Abstract
Perception represents not only discrete features and objects, but also information distributed in time and space. One intriguing example is perceptual averaging: we are surprisingly efficient at perceiving and reporting the average size of objects in spatial arrays or temporal sequences. Extracting such statistical summary representations (SSRs) is fast and accurate, but several fundamental questions remain about their underlying nature. We explored three such questions, investigating SSRs of size for static object arrays, and for a single continuously growing/shrinking object (as introduced in Albrecht & Scholl, in press, Psychological Science). Question 1: Are SSRs computed automatically, or only intentionally? When viewing a set of discs, observers completed three trials of a ‘decoy’ task, pressing a key when they detected a sudden luminance change. Observers also reported the discs′ average size on the final trial, but could receive these instructions either before the final display onset, or after its offset. Performance for the second (‘incidental averaging’) group was no worse than for the first (‘intentional averaging’) group – suggesting that some SSRs can be computed automatically. Question 2: Can SSRs be computed selectively from temporal subsets? Observers viewed a continuously growing/shrinking disc that changed color briefly during each trial. Observers were asked to average either the entire sequence, or only the differently-colored subset – via instructions presented either before the display onset, or after its offset. Performance was as accurate with subsets as with the whole – suggesting that SSRs can be temporally selective. Question 3: Can we simultaneously extract multiple SSRs from temporally overlapping sequences? In the same experiments, there was a small but reliable cost to receiving the instructions after the display offset – suggesting that SSRs cannot automatically compute multiple temporally-overlapping averages. Collectively, these and other results clarify both the flexibility and intrinsic limitations of perceptual averaging.
This work was supported by the EU-Project BACS FP6-IST-027140.