Context-Dependent Urgency Influences Speed–Accuracy Trade-Offs in Decision-Making and Movement Execution
Bibliographic record
Abstract
Speed-accuracy tradeoffs (SATs) exist in both decision-making and movement control, and are generally studied separately. However, in natural behavior animals are free to adjust the time invested in deciding and moving so as to maximize their reward rate. Here, we investigate whether shared mechanisms exist for SAT adjustment in both decisions and actions. Two monkeys performed a reach decision task in which they watched 15 tokens jump, one every 200 ms, from a central circle to one of two peripheral targets, and had to guess which target would ultimately receive the majority of tokens. The monkeys could decide at any time, and once a target was reached, the remaining token movements accelerated to either 50 ms ("fast" block) or 150 ms ("slow" block). Decisions were generally earlier and less accurate in fast than slow blocks, and in both blocks, the criterion of accuracy decreased over time within each trial. This could be explained by a simple model in which sensory information is combined with a linearly growing urgency signal. Remarkably, the duration of the reaching movements produced after the decision decreased over time in a similar block-dependent manner as the criterion of accuracy estimated by the model. This suggests that SATs for deciding and acting are influenced by a shared urgency/vigor signal. Consistent with this, we observed that the vigor of saccades performed during the decision process was higher in fast than in slow blocks, suggesting the influence of a context-dependent global arousal.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".