Netherlands

To succeed in social situations, we must learn how social cues predict subsequent events. How do we quickly form associations between a variety of social cues, such as individuals signaling their current emotion state, and social outcomes? To address this question, we developed a task in which participants viewed images of individuals conveying different emotions and searched among these images to gain rewards. Rewards were associated with either individuals’ identities or emotion cues. Across four experiments (N=720), individuals learned about rewards more efficiently from individual identity cues versus a wide variety of emotion cues. Participants also generalized cue-outcome associations more easily for individuals versus emotions. Learning was worse if participants experienced a change in the association rule, especially when switching from learning individual-based associations to emotion-based associations. Overall, we show that social cue type influences how associations between cues and rewards are learned, with implications for understanding learning in social contexts.

**Authors:**

Rista C Plate: University of Pennsylvania; Emily R. Perkins: University of Pennsylvania; Rebecca Waller: University of Pennsylvania; Martin Zettersten: Princeton University

CogSci 2024

Learning and generalizing associations between social cues and outcomes

Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
 
 [**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/07/Cogsci-2022-PAAG-Detailed-July-12final-1.pdf)
 [**The Detailed Program can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/08/DeArmond-CogSci2022-program-v113-final.pdf)

This conference requires a registration to attend. Please use the link below to secure your ticket. The website will be available to all registered attendees on July 25th. 

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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. 





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**
<div style="position:relative;padding-top:0;width:900px;height:500px;"><iframe style="position:absolute;border:none;width:100%;height:100%;left:0;top:0;" src="https://online.fliphtml5.com/ebtyf/cble/" seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe></div>


**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.

While the impact of social cognition on novel word learning has been extensively studied in monolingual populations, limited research has investigated its role in multilingual children with and without autism spectrum disorder. This study examined the role of multilingualism on the acquisition of novel English words under directly addressed and overhearing conditions. Participants included four groups of children with different language status (multilingual and monolingual and diagnostic status (typically developing and autistic). The results revealed that the learning preferences vary across participant groups depending on their language and diagnostic statuses. Additionally, dynamic patterns of novel word learning were unveiled, demonstrating the influence of English vocabulary proficiency on multilingual children's learning process. The findings highlighted the complex role of multilingualism on driving the formation of learning preference for typical developing and autistic children.

**Authors:**

Jinghong NING: PolyU; Anqi Hu: University of Delaware; Zhenghan Qi: Northeastern University; Li Sheng: PolyU

Novel Word Learning in Multilingual Children with and without Autism Spectrum Disorder: Roles of Social Cognition, Multilingualism and Vocabulary Proficiency

The recent surge in the use of AI-powered chatbots such as ChatGPT has led to new challenges in academia. These chatbots can enable student plagiarism and the submission of misleading content, undermining educational objectives. With plagiarism detectors unreliable in the face of this issue, educational institutions have been struggling to update their policies apace. This study assesses the effectiveness of sending warning messages - a common strategy used to discourage unethical use of ChatGPT - and investigates the use of the illusion of explanatory depth (IOED) paradigm as an alternative intervention. An international sample of students was asked to rate their understanding of, likelihood to use, and moral stance toward ChatGPT-generated text in assignments both before and after either reading a cautionary university message or explaining how ChatGPT works. Results showed that the explanation task did lead to the expected reduction in ratings of understanding, but despite this, neither moral acceptability nor likelihood to use decreased along with it. Similarly, reading the cautionary message neither resulted in a change in likelihood to use nor in moral acceptability, although it unexpectedly increased ratings of understanding. The results suggest that tackling students’ understanding of ChatGPT is insufficient when it comes to deterring its unethical use, and that future interventions might want to have students reflect on moral issues surrounding the use of AI-powered chatbots.

**Authors:**

Yomn Elsayed: Erasmus University Rotterdam; Steven Verheyen: Erasmus University Rotterdam

ChatGPT and the Illusion of Explanatory Depth

Studies on interactions between procedural and declarative learning have focused on largely on competition during encoding, consolidation, or use (retrieval). Less attention has been paid to interactions between the representations created by each system. In a behavioral study, we demonstrated that information from both declarative and procedural learning can contribute to response selection. Participants were instructed to use a completely diagnostic, verbalizable, shape-based rule to categorize exemplars and received feedback after each trial. However, the categories also differed probabilistically in their color distributions. Participants used both color (learned procedurally) and shape (learned declaratively) to categorize exemplars, making faster responses when both sources indicated the same category judgement, and slower when they conflicted. Debriefing confirmed that most participants were unaware of the color distributions (aware participants were analyzed separately). This result suggests that both the color (procedural) and shape (declarative) information contributed to response selection.

**Authors:**

Priya B. Kalra: University of Western Ontario; Laura Batterink: University of Western Ontario; John Paul Minda: University of Western Ontario

Procedural and Declarative Category Learning Simultaneously Contribute to Downstream Processes

Human moral judgments are both precise, with clear intuitions about right and wrong, and at the same time obscure, as they seem to result from principles whose logic often escapes us. The development of Artificial Intelligence (AI) applications requires an understanding of this subtle logic if we are to embed moral considerations in artificial systems. Reinforcement Learning (RL) algorithms have emerged as a valuable interactive tool for investigating moral behavior. However, being value-based algorithms, they face difficulty when it comes to explaining deontological, non-consequentialist moral judgments. Here, in a multi-agent learning scenario based on the Producer-Scrounger Game, we show that RL agents can converge towards apparently non-consequentialist outcomes, provided the algorithm accounts for the temporal value of actions. The implications of our findings extend to integrating morality into AI agents by elucidating the interplay between learning strategies, characteristics for accounting temporal values, and methods of considering the opponent’s payoff.

**Authors:**

Ali Shiravand: École Normale Supérieure - PSL; Jean-Baptiste André: École Normale Supérieure - PSL

Human-Like Moral Decisions by Reinforcement Learning Agents

Notifications convey important information, but they can also act as distractions, leading to resumption errors. Previous research has primarily focused on two types of notifications: pop-up notifications that appear quickly (1 second) and transparency reduction notifications that appear slowly (2 seconds). Pop-up notifications in an environment with perceptual feedback tend to result in the highest number of errors, while transparency reduction notifications may go unnoticed in an environment without feedback. To bridge this gap, the third variant of notification speed (1.5 seconds) was introduced in this study. The aim was to strike a balance between the noticeability of notification and minimizing the negative impact of attention redirection. Participants were instructed to perform the Modified Bourdon Test and close notifications. The findings revealed that the third variant, combining the features of pop-up and transparency reduction notifications, led to a decrease in resumption errors while still effectively capturing users' attention.

**Authors:**

Valeriya Sklemenova: HSE University; Anastasia Anufrieva: HSE University

Attentional sustainability of organizer users under fast and slow appearing notifications

The generation effect, demonstrating improved memory performance through self-generating information, was explored in this study. Participants engaged in semantic and perceptual generation tasks, where semantic tasks involved meaning-related associations, and perceptual tasks focused on surface characteristics. While previous studies separately examined these tasks, our project directly compared their impact. Experiment 1 revealed higher recognition performance for semantic generation over perceptual generation, with no significant difference in recognition across perceptual and semantic reading conditions. Experiment 2 incorporated judgments-of-learning (JOL) and no-JOL groups, demonstrating that participants accurately predicted and performed better on memory tasks involving the generation and semantic manipulations. Additionally, JOL-group participants outperformed the no-JOL group, suggesting that predicting one's memory performance enhances actual memory performance. Experiment 3 aimed to see the effects of the match between encoding and retrieval. Although the memory benefit for the JOL group diminished, it was still marginally significant, indicating the importance of the reactivity effect.

**Authors:**

Fatih Yavuz: Bilkent University; Miri Besken: Bilkent University

Investigation of The Generation Effect on Memory and Metamemory Through Semantic and Perceptual Cues

Despite the long-standing theoretical importance of the con- cept of illocutionary force in communication (Austin, 1975), quantitative measurement of it has remained elusive. The fol- lowing study seeks to measure the influence of illocutionary force on the degree to which subreddit community members maintain the concepts and ideas of previous community members’ comments when they reply to each other’s content. We leverage a information-theoretic framework implementing a measurement of linguistic convergence to capture how much of a previous comment can be recovered from its replies. To show the effect of illocutionary force, we then ask a large language model (LLM) to write a reply to the same previous comment as though it were a member of that subreddit community. Because LLMs inherently lack illocutionary intent but produce plausible utterances, they can function as a useful con- trol to test the contribution of illocutionary intent and the effect it may have on the language in human-generated comments. We find that LLMs indeed have statistically significant, lower entropy with prior comments than human replies to the same comments. While this says very little about LLMs on the basis of how they are trained, this difference offers a quantitative baseline to assess the effect of illocutionary force on the flow of information in online discourse.

**Authors:**

Zachary P Rosen: University of California, Los Angeles; Rick Dale: UCLA

LLMs Don’t “Do Things with Words” but Their Lack of Illocution Can Inform the Study of Human Discourse

The current study aimed to examine how the recognition of grey-scale photos of fearful or angry female bodies would be affected by three conditions: social situation (single person vs. facing vs. nonfacing dyad) the orientation of figures (upright vs. inverted); emotion complementarity (same vs. complementary). We hypothesized that the recognition of emotions would be the most accurate when either single or facing body pairs were presented, while the inversion would impair the perception of affective expressions. Facing bodies in fact had an advantage over nonfacing ones, same emotion condition also had higher accuracy than complementary, as well as the overall accuracy was higher for anger than fear, thus context was an important factor in differentiating between these two negative emotions. Inversion effect was not confirmed for emotions conveyed by bodies, therefore our results demonstrated that not only configural, but part-by-part analysis is also required for emotion recognition.
Keywords: body inversion effect, social interactions, bodily emotional expressions

**Authors:**

Gyopárka Barbara Lázár: Institute of Psychology; Beatrix Lábadi: Institute of Psychology University of Pécs

Inversion Effect of Emotional Bodies in Social Situations

The steady increase in time spent on smartphone applications and particularly on social networks, raises questions about the environmental and societal sustainability of such a phenomenon. Utility and enjoyment have a key role in such practices, but other factors such as passing time may also contribute. From May to November 2023, 5,028 people took part in a web survey aiming at producing durations prospectively using mobile applications like Facebook, Instagram, TikTok, Reading. The protocol introduces variables known to have an effect on time perception. On average, the produced durations were underestimated. This result is in line with the notion that tracking information and tracking time compete for the brain’s limited attentional resources and, hence, that attention plays a critical role in time estimation. Significant differences emerged between the applications tested. TikTok and Reading tasks appear the most underestimate but with opposite dynamics as the level of satisfaction and familiarity are lower for the first compared to the former. Among the variables studied to explain the difficulties in evaluating time spent, the importance of familiarity with the activity is undoubtedly something worth exploring in the context of the race between new algorithms and cognitive adaptability.

**Authors:**

Jean-Marc Josset: Orange Labs; Erwan Le Quentrec: Orange Innovation; Virginie van Wassenhove: CEA, INSERM

Our sweetest hours fly fastest… on smartphone

Electroencephalography (EEG) plays a pivotal role in the diagnosis of various neurological conditions, most notably major depressive disorder (MDD). However, deep learning-based methods currently employed for MDD detection tasks exhibit inadequate generalization capabilities, particularly across different EEG electrode channels, and demonstrate limited feature representation capacity. In this paper, we present a novel approach referred to as adaptive feature learning (AFL), which leverages kernel embedding to facilitate the learning of domain-invariant features across subjects within a reproducing kernel Hilbert space. This method aims to enhance the model's ability to generalize across multiple subjects' EEG signals. Furthermore, our research revealed that batch normalization (BN) layers within the existing MDD detection network frequently result in feature channel suppression, potentially compromising the representation power of the features. To address this issue, we propose channel activation (CA), which employs decorrelation to reactivate suppressed feature maps, thereby enhancing the model’s feature representation capability, particularly for subtle EEG changes. The effectiveness of the proposed methods is evaluated using the leave-one-subject-out protocol on MODMA and PRED+CT datasets, yielding detection accuracies of 90.56\% (MODMA) and 96.51\% (PRED+CT). Our experimental findings exhibit the superior performance of our method compared to state-of-the-art (SOTA) methods in terms of MDD recognition.

**Authors:**

Chenyang Xu: Tianjin University; Feiyi Fan: Institute of Computing Technology, Chinese Academy of Sciences; Jianfei Shen: Institute of Computing Technology, Chinese Academy of Sciences; Hanguang Wang: Tianjin University; Zhongyi Zhang: Tianjin University; Qinghao Meng: Tianjin University

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