United States

While extrinsic and intrinsic motivation have been well studied, the effects of altruistic motivation on motor performance remain largely unexplored. This study investigates the influence of altruistic motivation on brain–muscle coherence and its effect on improving time to task failure. Thirty-one participants performed two high-intensity isometric grip tasks to failure. The first trial was conducted without any extra motivational incentive, while the second trial was performed under one of three conditions: altruistic, extrinsic, or control. Electroencephalogram (EEG) and electromyogram (EMG) signals were recorded during both trials. Our results demonstrate that only the altruistic group improved their performance from the first to the second trial (68%; p = 0.004). The altruistic group exhibited increased EEG–EMG coherence in the alpha and beta bands and reduced delta coherence. These findings suggest that prosocial motivation restructures neural oscillatory activity, optimizing force control and endurance.

CogSci 2025

How Altruistic Motivation Synchronizes Brain-Muscle Coherence to Enhance Motor Performance

cognitive neuropsychology

electroencephalography (eeg)

cognitive neuroscience

motor control

empathy

sensory processing

emotion

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).
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AAMAS 2022

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

poster

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

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#### About

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.

#### Our History 

* **Society Creation**<br>
The Society was incorporated as a 501(c)(3) non-profit professional organization in Massachusetts in 1979. The organizing committee included Roger Schank, Allan Collins, Donald Norman, and a number of other scholars from psychology, linguistics, computer science, and philosophy. 
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* **Conference Creation**<br>
The first conference on cognitive science was held at La Jolla, California in August, 1979, and has occurred annually since then. The proceedings of each conference are published, and those from most years are available through Lawrence Erlbaum Associates, Inc. The annual proceedings of the Cognitive Science Conference represent a major source of information on new work and new ideas in the scientific study of thinking. In 1990, the Society, with help from an anonymous donor, established the David Marr Prize for the best student paper at each annual meeting.
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* **Journal Creation**<br>
The Journal, Cognitive Science, began publication in 1976, and is now published by Wiley-Blackwell. The Executive Editor is currently Richard P. Cooper of Birkbeck, University of London, and there are 18 Associate Editors and a 30-member editorial board. It serves as the premier outlet for research reports that intersect two or more disciplines. Copyrights for articles published in the journal are held by the Society. The Governing Board of the Cognitive Science Society voted in late 2006 to found a new journal, Topics in Cognitive Science (topiCS). The Editor in Chief is Wayne Gray, Cognitive Science Department, Rensselaer Polytechnic Institute. The journal seeks to fill a niche not occupied by Cognitive Science Journal or other cognitive science journals. Membership in the Society includes a subscription to Cognitive Science and TopiCS. Copyrights for articles published in the journal are held by the Society.
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#### Code of Conduct

By attending the CogSci 2025 Conference, you are required to adhere to the society’s **[Code of Conduct](https://drive.google.com/file/d/1ChPuihLy6jE_BWqfO7J2KKgX35JW2zsM/view?usp=sharing)**.
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The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Learning perception-action contingencies from the environment depends on attention, to efficiently control cognition and selectively process sensory information. Shifting attention between sensory modalities incurs a cost in humans, other primates, and rodents, resulting in slower learning in the new modality. Previous set-shifting work in rats showed that increased difficulty of perceptual discrimination in the preceding modality increases the following shift cost. We studied this in humans by manipulating perceptual attention in one sensory modality, titrating task demand by staircase design, to test the effect on perceptual contingency learning in another modality. To accommodate the complexity of human learning, we introduce a Bayesian method to decompose and estimate learning characteristics from performance data. This method identifies the completion of rule acquisition and consolidation, accounting for individual variation in learning. Results show the expected modality shift cost, and offer new evidence in humans that shift cost is exacerbated by prior demands on attention.

Higher perceptual attention cost slows contingency learning after a modality shift

The Rational Speech Act (RSA) model has been employed to explain word learning and inferences based on the costs of forms. Here, we focus on a hitherto untested and counterintuitive cost-based effect predicted by RSA: In learning a lexicon with two forms and meanings, learners should prefer an ambiguous costly form over an ambiguous cheap form. We demonstrate this prediction in an RSA model. We then measure reaction times and lexicon ratings in a novel word learning task to test whether a lexicon with an ambiguous costly form is less surprising than one with an ambiguous cheap form. We found no clear evidence for this effect in either measure. We discuss alternative explanations for documented cost-based effects, and the possibility that cost-based inferences may not occur during learning.

Testing counterintuitive predictions about cost-based inferences in learning from the Rational Speech Act model

Two experiments investigated how task demands influence sentence processing mechanisms, as reflected in neural responses at the disambiguating word. Participants read garden-path sentences with late-closure ambiguity (e.g., While the man hunted the deer ran into the woods) and answered comprehension questions while their brainwaves were recorded. Experiment 1 used standard questions (e.g., Did the man hunt the deer?), while Experiment 2 used "explicit" questions (e.g., Did the sentence explicitly say that the man hunted the deer?) to reduce inference-based responses. Results showed a typical P600 effect for ambiguous sentences in Experiment 1, indicating syntactic reanalysis. In Experiment 2, responses showed an N400 followed by Sustained Frontal Negativity, suggesting a shift toward plausibility evaluation and increased processing load. The explicit questions appear to alter underlying sentence processing mechanisms, prompting more effortful resolution of interpretive conflict beyond initial syntactic reanalysis.

What You Ask Affects What You Get: Task-Dependent ERPs in the Processing of Syntactically Ambiguous Sentences

Collaborative multi-brain motor imagery is an innovative brain-computer interface (BCI) paradigm that records and decodes brain signals from multiple individuals to collectively complete motor imagery tasks. However, existing decoding methods often rely on techniques such as averaging, concatenating, or cross-brain coupling of data or features, and lack coordination between single-brain and multi-brain decision-making in this context. To address this, we propose a dual-branch electroencephalogram (EEG) decoding method that jointly learns private and shared domain information. The method employs a Siamese network for private common spatial pattern (CSP) learning and a feature-sharing network for shared features, then combines the outputs for classification. Experiments with EEG data demonstrated a 10.27% improvement over the single-brain scenario and a 9% improvement over state-of-the-art methods. This approach effectively integrates private and shared domain learning, advancing collaborative BCI technology.

Dual-Branch EEG Decoding Method for Collaborative Multi-Brain Motor Imagery

Number words follow a set of principles that some have proposed to be universal. According to Hurford (1975), these principles include phrase-structure rules along with additional arithmetic constraints. However, languages vary in how transparently their number systems reflect these principles; for example, the English teens (e.g., twelve) and Turkish decades (e.g., yirmi) deviate from them. How do such irregularities influence speakers’ understanding of number word structure? To find out, we examined grammaticality judgments for novel number words among speakers of Chinese (with a transparent number system) and English and Turkish (with less transparent systems). While all groups demonstrated a better understanding of the phrase-structure rules than the arithmetic constraints, Chinese speakers showed more sensitivity to both the rules and the constraints than English or Turkish speakers. The results pinpoint ways in which the structure of number words is available to all speakers and ways in which it varies.

Cross-Linguistic Similarities and Differences in People’s Understanding of the Structure of Number Words

Patterns are fundamental to human cognition, enabling the recognition of structure and regularity across diverse domains. In this work, we focus on structural repeats, patterns that arise from the repetition of hierarchical relations within sequential data, and develop a candidate computational model of how humans detect and understand such structural repeats. Based on a weighted deduction system, our model infers the minimal generative process of a given sequence in the form of a Template program, a formalism that enriches context-free grammar with repetition combinators. Such representation efficiently encodes the repetition of sub-computations in a recursive manner. As a proof of concept, we demonstrate our model's capability on short sequences from music and action planning. The proposed model offers broader insights into the mental representations and cognitive mechanisms underlying human pattern recognition.

A Computational Cognitive Model for Processing Repetitions of Hierarchical Relations

Computational models of human learning can play a significant role in enhancing our knowledge about nuances in theoretical and qualitative learning theories and frameworks. There are many existing frameworks in educational settings that have shown to be verified using empirical studies, but at times we find these theories make conflicting claims or recommendations for instruction. In this study, we propose a new computational model of human learning, Procedural ABICAP, that reconciles the ICAP, Knowledge-Learning-Instruction (KLI), and cognitive load theory (CLT) frameworks for learning procedural knowledge. ICAP assumes that constructive learning generally yields better learning outcomes, while theories such as KLI and CLT claim that this is not always true. We suppose that one reason for this may be that ICAP is primarily used for conceptual learning and is underspecified as a framework for thinking about procedural learning. We show how our computational model, both by design and through simulations, can be used to reconcile different results in the literature. More generally, we position our computational model as an executable theory of learning that can be used to simulate various educational settings.

Reconciling Different Theories of Learning With an Agent-based Model of Procedural Learning

Category learning is an important ability that underlies complex cognitive processes such as object recognition and speech perception. Categories are ubiquitous across modalities and people differ greatly in their ability to learn novel categories. Here, we addressed a modality-specific cognitive individual difference that may relate to category learning – mental imagery. We examined how individual differences in self-reported auditory and visual mental imagery abilities related to individual differences in auditory and visual category learning. Overall, according to Bayesian analyses, there was anecdotal to moderate evidence for the null hypothesis that differences in self-reported modality-specific mental imagery are unrelated to differences in modality-specific category learning. These results have implications for theories of category learning and raise questions regarding the functions of mental imagery in cognitive processes such as categorization and learning.

Modality-Specific Mental Imagery Abilities are Unrelated to Modality-Specific Category Learning

Spatial ability is crucial in STEM and medical fields, including prosthetics and orthotics (P&O), which focuses on designing, fabricating and fitting of prostheses and orthoses. However, spatial ability in P&O practitioners remains unexplored. This study examined spatial ability in P&O master's students using mental rotation and cross-sectioning tasks, and assessed spatial anxiety, including imagery anxiety, mental manipulation anxiety, and navigation anxiety. At orientation, female students reported higher overall spatial anxiety, but there were no gender differences in spatial ability. Mental manipulation anxiety was negatively correlated with cross-sectioning ability in females (r = -0.44, p = 0.009) but not in males (r = -0.08, p = 0.080). After six months, female spatial anxiety decreased, and overall cross-sectioning ability improved. Gender differences in spatial anxiety decreased, though navigation anxiety remained higher in females (t(46) = 2.60, p = 0.01). P&O program participation appears to improve spatial ability and reduce spatial anxiety.

A Preliminary Investigation of Spatial Ability and Spatial Anxiety in Prosthetics and Orthotics Students 

During human communication, speakers balance informativeness and effort by tailoring their language to their audience. Large Language Models (LLMs) appear human-like in their communication and succeed at some tasks thought to involve social reasoning about interlocutors (in humans). Here, we tested audience design in LLMs using tasks modeled on (Isaacs & Clark, 1987). In Experiment 1, replicating findings with humans, LLMs produced longer responses when producing descriptions of pictures from a city while addressing an audience unfamiliar with that city and used more proper nouns when addressing a familiar audience. In Experiments 2 and 3, similar to previous findings with humans, LLMs used fewer words to describe pictures over the course of a multi-turn interaction. However, this pattern appeared to be sensitive to whether the user prompts also got shorter across turns, suggesting that efficient audience design in LLMs reflects patterns in training data and reinforcement learning, rather than an inherent drive
towards least effort.

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Higher perceptual attention cost slows contingency learning after a modality shift

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Popularity and Strict Popularity in Altruistic Hedonic Games and Minimum-Based Altruistic Hedonic Games

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