Why use a mirror to assess visual pursuit in prolonged disorders of consciousness? Evidence from healthy control participants
Bibliographic record
Abstract
BACKGROUND: Evidence of reliable smooth visual pursuit is crucial for both diagnosis and prognosis in prolonged disorders of consciousness (PDOC). However, a mirror is more likely than an object to elicit evidence of smooth pursuit. Our objective was to identify the physiological and/or cognitive mechanism underlying the mirror benefit. METHODS: We recorded eye-movements while healthy participants simultaneously completed a visual pursuit task and a cognitively demanding two-back task. We manipulated the stimulus to be pursued (two levels: mirror, ball) and the simultaneous cognitive load (pursuit only, pursuit plus two-back task) within subjects. RESULTS: Pursuit of the reflected-own-face in the mirror was associated with briefer fixations that occurred less uniformly across the horizontal plane relative to object pursuit. Secondary task performance did not differ between pursuit stimuli. The secondary task also did not affect eye movement measures, nor did it interact with pursuit stimulus. CONCLUSIONS: Reflected-own-face pursuit is no less cognitively demanding than object pursuit, but it naturally elicits smoother eye movements (i.e. briefer pauses to fixate). A mirror therefore provides greater sensitivity to detect smooth visual pursuit in PDOC because the naturally smoother eye movements may be identified more confidently by the assessor.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".