Activations in visual and attention-related areas predict and correlate with the degree of perceptual learning.
Repeated experience with a visual stimulus can result in improved perception of the stimulus, i.e., perceptual learning. To understand the underlying neural mechanisms of this process, we used functional magnetic resonance imaging to track brain activations during the course of training on a contrast discrimination task. Based on their ability to improve on the task within a single scan session, subjects were separated into two groups: "learners" and "nonlearners." As learning progressed, learners showed progressively reduced activation in both visual cortex, including Brodmann's areas 18 and 19 and the fusiform gyrus, and several cortical regions associated with the attentional network, namely, the intraparietal sulcus (IPS), frontal eye field (FEF), and supplementary eye field. Among learners, the decrease in brain activations in these regions was highly correlated with the magnitude of performance improvement. Unlike learners, nonlearners showed no changes in brain activations during training. Learners showed stronger activation than nonlearners during the initial period of training in all these brain regions, indicating that one could predict from the initial activation level who would learn and who would not. In addition, over the course of training, the functional connectivity between IPS and FEF in the right hemisphere with early visual areas was stronger for learners than nonlearners. We speculate that sharpened tuning of neuronal representations may cause reduced activation in visual cortex during perceptual learning and that attention may facilitate this process through an interaction of attention-related and visual cortical regions.
SciCrunch is a data sharing and display platform. Anyone can create a custom portal where they can select searchable subsets of hundreds of data sources, brand their web pages and create their community. SciCrunch will push data updates automatically to all portals on a weekly basis. User communities can also add their own data to scicrunch, however this is not currently a free service.