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Record W2101250831 · doi:10.1177/0956797610375451

Reflecting on God

2010· article· en· W2101250831 on OpenAlexaff
Michael Inzlicht, Alexa M. Tullett

Bibliographic record

VenuePsychological Science · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsPsychologyPriming (agriculture)FeelingNegativity effectAnxietyMeaning (existential)DistressError-related negativitySocial psychologyNeurophysiologyPower (physics)Anterior cingulate cortexNegationCognitive psychologyCognitionNeurosciencePsychotherapist

Abstract

fetched live from OpenAlex

The world is a vast and complex place that can sometimes generate feelings of uncertainty and distress for its inhabitants. Although religion is associated with a sense of meaning and order, it remains unclear whether religious belief can actually cause people to feel less anxiety and distress. To test the anxiolytic power of religion, we conducted two experiments focusing on the error-related negativity (ERN)-a neural signal that arises from the anterior cingulate cortex and is associated with defensive responses to errors. The results indicate that for believers, conscious and nonconscious religious primes cause a decrease in ERN amplitude. In contrast, priming nonbelievers with religious concepts causes an increase in ERN amplitude. Overall, examining basic neurophysiological processes reveals the power of religion to act as a buffer against anxious reactions to self-generated, generic errors-but only for individuals who believe.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.176
GPT teacher head0.531
Teacher spread0.355 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations162
Published2010
Admission routes1
Has abstractyes

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