Emotions do reliably co-occur with predicted facial signals: Comment on Durán and Fernández-Dols (2021).
Bibliographic record
Abstract
Durán and Fernández-Dols (see record 2022-03375-001) have done the field a service by conducting a meta-analytic review of the association between emotion experiences and facial expressions. Although they conclude that no meaningful association exists, our reading of their analyses suggest a different interpretation: The data that they report indicate an association of substantial magnitude-as large as 1.5 times the size of the average effect in social psychology and larger than 76% of meta-analytic effects previously reported throughout personality and social psychology (Gignac & Szodorai, 2016; Richard et al., 2003). Moreover, reexamination of some of the exclusion and classification choices made by Durán and Fernández-Dols (e.g., excluding intraindividual designs and studies purported to measure "amusement" from the primary analyses of "happiness") suggests that the observed large effects would be larger still if a more comprehensive set of studies had been included in their review. In sum, we conclude that Durán and Fernández-Dols' meta-analyses provide robust evidence that emotions do reliably co-occur with their predicted facial signals, although this conclusion is opposite to the one stated in their report. (PsycInfo Database Record (c) 2023 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 | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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".