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Surprise and Error: Common Neuronal Architecture for the Processing of Errors and Novelty

2012· article· en· W2160665922 on OpenAlexaff
Jan R. Wessel, Claudia Danielmeier, J. Bruce Morton, Markus Ullsperger

Bibliographic record

VenueJournal of Neuroscience · 2012
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsNoveltyFunctional magnetic resonance imagingNeurosciencePsychologySurpriseAnterior cingulate cortexElectroencephalographyElectrocorticographyNeural correlates of consciousnessCognitive psychologyComputer scienceCognitionCommunication

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.190
GPT teacher head0.400
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations238
Published2012
Admission routes1
Has abstractyes

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