Netherlands

Video recording is a widely used tool for studying animal behavior, especially in fields such as primatology. Primatologists rely on video data to analyze and research topics such as social grooming to uncover subtle mechanisms behind complex social behavior and structures. Insights into these social behaviors may provide us with a better understanding of our closest living relatives, but also new theories and insights into our own behavior. However, analyzing this type of data using manual annotation is currently a time-consuming task. Here we present an end-to-end pipeline to track chimpanzee (Pan troglodytes) poses using DeepLabCut (DLC) which then serves as input to a support vector machine. This classifier was trained to detect role transitions within grooming interactions. We replicate a recent study showing that DLC has usability value for chimpanzee data collected in natural environments. Our combined method of tracking and classification is remarkably successful in detecting the presence of grooming, indicating the directionality and a change in turn with an accuracy above 86% on unseen videos. We can identify particular pose features used in the classification of grooming, which will contribute to the exploration of turn-taking dynamics on a scale that would otherwise be difficult to attain with traditional methods. Finally, our pipeline can in principle be applied to recognize a variety of other socially interactive behaviors that are largely recognizable by (joint) postural states.

**Authors:**

Yana van de Sande MSc.: Radboud University; Wim Pouw: Radboud University Nijmegen; Lara M Southern: Osnabrück University

CogSci 2024

Automated Recognition of Grooming Behavior in Wild Chimpanzees

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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technical paper

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) 
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**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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#### **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 
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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

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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.

An important aspect of language comprehension is learning and generalizing complex lexical relations. For instance, having learned that the phrase preserve cucumbers predicts vinegar and that preserve berries predicts dehydrator, one should be able to infer that the novel phrase preserve peppers is more compatible with vinegar, because pepper is more similar to cucumber. We studied the ability to perform such (compositional) generalization in distributional models trained on an artificial corpus with strict semantic regularities. We found that word-encoding models failed to learn the multi-way lexical dependencies. Recurrent neural networks learned those dependencies but struggled to generalize to novel combinations. Only mini GPT-2, a minified version of the Transformer GPT-2, succeeded in both learning and generalization. Because successful generalization in our tasks requires capturing the relationship between a phrase and a word, we argue that mini GPT-2 acquired hierarchical representations that approximate phrase structure. Our results show that, compared to older models, Transformers are architecturally advantaged to perform compositional generalization.

**Authors:**

Shufan Mao: University of Illinois at Urbana Champaign; Philip A Huebner: Pattern; Jon Willits: University of Illinois at Urbana-Champaign

Compositional Generalization in Distributional Models of Semantics: Transformer-based Language Models are Architecturally Advantaged

Prediction error, both at the level of sentence meaning and at the level of the next presented word, has been shown to successfully account for N400 amplitudes. Here we investigate whether people differ in the representational level at which they implicitly predict upcoming language. We computed a measure of prediction error at the level of sentence meaning (termed semantic update) and a measure of prediction error at the level of the next word (surprisal). Both measures significantly accounted for N400 amplitudes even when the other measure was controlled for. Most important for current purposes, both effects were significantly negatively correlated. Moreover, random-effects model comparison showed that individuals differ in whether their N400 amplitudes are driven by semantic update only, by surprisal only, or by both, and that the most common model in the population was either semantic update or the combined model but clearly not the pure surprisal model.

**Authors:**

Milena Rabovsky: University of Potsdam; Alessandro Lopopolo Lopopolo: University of Potsdam; Daniel J. Schad: HMU Health and Medical University Potsdam

Interindividual differences in predicting words versus sentence meaning: Explaining N400 amplitudes using large-scale neural network models

Recent studies link surprisal —a measure of conditional probability of words in context—to the N400 component size in event-related potentials (ERP), supporting a role for predictive coding in language comprehension. An alternative account argues that N400 variations are better explained by a retrieval mechanism sensitive to the semantic similarity between a word and its preceding context. Because jokes often rely on the presence of unexpected words that relate to the prior context multiple ways, they afford observation of the relative importance of contextual predictability and contextual similarity. We employed state-of-the-art machine learning to assess the surprisal and contextual semantic similarity of critical words in jokes and control stimuli. Using regression models to predict ERP, we found contextual similarity best explains N400 and P600 responses, supporting the semantic similarity account. Additionally, jokes elicit enhanced N400 and P600 responses that go beyond that attributable to their surprisal and contextual semantic similarity.

**Authors:**

Haoyin Xu: University of California, San Diego; Masaki Nakanishi: 1986; Seana Coulson: UC San Diego

Revisiting Joke Comprehension with Surprisal and Contextual Similarity: Implication from N400 and P600 Components

Surprisal values from large language models (LLMs) have been used to model the amplitude of the N400.
This ERP component is sensitive not only to contextual word expectancy but also to semantic association, such that unexpected but associated words do not always induce an N400 increase.
While LLMs are also sensitive to association, it remains unclear how they behave in these cases.
Moreover, another ERP component, the P600, has shown graded sensitivity to plausibility-driven expectancy, while remaining insensitive to association; however, its relationship to LLM surprisal is not well researched yet.
In an rERP analysis, we evaluate surprisal values of two unidirectional transformers on their ability to model N400 and P600 effects observed in three German ERP studies isolating the effects of association, plausibility, and expectancy.
We find that surprisal predicts an N400 increase for associated but implausible words, even when no such increase was observed in humans.
Furthermore, LLM surprisal accounts for P600 effects elicited by violations of selectional restrictions, but captures neither P600 effects from more subtle script knowledge violations nor graded P600 modulations.
The results of our investigation call into question the extent to which LLM surprisal offers an accurate characterisation of the functional generators of either the N400 or P600.

**Authors:**

Benedict Krieger: Saarland University; Harm Brouwer: Tilburg University; Christoph Aurnhammer: Saarland University; Matthew W Crocker: Saarland University

On the limits of LLM surprisal as functional Explanation of ERPs

Speech perception involves storing and integrating sequentially presented items. Recent work in cognitive neuroscience has identified temporal and contextual characteristics in humans’ neural encoding of speech that may facilitate this temporal processing. In this study, we simulated similar analyses with representations extracted from a computational model that was trained on unlabelled speech with the learning objective of predicting upcoming acoustics. Our simulations revealed temporal dynamics similar to those in brain signals, implying that these properties can arise without linguistic knowledge. Another property shared between brains and the model is that the encoding patterns of phonemes support some degree of cross-context generalization. However, we found evidence that the effectiveness of these generalizations depends on the specific contexts, which suggests that this analysis alone is insufficient to support the presence of context-invariant encoding.

**Authors:**

Oli Danyi Liu: School of Informatics; Hao Tang: School of Informatics; Naomi H Feldman: University of Maryland; Sharon Goldwater: University of Edinburgh

A predictive learning model can simulate temporal dynamics and context effects found in neural representations of continuous speech

It has long been assumed that infants' ability to discriminate between languages stems from their sensitivity to speech rhythm, i.e., organized temporal structure of vowels and consonants in a language. However, the relationship between speech rhythm and language discrimination has not been directly demonstrated. Here, we use computational modeling and train models of speech perception with and without access to information about rhythm. We test these models on language discrimination, and find that access to rhythm does not affect the success of the model in replicating infant language discrimination results. Our findings challenge the relationship between rhythm and language discrimination, and have implications for theories of language acquisition.

**Authors:**

Ruolan Leslie Famularo: University of Maryland; Ali Aboelata: University of Maryland; Thomas Schatz: Aix-Marseille Université; Naomi H Feldman: University of Maryland

Language Discrimination May Not Rely on Rhythm: A Computational Study

This study delves into how various musical factors influence the experience of auditory illusions, building on Diana Deutsch's scale illusion experiments and subsequent studies. Exploring the interaction between scale mode and timbre, this study assesses their influence on auditory misperceptions, while also considering the impact of an individual's musical training and ability to discern absolute pitch. Participants were divided into non-musicians, musicians with absolute pitch, and musicians with relative pitch, and were exposed to stimuli modified across three scale modes (tonal, dissonant, atonal) and two timbres (same, different). The findings suggest that scale illusions occur less frequently with different timbres and vary with scale mode. Crucially, the absolute pitch ability appears to have a more significant impact on the perception of illusions than the duration of musical training. This research contributes to understanding the complex interplay between various factors in auditory perception and the mechanisms behind the experience of auditory illusions.

**Authors:**

Ahyeon Choi: Seoul National University; Younyoung Bang: Seoul National University; Jeong Mi Park: Seoul National University; Kyogu Lee: Seoul National University

The Effects of Musical Factors on the Perception of Auditory Illusions

Understanding spoken words requires listeners to integrate large amounts of linguistic information over time. There has been considerable debate about how semantic context preceding or following a target word affects its recognition, with preceding semantic context often viewed as a constraint on possible future words, and following semantic context as a mechanism for disambiguating previous ambiguous input. Surprisingly, no studies have directly compared whether the timing of semantic context influences spoken word recognition. The current study manipulates the acoustic-perceptual features of a target word, a semantic cue elsewhere in the sentence biasing toward one interpretation, and the location of the semantic context. We find that the two cues are additively integrated in participants' word identification responses, and that semantic context affects categorization the same regardless of where it appears relative to the target word. This suggests that listeners can optimally integrate acoustic-perceptual and semantic information across time.

**Authors:**

Wednesday Bushong: Wellesley College; Daniel LaMarche: University at Buffalo; Elayna A Espinal: University of Hartford; Zachary J. Longo: University of Hartford

Listeners Optimally Integrate Acoustic and Semantic Cues Across Time During Spoken Word Recognition

Analogical studies demonstrate that participants often fail to retrieve a well-learned base analog during the subsequent processing of a semantically-distant target analog. We evaluated whether presenting the target analog before the base analog increases analogical retrieval during hypothesis-generation. Experiment 1 revealed a higher rate of analogical retrieval when the target analog preceded the base analog, as compared to the traditional “base-target” sequence. Using a factorial design, Experiment 2 assessed whether spontaneously acknowledging the relevance of a subsequently encountered explanation for resuming a failed explanatory attempt requires the presence of structural similarities between the base and target situations. Results demonstrated that the primary contributor to spontaneous reactivation of a failed explanatory attempt is the presentation of an analogous phenomenon, while the presence of a useful explanation alone did not yield a significant impact. These findings contribute valuable insights to the dynamics of analogical retrieval and offer relevant implications for educational strategies.

**Authors:**

Leandro E. Rivas: IPEHCS, CONICET-UNCo; Sabina Leon: CONICET-UNCo; Maximo Trench: CONICET-UNCo

Dynamics of Analogical Retrieval: Evaluating Spontaneous Access by Reversing the Traditional Presentation Order of Analogs during a Hypothesis-Generation Task

Prospect Theory has been highly influential; however its experimental paradigm lacks higher orders of uncertainty. To introduce this, participants are asked to imagine themselves facing a choice between two bags containing 100,000 blue or red balls in unknown proportions. A red ball wins £500. Participants are shown samples from each bag; e.g., 5 balls from Bag 1 (3 red) and 100 balls from Bag 2 (55 red). The bags can be represented by distributions with Bag 1 having a higher mean probability estimate (60% vs 55%), but more variance (second order uncertainty) in that estimate. By varying observed frequencies and gain vs loss formats, we seek to determine if classic findings remain when higher order uncertainties are present. Results consistent with the four-fold pattern are seen for gains (uncertainty seeking at low probability values, uncertainty aversion at high probability values) but for losses, uncertainty aversion is seen at all values.

**Authors:**

Stephen H Dewitt: University College London; Michelle Lam: University College London; Andy Shi: University College London; Borys Tam: University College London; Samuel Henri Dupret: University College London; Toby D Pilditch: University College London; David Lagnado: University College London

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