Position Variance, Recurrence and Perceptual Learning

Stimulus arrays are inevitably presented at different positions on the retina in visual tasks, even those that nominally require fixation. In particular, this applies to many perceptual learning tasks. We show that perceptual inference or discrimination in the face of positional variance has a structurally different quality from inference about fixed position stimuli, involving a particular, quadratic, non-linearity rather than a purely linear discrimination. We show the advantage taking this non-linearity into account has for discrimination, and suggest it as a role for recurrent connections in area V1, by demonstrating the superior discrimination performance of a recurrent network. We propose that learning the feedforward and recurrent neural connections for these tasks corresponds to the fast and slow components of learning observed in perceptual learning tasks.

• Type: Conference
• Year: 2000
• Where: NIPS
• Authors: Zhaoping Li, Peter Dayan
• Keywords: NIPS 2000, NIPS 2007, Perceptual Inference, Perceptual Learning Tasks, Recurrent Neural Connections