Hit me with your best shock: Differences between cognitive and physical penalties in a decision based reaching task
Bibliographic record
Abstract
Many studies use of cognitive penalties as performance incentives, however there is a large gap in research involving how physical penalties affect decision making and risk taking. Cognitive penalties primarily include situations where there is potential for a loss of value. Physical penalties primarily come in the form of perceived pain or discomfort and may be particularly relevant for action decisions. The purpose of the present study is to distinguish differences in decision making behaviors as they relate to cognitive and physical penalties. Participants were presented with two target/penalty configurations and asked to choose between the two of them by aiming to one of targets. The target, when hit, yielded a reward. Critically the penalty regions differed based on whether they resulted in a loss of money (cognitive penalty) or a cutaneous electric shock (physical penalty). Each of these penalty types has a high and low valued version to compare effect of penalty magnitude. Four groups of participants emerged who utilized different strategies when performing the task: those who preferred cognitive penalties, those who preferred physical penalties, and those who changed their preference based on the magnitude of the penalty; those who were indifferent of the form of penalty presented. Results showed that participants who preferred physical penalties had higher risk-taking scores on the Evaluation of Risk (EVAR) questionnaire. Furthermore, these groups demonstrated differences in their movement trajectories. These results demonstrate that there are individual differences in motor risk-taking behavior.Acknowledgments: NSERC
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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) | 0.002 | 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".