The formation of new perceptual categories involves learning to extract that information from a wide range of often noisy sensory inputs, which is critical for selecting between a limited number of responses. To identify brain regions involved in visual classification learning under noisy conditions, we developed a task on the basis of the classical dot pattern prototype distortion task [M. I. Posner, Journal of Experimental Psychology, 68, 113–118, 1964]. Twenty-seven healthy young adults were required to assign distorted patterns of dots into one of two categories, each defined by its prototype. Categorization uncertainty was modulated parametrically by means of Shannon's entropy formula and set to the levels of 3, 7, and 8.5 bits/dot within subsets of the stimuli. Feedback was presented after each trial, and two parallel versions of the task were developed to contrast practiced and unpracticed performance within a single session. Using event-related fMRI, areas showing increasing activation with categorization uncertainty and decreasing activation with training were identified. Both networks largely overlapped and included areas involved in visuospatial processing (inferior temporal and posterior parietal areas), areas involved in cognitive processes requiring a high amount of cognitive control (posterior medial wall), and a cortico-striatal–thalamic loop through the body of the caudate nucleus. Activity in the medial prefrontal wall was increased when subjects received negative as compared with positive feedback, providing further evidence for its important role in mediating the error signal. This study characterizes the cortico-striatal network underlying the classification of distorted visual patterns that is directly related to decision uncertainty.

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