Rapid averaging? Not so fast!

Abstract

Previous research suggests that sets of similar items are represented using a rapid averaging mechanism that automatically extracts statistical properties within 50 ms. However, typically in these studies, displays are not masked, so it is possible that the sets are available for longer than this duration. In the present study, using masked displays, we (a) tested a newly proposed strategy for extracting the mean size of a set of circles, and (b) more precisely evaluated the time course of rapid averaging. The results indicate that when viewing conditions are poor, performance can be explained by assuming that observers rely on information from previous trials. In this study, observers required at least a 200-ms exposure time in order to derive the average size of a set of circles without relying on information from previously-viewed sets, suggesting that rapid averaging is not as fast as previously assumed and, therefore, that it may not be an automatic process.When shown a set of similar items, people can rapidly summarize the set according to statistical properties, such as the mean size. Ariely (2001) found that observers were able to determine the average size of a set of circles, but were unable to identify individual members of the set. Ariely interpreted this as evidence that the visual system can derive a statistical representation of the set without retaining specific information about the items within the set. Researchers have proposed that this is accomplished using a specialized averaging mechanism that evaluates all of the items in the set in parallel. Consistent with this proposal, Chong and Treisman (2005a) showed that averaging performance was better when attention was broadly distributed across a display than when attention was narrowly focused, suggesting that the specialized averaging mechanism operates preattentively, outside the focus of attention. Additional evidence for the automaticity of this process has been demonstrated through cuing and dual-task manipulations.

The capacity limitations of orientation summary statistics

“…For example, accurate summary formation requires a ten-fold increase in exposure time when the displays are masked (Whiting & Oriet, 2011), two summaries cannot be computed concurrently without cost (Brand, Oriet, & Tottenham, 2012), large set size effects abound when the items within a set are sufficiently heterogeneous (Marchant, Simons, & de Fockert, 2013), and summaries are susceptible to modulation by visual stages beyond the initial registration of features (Jacoby, Kamke, & Mattingley, 2013; see also Poltoratski & Xu, 2013). The range of effects cited in the SSR literature may also be accounted for by known psychophysical principles (Allik et al, 2013) or by existing cognitive mechanisms, such as visual working memory (Myzczek & Simons, 2008).…”

“…The default exposure duration was 200 ms (see Whiting & Oriet, 2011). A coarse tracking procedure altered the exposure duration, block-by-block, on the basis of performance in the simultaneous condition only.…”

Effects of ensemble complexity and perceptual similarity on rapid averaging of hue

“…Exhaustive ensemble processing implies the parallel integration of all the objects or items in an ensemble to provide a summary representation, often with inaccurate or no representation of the individual items. This has been a controversial suggestion as it postulates a mechanism that exceeds the limit of visual short-term memory (Alvarez, 2011) with some studies appearing to demonstrate exhaustive processing (e.g., Ariely, 2001Ariely, , 2008Chong & Treisman, 2003, 2005a, 2005b while others propose models that use subsampling can account for the accuracy of mean judgments without the need for a holistic mechanism (e.g., Marchant et al, 2013;Whiting & Oriet, 2011). Varying the number of elements in ensembles will provide an indication as to whether averaging is affected by an increase in the number of objects to average.…”

Temporal dynamics of ensemble statistics calculation using a neural network model

“…However, there exists some disagreement regarding the temporal dynamics of the cognitive process. Whereas Chong and Treisman (2003) propose a faster automatic process (withiñ 50 ms), Whiting and Oriet (2011) on the other hand suggest a slower timeline (around~200 ms). In the current work we investigated this dichotomy through a computational model developed for individuation (see Sengupta et al, 2014 for details).…”