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Neural network (connectionist) models are designed to encode image featuresand provide the building blocks for object and shape recognition. These modelsgenerally call for: a) initial diffuse connections from one neuron populationto another, and b) training to bring about a functional change in thoseconnections so that one or more high-tier neurons will selectively respond to aspecific shape stimulus. Advanced models provide for translation, size, androtation invariance. The present discourse notes that recent work on humanperceptual skills has demonstrated immediate encoding of unknown shapes thatwere seen only once. Further, the perceptual mechanism provided fortranslation, size, and rotation invariance. This finding represents a challengeto connectionist models that require many training trials to achieverecognition and invariance.

Rapid de novo shape encoding: a challenge to connectionist modeling