CogSci 2025

August 01, 2025

San Francisco, United States

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keywords:

behavioral science

quantitative behavior

concepts and categories

corpus studies

language production

statistics

bayesian modeling

psychology

linguistics

Classifier choice has been widely studied, with previous research highlighting the influence of semantic features such as shape and animacy. This study, however, demonstrates that classifier choice—specifically the selection between general and specific classifiers—is also influenced by taxonomic categorization, where nouns are divided into three levels based on specificity: basic (e.g., “apple”), superordinate (e.g., “fruit”), and subordinate (e.g., “golden apple”). A picture naming task was conducted and our findings reveal a tendency for individuals to favor specific classifiers when nouns are at the basic than at the subordinate level. This challenges the prevalent assumption that general classifiers are predominantly chosen. We attribute this tendency to a cognitive economy principle and propose a novel explanation for classifier choice based on the theory of Uniform Information Density, a perspective previously rejected in previous studies. Overall, this research suggests new directions for investigating the cognitive and linguistic factors influencing classifier choice.

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Towards a Formal Theory of the Need for Competence via Computational Intrinsic Motivation
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Towards a Formal Theory of the Need for Competence via Computational Intrinsic Motivation

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Erik Lintunen and 3 other authors

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