Attention and eye-movement control in reading: The selective reading paradigm.
Bibliographic record
Abstract
We introduced a novel paradigm for investigating covert attention and eye-movement control in reading. In 2 experiments, participants read sentence words (shown in blue color) while ignoring interleaved distractor strings (shown in orange color). Each single-line text display contained a target word and a critical distractor. Critical distractors were located just prior to the target in the text and were either words or symbol strings (e.g., @#%&). Target word availability for parafoveal processing (i.e., preview validity) was also manipulated. The results indicated much shallower processing of distractors than targets, and this pattern was more pronounced for symbol than word distractors. The influences of word frequency and fixation location on first-pass fixation durations on distractors were dramatically different than the well-documented pattern obtained in normal reading. Robust preview benefits were demonstrated both when the critical distractors were fixated and when the critical distractors were skipped. Finally, with the exception of larger preview benefits that were obtained in the condition in which the target and critical distractor were identical, the magnitude of the preview effect was largely unaffected by the nature of the critical distractor. Implications of the present paradigm and findings to the study of eye-movement control in reading are discussed. (PsycINFO Database Record
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".