Computing dynamic classification images from correlation maps

We used Pearson's correlation to compute dynamic classification images of biological motion in a point-light display. Observers discriminated whether a human figure that was embedded in dynamic white Gaussian noise was walking forward or backward. Their responses were correlated with the Gaussian noise fields frame by frame, across trials. The resultant correlation map gave rise to a sequence of dynamic classification images that were clearer than either the standard method of A. J. Ahumada and J. Lovell (1971) or the optimal weighting method of R. F. Murray, P. J. Bennett, and A. B. Sekuler (2002). Further, the correlation coefficients of all the point lights were similar to each other when overlapping pixels between forward and backward walkers were excluded. This pattern is consistent with the hypothesis that the point-light walker is represented in a global manner, as opposed to a fixed subset of point lights being more important than others. We conjecture that the superior performance of the correlation map may reflect inherent nonlinearities in processing biological motion, which are incompatible with the assumptions underlying the previous methods.