Similarity between Fourier transforms of objects predicts their experimental confusions

Two sets of stimuli were presented tachistoscopically to 4 subjects. On each trial, a single stimulus was presented, and the subject was required to identify the stimulus by verbal response. An exposure duration was chosen such that the subject's identification performance fell within a range from faultless identification to chance guessing. The object-identification data of each subject obtained for all stimulus exposures were pooled to form an object confusion matrix. A model of visual processing based on two-dimensional spatial frequency content (Fourier transforms) was used to predict confusions among stimulus pairs. The model properties that appear to be the most essential are those that allow it (1) to account for the obvious dependence of the Fourier transform on the choice of an origin point; and (2) choose the point of origin for each object separately, irrespective of other objects of the set. The point of origin of the reference frame, in which Fourier transforms are performed, is chosen so as to minimize the low-frequency phase component for each object. A high correlation (up to .96) between confusion matrices and model interobject distances was attained. The results demonstrate that such a distance measure gives a good prediction of object confusability.