Event-related and oscillatory signatures of response inhibition: A magnetoencephalography study with subclinical high and low impulsivity adults
Bibliographic record
Abstract
Abstract Inhibition, the ability to withhold a response or to stop an initiated response, is a necessary cognitive function that can be vulnerable to an impairment. High levels of impulsivity have been shown to impact response inhibition and/or cognitive task performance. The present study investigated the spectral and spatio-temporal dynamics of response inhibition, during a combined go/no-go/stop-signal task, using magnetoencephalography (MEG) in a healthy undergraduate student population. Participants were divided by their level of impulsivity, as assessed by self-report measures, to explore potential differences between high (n=17) and low (n=17) impulsivity groups. Results showed that individuals scoring high on impulsivity failed significantly more NOGO and STOP trials than those scoring low, but no significant differences were found between stop-signal reaction times. During NOGO and STOP conditions, high impulsivity individuals showed significantly smaller M1 components in posterior regions, which could suggest an attentional processing deficit. During NOGO trials, the M2 component was found to be reduced in individuals scoring high, possibly reflecting less pre-motor inhibition efficiency, whereas in STOP trials, the network involved in the stopping process was engaged later in high impulsivity individuals. The high impulsivity group also engaged frontal networks more during the STOP-M3 component only, possibly as a late compensatory process. The lack of response time differences on STOP trials could indicate that compensation was effective to some degree (at the expense of higher error rates). Decreased frontal delta and theta band power was observed in high impulsivity individuals, suggesting a possible deficit in frontal pathways involved in motor suppression, however, unexpectedly, increased delta and theta band power in central and posterior sensors was also observed, which could be indicative of an increased effort to compensate for frontal deficits. Individuals scoring highly also showed decreased alpha power in frontal sensors, suggesting decreased inhibitory processing, along with reduced alpha suppression in posterior regions, reflecting reduced cue processing. These results provide evidence for how personality traits, such as impulsivity, relate to differences in the neural correlates of response inhibition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".