A signal-detection account of item-based and ensemble-based visual change detection: A reply to Harrison, McMaster, and Bays

TL;DR

This paper argues that ensemble information influences visual change detection, challenging previous conclusions about how people use memory for individual items.

Contribution

The paper introduces a new interpretation of optimal summation as an ensemble-based strategy and redefines the reference level for item-based performance.

Findings

Performance in change detection is influenced by the statistics of the whole memory set, even when only one item is tested.

Optimal summation can be interpreted as a holistic, ensemble-based decision strategy.

Both the authors' and Harrison et al.'s observers outperformed the redefined minimum rule for item-based strategies.

Abstract

Growing empirical evidence shows that ensemble information (e.g., the average feature or feature variance of a set of objects) affects visual working memory for individual items. Recently, Harrison, McMaster, and Bays (2021) used a change detection task to test whether observers explicitly rely on ensemble representations to improve their memory for individual objects. They found that sensitivity to simultaneous changes in all memorized items (which also globally changed set summary statistics) rarely exceeded a level predicted by the so-called optimal summation model within the signal-detection framework. This model implies simple integration of evidence for change from all individual items and no additional evidence coming from ensemble. Here, we argue that performance at the level of optimal summation does not rule out the use of ensemble information. First, in two experiments, we show that, even if evidence from only one item is available at test, the statistics of the whole memory set affect performance. Second, we argue that optimal summation itself can be conceptually interpreted as one of the strategies of holistic, ensemble-based decision. We also redefine the reference level for the item-based strategy as the so-called “minimum rule,” which predicts performance far below the optimum. We found that that both our and Harrison et al. (2021)’s observers consistently outperformed this level. We conclude that observers can rely on ensemble information when performing visual change detection. Overall, our work clarifies and refines the use of signal-detection analysis in measuring and modeling working memory.

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