Surprise and Error: Common Neuronal Architecture for the Processing of Errors and Novelty
Bibliographic record
Abstract
According to recent accounts, the processing of errors and generally infrequent, surprising (novel) events share a common neuroanatomical substrate. Direct empirical evidence for this common processing network in humans is, however, scarce. To test this hypothesis, we administered a hybrid error-monitoring/novelty-oddball task in which the frequency of novel, surprising trials was dynamically matched to the frequency of errors. Using scalp electroencephalographic recordings and event-related functional magnetic resonance imaging (fMRI), we compared neural responses to errors with neural responses to novel events. In Experiment 1, independent component analysis of scalp ERP data revealed a common neural generator implicated in the generation of both the error-related negativity (ERN) and the novelty-related frontocentral N2. In Experiment 2, this pattern was confirmed by a conjunction analysis of event-related fMRI, which showed significantly elevated BOLD activity following both types of trials in the posterior medial frontal cortex, including the anterior midcingulate cortex (aMCC), the neuronal generator of the ERN. Together, these findings provide direct evidence of a common neural system underlying the processing of errors and novel events. This appears to be at odds with prominent theories of the ERN and aMCC. In particular, the reinforcement learning theory of the ERN may need to be modified because it may not suffice as a fully integrative model of aMCC function. Whenever course and outcome of an action violates expectancies (not necessarily related to reward), the aMCC seems to be engaged in evaluating the necessity of behavioral adaptation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
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".