Eye decide: eye movement initiation relates to decision accuracy in a go/no-go interception task
Bibliographic record
Abstract
Natural tasks, such as catching a fly, require a continuous readout of sensory information to decide whether, when, and where to act. These goal-directed actions are preceded by perceptual decisions relying on brain areas also involved in the planning and execution of eye movements. Recent studies showed that eye movements during or shortly after decision formation are modulated by decision outcome. For example, saccades are initiated earlier and faster in the decision-congruent direction in motion discrimination tasks. However, whether eye movements contribute to decision formation is not yet known. We tested observers in EyeStrike—a rapid manual interception task—allowing us to evaluate eye movements during go/no-go decisions. Observers (n=45) viewed a briefly presented (100–300 ms) moving target that followed a linear-diagonal trajectory either passing (“go” response required) or missing (“no-go” required) a strike box. Observers indicated their choice by intercepting the target inside the strike box (go) or by withholding a hand movement (no-go). The target elicited a combination of smooth pursuit and saccadic eye movements. The first saccade was reliably initiated ~240 ms after target onset. Hand movements were initiated shortly after the initial saccade onset (~180 ms), indicating that decision formation occurred prior to the initial saccade. Importantly, more accurate early pursuit was related to higher decision accuracy, reflected in a negative correlation between eye velocity error (between target onset and initial saccade) and decision accuracy. These results suggest that pursuit eye movements continuously update decision processes until the initiation of the first saccade.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".