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Cultural Neuroscience: Biology of the Mind in Cultural Contexts

2013· review· en· W2162964959 on OpenAlexaff
Heejung S. Kim, Joni Y. Sasaki

Bibliographic record

VenueAnnual Review of Psychology · 2013
Typereview
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsYork University
Fundersnot available
KeywordsCultural neurosciencePsychologySocial neuroscienceCognitive scienceCultural psychologyCultural diversityNeuroscienceCognitive psychologySocial cognitionSocial psychologyCognitionSociology

Abstract

fetched live from OpenAlex

This article provides a review of how cultural contexts shape and are shaped by psychological and neurobiological processes. We propose a framework that aims to culturally contextualize behavioral, genetic, neural, and physiological processes. Empirical evidence is presented to offer concrete examples of how neurobiological processes underlie social behaviors, and how these components are interconnected in larger cultural contexts. These findings provide some understanding of how the meanings shared by cultural experiences trigger a neurobiological, psychological, and behavioral chain of events, and how these events may be coordinated and maintained within a person. The review concludes with a reflection on the current state of cultural neuroscience and questions for the field to address.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.005
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.219
GPT teacher head0.537
Teacher spread0.319 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations267
Published2013
Admission routes1
Has abstractyes

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