Causal Prefrontal Contributions to Stop-Signal Task Performance in Humans
Bibliographic record
Abstract
The frontal lobes have long been implicated in inhibitory control, but a full understanding of the underlying mechanisms remains elusive. The stop-signal task has been widely used to probe instructed response inhibition in cognitive neuroscience. The processes involved have been modeled and related to putative brain substrates. However, there has been surprisingly little human lesion research using this task, with the few existing studies implicating different prefrontal regions. Here, we tested the effects of focal prefrontal damage on stop-signal task performance in a large sample of people with chronic focal damage affecting the frontal lobes (n = 42) and demographically matched healthy individuals (n = 60). Patients with damage to the left lateral, right lateral, dorsomedial, or ventromedial frontal lobe had slower stop-signal RT compared to healthy controls. There were systematic differences in the patterns of impairment across frontal subgroups: Those with damage to the left or right lateral and dorsomedial frontal lobes, but not those with ventromedial frontal damage, were slower than controls to "go" as well as to stop. These findings suggest that multiple prefrontal regions make necessary but distinct contributions to stop-signal task performance. As a consequence, stop-signal RT slowing is not strongly localizing within the frontal lobes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".