Human Pattern Recognition: Parallel Processing and Perceptual Learning

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Abstract
A new theory of visual object recognition by Poggio et al that is based on multidimensional interpolation between stored templates requires fast, stimulus-specific learning in the visual cortex. Indeed, performance in a number of perceptual tasks improves as a result of practice. We distinguish between two phases of learning a vernier-acuity task, a fast one that takes place within less than 20 min and a slow phase that continues over 10 h of training and probably beyond. The improvement is specific for relatively ‘simple’ features, such as the orientation of the stimulus presented during training, for the position in the visual field, and for the eye through which learning occurred. Some of these results are simulated by means of a computer model that relies on object recognition by multidimensional interpolation between stored templates. Orientation specificity of learning is also found in a jump-displacement task. In a manner parallel to the improvement in performance, cortical potentials evoked by the jump displacement tend to decrease in latency and to increase in amplitude as a result of training. The distribution of potentials over the brain changes significantly as a result of repeated exposure to the same stimulus. The results both of psychophysical and of electrophysiological experiments indicate that some form of perceptual learning might occur very early during cortical information processing. The hypothesis that vernier breaks are detected ‘early’ during pattern recognition is supported by the fact that reaction times for the detection of verniers depend hardly at all on the number of stimuli presented simultaneously. Hence, vernier breaks can be detected in parallel at different locations in the visual field, indicating that deviation from straightness is an elementary feature for visual pattern recognition in humans that is detected at an early stage of pattern recognition. Several results obtained during the last few years are reviewed, some new results are presented, and all these results are discussed with regard to their implications for models of pattern recognition.