We report a supervised category learning experiment in which the training phase contains both classification and observation learning blocks. To explain the use of different categorization strategies, we propose an account in which use of a stimuli dimension depends on how well the dimension is learned. Our results show that there is an overall preference for a unidimensional categorization based on the perfectly diagnostic dimension. The preference for unidimensional categorization is negatively correlated with how well participants learn the partially diagnostic dimensions. Preference for unidimensional categorization is also negatively correlated with the mean response time. Bayesian modeling results show that participants use a partially diagnostic dimension only when it is learned with a very high level of accuracy. Different strategies are used for categorization depending on how well the perfectly and partially diagnostic dimensions are learned.

Supervised category learning: When do participants use a partially diagnostic feature?


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Poster Session 1 -  C : Cognitive Science

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**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

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CogSci 2021

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.

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Supervised category learning: When do participants use a partially diagnostic feature?

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