Stuck in the middle: Kinematic evidence for optimal reaching in the presence of multiple potential reach targets
Bibliographic record
Abstract
Neurons in motor cortex simultaneously encode multiple potential reach targets prior to the unambiguous selection of a final motor plan (Cisek and Kalaska, 2005). Here we used hand-path trajectories during rapid reach responses (Song and Nakayama, 2006) to investigate behaviourally the simultaneous planning of multiple target reaches. Human participants made reaches to touch a single target in the presence of multiple potential targets on a touch screen. On each trial in Experiment 1, one or two possible targets (hollow circles) appeared on the screen in different spatial configurations. At movement onset, one circle was filled in and the participant's task was to touch the filled-in target within 750 ms of stimulus onset. When one target was presented and thus only one motor plan encoded, initial trajectories headed directly toward its location. Interestingly, when two possible targets appeared, requiring two motor plans to be encoded, initial trajectories headed almost exactly between the two target locations before correcting to the filled-in location. Experiment 2 was similar except that two or three possible targets appeared on the screen and these targets were either filled in before (early) or after (late) movement onset. On early trials, initial trajectories headed directly toward the filled-in target. On late trials, we replicated Experiment 1. Additionally, we showed that initial trajectories in three-target trials were biased toward the side of space with more possible targets. Experiment 3 further tested whether this bias was driven by the number of potential reach targets or their eccentricity. Analyses show that both properties affect initial trajectory heading. Taken together, these results are consistent with the hypothesis that potential targets are simultaneously encoded prior to movement onset. Moreover, our findings suggest the visuomotor system plans optimal trajectories with respect to the number and location of potential reach targets in cases of target uncertainty.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".