Hit me with your best shot: Optimal movement planning with constantly changing decision parameters.
Bibliographic record
Abstract
Humans are able to rapidly select movements that will achieve the individual’s goal while avoiding negative outcomes. Trommerhäuser et al. (2003) showed that, in a rapid aiming task, people concentrated their movements around an ‘optimal movement endpoint’ that was modeled based on the participants’ endpoint variability and the cost associated with a penalty circle that partially overlapped the target circle. Participants adjusted their endpoint when the penalty circle cost or distance between the two circles changed; however, penalty value only changed between blocks of trials. In typical daily interactions, the values associated with our movement goal vary. The purpose of the present study was to determine whether participants can adjust their endpoint when the distance between the target and penalty circles and the value of the penalty circle changed trial-to-trial. Participants aimed to a target circle in the presence of an overlapping penalty circle and received 100 points for contact with the target alone, and lost points for contact with the penalty region. In one block, the penalty circle for a given trial was either orange or red indicating that the cost was -100 or -600 points, respectively. In the other block, the penalty circle either overlapped the target circle by 9 or 13.5mm.There was a significant difference in endpoint between the two values within each distance and penalty block. However, when compared to the optimal endpoint calculated from the model, participants showed a significantly smaller shift in endpoint between the two penalty values, but an optimal shift in the distance block. We suggest participants are more optimal with a random changing of distance because the distance between the two circles is an intrinsic property of the visual stimuli, whereas the color associated with each the penalty value requires additional cognitive resources to interpret and predict the potential costs. Meeting abstract presented at VSS 2012
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".