Bibliographic record
Abstract
Optic flow is an important cue for human perception and locomotion and naturally triggers eye movements. Many aspects of visual perception benefit from eye movements, but it is unclear whether the perception of naturalistic optic flow is affected by eye movements. Here we investigate whether the perception of optic flow direction is limited or enhanced by eye movements by manipulating the instruction to observers (free viewing vs. fixation) during a direction discrimination task. Observers (n=23) viewed an optic flow pattern for 1 second and reported in which of the four quadrants (top left/right, bottom left/right) the focus of expansion (FOE) was located using a keypress; in all observers, the task was performed during fixation and free-viewing. Task difficulty was varied by manipulating the coherence of radial motion from the FOE (4%-64%). Threshold and slope measures of a psychometric function were compared across eye movement conditions. During free-viewing, observers tracked the optic flow pattern with a combination of saccades and smooth pursuit eye movements. During fixation, observers nevertheless made small-scale saccades and low-velocity pursuit. Despite differences in spatial scale eye movements during free viewing and fixation were similarly directed toward the FOE (saccades) and away from the FOE (smooth pursuit). Perceptual direction discrimination thresholds (p=.22, d=.26) and psychometric function slopes (p=.15, d=.30) were similar during free-viewing and fixation, indicating that FOE direction judgments were comparable across eye movement conditions. These findings suggest that optic flow patterns generate small and large-scale eye movements, but that their occurrence might not influence optic flow perception in a task that requires coarse localization of the FOE. Preliminary data of a control experiment requiring observers to discriminate luminance changes within the FOE differ between free viewing and fixation, indicating that the influence of eye movements on optic flow perception might be task-specific.
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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 | 0.000 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".