Reaching reflects ongoing deliberation prior to a decision
Bibliographic record
Abstract
We constantly make choices while moving, such as when navigating a crowded hallway. Studies that examine the interplay between decision-making and movement employ sudden target changes to evoke a rapid decision and motor response, whereas perceptual decision-making studies manipulate sensory evidence over time to influence the timing of a decision. In both cases, deliberation is hidden. Here we tested the hypothesis that decision-making and motor circuitry continuously interact during deliberation. We predicted that lateral hand movement would reflect the ongoing deliberation, prior to a decision. We extended the “tokens task” (Cisek, 2009) to require active forward movement prior to the final decision. Participants were required to move forward from a start position towards two potential targets. Once they left the start position, 15 tokens individually moved into one of the two targets. We manipulated the token patterns to influence the ongoing deliberation. Participants indicated the target expected to finish with the most tokens by both hitting the selected target with their reaching hand and pushing a button with their other hand. Critically, we measured the unconstrained lateral hand position, prior to the decision, to determine the influence of deliberation on movement. Across two experiments, the token patterns differentially impacted the lateral hand position prior to a decision (p < 0.003 for all comparisons), demonstrating that hand movements reflect a continuous readout of the ongoing deliberation. Our results support the idea that there is a continuous interaction between decision-making and motor circuitry.
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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 | 0.009 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".