Literature DB >> 18466413

Reading between the lies: identifying concealed and falsified emotions in universal facial expressions.

Stephen Porter1, Leanne ten Brinke.   

Abstract

The widespread supposition that aspects of facial communication are uncontrollable and can betray a deceiver's true emotion has received little empirical attention. We examined the presence of inconsistent emotional expressions and "microexpressions" (1/25-1/5 of a second) in genuine and deceptive facial expressions. Participants viewed disgusting, sad, frightening, happy, and neutral images, responding to each with a genuine or deceptive (simulated, neutralized, or masked) expression. Each 1/30-s frame (104,550 frames in 697 expressions) was analyzed for the presence and duration of universal expressions, microexpressions, and blink rate. Relative to genuine emotions, masked emotions were associated with more inconsistent expressions and an elevated blink rate; neutralized emotions showed a decreased blink rate. Negative emotions were more difficult to falsify than happiness. Although untrained observers performed only slightly above chance at detecting deception, inconsistent emotional leakage occurred in 100% of participants at least once and lasted longer than the current definition of a microexpression suggests. Microexpressions were exhibited by 21.95% of participants in 2% of all expressions, and in the upper or lower face only.

Entities:  

Mesh:

Year:  2008        PMID: 18466413     DOI: 10.1111/j.1467-9280.2008.02116.x

Source DB:  PubMed          Journal:  Psychol Sci        ISSN: 0956-7976


  31 in total

1.  Effects of the duration of expressions on the recognition of microexpressions.

Authors:  Xun-bing Shen; Qi Wu; Xiao-lan Fu
Journal:  J Zhejiang Univ Sci B       Date:  2012-03       Impact factor: 3.066

2.  The same with age: Evidence for age-related similarities in interpersonal accuracy.

Authors:  Vanessa L Castro; Derek M Isaacowitz
Journal:  J Exp Psychol Gen       Date:  2018-12-13

3.  Padova Emotional Dataset of Facial Expressions (PEDFE): A unique dataset of genuine and posed emotional facial expressions.

Authors:  A Miolla; M Cardaioli; C Scarpazza
Journal:  Behav Res Methods       Date:  2022-08-24

4.  Classification of emotional states via transdermal cardiovascular spatiotemporal facial patterns using multispectral face videos.

Authors:  Shaul Shvimmer; Rotem Simhon; Michael Gilead; Yitzhak Yitzhaky
Journal:  Sci Rep       Date:  2022-07-01       Impact factor: 4.996

5.  Cognitive-load approaches to detect deception: searching for cognitive mechanisms.

Authors:  Iris Blandón-Gitlin; Elise Fenn; Jaume Masip; Aspen H Yoo
Journal:  Trends Cogn Sci       Date:  2014-09       Impact factor: 20.229

6.  Exploring the movement dynamics of deception.

Authors:  Nicholas D Duran; Rick Dale; Christopher T Kello; Chris N H Street; Daniel C Richardson
Journal:  Front Psychol       Date:  2013-03-27

7.  Emotional intelligence and mismatching expressive and verbal messages: a contribution to detection of deception.

Authors:  Jerzy Wojciechowski; Maciej Stolarski; Gerald Matthews
Journal:  PLoS One       Date:  2014-03-21       Impact factor: 3.240

8.  Micro-Expressions of Fear During the 2016 Presidential Campaign Trail: Their Influence on Trait Perceptions of Donald Trump.

Authors:  Patrick A Stewart; Elena Svetieva
Journal:  Front Psychol       Date:  2021-06-02

9.  Effect of Acting Experience on Emotion Expression and Recognition in Voice: Non-Actors Provide Better Stimuli than Expected.

Authors:  Rebecca Jürgens; Annika Grass; Matthis Drolet; Julia Fischer
Journal:  J Nonverbal Behav       Date:  2015

10.  CASME II: an improved spontaneous micro-expression database and the baseline evaluation.

Authors:  Wen-Jing Yan; Xiaobai Li; Su-Jing Wang; Guoying Zhao; Yong-Jin Liu; Yu-Hsin Chen; Xiaolan Fu
Journal:  PLoS One       Date:  2014-01-27       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.