Basic Processes in Reading Aloud and Colour Naming: Towards a Better Understanding of the Role of Spatial Attention
Bibliographic record
Abstract
Whether or not lexical access from print requires spatial attention has been debated intensively for the last 30 years. Studies involving colour naming generally find evidence that "unattended" words are processed. In contrast, reading-based experiments do not find evidence of distractor processing. One theory ascribes the discrepancy to weaker attentional demands for colour identification. If colour naming does not capture all of a subject's attention, the remaining attentional resources can be deployed to process the distractor word. The present study combined exogenous spatial cueing with colour naming and reading aloud separately and found that colour naming is less sensitive to the validity of a spatial cue than is reading words aloud. Based on these results, we argue that colour naming studies do not effectively control attention so that no conclusions about unattended distractor processing can be drawn from them. Thus we reiterate the consistent conclusion drawn from reading aloud and lexical decision studies: There is no word identification without (spatial) attention.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".