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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".