Seeing Sets: Representation by Statistical Properties

TLDR

Sets of similar objects are common, but when attention shifts away, it is unclear what information the visual system retains about them. The study introduces a set representation to investigate what information is retained when attention is diverted. The authors tested the set representation using mean discrimination and member identification paradigms. Three experiments with sets of different‑sized spots showed that observers accurately perceive a set’s mean but know little about individual items, except their range, indicating that the visual system encodes overall statistical rather than individual properties.

Abstract

Sets of similar objects are common occurrences--a crowd of people, a bunch of bananas, a copse of trees, a shelf of books, a line of cars. Each item in the set may be distinct, highly visible, and discriminable. But when we look away from the set, what information do we have? The current article starts to address this question by introducing the idea of a set representation. This idea was tested using two new paradigms: mean discrimination and member identification. Three experiments using sets of different-sized spots showed that observers know a set's mean quite accurately but know little about the individual items, except their range. Taken together, these results suggest that the visual system represents the overall statistical, and not individual, properties of sets.