Canada

Semantic features have been playing a central role in investigating the nature of our conceptual representations. Yet the time and effort required to sample features from human raters has restricted their use to a limited set of manually curated concepts. Given recent success of transformer-based language models, we asked whether it was possible to use such models to automatically generate meaningful lists of properties for arbitrary object concepts and whether these models would produce features similar to those found in humans. We probed a GPT-3 model to generate semantic features for 1,854 objects and compared them to existing human feature norms. GPT-3 showed a similar distribution in the types of features and similar performance in predicting similarity, relatedness, and category membership. Together these results highlight the potential of large language models to capture important facets of human knowledge and yield a new approach for automatically generating interpretable feature sets.

CogSci 2022

Semantic features of object concepts generated with GPT-3

### 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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CogSci 2025

The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

technical paper

Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
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<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/07/Cogsci-2022-PAAG-Detailed-July-12final-1.pdf)
<br>[**The Detailed Program can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/08/DeArmond-CogSci2022-program-v113-final.pdf)
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The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





Facebook sends users friend suggestions when they have friends in common with others. However, how do people actually consider mutual friendships when inferring whether two individuals are friends? We examined how people (N=352) rely on numerical factors when judging whether individuals are friends based on their mutual connections. Participants saw two target individuals and their friends within a group, and judged if the targets were friends with each other. Experiment 1 manipulated the number of mutual friends and the number of friends each target had. Experiment 2 manipulated the proportion of these two factors. People were more likely to infer friendships when I) targets had more mutual friends; II) targets had fewer friends each; and III) there was a high proportion of mutual friends, which are all factors that impact the likelihood of targets’ friendship. In sum, people consider relevant numerical information about mutual friends when making friendship judgments.


Inferring friendships from mutual connections

Humans’ ability to attribute mental states to agents has been hypothesized to underpin our unique social behaviors. However, questions remain about the extent to which our representational Theory of Mind (ToM) is shared with non-human primates (NHPs). Here, we present a set of computational models each built to formalize a different representational theory of a foundational ToM component—understanding what others can see—and compare each model’s performance to that of NHPs across a range of previously published perspective-taking experiments. Our results show that multiple competing theories can account for NHPs’ perspective-taking abilities, including both human-like ToM and less complex mentalistic theories, but not simpler, non-mentalistic theories. This work supports the idea that NHPs may reason about others’ mental states when assessing their visual perspectives, and provides promising avenues for future work using computational modeling to determine if and how NHPs represent more complex mental states (e.g., ignorance, belief).

Multiple representational theories explain non-human primate perspective-taking: Evidence from computational modeling

Other people's mental states---what they want, what they know, and how they combine the two to act---are structured by the experiences that they've had. In line with this, we propose that inferences about other people's experiences are a central, but often neglected, aspect of human Theory of Mind. We explore this idea by presenting and testing a computational model that jointly infers others' desires, knowledge, and experience. We find that, by focusing inferences on others' experience, our model can make richer inferences about other's knowledge than would be otherwise possible. Our model quantitatively fits participant judgments on two experiments above an and beyond an alternative model. Overall, our work extends the richness of human Theory of Mind judgements that can be formalized as Bayesian inference over a generative model.

Integrating Experience into Bayesian Theory of Mind

Social learning is essential to survival. It is likely to evolve when it is more
efficient than asocial, trial-and-error learning. The consensus in cultural
evolutionary theory holds that some amount of environmental variability and
uncertainty about the best decisions are necessary for social learning to evolve.
However, current models for the evolution of social learning tend to conflate forms
of uncertainty, and rarely consider different ones in tandem. Moreover, many models
are limited by considering only two possible behaviors and environmental states. Here
we use evolutionary agent-based modeling to identify the complex ways in which
different forms of uncertainty affect social learning. We model a
time-varying environment with dozens of possible behaviors performed by agents
engaging in individual and social learning. We show that ambiguous payoffs, larger
possible decision sets, and shorter agent lifespans sometimes increase social
learning prevalence, as expected. However we also find 
which concrete uncertainty conditions cause evolution to select against social learning.


Some forms of uncertainty may suppress the evolution of social learning

We present a neural dynamic model that perceptually grounds nested noun phrases, i.e., noun phrases that contain further (possibly also nested) noun phrases as parts. The model receives input from the visual array and a representation of a noun phrase from language processing. It organizes a search for the denoted object in the visual scene. The model is a neural dynamic architecture of interacting neural populations which has clear interfaces with perceptual processes. It solves a set of theoretical challenges, including the problem of keeping a nested structure in short-term memory in a way that solves the problem of 2 and massive binding problem emphasized by Jackendoff (2002). The model organizes a search for the objects that are referenced in that structure. We motivate the model, demonstrate simulation results, and discuss how it differs from related models.

A Neural Dynamic Model Perceptually Grounds Nested Noun Phrases

Human language offers a powerful window into our thoughts -- we tell stories, give explanations, and express our beliefs and goals through words. Abundant evidence also suggests that language plays a developmental role in structuring our learning. Here, we ask: how much of human-like thinking can be captured by learning statistical patterns in language alone? We first contribute a new challenge benchmark for comparing humans and distributional large language models (LLMs). Our benchmark contains two problem-solving domains (planning and explanation generation) and is designed to require generalization to new, out-of-distribution problems expressed in language. We find that humans are far more robust than LLMs on this benchmark. Next, we propose a hybrid Parse-and-Solve model, which augments distributional LLMs with a structured symbolic reasoning module. We find that this model shows more robust adaptation to out-of-distribution planning problems, demonstrating the promise of hybrid AI models for more human-like reasoning.

Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks

Limited evidence shows that visual input can facilitate learning novel sound-to-meaning mappings that are crucial to learning a second language. However, the mechanisms by which visual information influences auditory learning are still unclear. Here, we investigate to what extent visual input can lead to effective learning in another domain. We trained atonal speakers with Mandarin tones in 4 conditions: Auditory Only (AO) where only auditory tones were given as input; Animated Contour (AC) where moving visual pitch contours indicating the dynamic changes of tones were given in addition to auditory tones; Static Contour (SC) where static visual pitch contours were given in addition to auditory tones; Incongruent Contour (IC) where mismatched pitch contours were given in addition to auditory tones. The results show the advantage of AC and SC over AO in learning tonal categories and that IC inhibits learning, suggesting that extracting ‘compatible’ properties cross modalities benefits learning most.

Cross-modal perceptual learning in learning a tonal language

Some linguistic features are more readily learned than others, and are thereby more likely to be maintained in diachronic language change, giving rise to typological universals. Less readily learned features may give rise to typological gaps. We consider an apparent typological gap—that a morphologically superlative determiner (e.g., gleebest in "gleebest of the cows") with a negative meaning is cross-linguistically unattested—and ask whether it reflects an underlying learning bias. We find 4-year-olds know that such determiners indicate quantity (replicating Wellwood, Gagliardi, & Lidz, 2016), but only when positive (‘most’), but not negative (‘least’). Importantly, the observed bias is not specific to the apparent typological gap: same-age children showed difficulty learning the negative meaning of a non-superlative determiner, though such meanings are attested. The data thus suggest that children are generally biased against negativity, consistent with much prior work on conceptual bias and language learning/processing.

"Most" is easy but "least" is hard: Novel determiner learning in 4-year-olds

Child-directed language (CDL) features words such as doggy, night-night, and tummy that are rarely used in adult-directed language (ADL). Characteristics of CDL variants, such as diminutivization and reduplication, explain why they may be learned and produced earlier by children. However, it is not yet clear how or when children switch to using ADL equivalents—dog, goodnight, stomach. Through analysis of speech transcripts from CHILDES and the Language Development Project corpus, we show that children significantly increase their production of ADL variants across age, with the average CDL-to-ADL transition point at 2.5 years. Many of the linguistic features that distinguish CDL vs. ADL registers (e.g., lexical and syntactic complexity) similarly differentiated the local speech contexts surrounding CDL vs. ADL variants in children’s input. Notably, these differences emerged even in speech that was primarily child-directed. Learners may therefore be able to capitalize on these linguistic cues to support their discovery of register along with context-appropriate CDL/ADL pair use.

From doggy to dog: Developmental shis in children's use of register-specific words

Historically, metaphors have facilitated creative change across multiple disparate domains. Similarly, human adults use metaphors to guide their everyday thinking and reasoning. While previous research in cognitive development has demonstrated that preschoolers understand metaphors, less is known about how preschoolers might learn from metaphors. The current experiments investigate whether preschoolers can use novel metaphors to make additional inferences about artifacts. Experiment 1 demonstrates that both four-year-olds and adults who hear novel positive and negative metaphors (e.g., “Daxes are clouds. Daxes are not suns.”) can form additional inferences based on these metaphor (e.g., that daxes let out water rather than light up). Experiment 2 conceptually replicates this result using a modified paradigm with only positive metaphors (e.g., “Daxes are clouds”). Taken together, these findings suggest that children can not only understand, but also learn from, metaphors. Consequently, metaphors may be a powerful learning mechanism in both adulthood and early childhood.

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Inferring friendships from mutual connections

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