Prosaccades and antisaccades under risk: penalties, rewards, and their spatial effects
Bibliographic record
Abstract
Background: Recent monkey studies suggest that the spatial location of reward cues influences saccadic programming, even when reward is not contingent on the location of the target and the saccade. This has been interpreted as an effect of reward operating through modulating attention in cortical areas involved in saccade generation. Objective: We examined human saccades to determine a) if reward and penalty differed in their effects, b) if these effects were greater in more cognitively demanding saccades, and c) if the contingency of financial consequences on stimulus location had spatially selective effects. Methods: Human subjects made prosaccades or antisaccades after motivational cues indicating if correct responses would be rewarded, incorrect ones penalized, or neither. In non-contingent sessions, financial consequences applied regardless of stimulus location, while in contingent sessions, they occurred only when the stimulus or response was at the same location as the motivational cue. Results: Financial motivation generally resulted in shorter latencies. This effect was similar for prosaccades and antisaccades, and greater for reward than for penalty. Motivation also improved antisaccade accuracy. However, while non-contingent sessions showed an inhibition-of-return-like effect for the location of the motivational cue, this did not differ between reward, penalty or neutral trials. When financial consequences were contingent on location, locations without financial consequences lost the benefits in reaction time and accuracy seen in non-contingent trials, while the locations with financial consequences maintained these benefits but did not show further gains in performance. Conclusions: Reward is more efficient than penalty in enhancing saccadic performance and this is similar for both automatic prosaccades and cognitively demanding antisaccades. With motivation, the saccadic system can not only enhance responses to multiple locations simultaneously, but also optimize movements only to locations where financial consequences apply, and not to those where they do not.
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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 | 0.002 |
| 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".