Neural mechanisms of motion perceptual learning in noise

Abstract

Practice improves our perceptual ability. However, the neural mechanisms underlying this experience-dependent plasticity in adult brain remain unclear. Here, we studied the long-term neural correlates of motion perceptual learning. Subjects' behavioral performance and BOLD signals were tracked before, immediately after, and 2 weeks after practicing a motion direction discrimination task in noise over six daily sessions. Parallel to the specificity and persistency of the behavioral learning effect, we found that training sharpened the cortical tuning in MT, and enhanced the connectivity strength from MT to the intraparietal sulcus (IPS, a motion decision-making area). In addition, the decoding accuracy for the trained motion direction was improved in IPS 2 weeks after training. The dual changes in the sensory and the high-level cortical areas suggest that learning refines the neural representation of the trained stimulus and facilitates the information transmission in the decision process. Our findings are consistent with the functional specialization in the visual cortex, and provide empirical evidence to the reweighting theory of perceptual learning at a large spatial scale. Hum Brain Mapp 38:6029-6042, 2017. © 2017 Wiley Periodicals, Inc.

Concentrations of Cortical GABA and Glutamate in Young Adults With Autism Spectrum Disorder

“…After concluding that the groups do not differ on any of the GABA+ or Glx measures, we turned to examine whether neurometabolite levels are associated with sensory sensitivity or with ASD symptomatology, and whether these associations differ in direction or magnitude between the groups, as previously suggested [Brix et al, 2015;Robertson et al, 2016]. We estimated visual sensitivity by measuring thresholds for contrast detection, a task thought to rely on early visual cortex computations [Boynton, Demb, Glover, & Heeger, 1999;Ress & Heeger, 2003], and thresholds for motion discrimination, a task associated with MT+ activation [Chen, Lu, Shao, Weng, & Fang, 2017;Turkozer, Pamir, & Boyaci, 2016]. We correlated these thresholds with GABA+ and Glx measurements in the corresponding ROIs in the visual cortex (left and right MT+ for motion and occipital for contrast), however no significant correlations were observed (|r 21-38 | < 0.29, uncorrected P > 0.217).…”

Glutamatergic facilitation of neural responses in MT enhances motion perception in humans

“…They also reported modest individual variability in the coherence-response function, but not whether such variability corresponds to differences in perception. A more recent study has found a correspondence between improved behavioral performance and fMRI response changes in MT following perceptual learning (Chen et al, 2017). Specifically, subjects with smaller thresholds in a motion discrimination paradigm following extensive training also showed greater sharpening of tuning within MT, as assessed by multi-voxel pattern analysis.…”

“…Here, we again chose to focus on neural processing within cortical area MT, in order to test our above hypothesis regarding a link between Glx, neural responses, and task performance. Neural responses within MT in both monkeys (Britten et al, 1992; Churan et al, 2008; Huk and Shadlen, 2005; Liu et al, 2016) and humans (Chen et al, 2017; Huk et al, 2001; Rees et al, 2000; Schallmo et al, 2018; Tadin et al, 2011; Turkozer et al, 2016) are known to be tightly linked to motion perception. In particular, studies in humans suggest that motion duration thresholds (Tadin, 2015; Tadin et al, 2003) – the amount of time that a stimulus needs to be presented to accurately discriminate motion direction – are shorter under conditions that elicit higher MT responses (Schallmo et al, 2018; Tadin et al, 2011; Turkozer et al, 2016), in agreement with our recent computational work (Schallmo et al, 2018).…”

Multi-Stage Cortical Plasticity Induced by Visual Contrast Learning

“…Alternatively, Mollon and Danilova (1996) hypothesized that learning occurs at a more central site but still predicts orientation and location specificity of learning. Models like improved readout or reweighting of representation neurons (e.g., V1) ( Poggio et al, 1992 ; Dorsher and Lu, 1998 ) and the involvement of high-level processes beyond the visual cortex ( Li W. et al, 2008 ) have been proposed in the last decades and received support from psychophysical ( Liu, 1999 ; Liu and Weinshall, 2000 ; Xiao et al, 2008 ; Zhang et al, 2010 ), neurophysiological ( Law and Gold, 2008 ), and brain imaging studies ( Chen et al, 2015 , 2017 ).…”