What Are You Looking At? Moving toward an Attentional Timeline in Insomnia: A Novel Semantic Eye Tracking Study
Bibliographic record
Abstract
STUDY OBJECTIVES: To date, cognitive probe paradigms have been used in different guises to obtain reaction time measurements suggestive of an attention bias towards sleep in insomnia. This study adopts a methodology which is novel to sleep research to obtain a continual record of where the eyes-and therefore attention-are being allocated with regard to sleep and neutral stimuli. DESIGN: A head mounted eye tracker (Eyelink II,SR Research, Ontario, Canada) was used to monitor eye movements in respect to two words presented on a computer screen, with one word being a sleep positive, sleep negative, or neutral word above or below a second distracter pseudoword. Probability and reaction times were the outcome measures. PARTICIPANTS: Sleep group classification was determined by screening interview and PSQI (> 8 = insomnia, < 3 = good sleeper) score. MEASUREMENTS AND RESULTS: Those individuals with insomnia took longer to fixate on the target word and remained fixated for less time than the good sleep controls. Word saliency had an effect with longer first fixations on positive and negative sleep words in both sleep groups, with largest effect sizes seen with the insomnia group. CONCLUSIONS: This overall delay in those with insomnia with regard to vigilance and maintaining attention on the target words moves away from previous attention bias work showing a bias towards sleep, particularly negative, stimuli but is suggestive of a neurocognitive deficit in line with recent research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".