Trace Inference, Curvature Consistency, and Curve Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Chromaticity and luminance as coding dimensions in visual search
Human Factors - Special issue: visual displays
What is the goal of sensory coding?
Neural Computation
Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training products of experts by minimizing contrastive divergence
Neural Computation
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
How Close Are We to Understanding V1?
Neural Computation
An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Journal of Cognitive Neuroscience
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Action and behavior: a free-energy formulation
Biological Cybernetics
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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.