Memory diffusion: Using generative AI to create an image database for memory research

When we memorize visual stimuli, their content is processed at multiple levels, ranging from the fine-grained perceptual details to the semantic concepts and categories.However, it is unclear to which extend low-and high-level information is maintained in memory over time.Real-world stimuli are not ideal for investigating this question, as they often exhibit strong correlations between processing levels: Conceptually similar objects tend to share similar visual features.Using generative AI we created a new database of 496 image pairs orthogonalizing semantic (word2vec) and perceptual (CoreNet-S) information.Specifically, we generated image pairs that either (a) depict objects from distinct semantic concepts but are perceptually similar, or (b) show the same object but are perceptually dissimilar.

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Publication Details

Journal
MPG.PuRe (Max Planck Society)
Published
2025-08-15
Primary Topic
Image Retrieval and Classification Techniques
Type
article
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article

Memory diffusion: Using generative AI to create an image database for memory research

F. Kamp, B. ; https://orcid.org/0000-0001-9752-932X Spitzer, F. Born
MPG.PuRe (Max Planck Society)
Image Retrieval and Classification Techniques
article

Memory diffusion: Using generative AI to create an image database for memory research

F. Kamp, B. ; https://orcid.org/0000-0001-9752-932X Spitzer, F. Born
article en

Abstract

When we memorize visual stimuli, their content is processed at multiple levels, ranging from the fine-grained perceptual details to the semantic concepts and categories.However, it is unclear to which extend low-and high-level information is maintained in memory over time.Real-world stimuli are not ideal for investigating this question, as they often exhibit strong correlations between processing levels: Conceptually similar objects tend to share similar visual features.Using generative AI we created a new database of 496 image pairs orthogonalizing semantic (word2vec) and perceptual (CoreNet-S) information.Specifically, we generated image pairs that either (a) depict objects from distinct semantic concepts but are perceptually similar, or (b) show the same object but are perceptually dissimilar.

MPG.PuRe (Max Planck Society)
Openalex Percentile: Top 29%
Image Retrieval and Classification Techniques
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