ReviewCognitive processing2024
Visual working memory for natural scenes: challenges and opportunities.
Review in Cognitive processing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- A population vector model of visual working memory for real-world scenes.Journal of experimental psychology. General · 2026Article
- A Population Vector Model of Visual Working Memory for Real-World Scenes.bioRxiv : the preprint server for biology · 2026Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Visual working memory is a fundamental cognitive process that people use thousands of times each day as they engage in visually guided behavior. Thus, it is important to understand how the natural visual input-which consists of complex, spatially organized, continuously varying features-is represented in visual working memory. However, most research has used arrays of discrete, artificial objects defined by simple features, and existing formal models of visual working memory cannot be applied to natural scenes. In this paper, we identify 3 key aspects of natural scenes that are not captured by existing formal models of visual working memory, along with 2 distinct types of attention that must be considered. The goal is to clearly define the challenges and opportunities for moving models of visual working memory from arrays of artificial objects to natural 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.