The point of no return in the Emotional Stop-Signal Task: A matter of affect or method?
Bibliographic record
Abstract
An affective variant of the Stop-Signal task was used to study the interaction between emotion and response inhibition (RI) in healthy young participants. The task involved the covert presentation of emotional faces as go stimuli, as well as a manipulation of motivation and affect by inducing a negative mood through the assignment of unfair punishment. In the literature on emotion and RI, there are contrasting findings reflecting the variability in the method used to calculate the RI latency, namely the Stop-Signal Reaction Time (SSRT). In fact, previous studies found both facilitatory and detrimental effects of affective manipulations over RI. However, they did not use the most robust SSRT estimation approach, namely the integration, casting some doubts on the reliability of the inferences. For these reasons, the present research draws attention on how the effect of the emotional manipulation may be due to a biased SSRT estimation. Specifically, the focus of our study was on how the effect of emotion on the SSRT may vary according to different estimation procedures, the mean and two variants of the integration method. We predicted that the effect of the emotional manipulation in the SST would depend on the SSRT estimation method employed. Indeed, a significant effect of emotion was only found when SSRT was estimated with the mean method. We conclude that the mean method should be avoided in the study of emotion and RI because it overestimates SSRT. Rather, the integration approach should be used for future research in this field, while also factoring in information about the participants' strategy in emotional contexts that require greater effortful control and offer a challenge to self-regulation both in health and disease.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".