Action-response mode modulates go/no-go decision accuracy
Bibliographic record
Abstract
Crossing a busy intersection requires a rapid decision whether to go or not. Decision accuracy generally improves over time, with longer accumulation of visual motion information. However, the time available to accumulate sensory information may be constrained by the complexity and movement time of the required action. This factor is rarely accounted for in perceptual decision-making studies, which typically rely on simple button presses as indicators of decision outcome. Here we ask whether action-response modes (manual button press vs. interceptive hand movement) directly affect the accuracy and timing of a go/no-go decision. We recorded eye and hand movements of n=10 human observers while they viewed a small disc moving toward a goal. The target disappeared 100, 200, or 300 ms after onset and observers had to indicate whether the target would miss (no-go decision required) or pass (go decision required) the goal by either inhibiting a response, or by executing a button press or interceptive hand movement, respectively. Decision accuracy increased with increasing target presentation duration, confirming that longer sensory accumulation improves decision accuracy. Across presentation durations, decision accuracy was significantly higher and less variable when observers indicated their decision by pressing a button, compared to when they manually intercepted the target. To compensate for different movement execution times, observers initiated their hand movements ~270 ms earlier and intercepted ~70 ms later when manually intercepting the target compared to pressing the button. These results indicate that observers had less time to form their decisions when performing goal-directed hand movements compared when they simply had to press a button. We propose that the more complex planning and execution of the interceptive hand movement competes with decision formation. Our results highlight critical differences in sensorimotor decision processes between simple button press and more complex hand movement tasks.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".