Extracting statistical information about shapes in the visual environment

Abstract

It is well known that observers can use so-called summary statistics of visual ensembles to simplify perceptual processing. The assumption has been that instead of representing feature distributions in detail the visual system extracts the mean and variance of visual ensembles. But recent evidence from implicit testing using a method called feature distribution learning showed that far more detail of the distributions is retained than the summary statistic literature indicates. Observers also encode higher-order statistics such as the kurtosis of feature distributions of orientation and color. But this sort of learning has not been shown for more intricate aspects of visual information. Here we tested the learning of distractor 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 learn the mean and range of the distributions. Previous demonstrations of feature 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 learning.