What is it about?
People often use simple unidimensional rules while learning artificial categories. Is this due to preference for simplicity? Or could it be because learning the family-resemblance structure of categories is difficult? We conducted two experiments to answer these questions. We used computational modelling to determine how learning affects the choice of the categorization strategy.
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Why is it important?
Our results show that preference for a unidimensional strategy decreases gradually as the family resemblance structure of the categories is learned more accurately. This helps in connecting the results of multiple different categorization experimental paradigms, which often report very different results. Our computational modelling results show that both visual attention and accurate learning must be taken into account while predicting the categorization strategy.
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This page is a summary of: Accurate knowledge about feature diagnosticities leads to less preference for unidimensional strategy., Journal of Experimental Psychology Learning Memory and Cognition, July 2022, American Psychological Association (APA), DOI: 10.1037/xlm0001151.
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