Bribing the eye: expected reward modulates smooth pursuit eye movements
Bibliographic record
Abstract
Introduction: Reward expectancies can have profound effects on behavioral choices: primates select the target that is associated with the highest expected reward or value. Previous studies on smooth pursuit eye movements, the eyes’ main response to visual motion, showed that eye velocity dynamically reflects selection in favor of targets with higher expected reward. The current study examined whether reward also modulates basic kinematics of human smooth pursuit: does expecting a higher reward make us track moving objects better? Methods: We recorded eye position in 22 untrained human observers who were instructed to accurately track a small spot of light, moving at constant speed across a computer monitor; luminance contrast of the spot was either high (exp1) or low (exp2). Expected reward was manipulated by using pictures of Canadian 5 or 25 cent coins as cues (presented for 1000 ms preceding stimulus motion) indicating a low or high-reward trial, respectively. The ratio of low- to high-reward trials was 4:1; reward cues were equal in size and luminance. Observers were told that for each high-reward trial 25 cents would be added to their remuneration as a reward for accurate tracking. A control condition without reward cues served as a baseline. Results: We found consistent effects of reward expectation on smooth pursuit in both experiments. In high-reward trials, pursuit was initiated faster (higher acceleration and velocity) and maintained with better accuracy (gain) and lower velocity error. High reward also resulted in smoother pursuit responses with fewer and smaller catch-up saccades. Conclusion: We found adaptive improvements of smooth pursuit as a result of high reward expectation across the entire pursuit response. Reward may increase neuronal sensitivity, thereby boosting the system’s capability of processing visual motion information for pursuit. Meeting abstract presented at VSS 2013
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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 | 0.001 |
| 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) | 0.002 | 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".