Evidence map›Paper›PMID 32173410›Full record

ArticleNeuroImage2020

An orthographic prediction error as the basis for efficient visual word recognition.

Benjamin Gagl, Jona Sassenhagen, Sophia Haan, Klara Gregorova, Fabio Richlan, Christian J Fiebach

Abstract read
In one paragraph

Article in NeuroImage, 2020. 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.

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  12. Detection of deviance in Japanese kanji compound words.Frontiers in human neuroscience · 2022
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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

6 authors.

Benjamin GaglDepartment of Psychology, Goethe University Frankfurt, Frankfurt/Main, Germany; Center for Individual Development and Adaptive Education of Children at Risk (IDeA), Frankfurt/Main, Germany. Electronic address: gagl@psych.uni-frankfurt.de.
Jona SassenhagenDepartment of Psychology, Goethe University Frankfurt, Frankfurt/Main, Germany.
Sophia HaanDepartment of Psychology, Goethe University Frankfurt, Frankfurt/Main, Germany.
Klara GregorovaDepartment of Psychology, Goethe University Frankfurt, Frankfurt/Main, Germany.
Fabio RichlanCentre for Cognitive Neuroscience, University of Salzburg, Salzburg, Austria.
Christian J FiebachDepartment of Psychology, Goethe University Frankfurt, Frankfurt/Main, Germany; Center for Individual Development and Adaptive Education of Children at Risk (IDeA), Frankfurt/Main, Germany; Brain Imaging Center, Goethe University Frankfurt, Frankfurt/Main, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Most current models assume that the perceptual and cognitive processes of visual word recognition and reading operate upon neuronally coded domain-general low-level visual representations - typically oriented line representations. We here demonstrate, consistent with neurophysiological theories of Bayesian-like predictive neural computations, that prior visual knowledge of words may be utilized to 'explain away' redundant and highly expected parts of the visual percept. Subsequent processing stages, accordingly, operate upon an optimized representation of the visual input, the orthographic prediction error, highlighting only the visual information relevant for word identification. We show that this optimized representation is related to orthographic word characteristics, accounts for word recognition behavior, and is processed early in the visual processing stream, i.e., in V4 and before 200 ​ms after word-onset. Based on these findings, we propose that prior visual-orthographic knowledge is used to optimize the representation of visually presented words, which in turn allows for highly efficient reading processes.

Indexed as

Computer SimulationModels, NeurologicalReadingAdolescentAdultBayes TheoremBrainBrain MappingElectroencephalographyFemaleHumansMagnetic Resonance ImagingMalePattern Recognition, VisualYoung AdultComputational modellingEEGfMRIHandwritingPredictive codingReaction timesVisual word recognition

Identifiers

PMID32173410
PMCPMC7284316

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

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.