Neurophysiological encoding of aversive prediction errors
Bibliographic record
Abstract
ABSTRACT: Aversive prediction error (PE) brain signals generated by unexpected pain or pain absence are crucial for learning to avoid future pain. Yet, the detailed neurophysiological origins of PE signaling remain unclear. In this study, we combined an instrumental pain avoidance task with computational modeling and magnetoencephalography to detect time-resolved activations underlying pain expectations and aversive PE signals in the human brain. The task entailed learning probabilistically changing cue-pain associations to avoid receiving a pain stimulus. We used an axiomatic approach to identify general aversive PE signals that encode the degree to which the outcome deviated from expectations. Our findings indicate that aversive PE signals are generated in the alpha band (8-12 Hz) by the midbrain/diencephalon, lateral orbitofrontal cortex, and ventrolateral prefrontal cortex approximately 150 milliseconds after outcome delivery. Moreover, alpha oscillations in these regions also encoded pain expectations before the outcome. We speculate that this may facilitate the rapid generation of PEs by allowing outcome-related nociceptive activity to be integrated with ongoing predictive signals. Finally, decisions to avoid pain recruited alpha oscillations in the anterior cingulate and dorsomedial prefrontal cortices, suggesting their active engagement in comparing predicted action values. Overall, our data reveal the rapid neurophysiological mechanisms underlying the generation of aversive PEs and subsequent decision-making.
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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".