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

AbstractSuccessful categorization requires a careful coordination of attention, representation, and decision making. Comprehensive theories that span levels of analysis are key to understanding the computational and neural dynamics of categorization. Here, we build on recent work linking neural representations of category learning to computational models to investigate how category decision making is driven by neural signals across the brain. We combine functional magnetic resonance imaging with hierarchical drift diffusion modelling to show that trial-by-trial fluctuations in neural activation from regions of occipital, cingulate, and lateral prefrontal cortices are linked to category decisions. Notably, lateral prefrontal cortex activation was also associated with exemplar-based model predictions of trial-by-trial category evidence. We propose that these brain regions underlie distinct functions that contribute to successful category learning.

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