Abstract
Studies in visual perceptual learning investigate the way
human performance improves with practice, in the context
of relatively simple (and therefore more manageable) visual
tasks. Building on the powerful tools currently available for
the training of Convolution Neural Networks (CNN), networks whose original architecture was inspired by the visual system, we revisited some of the open computational
questions in perceptual learning. We first replicated two
representative sets of perceptual learning experiments by
training a shallow CNN to perform the relevant tasks. These
networks qualitatively showed most of the characteristic behavior observed in perceptual learning, including the hallmark phenomena of specificity and its various manifestations in the forms of transfer or partial transfer, and learning enabling. We next analyzed the dynamics of weight
modifications in the networks, identifying patterns which
appeared to be instrumental for the transfer (or generalization) of learned skills from one task to another in the simulated networks. These patterns may identify ways by which
the domain of search in the parameter space during network re-training can be significantly reduced, thereby accomplishing knowledge transfer.