Generalization of form in visual pattern classification.
Human observers were trained to criterion in classifying compound Gabor signals with symmetry relationships, and were then tested with each of 18 blob-only versions of the learning set. Generalization to dark-only and light-only blob versions of the learning signals, as well as to dark-and-light blob versions was found to be excellent, thus implying virtually perfect generalization of the ability to classify mirror-image signals. The hypothesis that the learning signals are internally represented in terms of a 'blob code' with explicit labelling of contrast polarities was tested by predicting observed generalization behaviour in terms of various types of signal representations (pixelwise, Laplacian pyramid, curvature pyramid, ON/OFF, local maxima of Laplacian and curvature operators) and a minimum-distance rule. Most representations could explain generalization for dark-only and light-only blob patterns but not for the high-thresholded versions thereof. This led to the proposal of a structure-oriented blob-code. Whether such a code could be used in conjunction with simple classifiers or should be transformed into a propositional scheme of representation operated upon by a rule-based classification process remains an open question.
Rentschler
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I
Institute of Medical Psychology, University of Munich, Germany. ingo@imp.med.uni-muenchen.de
Barth
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E
Caelli
T
T
Zetzsche
C
C
Jüttner
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M
D000328:Adult; D003198:Computer Simulation; D005260:Female; D005556:Form Perception / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology; D008297:Male; D010364:Pattern Recognition, Visual / Q000145:classification*