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Shu-Guang Kuai, Zoe Kourtzi; Learning optimizes visual shape templates in the human brain. Journal of Vision 2011;11(11):1010. doi: 10.1167/11.11.1010.
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© ARVO (1962-2015); The Authors (2016-present)
Learning is known to improve the observers' efficiency in complex perceptual tasks and re-tune decision templates (i.e. the weight assigned to the different stimulus features based on task relevance). However, the neural mechanisms that mediate these learning-dependent changes in behavioural performance remain largely unknown. Here, we investigate whether learning alters behavioural and brain templates for visual shapes using classification image methods for the analysis of psychophysical and fMRI data. In particular, we used irregular pentagons comprised of thirty equally spaced Gaussian dots with position noise. Observers were instructed to discriminate between two types of stimuli that differed in the spatial arrangement of their shape segments. We compared behavioural performance and brain activations before and after five training sessions with stimuli presented at multiple levels of positional noise. For the analysis of the behavioural data, we used the trial-by-trial effect of noise to calculate classification images showing the stimulus parts that influence the observers' judgments. For the analysis of fMRI data, we trained a linear pattern classifier to discriminate the two types of stimuli when presented without position noise and tested the classifier's prediction on each stimulus trial. We then calculated classification images based on the prediction accuracy of the classifier in visual and parietal regions of interest. Our results showed that training improved perceptual efficiency for shape discrimination and decreased internal noise. Further, increased correlations between behavioural and brain templates with the performance of an ideal observer suggested that learning re-tunes the human templates. Interestingly, brain templates in higher occipitotemporal and posterior parietal regions rather than early visual areas improved after training, suggesting that learning tunes the representation of task-relevant features in higher visual circuits.
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