Netherlands

Large Language models (LLM) exhibit human-like proficiency in various tasks such as translation, question answering, essay writing, and programming. Emerging research explores the use of LLMs in collective problem-solving endeavors, such as tasks where groups try to uncover clues through discussions. Although prior work has investigated individual problem-solving tasks, leveraging LLM-powered agents for group consensus and decision-making remains largely unexplored. This research addresses this gap by (1) proposing an algorithm to enable free-form conversation in groups of LLM agents, (2) creating metrics to evaluate the human-likeness of the generated dialogue and problem-solving performance, and (3) evaluating LLM agent groups against human groups using an open source dataset. Our results reveal that LLM groups outperform human groups in problem-solving tasks. LLM groups also show a greater improvement in scores after participating in free discussions. In particular, analyses indicate that LLM agent groups exhibit more disagreements, complex statements, and a propensity for positive statements compared to human groups. The results shed light on the potential of LLMs to facilitate collective reasoning and provide insight into the dynamics of group interactions involving synthetic LLM agents.

**Authors:**

Yinuo Du: Software and Societal Systems Department; Prashanth Rajivan: University of Washington; Cleotilde Gonzalez: Carnegie Mellon University

CogSci 2024

Large Language Models for Collective Problem-Solving: Insights into Group Consensus Decision-Making

### Looking for Cogsci 2023? Please [click here](https://underline.io/events/405/reception) to switch to 2023 edition.


**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

The program committee for CogSci 2021 received 1,025 submissions, including 710 full papers, 275 member abstracts, 8 publication-based talks, as well as 15 proposals for symposia, and 17 for workshops and tutorials. After a rigorous review process, the committee selected 152 papers for oral presentation and inclusion in the conference proceedings (14.8%), 332 papers for poster presentation and inclusion in the proceedings (32.4%), and 175 papers for poster presentation with inclusion of abstracts in the proceedings (17.1%) We also selected 275 submitted member abstracts. In addition, we accepted 6 publication-based talks, 12 symposia, and 9 workshops and tutorials to make for an exciting and inclusive program.

We hope that you enjoy the conference this year!<br><br>

**Your Program Chairs,**
![](https://assets.underline.io/uploads/markdown_image/1/image/99984908caba5e6453e5fc5ba34f3507.jpg)

**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

<iframe width="560" height="315" src="https://www.youtube.com/embed/464h1m3iU68" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

To review the proceedings for the conference or review the searchable program, please visit the [website](https://cognitivesciencesociety.org/program/).


CogSci 2021

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.

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**<br>
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**<br>
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)<br>
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)<br>
1415: [Invited Symposium: Dynamics between minds and the environment](https://underline.io/events/465/sessions?eventSessionId=18016&searchGroup=lecture)<br>
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)<br>

**Friday, July 26**<br>
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)<br>
1030: [Rumelhart Symposium: Childhood as exploration](https://underline.io/events/465/sessions/18038/lecture/100346-childhood-as-exploration)<br>
1415: [Elman Prize Symposium](https://underline.io/events/465/sessions?eventSessionId=18053&searchGroup=lecture)<br>
1600: [Invited Symposium: Dynamics between minds](https://underline.io/events/465/sessions?eventSessionId=18065&searchGroup=lecture)<br>
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)<br>

**Saturday, July 27**<br>
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)<br>
1000: [Glushko Talks](https://underline.io/events/465/sessions?eventSessionId=18086&searchGroup=lecture)<br>
1500: [Invited Symposium: Dynamics within minds](https://underline.io/events/465/sessions?eventSessionId=18100&searchGroup=lecture)<br>


#### **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**<br>
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**<br>
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.
<br><br>

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To access the site please register [**here**](https://cognitivesciencesociety.org/registration/). 

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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.

Phonological anticipation, the ability to predict forthcoming words based on phonological cues, plays a pivotal role in language processing (Brunellière et al., 2018; Ito et al., 2018). Although predictive language abilities have been observed in infants, the mechanisms and developmental trajectories within native Spanish-speaking populations remain underexplored. This study delves into the investigation of phonological anticipation in Mexican Spanish-speaking infants through the utilization of eye-tracking methodology. The objective is to ascertain whether infants across different age groups can anticipate phonologically related words within semantically restrictive sentences. To achieve this, auditory sentences such as "La gallina pone... huevos" (The hen lays... eggs) were presented, accompanied by visual stimuli that included both phonologically related ("hueso" - bone) and unrelated competitor ("calcetín" - sock). Results indicate that 18- and 24-month-olds did not exhibit anticipation based solely on semantics, necessitating the auditory presentation. However, 30-month-olds demonstrated phonological anticipation, signaling developmental changes.

**Authors:**

Alejandra Mitzi Castellón-Flores: Universidad Nacional Autónoma de México; Armando Q Angulo-Chavira: Universidad Nacional Auntónoma de México; Natalia Arias-Trejo: Universidad Nacional Autónoma de Mexico, UNAM

Unveiling the Path to Phonological Anticipation: Insights from Infants' Eye Movements

We examine the role of referential contrast in the learning of homophone versus superordinate terms: when an unfamiliar word is mapped to a broad range of referents across learning instances, how do learners decide whether the word has distinct meanings or a single superordinate meaning? We hypothesize that learners can accomplish this via semantic contrast among candidate referents within the referential domains. Learners will be more likely to establish homophonous meanings when contrasting referents are from a neighboring category of the target, and more likely to establish a superordinate meaning when contrasting referents are from more distant categories. We also expect homophone learning to be more difficult because of their additional demands on learning and memory. Our predictions were borne out in a series of experiments and in a cross-situational model of word learning. Overall, this study offers new perspectives for how learners form hypotheses for novel word meanings.

**Authors:**

Daoxin Li: University of Pennsylvania; Christine Soh Yue: University of Pennsylvania; John Trueswell: University of Pennsylvania

Is it a bat or a thing? Referential contrast in the learning of homophones and superordinate terms

The current study used a real-time interactive “advisor-decider” task, in which advice given by one participant results in an onerous workload for another participant, to show that self-conscious affect based on performance in one domain shapes decisions to engage in prosocial behavior in an unrelated domain: Advisors that performed at or worse than the norm, in terms of giving incorrect advice, made more frequent subsequent charity donations. Intriguingly, when advisors were given social information about their performance relative to the norm, this pattern was reversed, such that advisors that performed worse than the norm made less frequent donations. We interpret this finding as reflecting a shift in the emotion driving the behavior, from guilt to shame. Consistent with this interpretation, trait measures of guilt proneness but not of shame proneness predicted an increase in both the probability and magnitude of donations.

**Authors:**

Lucila Arroyo: University of California, Irvine; Mimi Liljeholm: University of California, Irvine

Exogenous Self-Blame Modulates Charitable Giving

This study investigates the processing of structurally ambiguous relative clause (RC) constructions in Japanese by Persian-Japanese learners, examining the influence of their native language (L1) on second language (L2) processing. It challenges the universality of parsing strategies through a self-paced reading (SPR) task. The results indicate a preference for High Attachment (HA) and a stronger tendency towards NP-high when it's an animate noun, in both Persian and Japanese. Descriptive analyses further revealed a shift from Low Attachment (LA) to HA among native Japanese speakers, suggesting unforced revision. However, there was an absence of a clear animacy effect on their preference. These findings suggest parallel interactive mechanisms in sentence processing and the transfer of syntax and semantic information from L1 to L2. Moreover, the study underscores language-specific differences in sentence processing, emphasizing the impact of language dominance in cross-linguistic transfer and contributing to our understanding of bilingual sentence processing.

**Authors:**

Faezeh Zare: University of the Ryukyus ; Hideki Goya: University of the Ryukyus

Processing of Relative Clause Structural Ambiguity by Iranians' Japanese Learners - From the Perspective of the Effect of L1 and the Animacy of the Head Nouns on L2 Sentence Processing -

Exploration in sequential decision problems is a computationally challenging problem. Yet, animals exhibit effective exploration strategies, discovering shortcuts and efficient routes toward rewarding sites. Characterizing this efficiency in animal exploration is an important goal in many areas of research, from ecology to psychology and neuroscience to machine learning. In this study, we aim to understand the exploration behavior of animals freely navigating a complex maze with many decision points. We propose an algorithm based on a few simple principles of animal movement from foraging studies in ecology and formalized using reinforcement learning. Our approach not only captures the search efficiency and turning biases of real animals but also uncovers longer spatial and temporal dependencies in the decisions of animals during their exploration of the maze. Through this work, we aspire to unveil a novel approach in cognitive science of drawing interdisciplinary inspiration to advancing the field's understanding of complex decision-making.

**Authors:**

Umesh K Singla: Princeton University; Marcelo G Mattar: New York University

Temporal Persistence Explains Mice Exploration in a Labyrinth

Recent neuroscience studies suggest that the hippocampus encodes a low-dimensional ordered representation of evidence through sequential neural activity. Cognitive modelers have proposed a mechanism by which such sequential activity could emerge through the modulation of the decay rate of neurons with exponentially decaying firing profiles. Through a linear transformation, this representation gives rise to neurons tuned to a specific magnitude of evidence, resembling neurons recorded in the hippocampus. Here we integrated this cognitive model inside reinforcement learning agents and trained the agents to perform an evidence accumulation task designed to mimic a task used in experiments on animals. We found that the agents were able to learn the task and exhibit sequential neural activity as a function of the amount of evidence, similar to the activity reported in the hippocampus.

**Authors:**

James Mochizuki-Freeman: Indiana University; Md Rysul Kabir: Indiana University; Zoran Tiganj: Indiana University

Incorporating a cognitive model for evidence accumulation into deep reinforcement learning agents

Predicting Click-Through Rate (CTR) is crucial in product and content recommendation, as it involves estimating the likelihood of a user engaging with a specific advertisement or content link. This task encompasses understanding the complex cognitive processes behind human interactions with recommended content. Learning varied feature embeddings that reflect different cognitive responses in various circumstances is significantly important. However, traditional methods typically learn fixed feature representations, leading to suboptimal performance. Some recent approaches attempt to address this issue by learning bit-wise weights or augmented embeddings for feature representations, but suffer from uninformative or redundant features in the context. To tackle this problem, inspired by the Global Workspace Theory in conscious processing, which posits that only a specific subset of the product features are pertinent while the rest can be noisy and even detrimental to human-click behaviors, we propose a CTR model that enables Dynamic Embedding Learning  with Truncated Conscious Attention for CTR prediction, termed DELTA. DELTA contains two key components:

**Authors:**

Chen Zhu: Tsinghua University; Liang Du: IEG; Hong Chen: Tsinghua University; Shuang Zhao: Tencent; Zixun Sun: Tencent; Xin Wang: Tsinghua University; Wenwu Zhu: Tsinghua University

DELTA: Dynamic Embedding Learning with Truncated Conscious Attention for CTR Prediction

The current study examines the interaction between predisposed mood, perceptual processing, and induced mood using music. We conducted an experiment in which participants were asked to identify stimuli at global or local (G/L) perceptual levels with four different background music conditions, which had different valence and arousal ratings. We used BMIS to assess current mood and PHQ-9 and GAD-7 to assess depression and anxiety, and divided the participants into two groups: distress and no distress (encompassing both disorders). We found a main effect of background music on mood. However, the distress group showed an overall low mood. Further, we observed an overarching effect of predisposed mood, encompassing depression and anxiety, on individuals’ transient mood experience and perceptual task performance. Individuals in the non-distress group showed a larger global-precedence effect. The results are discussed in light of emotional reactivity theories and the theory of positive emotion.

**Authors:**

Manas Kabre: International Institute of information technology, Hyderabad; Priyanka Srivastava: International Institute of Information Technology, Hyd

Predisposed Mood and Music in Perceptual Judgement Task

Neural language models, particularly large-scale ones, have been consistently proven to be most effective in predicting brain neural activity across a range of studies. However, previous research overlooked the comparison of these models with psychologically plausible ones. Moreover, evaluations were reliant on limited, single-modality, and English cognitive datasets. To address these questions, we conducted an analysis comparing encoding performance of various neural language models and psychologically plausible models. Our study utilized extensive multi-modal cognitive datasets, examining bilingual word and discourse levels. Surprisingly, our findings revealed that psychologically plausible models outperformed neural language models across diverse contexts, encompassing different modalities such as fMRI and eye-tracking, and spanning languages from English to Chinese. Among psychologically plausible models, the one incorporating embodied information emerged as particularly exceptional. This model demonstrated superior performance at both word and discourse levels, exhibiting robust prediction of brain activation across numerous regions in both English and Chinese.

**Authors:**

Yunhao Zhang: State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS, Beijing, China.; Shaonan Wang: State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS, Beijing, China.; Xinyi Dong: Beijing Normal University; Jiajun Yu: China Agricultural University; Chengqing Zong: State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS, Beijing, China

Navigating Brain Language Representations: A Comparative Analysis of Neural Language Models and Psychologically Plausible Models

Counterfactual statements are famously difficult to process, and so are negated sentences and infrequent clause orders. Here, we argue that their combination can ease much of the processing cost when these difficult constructions align to clarify what is being referred to, thereby reducing referential uncertainty. In Experiment 1, we tested how affirmative and negative counterfactual statements (e.g., If there had been (no) zebras, there would have been (no) lions) are interpreted using a web-based eye-tracking paradigm. We found that negation facilitates processing, particularly when a Question under Discussion is about the actual state of affairs. In Experiment 2, reversing the clause order resulted in easier comprehension. These results provide support for a model of incremental language processing that puts the construal of semantic representations front and center.

**Authors:**

Ebru Evcen: University of California San Diego; Eva Wittenberg: Central European University

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