"Money, less problems": Wealth level affects the selection of optimal endpoint in an aiming task with positive and negative outcomes
Bibliographic record
Abstract
When aiming to a target circle that yields a small gain, people can aim to an optimal endpoint that is modeled based on the participants' endpoint variability and the cost associated with a penalty circle that partly overlaps the target. More recent research, however, has shown that people aim closer to the penalty area than optimal when a large loss is associated with the penalty area. Because participants started the task with no points, this strategy may be an attempt to increase target hits to increase wealth. Of note, research using cognitive decision making tasks shows that participants will avoid losses when they have a positive wealth level. The purpose of the present study is to determine whether people will have risk seeking or risk adverse strategies when they have different initial wealth levels. If participants are sensitive to their wealth level, they will aim further away from target center if they start with positive wealth than if they start with no wealth. Participants first played a lottery where they received either 0 or 5000pts. The participants then performed 200 trials where they received 100pts for target contact and -600pts for penalty contact. All participants started too close to the penalty area and then shifted their endpoints outward with experience. The participants who began with 5000pts achieved an optimal endpoint by the end of data collection where those who began with 0pts did not. Thus, aiming strategy can be affected by wealth.Acknowledgments: This research was supported through grants from the Natural Sciences and Engineering Research Council of Canada and the Ontario Ministry of Research and Innovation.
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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 | 0.002 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".