Distributed neural systems enable flexible attention updating during category learning

Abstract

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.

Fixation-gated Encoding Accounts for Attention Biases during Recognition and Categorization

“…Given that the context model estimates salience with considerably less constraint than the fixation-informed model, it is potentially the case that additional weighting of information occurs during the decision process. Indeed, our previous work has suggested that observers adapt to environmental changes by continuously updating how different sources of information impact their decisions (Galdo et al, 2021;Weichart, Evans, et al, 2021).…”