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Record W4289277970 · doi:10.1037/xge0001260

Revisiting the automaticity of reading: Electrophysiological recordings show that stroop words capture spatial attention.

2022· article· en· W4289277970 on OpenAlexfundno aff
Andrew D. Lowery, John J. McDonald

Bibliographic record

VenueJournal of Experimental Psychology General · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsAutomaticityStroop effectPsychologyCognitive psychologyReading (process)Automatism (medicine)Focus (optics)Color termN2pcCommunicationCognitionNeuroscienceVisual attentionComputer scienceArtificial intelligenceLinguistics

Abstract

fetched live from OpenAlex

Interference in the Stroop task is reduced when the word and color patch are placed at different locations and is diluted further by the presence of another distractor that is response neutral. Such dilution indicates that reading is not independent of an observer's attentional focus and thus is not a fully automatic process. So where does reading fall on the automaticity continuum? To address this question, we sought to determine whether an irrelevant word that appears abruptly in the field of view invariably draws attention to its location or whether observers can successfully ignore it while identifying a centrally presented target. In two experiments, electrical brain activity was recorded while healthy young adults participated in nonintegrated Stroop tasks. Irrelevant color words appearing randomly to the left or right of a target shape elicited an event-related potential component that reflects the spatial focusing of attention (posterior contralateral N2; N2pc). This N2pc was observed when participants discriminated the color of the target and when they discriminated the shape of the target. These findings demonstrate that color words reflexively capture spatial attention even when their meaning is unrelated to the task at hand. We conclude that although reading is not fully automatic, skilled readers cannot ignore words that appear abruptly in their field of view. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.121
GPT teacher head0.394
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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