The missing-colour effect: The attentional beam captures reading-relevant and reading-irrelevant information
Bibliographic record
Abstract
According to many models, reading is driven by an attentional beam. In two experiments, we investigated the specificity of the beam by testing its sensitivity to a reading-irrelevant feature: colour. More specifically, participants were asked to read either a black-and-white version or a multi-colour version of the text in which each letter was printed in a different colour. In addition, while reading for comprehension, participants either searched for a target letter ( t or d) or for a colour (pink or black). In Experiment 1, we used the Nelson–Denny reading test and in Experiment 2, we used an experimental text. In both the experiments, the typical missing-letter effect was observed with letters: Participants missed more letters in function than in content words. Most importantly, although the effect was smaller, this pattern of results was also observed when participants searched for a colour (e.g., pink or black letters in a multi-coloured passage). Our results suggest that the attentional beam involved in reading is sensitive to both reading-relevant and reading-irrelevant information.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".