Technique to extract relevant image features for visual tasks

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Abstract
Here we demonstrate a method for constructing stimulus classification images. These images provide information regarding the stimulus aspects the observer uses to segregate images into discrete response categories. Data are first collected on a discrimination task containing low contrast noise. The noises are then averaged separately for the stimulus-response categories. These averages are then summed with appropriate signs to obtain an overall classification image. We determine stimulus classification images for a vernier acuity task to visualize the stimulus features used to make these precise position discriminations. The resulting images reject the idea that the discrimination is performed by the single best discriminating cortical unit. The classification images show one Gabor-like filter for each line, rejecting the nearly ideal assumption of image discrimination models predicting no contribution from the fixed vernier line.

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