Distributed Neural Systems Support Flexible Attention Updating during Category Learning-猫眼课题宝

分布式神经系统支持类别学习期间灵活的注意力更新

DOI：

10.1162/jocn_a_01882

发表时间：

2022

期刊：

Journal of Cognitive Neuroscience

影响因子：

3.2

通讯作者：

中科院分区：

医学3区

文献类型：

作者：

Weichart, Emily R.;Evans, Daniel G.;Galdo, Matthew;Bahg, Giwon;Turner, Brandon M.

To accurately categorize items, humans learn to selectively attend to the stimulus dimensions that are most relevant to the task. Models of category learning describe how attention changes across trials as labeled stimuli are progressively observed. The Adaptive Attention Representation Model (AARM), for example, provides an account in which 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 processes of orienting, visual perception, memory retrieval, prediction error, and goal maintenance to facilitate learning. 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. Generalized linear model analyses revealed patterns of BOLD activation in the parietal cortex (orienting), visual cortex (perception), medial temporal lobe (memory retrieval), basal ganglia (prediction error), and pFC (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.