United States

A pedagogical tool in undergraduate oceanography and atmospheric science education is a water filled, rotating, tank used to model geophysical fluid flow on Earth. We used a typical classroom rotating tank to investigate students’ mental models of fluids in rotation. In two experiments, we conducted semi-structured interviews with participants (N=59) who predicted the behavior of water in rotation, explained their predictions, and attempted to make sense of observed demonstrations. We found participants had accessible mental models of fluid behavior based on analogies, however these mental models were wrong. Our results suggest that the behavior of rotating fluids is highly unintuitive and that without tangible opportunities for mental model formation, the mind adopts mental models representative of experiences with fluids to fill this gap. We discuss implications for education and suggest a better understanding of how humans reason about fluids can inform cognitive science while improving oceanography and atmospheric science instruction.

CogSci 2025

Mental models of fluids in solid body rotation: students’ reasoning about fluid processes modeled during oceanography and atmospheric science instruction

event cognition

psychology

education

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.

It has previously been shown that by using reinforcement learning (RL), agents can derive simple approximate and exact-restricted numeral systems that are similar to human ones (Carlsson, 21). However, it is a major challenge to show how more complex recursive numeral systems, similar to for example English, could arise via a simple learning mechanism such as RL.
Here, we introduce an approach towards deriving a mechanistic explanation of the emergence of efficient recursive number systems. We consider pairs of agents learning how to communicate about numerical quantities through a meta-grammar that can be gradually modified throughout the interactions. 
Utilising a slightly modified version of the meta-grammar of Hurford (1975), we demonstrate that our RL agents, shaped by the pressures for efficient communication, can effectively modify their lexicon towards Pareto-optimal configurations which are comparable to those observed within human numeral systems in terms of their efficiency.

Learning Efficient Recursive Numeral Systems via Reinforcement Learning

Previous work has shown that people’s arousal states become synchronized during natural communication. The present study examined the functional role of such synchrony. We recorded 10 personal stories, half happy, half sad, from a set of storytellers while continuously measuring electrodermal activity (EDA). A separate set of participants listened to the recordings while EDA was measured, and they completed a state-empathy questionnaire following each story. We predicted listeners would empathize more with storytellers when EDA synchrony was higher. Results revealed that synchrony did modulate empathy, but this depended on the valence of the story and the trait empathy level of the listener. Among people with low trait empathy, as synchrony increased, so did state empathy. Among people with high trait empathy, this correlation was negative. These relationships obtained only for sad stories. The findings point to intriguing individual differences in the functional role of arousal synchrony on empathy.

Individual Differences in the Functional Role of Arousal Synchrony on Empathy

People routinely make up stories to explain why things are called what they are called. These "intuitive etymological explanations" (IEEs) show up in children and adults, and even become cultural narratives shared across generations. Yet, they’re typically wrong. As a result, scholars have historically ignored them or treated them as mere curiosities that are irrelevant to our linguistic competence and even interfere with our theories of language evolution. Contrary to this view, we propose that IEEs may be a functional activity that people engage in to learn and maintain a massive, ever-changing lexicon. In Experiment 1, we find preliminary evidence that IEEs, whether self-generated or culturally-transmitted, can support word learning in comparison to control conditions in which participants engage with contextual word use (both self- generated and culturally-transmitted). In Experiment 2, we find that, despite being incorrect, culturally-transmitted IEEs can support word learning more than true etymologies. Across two preregistered experiments, our results suggest that intuitive etymological explanations, though typically incorrect, may facilitate language use by building structures of form and meaning out of our linguistic experience.

The functional view of intuitive etymological explanations

The single-celled protist Stentor roselii has long been observed to exhibit complex decision-making behaviors, yet existing machine learning and classical computational models have struggled to replicate its actions. In this paper, we propose a novel quantum-statistical framework to model S. roselii’s behavioral responses to environmental stimuli. By leveraging quantum circuits with amplitude dampening and memory effects, we construct a quantum behavioral model that captures the probabilistic and hierarchical nature of S. roselii’s decision making. Our results suggest that quantum statistical theory provides a powerful tool for representing and simulating biological decision processes.

A Quantum Statistical Model of Decision Making in a Single-Cell Eukaryote

This research examines how language (generic vs. specific) and speaker knowledge (knowledgeable vs. unknowledgeable) influence essentialist beliefs about a novel social category in children and adults. Across two studies (N = 448 children, 433 adults), adults were more likely to endorse essentialist beliefs when knowledgeable speakers used generic descriptions. Children’s responses varied by evaluation timing. In Study 1, when test questions were delayed, children’s essentialist beliefs were influenced by language but not speaker knowledge. In Study 2, when memory demands were reduced by having children evaluate claims immediately after hearing them, children showed sensitivity to speaker knowledge, mirroring adults’ responses. These findings highlight the role of language and contextual cues in shaping essentialist thought about social groups, suggesting that the effects of generics on social thought are dependent on the cultural expertise of the speaker.

Speaker knowledge modulates the effects of generic language on essentialist beliefs

Skilled readers show faster and longer saccades as they move efficiently through text. While studies of adolescents are quite sparse, teens are assumed to have adult-like behavior with rapid developmental trajectory of the oculomotor parameters (Blythe, 2014; Rayner, 1998; 2009). Age and reading skill effects on saccadic behavior were examined in young adults and adolescents reading naturalistic multi-line texts. Eye movements were recorded from 113 college students and 52 adolescents, who read publicly available English language PROVO corpus. Participants’ reading expertise was measured by vocabulary and reading comprehension tests. Linear mixed-effects regression models revealed that age interacted with reading expertise in how fast readers move through text (forward, regressive and return sweep saccades velocity). Age and vocabulary affected only teens. Individual differences emerged in a more heterogeneous population that is earlier on the developmental trajectory. The study found tighter than previously assumed coupling between oculomotor saccadic parameters and reading expertise.

Reading skill affects reading saccades well into late childhood

How do words evolve in their usage and meaning over time? We investigate the relationship between word frequency, semantic richness, and network centrality through longitudinal analysis of the Corpus of Historical American English (1820–2019). Using measures of semantic richness and network position, we find that a word’s betweenness centrality—its tendency to bridge different semantic domains—consistently predicts both its future semantic richness and frequency of use. This relationship strengthens over longer time intervals, with the strongest effects observed across a 100-year span. Notably, while frequency and semantic richness are correlated as established in the literature, our results indicate that there was no directional relationship between frequency and semantic richness, while network centrality exerts a significant influence on both of these factors. Our results suggest that a word’s position within the semantic network might play a crucial role in its evolution: words that bridge different semantic domains are more likely to develop new meanings and change in frequency over time. These findings offer new insights into the mechanisms driving language change.

Evolution on the Lexical Workbench: Disentangling Frequency, Centrality, and Polysemy in Language Evolution

Recent research has shown that people can learn more new concepts than the number of examples they are presented with. However, these results relied on strong assumptions about what skills and prior knowledge are required to perform this kind of less-than-one-shot learning. This has included having participants disentangle soft labels that fuzzily map stimuli to multiple concepts, interpret continuous feature weights, and parse complex compositional statements. We propose a novel minimal paradigm that strips away these assumptions to explore how efficiently people can simultaneously learn visual and symbolic concepts. We show theoretically that it should be possible to learn up to $2^{k-1}$ binary features from $k$ examples, and to learn up to $2^{2^{k-1}}$ unique combinations of those features.  We validate this empirically, showing that people may be able to learn as many as 8 novel binary features and up to 256 concepts corresponding to unique compositions of those features from just 4 examples.

Learning a Doubly-Exponential Number of Concepts From Few Examples

Mechanical reasoning is a hallmark of human intelligence, defined by its ubiquitous yet irreplaceable role in human activities ranging from routine tasks to civil engineering. Embedding machines with mechanical reasoning is therefore an important step towards building human-level artificial intelligence. Here, we leveraged 155 cognitive experiments to test the understanding of system stability, gears and pulley systems, leverage principle, inertia and motion, and fluid mechanics in 26 vision language models. Results indicate that VLMs consistently perform worse than humans on all domains, while demonstrate significant difficulty in reasoning about gear systems and fluid mechanics. Notably, their performance on these tasks do not improve as number of parameters increase, suggesting that current attention-based architecture may fail to grasp certain underlying mechanisms required for mechanical reasoning, particularly those pertaining to mental simulations.

Probing Mechanical Reasoning in Large Vision Language Models

Caregiving helps learners survive in the present and ultimately thrive independently without their caregiver in the future. While some caregiving provides immediate benefits, other actions focus on long-term development, even if they cause short-term discomfort or setbacks. For example, a parent might allow their child to fail in a game to learn a useful lesson about the value of perseverance. Here, we develop a probabilistic model of caregiving with a recursive theory of mind using the Memo programming language that captures these intuitions. The model considers learners as POMDP planners, and plans over such learners to intervene on their beliefs in a way that will be valuable in the future. As predicted by the model, participants favor improving learners' knowledge over immediate efficiency, but only when that knowledge has future value. Effective caregivers thus think several moves ahead, accepting short-term costs to prepare learners for long-term success.

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Learning Efficient Recursive Numeral Systems via Reinforcement Learning

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