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Record W2106738367 · doi:10.1177/0022022110395139

Affective Information in Context and Judgment of Facial Expression

2011· article· en· W2106738367 on OpenAlexafffund
Kenichi Ito, Takahiko Masuda, Koichi Hioki

Bibliographic record

VenueJournal of Cross-Cultural Psychology · 2011
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsPriming (agriculture)PsychologyFacial expressionContext (archaeology)Cognitive psychologySocial psychologyGeneralizationCross-culturalContrast (vision)Expression (computer science)Cultural diversityFace (sociological concept)LinguisticsCommunicationSociologyComputer scienceGeographyEpistemologyArtificial intelligence

Abstract

fetched live from OpenAlex

Previous research in cultural psychology suggests that North Americans are less likely than their East Asian counterparts to be sensitive to contextual information. By contrast, much evidence suggests that even North Americans’ judgments are highly influenced by affective priming information, the effect of which can be seen as another type of contextual cue. However, the magnitude of such a priming effect has not been comprehensively tested in a cross-cultural context. Taking advantage of the methodology of the affective priming paradigm, we conducted two studies, in which we manipulated (a) the timing of priming information (simultaneous vs. sequential) and (b) the type of affective information (background landscape vs. background human figures), in which European Canadians and Japanese judged target faces that showed either happy or sad facial expressions in the focal area of the scene. The results in general indicate that a similar degree of contextual effect occurs in members of both cultures. The issue of generalization of cross-cultural findings and the necessity of overarching more than one research paradigm are discussed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.686

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.113
GPT teacher head0.442
Teacher spread0.329 · 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 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

Citations40
Published2011
Admission routes2
Has abstractyes

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