Seeing Sets: Representation by Statistical Properties

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.

[1]  Ernst Mach,et al.  The Analysis of Sensations. , 1916 .

[2]  J. Gaddum.  Probit Analysis , 1948, Nature.

[3]  H. Helson.  Adaptation-level theory : an experimental and systematic approach to behavior , 1964 .

[4]  A. Linksz.  Outlines of a Theory of the Light Sense. , 1965 .

[5]  Stanley Coren,et al.  Size contrast as a function of figural similarity , 1974 .

[6]  I. Biederman.  Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[7]  Andrew B. Watson,et al.  The cortex transform: rapid computation of simulated neural images , 1987 .

[8]  J. Duncan,et al.  Visual search and stimulus similarity. , 1989, Psychological review.

[9]  Susan L. Franzel,et al.  Guided search: an alternative to the feature integration model for visual search. , 1989, Journal of experimental psychology. Human perception and performance.

[10]  C. Blakemore,et al.  Vision: The iconic bottleneck and the tenuous link between early visual processing and perception , 1990 .

[11]  A. Treisman.  Search, similarity, and integration of features between and within dimensions. , 1991, Journal of experimental psychology. Human perception and performance.

[12]  Robert P. Abelson,et al.  Velocity relation : satisfaction as a function of the first derivative of outcome over time , 1991 .

[13]  Andrew P. Duchon,et al.  The human visual system averages speed information , 1992, Vision Research.

[14]  D. Kahneman,et al.  When More Pain Is Preferred to Less: Adding a Better End , 1993 .

[15]  W. D. Ross,et al.  A Neural Theory of Attentive Visual Search : Interactions of Boundary , Surface , Spatial , and Object Representations By : Stephen Grossberg , 2004 .

[16]  B. Julesz.  Dialogues on Perception , 1994 .

[17]  P. Atchley,et al.  Discrimination of speed distributions: Sensitivity to statistical properties , 1995, Vision Research.

[18]  D. Ariely.  Combining experiences over time: the effects of duration, intensity changes and on‐line measurements on retrospective pain evaluations , 1998 .

[19]  R. Rosenholtz.  A simple saliency model predicts a number of motion popout phenomena , 1999, Vision Research.

[20]  A. Tversky,et al.  Choices, Values, and Frames , 2000 .

[21]  Daniel Kahneman,et al.  Evaluation by Moments: Past and Future , 2002 .