Voluntary Modulation of Anterior Cingulate Response to Negative Feedback
Bibliographic record
Abstract
Anterior cingulate and medial frontal cortex (dACC/mFC) response to negative feedback represents the actions of a generalized error-monitoring system critical for the management of goal-directed behavior. Magnitude of dACC/mFC response to negative feedback correlates with levels of post-feedback behavioral change, and with proficiency of operant learning processes. With this in mind, it follows that an ability to alter dACC/mFC response to negative feedback may lead to representative changes in operant learning proficiency. To this end, the present study investigated the extent to which healthy individuals would show modulation of their dACC/mFC response when instructed to try to either maximize or minimize their neural response to the presentation of contingent negative feedback. Participants performed multiple runs of a standard time-estimation task, during which they received feedback regarding their ability to accurately estimate a one-second duration. On Watch runs, participants were simply instructed to try to estimate as closely as possible the one second duration. On Increase and Decrease runs, participants performed the same task, but were instructed to "try to increase [decrease] their brain's response every time they received negative feedback". Results indicated that participants showed changes in dACC/mFC response under these differing instructional conditions: dACC/mFC activity following negative feedback was higher in the Increase condition, and dACC activity trended lower in the Decrease condition, compared to the Watch condition. Moreover, dACC activity correlated with post-feedback performance adjustments, and these adjustments were highest in the Increase condition. Potential implications for neuromodulation and facilitated learning are discussed.
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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 | 0.000 | 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 | 0.000 |
| 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".