Feature-specificity in visual statistical summary processing.

Abstract

Visual statistical summary processing enables people to extract the average feature of a set of items rapidly and accurately. Previous studies have demonstrated independent mechanisms for summarizing low (e.g. color, orientation) and high-level (facial identity, emotion) visual information. However, no study to date has conclusively determined whether there are feature-specific summarization mechanisms for low-level features or whether there are low-level, feature agnostic summarization mechanisms. To address this issue, we asked participants to report either the average orientation or the average size from a set of lines where both features varied. Participants completed these tasks either in single-task or mixed-task conditions; in the latter, successful performance required extraction of both summaries concurrently. If there were feature-specific summarization mechanisms that could operate in parallel, then errors in mean size and mean orientation tasks should be independent, in both single and mixed task conditions. On the other hand, a central domain-general mechanism for low-level summarization would imply a correlation between errors for both features and greater error in the mixed than single task trials. In Experiment 1, we found that there was no correlation between the mean size and mean orientation errors and performance was similar across single and mixed-task conditions, suggesting that there may be independent summarization mechanisms for size and orientation features. To further test the feature-specificity account, in Experiment 2 and 3 (with mask), we manipulated the display duration to determine whether there were any differences in the summarization of earlier (orientation) vs. later (size) features. While these experiments replicated the pattern of results observed in Experiment 1, at shorter display durations, no differences emerged across features. We argue that our data is consistent with independent, multi-level feature-specific statistical summary mechanisms for low-level visual features.