Electrophysiological measures reveal the role of anterior cingulate cortex in learning from unreliable feedback
Bibliographic record
Abstract
Although a growing number of studies have investigated the neural mechanisms of reinforcement learning, it remains unclear how the brain responds to feedback that is unreliable. A recent theory proposes that the reward positivity (RewP) component of the event-related brain potential (ERP) and frontal midline theta (FMT) power reflect separate feedback-related processing functions of anterior cingulate cortex (ACC). In the present study, the electroencephalogram (EEG) was recorded from participants as they engaged in a time estimation task in which feedback reliability was manipulated across conditions. After each response, they received a cue that indicated that the following feedback stimulus was 100%, 75%, or 50% reliable. The results showed that participants' time estimates adjusted linearly according to the feedback reliability. Moreover, presentation of the cue indicating 100% reliability elicited a larger RewP-like ERP component than the other cues did, and feedback presentation elicited a RewP of approximately equal amplitude for all of the three reliability conditions. By contrast, FMT power elicited by negative feedback decreased linearly from the 100% condition to 75% and 50% condition, and only FMT power predicted behavioral adjustments on the following trials. In addition, an analysis of Beta power and cross-frequency coupling (CFC) of Beta power with FMT phase suggested that Beta-FMT communication modulated motor areas for the purpose of adjusting behavior. We interpreted these findings in terms of the hierarchical reinforcement learning account of ACC, in which the RewP and FMT are proposed to reflect reward processing and control functions of ACC, respectively.
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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 | 0.003 |
| 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".