Distributed neural systems enable flexible attention updating during category learning

In order to accurately categorize novel items, humans learn to selectively attend to stimulus dimensions that are most relevant to the task. Models of category learning describe the interconnected cognitive processes that contribute to selective attention as observations of stimuli and category feedback are progressively acquired. The Adaptive Attention Representation Model (AARM), for example, provides an account whereby categorization decisions are based on the perceptual similarity of a new stimulus to stored exemplars, and dimension-wise attention is updated on every trial in the direction of a feedback-based error gradient. As such, attention modulation as described by AARM requires interactions among orienting, visual perception, memory retrieval, error monitoring, and goal maintenance in order to facilitate learning across trials. The current study explored the neural bases of attention mechanisms using quantitative predictions from AARM to analyze behavioral and fMRI data collected while participants learned novel categories. GLM analyses revealed patterns of BOLD activation in the parietal cortex (orienting), visual cortex (perception), medial temporal lobe (memory retrieval), basal ganglia (error monitoring), and prefrontal cortex (goal maintenance) that covaried with the magnitude of model-predicted attentional tuning. Results are consistent with AARM’s specification of attention modulation as a dynamic property of distributed cognitive systems.