Nonlinear ideal observation and recurrent preprocessing in perceptual learning

Abstract

Residual micro-saccades, tremor and fixation errors imply that, on different trials in visual tasks, stimulus arrays are inevitably presented at different positions on the retina. Positional variation is likely to be specially important for tasks involving visual hyperacuity, because of the severe demands that these tasks impose on spatial resolution. In this paper, we show that small positional variations lead to a structural change in the nature of the ideal observer's solution to a hyperacuity-like visual discrimination task such that the optimal discriminator depends quadratically rather than linearly on noisy neural activities. Motivated by recurrent models of early visual processing, we show how a recurrent preprocessor of the noisy activities can produce outputs which, when passed through a linear discriminator, lead to better discrimination even when the positional variations are much larger than the threshold acuity of the task. Since, psychophysically, hyperacuity typically improves greatly over the course of perceptual learning, we discuss our model in the light of results on the speed and nature of learning.