of Universty/ of Reykj Sigma 4k, b anders bal.:2 for B An, Coonition, and BeLavior, Radboud University, Nbniegen, the Netherlands ARTICLE INFO ABSTRACT

It is well known that obs_rvas can us. so-called summary statistics of visual ensembles to slmplily perceptual processing. rne assumption has been that instead or representing feature distributions in detail the visual system extracts the mean and variance of visual ensembles. But recent evidence Mom implicit testing using a method called feature distribution learning showed that far more detail of the distributions is retained Lan the summary statistic literature indicates. Observers also encode higher order statistics such as the _mitosis of feature distributions of orientation and color. But this sort of learning has not been shown for more intricate aspects of visual infmmation Here we tested the leaning of disnactor ensembles for shape, using the feature distribution learning method. Using a linearized circular shape space, we found that learning of detailed distributions of shape does not occur for this shape space while observers were able to lean the mean and range of the distributions frevious demonstrations of feanue distribution learning involved simpler feature dimensions than the more complex shape space tested here, and our findings may therefore reveal important boundary conditions of feature distribution lemming.