Evidence map›Paper›PMID 41948078›Full record

ArticleOpen mind : discoveries in cognitive science2026

Chunking, Attraction, Repulsion, and Ensemble Effects Are Ubiquitous in Visual Working Memory.

Chaipat Chunharas, Timothy F Brady

Abstract read
In one paragraph

Article in Open mind : discoveries in cognitive science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Chaipat ChunharasCognitive Clinical & Computational Neuroscience Center of Excellence, Department of Internal Medicine, Faculty of Medicine, Chulalongkorn University; Chula Neuroscience Center, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand.ORCID https://orcid.org/0000-0003-1074-0160
Timothy F BradyDepartment of Psychology, University of California, San Diego, La Jolla, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

What happens when there are many different objects that must be maintained simultaneously in visual working memory? Prominent models focus largely on individual objects in describing memory limits: e.g., claims that we can remember only a fixed number of items, or that a single resource limit describes the cost of holding additional items. While some acknowledge interactions between items, these interactions are not given a prominent role in most models. Here we show that instead of items being represented independently, the visual display is always compressed by utilizing clusters of items and the gist of the display. We reanalyze data from 11 previously available datasets (comprising 137,986 trials in total). We find strong evidence for non-independent representations, including chunking and use of the 'gist' of the display, which is present in nearly every study at every set size. We then present a model for understanding this chunking and the resulting systematic biases (e.g., items attracting and repelling one another) based on psychophysical similarity. Overall, this work provides strong evidence that in order to understand visual working memory, we need to consider how our memory system takes advantage of the relationship between items. This is in contrast to the way the majority of the field studies visual working memory and suggests a major paradigm shift is required to think of memory in terms of clusters, chunks, and gist rather than in terms of independent items.

Indexed as

chunkingensemble perceptionvisual working memoryworking memory capacity

Identifiers

PMID41948078
PMCPMC13053025

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Registered trials

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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.