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Abstract:
neurons- during a visual task is an important pre-requisite for computational models of visual cognition. We describe a technique for estimating high-dimensional decision-images, and apply the method to a psychophysical gender discrimination task. The use of regularization makes it possible to map out decision-images using a relatively small number of stimuli.
Statistical analysis of the result shows a remarkable fit to the datasets collected—remarkable, as gender discrimination is a rather high-level visual task, and thus believed to be complex, but our model is conceptually rather simple. We demonstrate that the decision-images are sensitive to subtle changes in lighting, texture, and pose, and to individual differences in gender discrimination exhibited by our subjects.
We show how decision-images can be used to create new stimuli, and how the approach can be generalized to non-linear and multi-scale decision-images. In addition, connections to reverse correlation techniques for receptive field estimation are described.
