Bibliographic record
Abstract
Humans need to rapidly select movements that achieve their goal while avoiding negative outcomes. The processes leading to these decisions have only recently been studied. In the typical paradigm used to gain insight into the decision process, participants aim to a target circle that is overlapped by a penalty circle. They receive 100 points for hitting the target, and lose points for hitting the penalty region. Previous research has shown that participants generally behave like a rational decision maker by adapting their endpoint when the distance between the target and penalty circle and the penalty value changes (although some suboptimal selection has been noted). The overall purpose of the research reported in the present thesis was to determine if there are contexts when participants’ behaviour is suboptimal in a rapid, motor decision making tasks. Taken together, the results from four studies showed that: 1) participants require experience and feedback to aim to optimal locations; 2) participants often aimed closer to target center than optimal; and, 3) probability (represented through spatial parameters) has more influence over participant’s motor decisions than does the value of the penalty. Therefore, participants’ actions do not necessarily conform to a rational model of decision making; rather, there are consistent biases arising in the selection, planning, and execution of actions in specific contexts. These findings and conclusions can lead to a more descriptive understanding of motor decision making to provide information that is in addition to prescriptive models of rational behaviour.
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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 | 0.008 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".