ArticleCognition2021
Meaning maps capture the density of local semantic features in scenes: A reply to Pedziwiatr, Kümmerer, Wallis, Bethge & Teufel (2021).
Article in Cognition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- The temporal dynamics of semantic guidance in scenes.Attention, perception & psychophysics · 2026Article
- Causal reasoning guides visual exploration.Experimental brain research · 2026Article
- Saliency models perform best for women's and young adults' fixations.Communications psychology · 2023Article
- Look at what I can do: Object affordances guide visual attention while speakers describe potential actions.Attention, perception & psychophysics · 2022Article
- 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
- Disrupted object-scene semantics boost scene recall but diminish object recall in drawings from memory.Memory & cognition · 2021Article
- Deep saliency models learn low-, mid-, and high-level features to predict scene attention.Scientific reports · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
Pedziwiatr, Kümmerer, Wallis, Bethge, & Teufel (2021) contend that Meaning Maps do not represent the spatial distribution of semantic features in scenes. We argue that Pesziwiatr et al. provide neither logical nor empirical support for that claim, and we conclude that Meaning Maps do what they were designed to do: represent the spatial distribution of meaning in scenes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.