Value-driven effects on perceptual averaging

Abstract

Copyright © The Author(s) 2022. Perceptual averaging refers to a strategy of encoding the statistical properties of entire sets of objects rather than encoding individual object properties, potentially circumventing the visual system’s strict capacity limitations. Prior work has shown that such average representations of set properties, such as its mean size, can be modulated by top-down and bottom-up attention. However, it is unclear to what extent attentional biases through selection history, in the form of value-driven attentional capture, influences this type of summary statistical representation. To investigate, we conducted two experiments in which participants estimated the mean size of a set of heterogeneously sized circles while a previously rewarded color singleton was part of the set. In Experiment 1, all circles were gray, except either the smallest or the largest circle, which was presented in a color previously associated with a reward. When the largest circle in the set was associated with the highest value (as a proxy of selection history), we observed the largest biases, such that perceived mean size scaled linearly with the increasing value of the attended color singleton. In Experiment 2, we introduced a dual-task component in the form of an attentional search task to ensure that the observed bias of reward on perceptual averaging was not fully explained by focusing attention solely on the reward-signaling color singleton. Collectively, findings support the proposal that selection history, like bottom-up and top-down attention, influences perceptual averaging, and that this happens in a flexible manner proportional to the extent to which attention is captured.