Training Emotion Recognition Accuracy: Results for Multimodal Expressions and Facial Micro Expressions.

Lillian Döllinger,Stephan Hau,Tanja Bänziger,Lennart Björn Högman,Irena Makower,Håkan Fischer,Petri Laukka

doi:10.3389/fpsyg.2021.708867

Lillian Döllinger, Stephan Hau + Show 5 more

Open Access

https://doi.org/10.3389/fpsyg.2021.708867

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Abstract

Nonverbal emotion recognition accuracy (ERA) is a central feature of successful communication and interaction, and is of importance for many professions. We developed and evaluated two ERA training programs—one focusing on dynamic multimodal expressions (audio, video, audio-video) and one focusing on facial micro expressions. Sixty-seven subjects were randomized to one of two experimental groups (multimodal, micro expression) or an active control group (emotional working memory task). Participants trained once weekly with a brief computerized training program for three consecutive weeks. Pre-post outcome measures consisted of a multimodal ERA task, a micro expression recognition task, and a task about patients' emotional cues. Post measurement took place approximately a week after the last training session. Non-parametric mixed analyses of variance using the Aligned Rank Transform were used to evaluate the effectiveness of the training programs. Results showed that multimodal training was significantly more effective in improving multimodal ERA compared to micro expression training or the control training; and the micro expression training was significantly more effective in improving micro expression ERA compared to the other two training conditions. Both pre-post effects can be interpreted as large. No group differences were found for the outcome measure about recognizing patients' emotion cues. There were no transfer effects of the training programs, meaning that participants only improved significantly for the specific facet of ERA that they had trained on. Further, low baseline ERA was associated with larger ERA improvements. Results are discussed with regard to methodological and conceptual aspects, and practical implications and future directions are explored.

Highlights

Nonverbal emotion recognition accuracy (ERA)—the ability to correctly infer another person’s emotional state from their nonverbal behavior, including facial expressions, bodily postures and voice—is a crucial part of social interactions
All participants became more accurate at detecting emotional expressions as assessed via the Emotion Recognition Assessment in Multiple Modalities test (ERAM) total score, post-hoc Aligned Rank Transform (ART) contrast analyses showed that the pre–post difference of the multimodal training group was significantly higher than the pre–post difference of the micro expression training group, χ2 (1, N = 44) = 9.06, p = 0.005; and significantly higher than the pre–post difference of the CT group, χ2 (1, N = 44) = 11.57, p = 0.002
The present study shows that multimodal ERA and micro expressions ERA can be successfully trained using computerized training

Summary

Introduction

Nonverbal emotion recognition accuracy (ERA)—the ability to correctly infer another person’s emotional state from their nonverbal behavior, including facial expressions, bodily postures and voice—is a crucial part of social interactions. Even though healthy adults are relatively accurate at recognizing others people’s emotions, there are important individual differences in ERA, and low ERA is associated with personal and interpersonal drawbacks (see e.g., Hall et al, 2009; Schlegel et al, 2017). Emotion recognition training has been shown to improve ERA (for reviews, see e.g., Blanch-Hartigan and Ruben, 2013; Rebeschini et al, 2019), and first evidence even suggests positive real-life outcomes of ERA training (Schlegel, 2021). Previous studies on ERA training are reviewed and the contribution of the present study toward bridging scientific and methodological gaps will be outlined

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