ArticleCognition2021
Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations.
Article in Cognition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 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
- Pre-viewing context and visual attention in museum scenes: an exploratory eye-tracking study.Frontiers in psychology · 2026Article
- Easy does it: Selection during interactive search tasks is biased towards objects that can be examined easily.Attention, perception & psychophysics · 2025Article
- Quantifying task-related gaze.Attention, perception & psychophysics · 2024Article
- Visual search patterns during exploration of naturalistic scenes are driven by saliency cues in individuals with cerebral visual impairment.Scientific reports · 2024Article
- Meaning maps detect the removal of local semantic scene content but deep saliency models do not.Attention, perception & psychophysics · 2022Article
- Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps.Journal of vision · 2022Article
- Low-level factors increase gaze-guidance under cognitive load: A comparison of image-salience and semantic-salience models.PloS one · 2022Article
- Disrupted object-scene semantics boost scene recall but diminish object recall in drawings from memory.Memory & cognition · 2021Article
- Meaning maps capture the density of local semantic features in scenes: A reply to Pedziwiatr, Kümmerer, Wallis, Bethge & Teufel (2021).Cognition · 2021Article
- Cognitive load influences oculomotor behavior in natural scenes.Scientific reports · 2021Article
- Salience-based object prioritization during active viewing of naturalistic scenes in young and older adults.Scientific reports · 2020Article
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5 authors.
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Abstract
Eye movements are vital for human vision, and it is therefore important to understand how observers decide where to look. Meaning maps (MMs), a technique to capture the distribution of semantic information across an image, have recently been proposed to support the hypothesis that meaning rather than image features guides human gaze. MMs have the potential to be an important tool far beyond eye-movements research. Here, we examine central assumptions underlying MMs. First, we compared the performance of MMs in predicting fixations to saliency models, showing that DeepGaze II - a deep neural network trained to predict fixations based on high-level features rather than meaning - outperforms MMs. Second, we show that whereas human observers respond to changes in meaning induced by manipulating object-context relationships, MMs and DeepGaze II do not. Together, these findings challenge central assumptions underlying the use of MMs to measure the distribution of meaning in images.
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