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Identifying the neural dynamics of category decisions with computational model-based fMRI

Abstract

Successful categorization requires a careful coordination ofattention, representation, and decision making. Comprehensivetheories that span levels of analysis are key to understandingthe computational and neural dynamics of categorization. Here,we build on recent work linking neural representations ofcategory learning to computational models to investigate howcategory decision making is driven by neural signals across thebrain. We combine functional magnetic resonance imagingwith hierarchical drift diffusion modelling to show that trial-by-trial fluctuations in neural activation from regions ofoccipital, cingulate, and lateral prefrontal cortices are linked tocategory decisions. Notably, lateral prefrontal cortex activationwas also associated with exemplar-based model predictions oftrial-by-trial category evidence. We propose that these brainregions underlie distinct functions that contribute to successfulcategory learning.

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