Knowing good from bad: differential activation of human cortical areas by positive and negative outcomes
Bibliographic record
Abstract
Previous research has identified a component of the event-related brain potential (ERP), the feedback-related negativity, that is elicited by feedback stimuli associated with unfavourable outcomes. In the present research we used event-related functional magnetic resonance imaging (fMRI) and electroencephalographic (EEG) recordings to test the common hypothesis that this component is generated in the caudal anterior cingulate cortex. The EEG results indicated that our paradigm, a time estimation task with trial-to-trial performance feedback, elicited a large feedback-related negativity (FRN). Nevertheless, the fMRI results did not reveal any area in the caudal anterior cingulate cortex that was differentially activated by positive and negative performance feedback, casting doubt on the notion that the FRN is generated in this brain region. In contrast, we found a number of brain areas outside the posterior medial frontal cortex that were activated more strongly by positive feedback than by negative feedback. These included areas in the rostral anterior cingulate cortex, posterior cingulate cortex, right superior frontal gyrus, and striatum. An anatomically constrained source model assuming equivalent dipole generators in the rostral anterior cingulate, posterior cingulate, and right superior frontal gyrus produced a simulated scalp distribution that corresponded closely to the observed scalp distribution of the FRN. These results support a new hypothesis regarding the neural generators of the FRN, and have important implications for the use of this component as an electrophysiological index of performance monitoring and reward processing.
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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 | 0.002 |
| 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".