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Record W2137228206 · doi:10.1037/a0035179

System justification and electrophysiological responses to feedback: Support for a positivity bias.

2013· article· en· W2137228206 on OpenAlexafffund
Shona M. Tritt, Elizabeth Page‐Gould, Jordan B. Peterson, Michael Inzlicht

Bibliographic record

VenueJournal of Experimental Psychology General · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersOntario Ministry of Research and InnovationSocial Sciences and Humanities Research Council of CanadaCanada Foundation for Innovation
KeywordsNegativity effectNegative feedbackPsychologySalientEvent-related potentialElectrophysiologyPositive feedbackIdeologyNeural correlates of consciousnessElectroencephalographySocial psychologyCognitive psychologyDevelopmental psychologyNeurosciencePoliticsCognitionPolitical science

Abstract

fetched live from OpenAlex

Conservatives, compared to liberals, are consistently found to exhibit physiological sensitivity to aversive stimuli. However, it remains unknown whether conservatives are also sensitive to salient positively valenced stimuli. We therefore used event-related potentials to determine the relationship between system justification (SJ), a fundamental component of conservative political ideology, and neural processing of negative and positive feedback. Participants (N = 29) filled out questionnaire assessments of SJ. Feedback-related negativity (FRN), an event-related potential component thought to index activity in neural regions associated with reward processing, was assessed in response to positive and negative feedback on a time estimation task. A significant interaction was noted between SJ and feedback type in predicting FRN. Simple effects tests suggested that SJ predicted greater FRN in response to positive but not to negative feedback. Conservatives may experience salient positive information with a heightened intensity.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.495

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 studies0.0000.000
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 head0.188
GPT teacher head0.434
Teacher spread0.246 · 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

Citations8
Published2013
Admission routes2
Has abstractyes

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