ArticleAttention, perception & psychophysics2022
Meaning maps detect the removal of local semantic scene content but deep saliency models do not.
Article in Attention, perception & psychophysics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Conceptual priorities shape individual gaze patterns during naturalistic visual attention.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- The role of event understanding in guiding attentional selection in real-world scenes: The Scene Perception & Event Comprehension Theory (SPECT).Attention, perception & psychophysics · 2026Review
- Frontotemporal network interactions causally support rapid concreteness judgments during reading.PLoS biology · 2026Article
- Visual memory.Attention, perception & psychophysics · 2026Review
- Meaning maps predict reaction time in change detection.Visual cognition · 2025Article
- Searching for meaning: Local scene semantics guide attention during natural visual search in scenes.Quarterly journal of experimental psychology (2006) · 2023Article
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2 authors.
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Abstract
Meaning mapping uses human raters to estimate different semantic features in scenes, and has been a useful tool in demonstrating the important role semantics play in guiding attention. However, recent work has argued that meaning maps do not capture semantic content, but like deep learning models of scene attention, represent only semantically-neutral image features. In the present study, we directly tested this hypothesis using a diffeomorphic image transformation that is designed to remove the meaning of an image region while preserving its image features. Specifically, we tested whether meaning maps and three state-of-the-art deep learning models were sensitive to the loss of semantic content in this critical diffeomorphed scene region. The results were clear: meaning maps generated by human raters showed a large decrease in the diffeomorphed scene regions, while all three deep saliency models showed a moderate increase in the diffeomorphed scene regions. These results demonstrate that meaning maps reflect local semantic content in scenes while deep saliency models do something else. We conclude the meaning mapping approach is an effective tool for estimating semantic content in scenes.
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