Noise reduction as a unified mechanism of perceptual learning in both artificial and biological visual systems

Abstract

Although signal enhancement and/or noise reduction have been proposed as key computational mechanisms of visual perceptual learning (VPL), their links to behavioral and neural consequences of VPL remain elusive. To better bridge previous theoretical and empirical findings, we built a deep neural network (DNN) model of VPL. The DNN is a Siamese neural network that inherits the first five convolutional layers from the pretrained AlexNet to emulate the early visual system and appends one linear readout layer to make binary perceptual decisions. We trained it on an orientation discrimination task consisting of Gabor stimuli with varying levels of external noises. After training, the DNN model reproduced several key psychophysical, human imaging, and neurophysiological findings in VPL literature: (1) training uniformly shifts down the behavioral Threshold vs. Noise functions; (2) training improves stimulus decoding accuracy at the population level in the last four layers; (3) training sharpens the orientation tuning curves of individual neurons in the first two layers and reduces Fano factors and inter-neuron noise correlations in all layers. Furthermore, we used an information-theoretic approach to analyze two high-dimensional distributions of population responses that correspond to the two Gabor stimuli being discriminated. The results showed that VPL improves population codes primarily by reducing the (co)variance of population responses (i.e., noise reduction) rather than enlarging the Euclidean distance between the two response distributions (i.e., signal enhancement). Most importantly, our model generates novel predictions that VPL systematically warps and rotates the two response distributions in high-dimensional representational spaces. These predictions were supported by the results of a human fMRI experiment on perceptual learning of motion direction discrimination. Taken together, our DNN model can reproduce a broad range of psychophysical, human imaging, and neurophysiological findings reported in VPL literature. Systematic analyses of population responses strongly support the noise reduction theory of VPL.