Online movements reflect ongoing deliberation
Bibliographic record
Abstract
ABSTRACT From navigating a crowded hallway to skiing down a treacherous hill, humans are constantly making decisions while moving. Insightful past work has provided a glimpse of decision deliberation at the moment of movement onset. Yet it is unknown whether ongoing deliberation can be expressed during movement, following movement onset and prior to any decision. Here we tested the idea that an ongoing deliberation continually influences motor processes—prior to a decision—directing online movements. Over three experiments, we manipulated evidence to influence deliberation during movement. The deliberation process was manipulated by having participants observe evidence in the form of tokens that moved into a left or right target. Supporting our hypothesis we found that lateral hand movements reflected deliberation, prior to a decision. We also found that a deliberation urgency signal, which more heavily weighs later evidence, was fundamental to predicting decisions and explains past movement behaviour in a new light. Our paradigm promotes the expression of ongoing deliberation through movement, providing a powerful new window into understanding the interplay between decision and action.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".