Netherlands

Throughout evolutionary history, and in everyday lives, it has been a crucial task to identify good and reliable cooperation partners. A good way of assessing potential partners’ quality and willingness is to engage in conversation with them. We investigated if non-verbal behaviours during such conversations can be reliable indicators of interactants’ cooperativeness – in contrast to the semantic content of utterances that can be easily faked. Specifically, we predicted that interactants who align in their use of non-verbal behaviours would also act more cooperatively in other tasks beyond the conversation. To test this, we analyzed gestures in the British TV game show Golden Balls, where contestants discussed and faced a game-theoretic decision to split or steal a monetary prize. Results suggest that individuals choosing to split indeed align their non-verbal behaviours more than those choosing to steal. This implies that subtle movements can serve as reliable indicators of trustworthy cooperation partners.

**Authors:**

Klaudia Karkowska: Nicolaus Copernicus University; Darya Namednikava: Centre for Language Evolution Studies, Nicolaus Copernicus University ; Marek Placiński: Nicolaus Copernicus University in Toruń; Michael Pleyer: Nicolaus Copernicus University in Toruń; Theresa Matzinger: University of Vienna

CogSci 2024

The relationship between non-verbal alignment and cooperativeness in a game theory-based TV show

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** 
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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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#### **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.

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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

The process of adaptation to the partner in the course of an interaction is still not well understood. In the case of explanatory dialogues, to provide satisfying explanations, explainers have to consider the needs of the explainees. This requires mental representations of the explainees, i.e., “partner models”. Little is known about whether and how modifications of partner models during an explanation take place. We assumed that they get informed by the interactive behaviour of the explainee and investigated partner models in dependence on explainees’ verbal moves. A total of 59 dyadic explanations were investigated in an observation study. The comparison of the partner models before and after the explanation showed changes regarding, e.g., knowledge, interest in the explanation, cooperation, and mood. Moves such as questions as well as summarising and paraphrasing information given by the explainee were associated with the partner model dimensions interest in the explanation and co-construction.

**Authors:**

Heike M. Buhl: Paderborn University; Josephine Beryl Fisher: University Paderborn; Katharina Rohlfing: Paderborn University

Changes in Partner Models – Effects of Adaptivity in the Course of Explanations

Numbers are not only used for quantification (cardinals), but also for sequencing (ordinals), and identifying entities (nominals). For example, the sentence “Player number 23 took 2nd place by scoring 3 goals” features nominal, ordinal, and cardinal uses of numbers, in that order. Claims about the relative prevalence of these uses (Wiese, 2004, Niederer 2005) have never been tested. We present the first large-scale analysis of 3,600 numbers in context, showing that cardinal uses are dominant (83.4%), followed by ordinals (11.8%), and then nominals (4.8%). Round numbers, which are associated with approximation, dominate for cardinals (76.4%) but not ordinals (31.1%) or nominals (23.3%). The prevalence of round numbers increases with magnitude only for cardinals. We discuss implications for the logarithmic scaling of the mental number line (Dehaene & Mehler, 1992), the approximate number system (e.g., Rinaldi & Marelli, 2020), and children’s acquisition of number concepts (e.g., Colomé & Noël, 2012).

**Authors:**

Greg Woodin: University of Birmingham; Bodo Winter: University of Birmingham

Numbers in context: Cardinals, ordinals, and nominals

It is now well-established that the visual features of objects influence the sounds we make to refer to them. This is called sound symbolism. We present the results of a two-part study that explores the extent to which the visual features of writing systems correspond to the smallest spoken units of language. In Study 1, participants (n = 322) classified the shape of a set of glyphs, representative of the world’s script families. The purpose was to create an open-source database of normed glyphs for future research in cognitive linguistics. In Study 2, participants (n = 73) were prompted to select either a round or angular glyph after hearing one of two kinds of phonemes (vowel or consonant) from the International Phonetic Alphabet. Results from a logistic regression suggest that the type of sound had a significant effect on the choice of glyph, and that vowel sounds increased the likelihood of choosing round glyphs by 30%. The significant correlation between what subjects heard and their choice of glyph suggests that the effect may extend to such sound symbolic relations in real-world writing systems. Our ongoing research seeks to substantiate these findings with increased glyph contrast and more diverse populations.

**Authors:**

Alexander Porto: Duquesne University; Alexander Basalyga: Independent Researcher; Mr. Nikolai Huckle: SCHUFA Holding AG; Julio Santiago: University of Granada; Elizabeth Fein: Duquesne University; Alexander Kranjec: Duquesne University

Sound Symbolism Across Diverse Writing Systems

Large Language Models (LLMs), which match or exceed human performance on many linguistic tasks, are nonetheless commonly criticized for not “understanding” language. These critiques are hard to evaluate because they conflate “understanding” with reasoning and common sense—abilities that, in human minds, are dissociated from language processing per se. Here, we instead focus on a form of understanding that is tightly linked to language: mapping sentence structure onto an event description of “who did what to whom” (thematic roles). Whereas LLMs can be directly trained to solve to this task, we asked whether they naturally learn to extract such information during their regular, unsupervised training on word prediction. In two experiments, we evaluated sentence representations in two commonly used LLMs—BERT and GPT-2. Experiment 1 tested hidden representations distributed across all hidden units, and found an unexpected pattern: sentence pairs that had opposite (reversed) agent and patient, but shared syntax, were represented as more similar than pairs that shared the same agent and same patient, but differed in syntax. In contrast, human similarity judgments were driven by thematic role assignment. Experiment 2 asked whether thematic role information was localized to a subset of units and/or to attention heads. We found little evidence that this information was available in hidden units (with one exception). However, we found attention heads that reflected thematic roles independent of syntax. Therefore, some components within LLMs capture thematic roles, but such information exerts a much weaker influence on their sentence representations compared to its influence on human judgments.

**Authors:**

Joseph Denning: UCLA; Xiaohan (Hannah) Guo: The University of Chicago; Bryor Snefjella: UCLA; Idan A Blank: University of California, Los Angeles

Do Large language Models know who did what to whom?

Empirical evidence shows that gendered languages influence speaker’s perception of gender of inanimate nouns. In this framework, we aimed to explore whether grammatical gender can override gender stereotypes. One hundred fourteen native Greek speakers whose second language was English were asked to match stereotypically male- and female-associated nouns presented in Greek or in their English translation with a male or female face. The nouns denoted agency and communality. Participants were presented with nouns both congruent and incongruent in terms of conceptual and grammatical gender. Responses for both Greek and English nouns were provided consistently with gender stereotypes. Critically, although responses were not dominated by grammatical gender, for a female-associated noun, a grammatically masculine gender would reduce female responses. Moreover, participants assigned a male face faster for male-associated nouns than for female associated nouns irrespective of grammatical gender. Male participants provided fewer female responses for agentic nouns than female participants.

**Authors:**

Nikoletta Galeraki: Deree, The American College of Greece; Eleni Orfanidou: Deree, The American College of Greece

Can Grammatical Gender Override Gender Stereotypes?

The present study examines the feasibility of conducting iconic artificial language learning (ALL) experiments in a fieldwork setting. We taught the pictographic language from Shapiro and Steinert-Threlkeld (2023) to speakers of San Martín Peras Mixtec in Oaxaca, Mexico. In a qualitative analysis, we explore whether these speakers display similar word-ordering behaviors to those observed among other populations, while developing insights for future ALL field experiments. We show that iconic ALL offers a promising path forward for considering understudied communities in the cognitive sciences.

**Authors:**

Naomi Tachikawa Shapiro: Radboud Universiteit; Andrew Hedding: University of Washington; Shane Steinert-Threlkeld: University of Washington

Iconic Artificial Language Learning in the Field: An Experiment with San Martín Peras Mixtec Speakers

The present study investigated the effects of presentational timing, operationalized as different levels of temporal overlap, on cross-modal priming of written words. We used a paradigm where the playback of spoken word primes was shifted relative to the presentation of written targets (asynchronous, partially overlapping, and synchronous presentation). Our participants (n = 48) carried out a speeded lexical decision task on the written targets. Presenting the spoken primes, albeit the words’ onset, before the written targets reduced lexical decision times to both words and pseudowords. Asynchronous presentation of the spoken primes resulted in the largest difference between word and pseudoword response times. We discuss our results in relation to the mental structure of human word knowledge and in the context of word form acquisition.

**Authors:**

Johanna Funk: Philipps University of Marburg; Louis Terenci Hessler Carbonell: Philipps Universität Marburg ; Denise Al-Rubaye-Jung: DSA; Michelle Morgenstern: Philipps-Universität Marburg; Elisa Schmied: Philipps-Universität Marburg ; Jasemina Tzallas: Philipps-Universität Marburg ; Florian Hintz: Philipps University

Cross-modal priming of written words across different timing conditions

What words are central in our semantic representations? In this experiment, we compared the core vocabulary derived from different association-based and language-based distributional models of semantic representation. Our question was: what kinds of words are easiest to guess given the surrounding sentential context? This task strongly resembles the prediction tasks on which distributional language models are trained, so core words from distributional models might be expected to be easier to guess. Results from 667 participants revealed that people's guesses were affected by word predictability, but that aspects of their performance could not be explained by distributional language models and were better captured by association-based semantic representations.

**Authors:**

Andrew Wang: The University of Melbourne; Simon De Deyne: University of Melbourne; Meredith McKague: University of Melbourne; Andrew Perfors: University of Melbourne

Word prediction is more than just predictability: An investigation of core vocabulary

It has been proposed that Chinese classifiers facilitate efficient communication by reducing the noun uncertainty in context. Although recent evidence has undermined this proposal, it was obtained using the common method of equating noun occurrence probabilities with corpus frequencies. This method implicity assumes words occur uniformly across contexts, yet this is inconsistent with empirical findings showing word distributions to be bursty. We hypothesized that if language users are sensitive to burstiness, and if classifiers provide information about upcoming nouns, this information will be less important in reducing uncertainty about noun after their first mention. We show that classifier usage provides more information at earlier mentions of nouns and and less information at later mentions, and that the actual classifier distribution appears inconsistent with previous proposals. These results support the idea that classifiers facilitate efficient communication and indicate that language users representations of lexical probabilities in context are dynamic.

**Authors:**

Sihan Chen: MIT; Edward Gibson: Massachusetts Institute of Technology; Michael Ramscar: University of Tübingen

Availability, informatively and bustiness: Why average corpus measures are an inaccurate guide to surprisal in language

Recent advances in generative language models, such as ChatGPT, have demonstrated an uncanny ability to produce texts that appear to be comparable to those produced by humans. Several key empirical results related to human processing of language, such as analogical reasoning, have been replicated using these models. Nevertheless, there are some important differences between the language generated by these models and language produced by humans. In this paper, I examine how LLMs performs on two pronoun disambiguation tasks reported on by Rhode, Levy, and Kehler (2011) and Sagi and Rips (2014). While LLMs performed reasonably in these tasks, their responses demonstrate stronger language-based biases while the influence of world knowledge, such as causal relationships, was lessened. Because LLMs replicate language produced by humans, these results can help shed light on which aspects of language use are directly encoded in language and which require additional reasoning faculties beyond language processing.

**Authors:**

Eyal Sagi: University of St. Francis

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