Literature DB >> 15502685

Detecting deception in facial expressions of pain: accuracy and training.

Marilyn L Hill1, Kenneth D Craig.   

Abstract

Clinicians tend to assign greater weight to nonverbal expression than to patient self-report when judging the location and severity of pain. However, patients can be successful at dissimulating facial expressions of pain, as posed expressions resemble genuine expressions in the frequency and intensity of pain-related facial actions. The present research examined individual differences in the ability to discriminate genuine and deceptive facial pain displays and whether different models of training in cues to deception would improve detection skills. Judges (60 male, 60 female) were randomly assigned to 1 of 4 experimental groups: 1) control; 2) corrective feedback; 3) deception training; and 4) deception training plus feedback. Judges were shown 4 videotaped facial expressions for each chronic pain patient: neutral expressions, genuine pain instigated by physiotherapy range of motion assessment, masked pain, and faked pain. For each condition, the participants rated pain intensity and unpleasantness, decided which category each of the 4 video clips represented, and described cues they used to arrive at decisions. There were significant individual differences in accuracy, with females more accurate than males, but accuracy was unrelated to past pain experience, empathy, or the number or type of facial cues used. Immediate corrective feedback led to significant improvements in participants' detection accuracy, whereas there was no support for the use of an information-based training program.

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Year:  2004        PMID: 15502685     DOI: 10.1097/00002508-200411000-00006

Source DB:  PubMed          Journal:  Clin J Pain        ISSN: 0749-8047            Impact factor:   3.442


  7 in total

1.  Automated Pain Detection in Facial Videos of Children using Human-Assisted Transfer Learning.

Authors:  Xiaojing Xu; Kenneth D Craig; Damaris Diaz; Matthew S Goodwin; Murat Akcakaya; Büşra Tuğçe Susam; Jeannie S Huang; Virginia R de Sa
Journal:  CEUR Workshop Proc       Date:  2018-07

2.  The Delaware Pain Database: a set of painful expressions and corresponding norming data.

Authors:  Peter Mende-Siedlecki; Jennie Qu-Lee; Jingrun Lin; Alexis Drain; Azaadeh Goharzad
Journal:  Pain Rep       Date:  2020-10-21

3.  Automatic decoding of facial movements reveals deceptive pain expressions.

Authors:  Marian Stewart Bartlett; Gwen C Littlewort; Mark G Frank; Kang Lee
Journal:  Curr Biol       Date:  2014-03-20       Impact factor: 10.834

4.  Depression augments activity-related pain in women but not in men with chronic musculoskeletal conditions.

Authors:  H Adams; P Thibault; N Davidson; M Simmonds; A Velly; M J L Sullivan
Journal:  Pain Res Manag       Date:  2008 May-Jun       Impact factor: 3.037

5.  Facial expressions of pain modulate observer's long-latency responses in superior temporal sulcus.

Authors:  Miiamaaria V Kujala; Topi Tanskanen; Lauri Parkkonen; Riitta Hari
Journal:  Hum Brain Mapp       Date:  2009-12       Impact factor: 5.038

6.  Pain behavior mediates the relationship between perceived injustice and opioid prescription for chronic pain: a Collaborative Health Outcomes Information Registry study.

Authors:  Junie S Carriere; Marc-Olivier Martel; Ming-Chih Kao; Michael Jl Sullivan; Beth D Darnall
Journal:  J Pain Res       Date:  2017-03-07       Impact factor: 3.133

7.  Machine learning methods for automatic pain assessment using facial expression information: Protocol for a systematic review and meta-analysis.

Authors:  Dianbo Liu; Dan Cheng; Timothy T Houle; Lucy Chen; Wei Zhang; Hao Deng
Journal:  Medicine (Baltimore)       Date:  2018-12       Impact factor: 1.817

  7 in total

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