Generalization of form in visual pattern classification.

Human observers were trained to criterion in classifying compound
Gabor signals with sym- metry relationships, and were then tested
with each of 18 blob-only versions of the learning set. General-
ization 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 propo- sitional scheme of representation
operated upon by a rule-based classification process remains an
open question.