Building ensemble representations: How the shape of preceding distractor distributions affects visual search.

Perception allows us to extract information about regularities in the environment. Observers can quickly determine summary statistics of a group of objects and detect outliers. The existing body of research has, however, not revealed how such ensemble representations develop over time. Moreover, the correspondence between the physical distribution of features in the external world and their potential internal representation as a probability density function (PDF) by the visual system is still unknown. Here, for the first time we demonstrate that such internal PDFs are built during visual search and show how they can be assessed with repetition and role-reversal effects. Using singleton search for an oddly oriented target line among differently oriented distractors (a priming of pop-out paradigm), we test how different properties of previously observed distractor distributions (mean, variability, and shape) influence search times. Our results indicate that observers learn properties of distractor distributions over and above mean and variance; in fact, response times also depend on the shape of the preceding distractor distribution. Response times decrease as a function of target distance from the mean of preceding Gaussian distractor distributions, and the decrease is steeper when preceding distributions have small standard deviations. When preceding distributions are uniform, however, this decrease in response times can be described by a two-piece function corresponding to the uniform distribution PDF. Moreover, following skewed distributions response times function is skewed in accordance with the skew in distributions. Indeed, internal PDFs seem to be specifically tuned to the observed feature distribution.