Human Pattern Recognition:
Parallel Processing and Perceptual Learning


Manfred Fahle


Abstract

A new theory of visual object recognition that is based on multi-dimensional interpolation between stored templates (Poggio & Girosi, 1990; Poggio, Fahle & Edelman, 1992) 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 minutes and a slow phase that continues over 10 hours 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 specific for the eye through that 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. 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 of both psychophysical and 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.

Here, I review several results obtained during the last few years, present some new results and discuss all these results regarding their implications for models of pattern recognition.