Evidence map›Paper›PMID 31697615›Full record

ArticlePsychological science2019

Reading Increases the Compositionality of Visual Word Representations.

Aakash Agrawal, K V S Hari, S P Arun

Abstract read
In one paragraph

Article in Psychological science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
  2. Article
  3. Shape and word parts combine linearly in the Bouba-Kiki effect.Attention, perception & psychophysics · 2025
    Article
  4. From retinotopic to ordinal coding: Dissecting the cortical stages of visual word recognition.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Using compositionality to understand parts in whole objects.The European journal of neuroscience · 2022
    Review
  12. Article
  13. Article
  14. Emergence of a compositional neural code for written words: Recycling of a convolutional neural network for reading.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  15. Article
  16. 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

3 authors.

Aakash AgrawalCentre for BioSystems Science and Engineering, Indian Institute of Science.
K V S HariDepartment of Electrical Communication Engineering, Indian Institute of Science.
S P ArunCentre for Neuroscience, Indian Institute of Science.ORCID 0000-0001-9602-5066

Funding

DBT-Wellcome Trust India Alliance IA/S/17/1/503081Wellcome Trust
6 · The paper itself

Abstract

Reading causes widespread changes in the brain, but its effect on visual word representations is unknown. Learning to read may facilitate visual processing by forming specialized detectors for longer strings or by making word responses more predictable from single letters-that is, by increasing compositionality. We provided evidence for the latter hypothesis using experiments that compared nonoverlapping groups of readers of two Indian languages (Telugu and Malayalam). Readers showed increased single-letter discrimination and decreased letter interactions for bigrams during visual search. Importantly, these interactions predicted subjects' overall reading fluency. In a separate brain-imaging experiment, we observed increased compositionality in readers, whereby responses to bigrams were more predictable from single letters. This effect was specific to the anterior lateral occipital region, where activations best matched behavior. Thus, learning to read facilitates visual processing by increasing the compositionality of visual word representations.

Indexed as

AdultBrainFemaleHumansLanguageLearningMaleNeuroimagingOccipital LobeReadingRecognition, PsychologySpatial Processingneuroimagingobject recognitionopen datareadingvisual search

Identifiers

PMID31697615
PMCPMC6912929

What OpenQuestion holds

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Registered trials

None linked

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.