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Record W4286634255 · doi:10.3389/fnhum.2022.886971

RETRACTED: Do Surrounding People's Emotions Affect Judgment of the Central Person's Emotion? Comparing Within Cultural Variation in Holistic Patterns of Emotion Perception in the Multicultural Canadian Society

2022· article· en· W4286634255 on OpenAlexafffundabout
Takahiko Masuda, Shuwei Shi, Pragya Varma, Delaney G. Fisher, Safi Shirazi

Post-publication record

NatureRetraction
ReasonConcerns/Issues about Article;Concerns/Issues about Human Subject Welfare;Concerns/Issues about Referencing/Attributions;Error in Results and/or Conclusions;Error in Text;Investigation by Journal/Publisher;
Date8/23/2024 0:00
Flagged by OpenAlex?Yes

Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.

Bibliographic record

VenueFrontiers in Human Neuroscience · 2022
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Alberta
FundersNational Institute of Environmental Health SciencesSocial Sciences and Humanities Research Council of Canada
KeywordsAffect (linguistics)PsychologyPerceptionVariation (astronomy)MulticulturalismEmotion perceptionSocial psychologyCognitive psychologyCommunication

Abstract

fetched live from OpenAlex

Previous studies in cultural psychology have suggested that when assessing a target person's emotion, East Asians are more likely to incorporate the background figure's emotion into the judgment of the target's emotion compared to North Americans. The objective of this study was to further examine cultural variation in emotion perception within a culturally diverse population that is representative of Canada's multicultural society. We aimed to see whether East-Asian Canadians tended to keep holistic tendencies of their heritage culture regarding emotion perception. Participants were presented with 60 cartoon images consisting of a central figure and four surrounding figures and were then asked to rate the central figure's emotion; out of the four cartoon figures, two were female and two were male. Each character was prepared with 5 different emotional settings with corresponding facial expressions including: extremely sad, moderately sad, neutral, moderately happy, and extremely happy. Each central figure was surrounded by a group of 4 background figures. As a group, the background figures either displayed a sad, happy, or neutral expression. The participant's task was to judge the intensity of the central figures' happiness or sadness on a 10-point Likert scale ranging from 0 (not at all) to 9 (extremely). For analysis, we divided the participants into three groups: European Canadians (N = 105), East Asian Canadians' (N = 104) and Non-East Asian/Non-European Canadians (N = 161). The breakdown for the Non-East Asian/Non-European Canadian group is as follows: 94 South Asian Canadians, 25 Middle Eastern Canadians, 23 African Canadians, 9 Indigenous Canadians, and 10 Latin/Central/South American Canadians. Results comparing European Canadians and East Asian Canadians demonstrated cultural variation in emotion judgment, indicating that East Asian Canadians were in general more likely than their European Canadian counterparts to be affected by the background figures' emotion. The study highlights important cultural variations in holistic and analytic patterns of emotional attention in the ethnically diverse Canadian society. We discussed future studies which broaden the scope of research to incorporate a variety of diverse cultural backgrounds outside of the Western educational context to fully comprehend cultural variations in context related attentional patterns.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.567

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.087
GPT teacher head0.327
Teacher spread0.240 · 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

Citations7
Published2022
Admission routes3
Has abstractyes

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