PMID- 32584067
OWN - NLM
STAT- MEDLINE
DCOM- 20220606
LR  - 20220716
IS  - 1931-1516 (Electronic)
IS  - 1528-3542 (Print)
IS  - 1528-3542 (Linking)
VI  - 22
IP  - 4
DP  - 2022 Jun
TI  - Alterations in facial expressions of emotion: Determining the promise of 
      ultrathin slicing approaches and comparing human and automated coding methods in 
      psychosis risk.
PG  - 714-724
LID - 10.1037/emo0000819 [doi]
AB  - Alterations in facial expressions of emotion are a hallmark of psychopathology 
      and may be present before the onset of mental illness. Technological advances 
      have spurred interest in examining alterations based on "thin slices" of behavior 
      using automated approaches. However, questions remain. First, can alterations be 
      detected in ultrathin slices of behavior? Second, how do automated approaches 
      converge with human coding techniques? The present study examined ultrathin 
      (i.e., 1-min) slices of video-recorded clinical interviews of 42 individuals at 
      clinical high risk (CHR) for psychosis and 42 matched controls. Facial 
      expressions of emotion (e.g., joy, anger) were examined using two automated 
      facial analysis programs and coded by trained human raters (using the Expressive 
      Emotional Behavior Coding System). Results showed that ultrathin (i.e., 1-min) 
      slices of behavior were sufficient to reveal alterations in facial expressions of 
      emotion, specifically blunted joy expressions in individuals at CHR (with 
      supplementary analyses probing links with attenuated positive symptoms and 
      functioning). Furthermore, both automated analysis programs converged in the 
      ability to detect blunted joy expressions and were consistent with human coding 
      at the level of both second-by-second and aggregate data. Finally, there were 
      areas of divergence across approaches for other emotional expressions beyond joy. 
      These data suggest that ultrathin slices of behavior can yield clues about 
      emotional dysfunction. Further, automated approaches (which do not require 
      lengthy training and coder time but do lend well to mobile assessment and 
      computational modeling) show promise, but careful evaluation of convergence with 
      human coding is needed. (PsycInfo Database Record (c) 2022 APA, all rights 
      reserved).
FAU - Gupta, Tina
AU  - Gupta T
AUID- ORCID: 0000-0003-0160-3201
AD  - Department of Psychology.
FAU - Haase, Claudia M
AU  - Haase CM
AD  - School of Education and Social Policy.
FAU - Strauss, Gregory P
AU  - Strauss GP
AD  - Department of Psychology.
FAU - Cohen, Alex S
AU  - Cohen AS
AD  - Department of Psychology.
FAU - Ricard, Jordyn R
AU  - Ricard JR
AD  - School of Education and Social Policy.
FAU - Mittal, Vijay A
AU  - Mittal VA
AD  - Department of Psychology.
LA  - eng
GR  - R21 MH115231/MH/NIMH NIH HHS/United States
GR  - R21 MH110374/MH/NIMH NIH HHS/United States
GR  - F31 MH121018/MH/NIMH NIH HHS/United States
GR  - R01 MH112545/MH/NIMH NIH HHS/United States
GR  - R01 MH116039/MH/NIMH NIH HHS/United States
GR  - R21 MH119677/MH/NIMH NIH HHS/United States
GR  - MH/NIMH NIH HHS/United States
PT  - Journal Article
DEP - 20200625
PL  - United States
TA  - Emotion
JT  - Emotion (Washington, D.C.)
JID - 101125678
SB  - IM
MH  - Anger
MH  - Emotions
MH  - Face
MH  - *Facial Expression
MH  - Humans
MH  - *Psychotic Disorders
PMC - PMC7759595
MID - NIHMS1623786
EDAT- 2020/06/26 06:00
MHDA- 2022/06/07 06:00
PMCR- 2021/12/25
CRDT- 2020/06/26 06:00
PHST- 2020/06/26 06:00 [pubmed]
PHST- 2022/06/07 06:00 [medline]
PHST- 2020/06/26 06:00 [entrez]
PHST- 2021/12/25 00:00 [pmc-release]
AID - 2020-44597-001 [pii]
AID - 10.1037/emo0000819 [doi]
PST - ppublish
SO  - Emotion. 2022 Jun;22(4):714-724. doi: 10.1037/emo0000819. Epub 2020 Jun 25.