Emotional Context Influences Micro-Expression Recognition

Zhang, Ming; Fu, Qiuping; Chen, Yu‐Hsin; Fu, Xiaolan

doi:10.1371/journal.pone.0095018

Cited by 34 publications

(41 citation statements)

References 34 publications

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“…These are expressions lasting between 1/25 and 1/5 s. Because of their short time, they may be difficult to capture for an observer and may require advanced video systems . They are also regarded as an expression of the transition between the previous and the current emotion …”

Section: Different Aspects Of Emotional Expressionmentioning

confidence: 99%

Emotional expression in psychiatric conditions: New technology for clinicians

Grabowski

Rynkiewicz

Lassalle

et al. 2018

Psychiatry Clin Neurosci

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Aim Emotional expressions are one of the most widely studied topics in neuroscience, from both clinical and non‐clinical perspectives. Atypical emotional expressions are seen in various psychiatric conditions, including schizophrenia, depression, and autism spectrum conditions. Understanding the basics of emotional expressions and recognition can be crucial for diagnostic and therapeutic procedures. Emotions can be expressed in the face, gesture, posture, voice, and behavior and affect physiological parameters, such as the heart rate or body temperature. With modern technology, clinicians can use a variety of tools ranging from sophisticated laboratory equipment to smartphones and web cameras. The aim of this paper is to review the currently used tools using modern technology and discuss their usefulness as well as possible future directions in emotional expression research and treatment strategies. Methods The authors conducted a literature review in the PubMed, EBSCO, and SCOPUS databases, using the following key words: ‘emotions,’ ‘emotional expression,’ ‘affective computing,’ and ‘autism.’ The most relevant and up‐to‐date publications were identified and discussed. Search results were supplemented by the authors’ own research in the field of emotional expression. Results We present a critical review of the currently available technical diagnostic and therapeutic methods. The most important studies are summarized in a table. Conclusion Most of the currently available methods have not been adequately validated in clinical settings. They may be a great help in everyday practice; however, they need further testing. Future directions in this field include more virtual‐reality‐based and interactive interventions, as well as development and improvement of humanoid robots.

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Section: Different Aspects Of Emotional Expressionmentioning

confidence: 99%

Emotional expression in psychiatric conditions: New technology for clinicians

Grabowski

Rynkiewicz

Lassalle

et al. 2018

Psychiatry Clin Neurosci

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show abstract

“…The order of the combination of the model sets and the duration conditions was counterbalanced across participants by balanced Latin square. The micro-expressions were presented by employing the JACBART paradigm ( Matsumoto et al, 2000 ; Matsumoto and Hwang, 2011 ; Shen et al, 2012 ; Zhang et al, 2014 , 2018 ; Svetieva and Frank, 2016 ), in which the expression image (presented for 50, 150, 333, or 450 ms) was sandwiched in between two 1 s presentations of the same expresser’s neutral face (see Figure 2 ). After that, participants were asked to select a single emotion label from a list provided that corresponded to the expression just displayed.…”

Section: Studymentioning

confidence: 99%

“…Only a few studies have investigated this subject. For example, researchers found that, factors like emotional context, duration of facial expressions, age, personality, or profession can affect micro-expression recognition (e.g., Matsumoto et al, 2000 ; Hall and Matsumoto, 2004 ; Hurley, 2012 ; Shen et al, 2012 ; Hurley et al, 2014 ; Zhang et al, 2014 , 2018 ; Shen et al, 2016b ; Svetieva and Frank, 2016 ; Demetrioff et al, 2017 ). Evidence also suggests that there are different EEG/ERPs neural mechanisms underlying the recognition of micro-expressions and macro-expressions (i.e., the typical facial expressions, usually last between 0.5 and 4 s) ( Shen et al, 2016a ).…”

Section: Introductionmentioning

confidence: 99%

A False Trail to Follow: Differential Effects of the Facial Feedback Signals From the Upper and Lower Face on the Recognition of Micro-Expressions

Zeng

Zhang

et al. 2018

Front. Psychol.

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Micro-expressions, as fleeting facial expressions, are very important for judging people’s true emotions, thus can provide an essential behavioral clue for lie and dangerous demeanor detection. From embodied accounts of cognition, we derived a novel hypothesis that facial feedback from upper and lower facial regions has differential effects on micro-expression recognition. This hypothesis was tested and supported across three studies. Specifically, the results of Study 1 showed that people became better judges of intense micro-expressions with a duration of 450 ms when the facial feedback from upper face was enhanced via a restricting gel. Additional results of Study 2 showed that the recognition accuracy of subtle micro-expressions was significantly impaired under all duration conditions (50, 150, 333, and 450 ms) when facial feedback from lower face was enhanced. In addition, the results of Study 3 also revealed that blocking the facial feedback of lower face, significantly boosted the recognition accuracy of subtle and intense micro-expressions under all duration conditions (150 and 450 ms). Together, these results highlight the role of facial feedback in judging the subtle movements of micro-expressions.

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“…Aiming to solve this problem, based on the JACBART, Zhang et al ( 2014 ) were the first to explore the role of neutral, sad and happy contexts in the ME recognition task. The results showed that the ACC of recognizing all MEs was decreased in the sad context compared to the neutral and happy contexts, which suggests that participants’ performance in the ME recognition task is influenced by the context.…”

Section: Introductionmentioning

confidence: 99%

“…This study was a further refinement of the JACBART. However, the types of context that were adopted in Zhang et al’s ( 2014 ) study were still limited. Soon after that, Zhang et al ( 2017 ) examined the ME recognition characteristics of college students in fearful, sad, disgusting, angry, surprised and happy contexts.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Ecological Micro-Expression Recognition in Patients with Depression and Healthy Individuals

Zhu

Zhang

Zhiying

et al. 2017

Front. Behav. Neurosci.

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Previous studies have focused on the characteristics of ordinary facial expressions in patients with depression, and have not investigated the processing characteristics of ecological micro-expressions (MEs, i.e., MEs that presented in different background expressions) in these patients. Based on this, adopting the ecological MEs recognition paradigm, this study aimed to comparatively evaluate facial ME recognition in depressed and healthy individuals. The findings of the study are as follows: (1) background expression: the accuracy (ACC) in the neutral background condition tended to be higher than that in the fear background condition, and the reaction time (RT) in the neutral background condition was significantly longer than that in other backgrounds. The type of ME and its interaction with the type of background expression could affect participants’ ecological MEs recognition ACC and speed. Depression type: there was no significant difference between the ecological MEs recognition ACC of patients with depression and healthy individuals, but the patients’ RT was significantly longer than that of healthy individuals; and (2) patients with depression judged happy MEs that were presented against different backgrounds as neutral and judged neutral MEs that were presented against sad backgrounds as sad. The present study suggested the following: (1) ecological MEs recognition was influenced by background expressions. The ACC of happy MEs was the highest, of neutral ME moderate and of sadness and fear the lowest. The response to the happy MEs was significantly shorter than that of identifying other MEs. It is necessary to conduct research on ecological MEs recognition; (2) the speed of patients with depression in identifying ecological MEs was slower than of healthy individuals; indicating that the patients’ cognitive function was impaired; and (3) the patients with depression showed negative bias in the ecological MEs recognition task, reflecting the lack of happy ME recognition ability and the generalized identification of sad MEs in those patients.

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Emotional Context Influences Micro-Expression Recognition

Cited by 34 publications

References 34 publications

Emotional expression in psychiatric conditions: New technology for clinicians

Emotional expression in psychiatric conditions: New technology for clinicians

A False Trail to Follow: Differential Effects of the Facial Feedback Signals From the Upper and Lower Face on the Recognition of Micro-Expressions

Comparison of Ecological Micro-Expression Recognition in Patients with Depression and Healthy Individuals

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