Looking before you leap: A theory of motivated control of action
Bibliographic record
Abstract
The acquisition of volitional control depends, in part, on developing the ability to countermand a planned action. Many tasks have been used to tap the efficiency of this process, but few studies have investigated how it may be modulated by participants' motivation. Multiple mechanisms may be involved in the deliberate exercise of caution when incentives are provided. For example, control may involve modulation of the efficiency of the countermanding process, and/or inhibitory modulation of the impulse to go. One of the most commonly used paradigms to assess control of action is the Stop Signal Task, in which a primary Go stimulus is occasionally followed by a countermanding Stop signal, allowing a Stop Signal Reaction Time (SSRT) to be inferred as the outcome of a "horse race" between the go and countermanding processes. Here, we present a computational model in which high task motivation modulates proactive pre-stimulus inhibition of the go response. This allows responses to be calibrated so as to fall within a time-window that maximizes the probability of success, regardless of trial type, but does not decrease the observed SSRT. We report empirical support for the model from a sample of typically developing children, and discuss the broader implications for operationalizing measures of volitional control.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".