Simultaneous encoding of the direction and orientation of potential targets during reach planning: evidence of multiple competing reach plans
Bibliographic record
Abstract
Reaches performed in many natural situations involve selecting a specific target from a number of alternatives. Recent studies show that before reaching, multiple potential reach targets are encoded in brain regions involved in action control and that, when people are required to initiate the reach before the target is specified, initial hand direction is biased by the spatial distribution of potential targets. These findings have led to the suggestion that the brain, during planning, simultaneously prepares multiple reaches to potential targets. In addition to hand direction, reach planning often involves specifying other parameters such as wrist orientation. For example, when posting a letter in a mail slot, both the location and orientation of the slot must be encoded to control hand direction and orientation. Therefore, if the brain prepares multiple reaches to potential targets and if these targets require the specification of hand direction and orientation, then both of these variables should be biased by the spatial distribution of potential targets. To test this prediction, we examined a task in which participants moved a hand-held rectangular tool toward multiple rectangular targets of varying location and orientation, one of which was selected, with equal probability as the actual target after movement initiation. We found that initial hand direction and orientation were biased by the spatial distributions of potential target locations and orientations, respectively. This result is consistent with the idea that the brain, in cases of target uncertainty, simultaneously plans fully specified reaching movements to all potential targets.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".