Reading the Room: Emotional Valence Appraisal in Adolescent and Adult Expressers
Bibliographic record
Abstract
Whether someone is expressing a positive emotion (i.e. happiness) or a negative emotion (i.e. anger) can determine how we interact with them (McArthur and Baron, 1983). Hence, our perception of emotional valence (i.e., whether an emotion is pleasant or unpleasant; Barrett, 2006) can influence how we approach others. Research on the perception of emotion in others has focused on how we view children and adults’ emotional expressions, but not those of adolescents. We have an incomplete understanding of how emotion perception is affected by the expresser’s age. The current study assessed variations in perceivers’ ratings of emotional valence when viewing stimulus recordings of adolescent or adult expressers portraying different emotions. Using the lmerTest package in R Studio, linear mixed effects models were performed to investigate the effect of expresser age on raters’ perceptions of emotional valence in each recording. Models were built by adding fixed effects of stimulus age (adolescent or adult), emotion type (happiness, sadness, fear, anger, or neutral), and the interaction between stimulus age and emotion type in sequence. Significance was determined via likelihood ratio tests comparing each model to the previous one. There was a significant main effect of emotion type (happiness, sadness, fear, anger, or neutral) on emotional valence ratings. There was no main effect of stimulus age on emotional valence ratings; however, there was an interaction between emotion type and stimulus age. Compared to adolescents, adults were rated more negatively when expressing fear and sadness and more positively when expressing happiness. Results suggest that there are differences in how positively/negatively adult and adolescent expressers are perceived, depending on the type of emotion being expressed. This research can inform advertising: for instance, adults expressing happiness may be perceived more positively than adolescents, which could increase the likelihood that customers will purchase their products. References Barrett, L.F. (2006) Valence is a basic building block of emotional life. Journal of Research in Personality, 40(1), 35-55. https://doi.org/10.1016/j.jrp.2005.08.006. McArthur, L. Z. & Baron, R. M. (1983). Toward an ecological theory of social perception. Psychological Review, 90(3), 215–238. https://doi.org/10.1037/0033-295X.90.3.215
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".