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Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

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Limited memory for ensemble statistics in visual change detection.

William J Harrison¹, Jessica M V McMaster², Paul M Bays²

¹Department of Psychology, University of Cambridge, Downing Street, Cambridge CB2 3EB, UK; Queensland Brain Institute, The University of Queensland, QBI Building 79, St Lucia, QLD 4072, Australia; School of Psychology, The University of Queensland, McElwain Building 24a, St Lucia, QLD 4072, Australia.

|June 1, 2021

Summary

This summary is machine-generated.

Working memory may use ensemble statistics, like variance, for visual change detection, especially under high cognitive load. However, memory for the average feature value does not appear to aid this process.

Keywords:

Change detection Ensemble statistics Optimal observer model Short term memory Signal detection theory Visual working memory

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Area of Science:

Cognitive Psychology
Neuroscience
Visual Perception

Background:

Working memory models often focus on individual item representations.
Ensemble statistics (e.g., mean, variance) represent higher-order scene regularities.
The role of ensemble statistics in working memory for visual change detection is not well understood.

Purpose of the Study:

To investigate if ensemble statistics are stored in working memory.
To determine if ensemble statistics aid in detecting changes in the visual environment.
To compare the contribution of ensemble statistics versus individual item memory.

Main Methods:

Utilized change detection tasks with controlled alterations in ensemble mean and variance.
Compared observer sensitivity to predictions from an optimal summation model.
Conducted six experiments manipulating task difficulty and type of ensemble change.

Main Results:

Observers outperformed the optimal summation model in detecting changes in stimulus variance, particularly under high task difficulty.
Memory for stimulus variance significantly contributed to change detection sensitivity under specific high-demand conditions.
No significant evidence supported the hypothesis that the ensemble mean is stored or utilized in working memory for change detection.

Conclusions:

Visual change detection is primarily limited by individual feature memory uncertainty.
Memory for stimulus variance can enhance change detection, but only under specific high working memory load conditions.
Current working memory models may need to incorporate ensemble statistical representations, particularly variance, to fully explain visual perception.