The Influence of Expected Value on Saccadic Preparation
Bibliographic record
Abstract
Basing higher-order decisions on expected value (reward probability x reward magnitude) maximizes an agent's accruement of reward over time. The goal of this study was to determine whether the advanced preparation of simple actions reflected the expected value of the potential outcomes. Human subjects were required to direct a saccadic eye movement to a visual target that was presented either to the left or right of a central fixation point on each trial. Expected value was manipulated by adjusting the probability of presenting each target and their associated magnitude of monetary reward across 15 blocks of trials. We found that saccadic reaction times (SRTs) were negatively correlated to the relative expected value of the targets. Occasionally, an irrelevant visual distractor was presented before the target to probe the spatial allocation of saccadic preparation. Distractor-directed errors (oculomotor captures) varied as a function of the relative expected value of, and the distance of distractors from, the potential valued targets. SRTs and oculomotor captures were better correlated to the relative expected value of actions than to reward probability, reward magnitude, or overall motivation. Together, our results suggest that the level and spatial distribution of competitive dynamic neural fields representing saccadic preparation reflect the relative expected value of the potential actions.
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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 | 0.009 |
| 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 | 0.000 | 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".