Evaluating past emotions in changing facial expressions: The role of current emotions and culture.
Bibliographic record
Abstract
expressions. While researchers have recently focused on perceptions of current expressions, little is known about how past expressions are gauged and about cultural differences in this process. The present research investigated whether and how evaluations of past facial expressions are influenced by subsequent expressions, and whether this process varies across East Asian and Western cultures. Specifically, Chinese and Canadian participants judged the degree of positivity/negativity of past expressions after viewing expressions that changed from past emotions-low-intensity smiles (Experiment 1), high-intensity smiles (Experiment 2), and anger (Experiment 3)-to current positive or negative emotions (collected between 2019 and 2020). All three experiments consistently found an assimilation effect, whereby past expressions were rated more positively when the current expression was positive than when the current expression was negative. Moreover, this assimilation effect was consistently greater in Chinese than in Canadian participants. Together, these findings suggest that the interpretation of past facial expressions assimilates toward the valence of subsequent expressions and that the impact of this temporal emotional context is more pronounced in Eastern relative to Western cultures. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.006 |
| 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 | 0.000 |
| 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".