Effects of directional expectations on motion perception and pursuit eye movements
Bibliographic record
Abstract
Expectations about future motions can influence both perceptual judgements and pursuit eye movements. However, it is not known whether these two effects are due to shared processing, or to separate mechanisms with similar properties. We have addressed this question by providing subjects with prior information about the likely direction of motion in an upcoming random-dot motion display and measuring both the perceptual judgements and pursuit eye movements elicited by the stimulus. We quantified the subjects' responses by computing oculometric curves from their pursuit eye movements and psychometric curves from their perceptual decisions. Our results show that directional cues caused similar shifts in both the oculometric and psychometric curves toward the expected motion direction, with little change in the shapes of the curves. Prior information therefore biased the outcome of both eye movement and perceptual decisions without systematically changing their thresholds. We also found that eye movement and perceptual decisions tended to be the same on a trial-by-trial basis, at a higher frequency than would be expected by chance. Furthermore, the effects of prior information were evident during pursuit initiation, as well as during pursuit maintenance, indicating that prior information likely influenced the early processing of visual motion. We conclude that, in our experiments, expectations caused similar effects on both pursuit and perception by altering the activity of visual motion detectors that are read out by both the oculomotor and perceptual systems. Applying cognitive factors such as expectations at relatively early stages of visual processing could act to coordinate the metrics of eye movements with perceptual judgements.
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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 | 0.008 |
| 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".