Dimensions of Vulnerability in Visual Working Memory: An AI-Driven Approach to Perceptual Comparison
Yuang Cao, Jiachen Zou, Chen Wei, Quanying Liu

TL;DR
This study introduces an AI-driven framework to investigate how perceptual comparison of naturalistic visual stimuli influences memory distortions, revealing that visual dimensions are more susceptible to distortion than semantic ones.
Contribution
The paper presents a novel AI-based method to generate naturalistic stimuli for studying memory biases, advancing understanding of perceptual features affecting visual working memory vulnerability.
Findings
Similar visual dimensions induce memory distortions.
Visual dimensions are more prone to distortion than semantic dimensions.
AI-generated stimuli effectively elicit perceptual memory biases.
Abstract
Human memory exhibits significant vulnerability in cognitive tasks and daily life. Comparisons between visual working memory and new perceptual input (e.g., during cognitive tasks) can lead to unintended memory distortions. Previous studies have reported systematic memory distortions after perceptual comparison, but understanding how perceptual comparison affects memory distortions in real-world objects remains a challenge. Furthermore, identifying what visual features contribute to memory vulnerability presents a novel research question. Here, we propose a novel AI-driven framework that generates naturalistic visual stimuli grounded in behaviorally relevant object dimensions to elicit similarity-induced memory biases. We use two types of stimuli -- image wheels created through dimension editing and dimension wheels generated by dimension activation values -- in three visual working…
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