The Impact of Strategic Trajectory Optimization on Illusory Target Biases During Goal-Directed Aiming
Bibliographic record
Abstract
During rapid aiming, movements are planned and executed to avoid worst-case outcomes that require time and energy to correct. As such, downward movements initially undershoot the target to avoid corrections against gravity. Illusory target context can also impact aiming bias. Here, the authors sought to determine how strategic biases mediate illusory biases. Participants aimed to Müller-Lyer figures in different directions (forward, backward, up, down). Downward biases emerged late in the movement and illusory biases emerged from peak velocity. The illusory effects were greater for downward movements at terminal endpoint. These results indicate that strategic biases interact with the limb-target control processes associated with illusory biases. Thus, multiple control processes during rapid aiming may combine and later affect endpoint accuracy (D. Elliott et al., 2010 Elliott, D., Hansen, S., Grierson, L. E. M., Lyons, J., Bennett, S. J., & Hayes, S. J. (2010). Goal-directed aiming: two components but multiple processes. Psychological Bulletin, 136, 1023–1044.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".