Predictive coding as a model of the V1 saliency map hypothesis

TY - JOUR

T1 - Predictive coding as a model of the V1 saliency map hypothesis

AU - Spratling, M. W.

PY - 2012/2

Y1 - 2012/2

N2 - The predictive coding/biased competition (PC/BC) model is a specific implementation of the predictive coding theory that has previously been shown to provide a detailed account of the response properties of orientation tuned cells in primary visual cortex (V1). Here it is shown that the same model can successfully simulate psychophysical data relating to the saliency of unique items in search arrays, of contours embedded in random texture, and of borders between textured regions. This model thus provides a possible implementation of the hypothesis that V1 generates a bottom-up saliency map. However, PC/BC is very different from previous models of visual salience, in that it proposes that saliency results from the failure of an internal model of simple elementary image components to accurately predict the visual input. Saliency can therefore be interpreted as a mechanism by which prediction errors attract attention in an attempt to improve the accuracy of the brain's internal representation of the world.

AB - The predictive coding/biased competition (PC/BC) model is a specific implementation of the predictive coding theory that has previously been shown to provide a detailed account of the response properties of orientation tuned cells in primary visual cortex (V1). Here it is shown that the same model can successfully simulate psychophysical data relating to the saliency of unique items in search arrays, of contours embedded in random texture, and of borders between textured regions. This model thus provides a possible implementation of the hypothesis that V1 generates a bottom-up saliency map. However, PC/BC is very different from previous models of visual salience, in that it proposes that saliency results from the failure of an internal model of simple elementary image components to accurately predict the visual input. Saliency can therefore be interpreted as a mechanism by which prediction errors attract attention in an attempt to improve the accuracy of the brain's internal representation of the world.

U2 - 10.1016/j.neunet.2011.10.002

DO - 10.1016/j.neunet.2011.10.002

M3 - Article

VL - 26

SP - 7

EP - 28

JO - Neural Networks

JF - Neural Networks

IS - N/A

M1 - N/A

ER -