Confidence-based integrated reweighting model of task-difficulty explains location-based specificity in perceptual learning.

ABSTRACT: Perceptual learning is classically thought to be highly specific to the trained stimuli's retinal locations. However, recent research using a novel double-training paradigm has found dramatic transfer of perceptual learning to untrained locations. These results challenged existing models of perceptual learning and provoked intense debate in the field. Recently, Hung and Seitz (2014) showed that previously reported results could be reconciled by considering the details of the training procedure, in particular, whether it involves prolonged training at threshold using a single staircase procedure or multiple staircases. Here, we examine a hierarchical neural network model of the visual pathway, built upon previously proposed integrated reweighting models of perceptual learning, to understand

SUBMITTER: Talluri BC 

PROVIDER: S-EPMC4699478 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature