Insights into motion perception by observer modeling.

The statistical efficiency of human observers performing a simplified version of the motion detection task of Salzman and Newsome [Science 264, 231 (1994)] is high but not perfect. This reduced efficiency may be caused by noise internal to the observers or by the observers' using strategies that are different from that used by an ideal machine. We therefore investigated which of three simple models best accounts for the observers' performance. The models compared were a motion detector that uses the proportion of dots in the first frame that move coherently (as would an ideal machine), a model that bases its decision on the number of dots that move, and a model that differentially weights motions that occur at different locations in the visual field (for instance, differentially weights the point of fixation and the periphery). We compared these models by explicitly modeling the human observers' performance. We recorded the exact stimulus configuration on each trial together with the observer's response, and, for the different models, we found the parameters that best predicted the observer's performance in a least-squares sense. We then used N-fold cross validation to compare the models and hence the associated hypotheses. Our results show that the performance of observers is based on the proportion, not the absolute number, of dots that are moving and that there was no evidence of any differential spatial weighting. Whereas this method of modeling the observers' response is demonstrated only for one simple psychophysical paradigm, it is general and can be applied to any psychophysical framework in which the entire stimulus can be recorded.