Affective Information in Context and Judgment of Facial Expression
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".