Dissociation of response inhibition and performance monitoring in the stop signal task using event‐related fMRI
Bibliographic record
Abstract
We examined the neural substrate of motor response inhibition and performance monitoring in the stop signal task (SST) using event-related functional magnetic resonance imaging (fMRI). The SST involves a go task and the occasional requirement to stop the go response. We posit that both the go and the stop phases of the SST involve components of inhibition and performance monitoring. The goal of this study was to determine whether inhibition and performance monitoring during go and stop phases of the task activated different networks. We isolated go-phase activities underlying response withholding, monitoring, and sensorimotor processing and contrasted these with successful inhibition to identify the substrate of response inhibition. Error detection activity was isolated using trials in which a stop signal appeared but the response was executed. These trials were modeled as a hand-specific go trial followed by error processing. Cognitive go-phase processes included response withholding and monitoring and activated right prefrontal and midline networks. Response withdrawal additionally activated right inferior frontal gyrus and basal ganglia (caudate). Error detection invoked by failed inhibition activated dorsal anterior cingulate cortex (dACC) and right middle frontal Brodmann's area 9. Our results confirm that there are distinct aspects of inhibition and performance monitoring functions which come into play at various phases within a given trial of the SST, and that these are separable using fMRI.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".