Revisiting the automaticity of reading: Electrophysiological recordings show that stroop words capture spatial attention.
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".