are ubiquitous in everyday life and for our continued existence as a species. We introduce a new risky-choice task that can be used to study this topic: The Extinction Gambling Task. Here, we investigate two versions of this task: a Keep variant, where participants cannot accumulate any more earnings after the extinction event, and a Lose variant, where extinction also wipes out all previous earnings. We derive optimal solutions for both variants and compare them to behavioural data. Our findings suggest that people understand the difference between the two variants and their behaviour is qualitatively in line with the optimal solution. Further, we find evidence for risk-aversion in the Keep condition but not in the Lose condition. We hope that this task can facilitate further research on this vital topic.0

Introducing the Extinction Gambling Task

To date, no account of lie-truth judgement formation has been capable of explaining how core cognitive mechanisms such as memory encoding and retrieval are employed to reach a judgement of either truth or lie. One account, the Adaptive Lie Detector theory (ALIED: Street, Bischof, Vadillo, & Kingstone, 2016) is sufficiently well defined that its assumptions may be implemented in a computational model. In this paper we describe our attempt to ground ALIED in the representations and mechanisms of the ACT-R cognitive architecture and then test the model by comparing it to human data from an experiment conducted by Street et al. (2016). The model provides a close fit to the human data and a plausible mechanistic account of how specific and general information are integrated in the formation of truth-lie judgements.0

Combining individuating and context-general cues in lie detection

Human learning does not stop at solving a single problem. Instead, we seek new challenges, define new goals, and come up with new ideas. What drives people to disrupt the existing conceptual landscape and create new things? Here, we examine the decision to create new things under different levels of expected returns. We formalize innovation as stochastically recombining existing ideas, where successful and more complex combinations generate higher returns. This formalization allows us to cast innovation-seeking as a Markov decision process, and derive optimal policies under different settings. Data collected through an online behavioral experiment confirm our prediction that people should invest more time and effort in seeking innovations when they know the chances of success are high and the potential new ideas would be rewarding. However, people also deviate from being optimal, both innovating more and less than they should in different settings.0

A Rational Model of Innovation by Recombination

Helping is a universal human behavior, and is a core aspect of a functioning society. However, the decision to provide help, and what type of help to provide, is a complex cognitive calculation that weights many costs and benefits simultaneously.  In this paper, we explore how various costs influence the moment-to-moment decision to help in a simple video game. Participants were paired with another human participant and were asked to make repeated decisions that could benefit either themselves or their partner.  
Several preregistered manipulations altered the cost each person paid for actions in the environment, the intrinsic resource capacity of individuals to perform the task, the visibility of the other player's score, and the affordances within the environment for helping.  The results give novel insight into the cost-benefit analyses that people apply when providing help, and highlight the role of reciprocity in influencing helping decisions.0

Moment-to-moment decisions of when and how to help another person

When do children understand that number words (such as ‘five’) refer to exact quantities and that the same number word can be used to label two sets whose items correspond 1-to-1 (e.g., if each bunny has a hat, and there are five hats, then there are five bunnies)? Two studies with English-speaking 2- to 5-year-olds revealed that children who could accurately count large sets (CP knowers) were able to infer that sets exhibiting 1-to-1 correspondence share the same number word, but not children who could not accurately count large sets (subset knowers). However, not all CP knowers made this inference, suggesting that learning to construct and label large sets is a critical but insufficient step in discovering that numbers represent exact quantities. CP knowers also failed to identify 1-to-1 corresponding sets when faced with sets that had an off-by-one difference, suggesting that children who could accurately count large sets used approximate magnitude to establish set equality, rather than 1-to-1 correspondence. These results suggest that children’s initial intuitions about numerical and set equality are based on approximation, not 1-to-1 correspondence, and that this occurs well after they have learned to count and construct large sets.0

‘Five’ is the number of bunnies and hats: Children’s understanding of cardinal extension and exact number

People’s mental states constantly change as they navigate and interact with their environment. Accordingly, social reasoning requires us not only to represent mental states but also to understand the ways in which mental states tend to change. Despite their importance, relatively little is known about children’s understanding of the dynamics of mental states. To explore this question, we studied a common type of mental state change: knowledge gain. Specifically, we studied whether five- and six-year-olds distinguish between agents who gain knowledge and those who lose knowledge. In one condition, children saw an agent answer a two-alternative choice question incorrectly, followed by an identical-looking agent who answered the same question correctly (i.e., gaining knowledge). In another condition, children saw the reverse pattern (i.e., losing knowledge). Children were more likely to infer they had seen two different agents in the knowledge loss condition relative to the knowledge gain condition. These results suggest that children have intuitions about how epistemic states change and open new questions about children’s naive theories of mental state dynamics.0

Children’s Expectations About Epistemic Change

When children use online apps, they often share personal information, such as their name, address, and birthday. In the present study, we investigated the mental models children use to reason about what apps are allowed to do with personal data after it has been willingly shared with an app. 57 children ages 8- to 11-years-old were read a story in which they were asked to judge whether an online game (app) was allowed or not allowed to perform four different actions: looking, saving, selling, and showing. We compared these judgments to a comparison condition where we asked children what users themselves should be allowed to do with their data. We found that children viewed the app as less permitted to act on the data than users as well as some further differences by action-type. Our findings suggest that children use something akin to a “lending” model to conceptualize data transfers, in which apps have less rights than users despite the data being willingly transferred to the app. Our findings also suggest that children differentiate among the uses of information as children think certain actions by the app are less permissible than others (e.g., looking is more permissible than selling).0

An Investigation of Children’s Reasoning about Data Transfers

We describe the verbs pursue, chase, and follow as “tracking” verbs because they share conceptual similarities: they are all motion verbs that describe a dynamic spatial relation between two entities, as in “the cat chased the mouse”. What distinguishes them from one another? If, as some cognitive scientists argue, mental simulations underlie the way the mind processes all motion verbs — including those that describe static scenarios, such as run in “the road runs through the desert” — then those simulations may explain the differences between tracking verbs. For instance, chase and pursue may describe conceptually faster motion than follow. We tested this hypothesis in two experiments. The studies presented participants with imagery of one car chasing another along a straight road. In Experiment 1, participants estimated the distance that the pursued car would travel 3 seconds into the future by dragging a slider to an appropriate point on the road. In Experiment 2, participants estimated the distance by selecting from several distance options on a logarithmic scale. Both studies validated the hypothesis that chase and pursue describe faster motion, i.e., participants reliably estimated shorter distances for descriptions that included those verbs. We place the results in the context of broader theories of pursuit perception and verb comprehension.

**Authors:**

Maria Kon: U.S. Naval Research Laboratory; Sangeet Khemlani: U.S. Naval Research Laboratory

How spatial simulations distinguish “tracking” verbs

Do metaphors for learning influence how well we remember new information? We tested whether reading a learning-oriented metaphor (i.e., focusing on learning processes and outcomes) versus a self-referential metaphor (i.e., focusing on motivational or emotional aspects of learning) can affect the quality of learning (contrast adapted from Wegner et al., 2020). Participants either read a paragraph likening learning to a long hiking tour (self-referential condition), expanding a library in one’s mind (learning-oriented condition), or no passage (no metaphor condition). Then participants learned a new mnemonic technique, the Method of Loci, and had to apply it to a sentence learning task. The effect of metaphor on sentence memory depended on participants’ education level. People with college degrees learned better in the self-referential condition than the learning-oriented condition, whereas people without college degrees showed the opposite pattern. These findings identify novel individual differences in how learning metaphors causally influence learning outcomes.

**Authors:**

Alexia Toskos Dils: Purchase College, SUNY

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Attribution of Responsibility Between Agents in a Causal Chain of Events

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