Don't go changing on me: Consistent feedback is necessary for optimal endpoint selection in the context of changing rewards
Bibliographic record
Abstract
When values - reward or penalties - change in the aiming environment, participants must adjust their endpoint to maximize their gain. Previous research has shown that participants need to consistent feedback to aim to an optimal endpoint in the context of changing penalties. Other research has shown that participants weight positive and negative values differently in cognitive decision-making tasks but no research has examined the effect of manipulating rewards on endpoint selection. The purpose of the present study was first, to determine if participants adjust their endpoint in the context of changing rewards and secondarily, whether participants need consistent feedback to do so. Participants aimed to a target that was overlapped by a penalty region. Participants gained points for hitting the target but lost points for hitting the penalty region. The reward was set at either 100 or 600 points and the reward changed trial-to-trial (Random Condition) or only between blocks of trials (Blocked Condition). If participants need consistent feedback to aim optimally, there should only be a difference in endpoint between reward values in the Blocked condition where participants receive consistent feedback from aiming in the same value context on each trial. There was a significant interaction between reward and blocking condition where participants adjusted their endpoints with changing reward in the Blocked but not Random condition. Further, there was a correlation between endpoint selection and participants' risk sensitivity as measured through a questionnaire. The results indicate that participants can adjust their endpoints to changing reward values but only with consistent feedback. Acknowledgments: Research was funded through an NSERC Discovery Grant
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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 | 0.003 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".