Activations in Visual and Attention-Related Areas Predict and Correlate with the Degree of Perceptual Learning

Abstract

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.

Individual variability in functional connectivity predicts performance of a perceptual task

“…The behavioral component identified by the factor analysis (i.e., PC1) combined aspects of initial performance (i.e., predisposition), the rate of performance improvement, and the quantity of practice required to reach criterion. Our observers were highly variable in their initial performance, a finding concordant with previous studies of complex visual tasks (1) as well as perceptual learning (2)(3)(4)6). Interestingly, task fitness was positively correlated with initial performance, and negatively correlated with the rate of learning and the number of blocks to criterion.…”

“…Thus, individuals who perform better initially tend to exhibit slower improvement. Although the physiological correlates of perceptual learning have been well documented at the level of individual synapses (10), neurons (11,12), and largescale networks (6,(13)(14)(15)(16), it is largely unknown whether the state of the brain before training influences future performance or the rate of acquisition of a novel task. Here we investigate the extent to which performance of a novel perceptual task can be predicted on the basis of physiological measures evaluated before training.…”

Learning Alters the Tuning of Functional Magnetic Resonance Imaging Patterns for Visual Forms

“…First, we provide evidence for learning-dependent changes related to neural sensitivity rather than simply overall responsiveness (i.e., increased or decreased fMRI responses) to trained stimuli as reported in previous imaging studies (Kourtzi et al, 2005;Sigman et al, 2005;Op de Beeck et al, 2006;Mukai et al, 2007;Yotsumoto et al, 2008). This previous work does not allow us to discern whether learningdependent changes in fMRI signals relate to changes in the overall magnitude of neural responses or changes in neuronal selectivity of neural populations.…”

Flexible Learning of Natural Statistics in the Human Brain

“…First, previous psychophysical studies have shown that learning enhances the ability of observers to detect targets in noise (Brady and Kersten 2003;Dosher and Lu 1998;Eckstein et al 2004;Fahle 2004;Fine and Jacobs 2002;Furmanski and Engel 2000;Gilbert et al 2001;Gold et al 1999;Kovacs et al 1999;Li and Gilbert 2002;Li RW et al 2004;Polat and Sagi 1994;Sagi and Tanne 1994;Sigman and Gilbert 2000). Further, previous imaging studies have shown that learning changes cortical responses in accordance with the level of behavioral improvement (Dolan et al 1997;Gauthier et al 1999;Grill-Spector et al 2000;Kourtzi et al 2005;Mukai et al 2007;Op de Beeck et al 2006;Schwartz et al 2002;Sigman et al 2005;Yotsumoto et al 2008). However, our study focuses on how learning determines the principles that facilitate contour linking in cluttered scenes.…”