Related Experiment Video
Updated: Aug 13, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
Published on: June 30, 2020
Event-related fMRI of category learning: differences in classification and feedback networks
Deborah M Little1, Silvia S Shin, Shannon M Sisco
1Center for Stroke Research, Department of Neurology and Rehabilitation, University of Illinois at Chicago, 60612, USA. little@uic.edu
Abstract:
Eighteen healthy young adults underwent event-related (ER) functional magnetic resonance imaging (fMRI) of the brain while performing a visual category learning task. The specific category learning task required subjects to extract the rules that guide classification of quasi-random patterns of dots into categories. Following each classification choice, visual feedback was presented. The average hemodynamic response was calculated across the eighteen subjects to identify the separate networks associated with both classification and feedback. Random-effects analyses identified the different networks implicated during the classification and feedback phases of each trial. The regions included prefrontal cortex, frontal eye fields, supplementary motor and eye fields, thalamus, caudate, superior and inferior parietal lobules, and areas within visual cortex. The differences between classification and feedback were identified as (i) overall higher volumes and signal intensities during classification as compared to feedback, (ii) involvement of the thalamus and superior parietal regions during the classification phase of each trial, and (iii) differential involvement of the caudate head during feedback. The effects of learning were then evaluated for both classification and feedback. Early in learning, subjects showed increased activation in the hippocampal regions during classification and activation in the heads of the caudate nuclei during the corresponding feedback phases. The findings suggest that early stages of prototype-distortion learning are characterized by networks previously associated with strategies of explicit memory and hypothesis testing. However as learning progresses the networks change. This finding suggests that the cognitive strategies also change during prototype-distortion learning.

