Reconstructing population tuning functions to quantify the effects of category learning on visual perception
Year: 2021
Type: dissertation
Abstract: Category learning and visual perception are fundamentally interactive processes, such that successful categorization often depends on the ability to make fine visual discriminations between stimuli that vary on continuously valued dimensions. Research suggests that category learning can improve perceptual discrimination along the stimulus dimensions that predict category membership, and that these perceptual enhancements are a byproduct of functional plasticity in the visual system (sensory modulation). However, the precise mechanisms underlying learning-dependent sensory modulation in categorization are not well understood. We hypothesized that category learning may lead to a representational sharpening of underlying sensory populations tuned to the category boundary. Furthermore, we expected such sharpening to occur largely during active learning of new categories. These hypotheses were tested using fMRI and a theoretically-constrained model of vision to quantify changes in the shape of orientation-selective population responses while subjects (N = 23) learned to categorize physically identical stimuli based on an orientation (line angle) rule or an orthogonal spatial frequency (line width) rule. All subjects successfully learned the category rules, and learning performance was matched between groups. Consistent with our predictions, modeling results revealed sharpened representations of presented stimulus orientations in visual cortex for subjects who learned rules based on orientation versus spatial frequency. Moreover, these representations varied as a function of distance from the category boundary, such that the orientation tuning curves for challenging stimuli near the boundary were significantly sharper than those for stimuli at the category centers. These results support an efficient model of plasticity wherein only the sensory populations tuned to the most behaviorally relevant regions of feature space are sharpened.
Author: Sean O’Bryan
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W3203381141