Evidence map›Paper›PMID 30941073›Full record

ArticleFrontiers in psychology2019

Dynamic Facial Expression of Emotion and Observer Inference.

Klaus R Scherer, Heiner Ellgring, Anja Dieckmann, Matthias Unfried, Marcello Mortillaro

Abstract read
In one paragraph

Article in Frontiers in psychology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Klaus R SchererDepartment of Psychology and Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland.
Heiner EllgringDepartment of Psychology, University of Würzburg, Würzburg, Germany.
Anja DieckmannGfK Verein, Nuremberg, Germany.
Matthias UnfriedGfK Verein, Nuremberg, Germany.
Marcello MortillaroDepartment of Psychology and Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Research on facial emotion expression has mostly focused on emotion recognition, assuming that a small number of discrete emotions is elicited and expressed via prototypical facial muscle configurations as captured in still photographs. These are expected to be recognized by observers, presumably via template matching. In contrast, appraisal theories of emotion propose a more dynamic approach, suggesting that specific elements of facial expressions are directly produced by the result of certain appraisals and predicting the facial patterns to be expected for certain appraisal configurations. This approach has recently been extended to emotion perception, claiming that observers first infer individual appraisals and only then make categorical emotion judgments based on the estimated appraisal patterns, using inference rules. Here, we report two related studies to empirically investigate the facial action unit configurations that are used by actors to convey specific emotions in short affect bursts and to examine to what extent observers can infer a person's emotions from the predicted facial expression configurations. The results show that (1) professional actors use many of the predicted facial action unit patterns to enact systematically specified appraisal outcomes in a realistic scenario setting, and (2) naïve observers infer the respective emotions based on highly similar facial movement configurations with a degree of accuracy comparable to earlier research findings. Based on estimates of underlying appraisal criteria for the different emotions we conclude that the patterns of facial action units identified in this research correspond largely to prior predictions and encourage further research on appraisal-driven expression and inference.

Indexed as

affect burstsappraisal theory of emotion expressiondynamic facial emotion expressionemotion enactmentemotion recognition

Identifiers

PMID30941073
PMCPMC6434775

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.