Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2023 Aug 1.
Published in final edited form as: Early Interv Psychiatry. 2021 Nov 1;16(8):875–882. doi: 10.1111/eip.13233

Genuine and non-genuine smiles in individuals meeting criteria for a clinical high-risk syndrome

Jordyn R Ricard 1,*, Tina Gupta 2, Teresa Vargas 3, Claudia M Haase 4, Vijay A Mittal 5
PMCID: PMC9056581  NIHMSID: NIHMS1750390  PMID: 34725928

Abstract

Aim:

Psychosis is characterized by both alterations in emotional functioning and environmental stressors including bullying victimization. Recent evidence suggests that some alterations in emotional functioning (e.g., blunted positive facial expressions) are already present in the psychosis risk period. Yet, some clinically relevant facial emotions have not been investigated such as genuine smiles (thought to reflect genuine positive emotions) and non-genuine smiles (thought to fake positive or mask negative emotions) in individuals meeting criteria for a clinical high-risk (CHR) syndrome. Further, despite a compelling conceptual basis to suggest a link between affective expression and exposure to environmental stress, to date, no investigations have sought to examine this association. Here, we aim to assess differences between a sample of CHR (N = 65) and control (N = 67) individuals in genuine and non-genuine smiles and associations with bullying victimization.

Methods:

Smiles (i.e., genuine; non-genuine) were objectively coded on a second-by-second basis using the Facial Action Coding System (FACS) during a digitally-recorded clinical interview segment. Bullying victimization was measured via parent report.

Results:

Findings revealed that the CHR group (1) showed blunted genuine (but not non-genuine) smiles compared to controls. Moreover, (2) bullying victimization was related to blunted genuine smiles, but not non-genuine smiles.

Conclusion:

These findings expand our understanding of emotional alterations in this group with implications for diagnosis (highlighting blunted genuine smiles as a specific marker) and etiology (underscoring the role of bullying victimization in the etiology of emotional dysfunction).

Keywords: Clinical high-risk, positive emotions, genuine and non-genuine smiles, bullying victimization, psychosis spectrum

Introduction

Alterations in emotional functioning are a hallmark of psychosis (Andreasen, 1982; Kirkpatrick, Fenton, Carpenter, & Marder, 2006; Kring & Caponigro, 2010; Kring & Elis, 2013). Additionally, studies show that alterations in facial expressions of emotion are present among individuals with a clinical high-risk (CHR) syndrome (Gupta, Haase, Strauss, Cohen, & Mittal, 2019; Gupta et al., 2020; Miller et al., 2003). These individuals are at a heightened risk for transitioning to a psychotic disorder in a short period of time (Cannon et al., 2008; Fusar-Poli et al., 2012; Fusar-Poli et al., 2013) and have high comorbidity rates with other non-psychotic disorders (e.g., mood disorders) (Fusar-Poli et al., 2014). Furthermore, blunted facial expressions are often observed in non-psychotic disorders such as those with depression (Sobin & Sackeim, 1997; Davies et al., 2016). Emerging work has suggested that rather than blunting across the board, individuals in this group show selective blunting of positive (but not negative) facial expressions (Gupta et al., 2019). While our understanding of emotional functioning prior to the onset of psychosis is growing, an area of research that is more established is the role of bullying victimization in the etiology of psychosis (Corcoran et al., 2003; Vargas, Damme, & Mittal, 2019). To date, the relationship between facial expressions and bullying victimization in this group is unknown. Understanding these relationships can inform the etiology of psychosis, development of intervention and prevention strategies, and emotional processing conceptualizations.

Alterations in emotional functioning are already present in the prodromal period among individuals with a CHR syndrome (Addington, Penn, Woods, Addington, & Perkins, 2008; Amminger et al., 2011; Gupta, Cowan, Strauss, Walker, & Mittal, 2021; Kohler et al., 2014; Strauss, Pelletier-Baldelli, Visser, Walker, & Mittal, 2020). Previous work has shown that blunting in select facial expressions, particularly blunted expressions of joy (i.e., smiling), is one notable feature in CHR groups (Gupta et al., 2019; Gupta et al., 2020; Strauss et al., 2020). Smiling serves important functions in daily life and is related to work outcomes, well-being, and mental health (Barger & Grandey, 2006; Harker & Keltner, 2001; Keltner & Gross, 1999; Papa & Bonanno, 2008). Furthermore, they play a key role in the emotion generation process (Levenson, 1999); provide important interoceptive cues (James, 1884; Noah, Schul, & Mayo, 2018); and they play a key role in social relationships (Gray, Ishii, & Ambady, 2011; Keltner, 2009).

Yet, not all smiles are the same. Smiles can include genuine (i.e., smiles involving the movement of two facial muscles – the zygomatic major muscle (lip corner raise) and the orbicularis oculi muscle (cheek raise to form crow’s feet around the eyes) and non-genuine smiles (i.e., smiles without movement of the orbicularis oculi muscle) (Ekman & Friesen, 1982), and they differ in terms of anatomy, correlates, and consequences (Ekman, Davidson, & Friesen, 1990; Ekman & Friesen, 1982; Ekman, Hager, & Friesen, 1981; Frank, Ekman, & Friesen, 1993; Keltner & Bonanno, 1997; Papa & Bonanno, 2008). Genuine smiles are thought to reflect genuine positive emotions (e.g., enjoyment, amusement) and are difficult to voluntarily produce. Furthermore, many of the existing studies find that when smiles have benefits, these benefits stem specifically from genuine (rather than non-genuine) smiles (Davidson, Ekman, Saron, Senulis, & Friesen, 1990; Ekman et al., 1990; Ekman & Friesen, 1982; Keltner & Bonanno, 1997; Papa & Bonanno, 2008; Soussignan, 2002). Non-genuine smiles can be produced voluntarily and are thought to communicate positive emotion that is not really there or mask negative emotions (e.g., embarrassment) (Davidson et al., 1990; Ekman & Friesen, 1982; Keltner & Bonanno, 1997; Papa & Bonanno, 2008; Soussignan, 2002). To date, there is some work documenting reduced genuine smiles in samples of individuals diagnosed with schizophrenia (Krause, Steimer, Sänger-Alt, & Wagner, 1989); however, there are no studies examining these smiles prior to the onset of psychosis.

Research on sources of emotional alterations in individuals with a CHR syndrome is sparse, but one candidate source is environmental stress – which can include bullying victimization (Tognin et al., 2020). There is evidence of relationships between facial expressions and broader environmental stressors such as a history of sexual harassment in individuals diagnosed with schizophrenia (Yildirim et al., 2014). In CHR groups, increasing evidence indicates that individuals endorsing CHR syndromes are more likely to report experiencing bullying victimization when compared to healthy controls (Catone et al., 2015; Valmaggia et al., 2015). To our knowledge, there is one study that has assessed the relationship between emotional processes and bullying victimization in individuals endorsing CHR syndromes (Tongin et al., 2020), with no studies investigating relationships between smiles and bullying victimization. Given that adolescence is a sensitive developmental period with increasing demands that result in increased stress exposure (Fuhrmann, Knoll, & Blakemore, 2015; Konrad, Firk, & Uhlhaas, 2013), and smiles serve adaptive functions, there is a utility in investigating the interplay between smiles and bullying victimization in this risk group.

The present study sought to determine whether individuals with a CHR syndrome exhibit alterations in specific types of smiles, genuine and non-genuine smiles, when compared to healthy controls. Based on prior work (Gupta et al., 2019), we expected the CHR group to display blunting in smiles when compared to controls. Further, the present study sought to examine links between smiles and bullying victimization in the CHR group. Given evidence of links between emotional processes and environmental stress in both schizophrenia and CHR groups (Tognin et al., 2020; Yildirim et al., 2014), we hypothesized that reduced smiles would be related to bullying victimization in this group.

Method

Participants and Procedure

A total of 132 participants (65 individuals with a CHR syndrome and 67 healthy controls), aged 12-21 (M = 18.30, SD = 2.33) were recruited using a variety of methods (e.g., e-mail announcements, newspaper advertisements, flyers, community health referrals through the Adolescent Development and Preventive Treatment (ADAPT) Program. Although the sample was not entirely composed of referred participants, participants were, for the large part, help-seeking and quite symptomatic. Along these lines, self/family referrals have been found to be a recruitment stream in our studies as well as across other sites (Addington et al., 2012; Addington et al., 2015; Yee et al., 2020). Exclusion criteria for all participants included the presence of neurological disorders, head injury, substance dependence, and IQ < 70. Furthermore, individuals that met for a psychotic disorder (e.g., schizophrenia) were excluded. The University of Colorado Boulder Institutional Review Board approved the study.

The Structured Interview for Prodromal Syndromes (SIPS) (Miller et al., 2003) was used to identify a CHR syndrome – a standardized approach in the field (McGlashan et al., 2001). This was defined by endorsing attenuated positive symptoms, having a family history of psychosis, or a decline in global functioning accompanying the presence of schizotypal personality disorder (Miller et al., 2003). The Structured Clinical Interview for the DSM-IV Axis I Disorders (SCID) was also administered to rule out a psychotic disorder diagnosis (First, Gibbon, Spitzer, Benjamin, & Williams, 1997). Training of interviewers was conducted during a two-month period for both clinical interviews (i.e., SIPS and SCID), and all interviewers were reliable (κ ≥ .80). All participants included in this study consented to being video recorded during clinical interviews and all interviews were videotaped.

Measures

Smiles.

Smiles were objectively measured for in a sample of participants with available and usable video data (N = 84; 42 with a CHR syndrome and 42 controls) using the Facial Action Coding System (FACS); a comprehensive, anatomically based system for objectively measuring visually discernible facial activity (Ekman & Friesen, 1978). FACS uses action units (AUs) to describe facial muscle activity (e.g., AU 6 is the label for cheeks raising) that are labeled by trained coders in static images or in video segments (labels occur on a second-by-second basis in the video) and is considered a well-established approach to facial coding. The first 1-minute of the SIPS interview was FACS coded with another trained rater to establish inter-rater agreement in line with previous studies analyzing facial expressions in CHR samples (Gupta et al., 2020) as well as healthy populations (Ambady & Rosenthal, 1992). Each coder completed FACS training (estimated to take between 50-100 hours) and successfully passed the FACS certification test to demonstrate proficiency (Cohn, Ambadar, & Ekman, 2007). The content of the interviews contained neutral information (e.g., discussing information about demographics such as asking about age) (Gupta et al., 2019). Additionally, interviews were FACS coded with special attention to action units that are related to the emotional category of smiles. Specifically, the following action units were coded: AU1 (inner brow raise), AU2 (outer brow raise), AU4 (brow lower), AU5 (upper lid raise), AU6 (cheek raise), AU12 (lip corner puller), and AU25 (lips part). An additional category “AU12 mixed with” was coded when AU12 occurred together with an additional AU such as AU7 (lid tightener), AU9 (nose wrinkle), AU10 (upper lip raise), AU14 (dimpler), AU15 (lip corner depressor), AU20 (lip stretch), and AU24 (lip presser) because some of these AUs could act as disqualifiers of a genuine smile. The intensity of each action unit (0 = no expression, 1 = slight, 2 = moderate, 3 = strong) was coded.

Genuine and non-genuine smiles.

Based on prior work (Johnson et al., 2017), genuine smiles were defined as the movement of both the zygomatic major muscle (i.e., AU 12; lip corner raise) as well as the orbicularis oculi muscle (i.e., AU 6; cheek raise). Genuine smiles were calculated by taking the average intensity of the apexes of the AU 12s that were expressed during the duration of participants’ genuine smiles. Non-genuine smiles were defined as the movement of the zygomatic major muscle (i.e., AU 12; lip corner raise), but did not involve the movement of the orbicularis oculi muscle (i.e., AU 6; cheek raise) around the eyes. In addition, genuine smiles that were accompanied by additional muscle movements (e.g., AUs 4, 7, 9, 10, 14, 15, 20, 24) served as disqualifiers of genuine smiles and were defined as non-genuine smiles. Non-genuine smiles were calculated by taking the average intensity of the apexes of the AU 12s that were expressed during the duration of participants’ non-genuine smiles. This criteria for coding genuine and non-genuine smiles was consistent with Johnson and colleagues (Johnson et al., 2017). To assess intercoder agreement, two coders coded an overlap of eight videos (10%) of participants’ videos. Interrater agreement was calculated using Cronbach’s alpha and was satisfactory (.76).

Bullying victimization.

Bullying victimization data were available for a sample of N = 104 and were measured via parental report. Specifically, parents completed the Autism-Tics, ADHD, and other Comorbidities inventory (A-TAC) which asks, (1) “Is he/she easily teased?” (1 = yes, 0 = no) and (2) “Has she/he been bullied by other children in school?” (1 = yes, 0 = no) (Larson et al., 2013) as well as the Social Responsiveness Scale (SRS), which asks whether the child “gets teased a lot” (1 = yes, 0 = no) (Constantino et al., 2003). Bullying victimization was dichotomized, and scores ranged from 0 (no bullying) to 1 (endorsed at least one of the three items).

Statistical Approach

To assess for demographic differences between groups, independent t-tests and chi-square tests were employed for continuous and categorical variables, respectively. In terms of normality tests, an examination of skew and kurtosis suggested our data were not highly skewed and, thus, parametric tests were applied. Given high rates of comorbidity within CHR groups (Fusar-Poli et al., 2014), Pearson correlations were used to assess whether blunted smiles were related to depressive symptoms, which were measured using the Beck Depression Inventory (BDI; Beck, Steer, Brown, 1996). To determine group differences in smiles and bullying victimization, independent sample t-tests were employed. Pearson correlations were used to investigate relationships between smiles and bullying victimization within a subsample of the CHR group with available data (N = 56). Overall, our sample size included a total of 84 participants with available and usable video data for FACS coding and 104 individuals with available bullying parent data. It is important to note that controlling for sex and age did not change the direction or magnitude of the findings. Analyses assessing frequency were also employed in efforts to support interpretation of intensity findings and ensure individuals were displaying facial expressions. Frequency findings are indicated in footnotes below.

Results

Sociodemographic Characteristics

As shown in Table 1, the CHR group did not differ from controls in age, t(130) = 1.56, p = .12, or parental education, t(124) = −.28, p = .78, but did differ in biological sex, χ2(1) = 5.15, p = .02. Additionally, the CHR group was more likely to report bullying victimization, χ2(1) = 5.67, p = .02, compared to controls. Notably, the CHR group showed a range of frequencies of smiles for genuine (0 – 31) and non-genuine (2 – 45) smiles. The average frequency was 6.67 (8.05) for genuine smiles and 19.19 (10.86) for non-genuine smiles. For the control group, the average frequency was 19.19 (14.10) for genuine smiles and 17.45 (7.98) for non-genuine smiles. These frequency data suggest there were ample instances of smiles during the video clip. In the CHR group, depressive symptoms were not associated with genuine, r = .15, p = .37, or non-genuine smiles, r = −.02, p = .89.

Table 1.

Demographic and symptom details

CHR Control Statistic p
N = 132†
Age 18.62 (1.81) 17.99 (2.73) t(130) = 1.56 .12
Biological Sex χ2(1) = 5.15 .02
 Male 40 28
 Female 25 39
 Total 65 67
Parent Education 31.21 (4.40) 31.46 (5.59) t(124) = −.28 .78
BDI 19.25 (11.28) 4.00 (4.28) t(78) = 8.12 <.001
Positive Symptoms 11.83 (4.58) .64 (1.36) t(130) = 19.15 <.001
Bullying Victimization χ2(1) = 5.67 .02
 History of BV 25 14
 No History of BV 26 39
 Total 51 53
N = 84‡
Smiles
 Genuine Smiles 1.96 (1.53) 2.79 (.56) t(82) =−3.29 .001
 Non-Genuine Smiles 1.75 (.39) 1.75 (.37) t(82) =−.09 .93
Age 18.91 (1.91) 18.12 (2.61) t(82) = 1.58 .12
Biological Sex χ2(1) = 5.93 .02
 Male 23 12
 Female 19 30
 Total 42 42
Parent Education 31.03 (4.20) 31.49 (5.68) t(79) =−.42 .68
BDI 19.35 (11.28) 4.00 (4.28) t(78) = 8.12 <.001
Positive Symptoms 11.69 (4.70) .45 (1.20) t(82) = 15.01 <.001
Open in a new tab

Note. Individuals meeting criteria for a clinical high-risk syndrome (CHR); Parental education is the average of mother and father education combined; Parent education and age are in years; Biological sex is represented in counts; Mean and standard deviations for age, parental education, positive symptoms, bullying victimization, and smiles are shown; Positive symptoms are average sum scores from the Structured Interview for Psychosis-Risk Syndromes (SIPS); Bullying victimization is represented in counts.

†

N = 132 was the sample size used to conduct analyses of group differences in age, biological sex, parental education, positive symptoms, depressive symptoms, and bullying victimization.

‡

N = 84 was the sample size used to conduct analyses of differences in smiles.

Group Differences in Genuine and Non-Genuine Smiles

As shown in Figure 1, findings revealed the CHR group showed significantly blunted genuine smiles when compared to controls, t(82) = −3.29, p = .001; d = .72. However, there were no significant differences in non-genuine smiles between groups, t(82) = −.09, p = .93.1

Figure 1.

Figure 1.

Open in a new tab

Genuine and non-genuine smile intensity in those meeting criteria for a clinical high-risk syndrome and controls.

Note. * p < 05. Error bars represent standard errors.

Associations between Smiles and Bullying Victimization within the CHR Group

As shown in Figure 2, those in the CHR group who had blunted genuine smiles were more likely to report bullying victimization, r = −.40, p = .04, but bullying victimization was not associated with non-genuine smiles, r = −.15, p = .45.2

Figure 2.

Figure 2.

Open in a new tab

Association between bullying victimization and smiles in those considered meeting criteria for a clinical high-risk syndrome.

Note. Genuine smiles (G); Non-genuine smiles (NG); * p ≤ .05. N = 51.

Discussion

Blunting in positive emotions broadly (Kring & Elis, 2013) and at the level of facial expressions specifically (Krause et al., 1989) are hallmark features of psychosis. Drawing from affective science, the present study was the first to use an objective, anatomically based coding system, FACS (Ekman & Friesen, 1978), to examine genuine and non-genuine smiles in individuals with a CHR syndrome. Findings revealed that the CHR group showed blunted genuine smiles, but no alterations in non-genuine smiles, when compared to healthy controls. Furthermore, the present study found that those in the CHR group who were more likely to report bullying victimization showed blunted genuine smiles.

Findings suggesting blunted facial expressions of joy (i.e., smiles) are in line with previous studies (Gupta et al., 2020). Facial expressions of emotions can serve as windows into our emotional lives, signal emotional states to others, and help in the creation and maintenance of social bonds (Gray et al., 2011; Keltner, 2009; Keltner & Kring, 1998). A novel aspect of this work was the use of the FACS-based approach in assessing facial expressions. This method revealed unparalleled specificity showing blunting in genuine, but not non-genuine smiles in the CHR group. These data are in line with very early work in schizophrenia suggesting blunting only in genuine smiles are observed (and not non-genuine) (Krause et al., 1989). Given the central importance of genuine (but not non-genuine) smiles for long-term well-being and social functioning (Harker & Keltner, 2001) for individuals and caregivers (Lwi et al., 2019), this finding raises the question of whether blunted genuine smiles in the CHR group may not only serve as an early psychopathological marker but might also predict deterioration of well-being and social relationships prospectively.

These findings indicating the CHR group reported more bullying victimization compared to controls is in line with previous studies as well (Mayo et al., 2017; Vargas, Damme, et al., 2019). Furthermore, results suggesting bullying victimization is related to reduced genuine smiles in the CHR group offer important clues. There exists an enormous body of work documenting the negative long-term effects of environmental stress (i.e., trauma, bullying victimization) on emotional functioning (Burns, Jackson, & Harding, 2010; Carvalho Fernando et al., 2014; Cicchetti & Rogosch, 2009; Pechtel & Pizzagalli, 2011). Furthermore, because genuine smiles are thought to reflect “felt” emotion (Ekman & Friesen, 1982) and are also preferred in social situations (Shore & Heerey, 2011), it is possible that associations between genuine smiles and bullying victimization reflect reduced feelings of positive emotion which can influence social relationships. While our study used correlational analyses and cannot infer causation, these data suggest smiles and bullying, and possibly environmental stress more generally, are interconnected. Further research is necessary to understand the directional relationship (e.g., does bullying victimization lead to reductions in facial expressions, and/or do reduced facial expressions lead to bullying victimization?).

While the present study had several strengths (e.g., first to examine specific smile type, used a FACS based approach), there are also limitations to discuss that offer opportunities for future directions. For example, smiles were measured during the first minute of a clinical interview. Additional research could determine generalizability across other clinical interview segments. Related, the use of video clips may produce context-specific facial expressivity (e.g., facial expressions may be less intense because of the content discussed during this interview). Future work should consider assessing facial expressivity in different contexts such as when using an evocative film clip task, in naturalistic contexts, during dyadic conversations, as well as when completing various cognitive tasks. Additionally, future work may benefit from further assessing both the frequency and intensity of smiles. Moreover, the present study did not find a relationship between smiles and depressive symptoms in the CHR group. While this relationship suggests the blunted smiles in the CHR group may not be better explained by affective symptoms, it will be valuable for future studies to continue to understand relationships with comorbidity using different measures of depression and larger sample sizes. Also, bullying victimization was dichotomized across different measures to encompass a metric to assess bullying and teasing. There are additional measures that can be used to assess bullying and other environmental stressors (Rigby & Slee, 1993; Slee & Rigby, 1993). Future work would also benefit from teasing apart bullying victimization into different categories (e.g., teasing vs. bullying) (Cornell, Gregory, Huang, & Fan, 2013), and using continuous measures that extend outside self-report (Vargas, Zou, Conley, & Mittal, 2019). While the sample size afforded adequate statistical power to detect medium effect sizes for the main research questions, additional studies with larger samples and longitudinal designs are needed. In addition, a critical future direction is assessing sex differences in smiles and bullying victimization (Barlett & Coyne, 2014; Turner, Exum, Brame, & Holt, 2013). Finally, assessing facial expressions and links with bullying victimization between subgroups (e.g., genetic, attenuated positive symptoms, brief intermittent psychotic symptoms) is an important future direction and can inform vulnerability models and treatments (Fusar-Poli et al., 2016).

Acknowledgements

Funding for this work was provided by National Institute of Mental Health Grants R21MH110374 to V.A.M, R21MH115231 to C.M.H. and V.A.M, R01MH116039 to G.P.S and V.A.M, R01MH112545 to V.A.M, L.E, J.S, R21MH119677 to M.G. and V.A.M, F31MH119776 to T.V., and F31MH121018 to T.G.

Footnotes

1

The CHR group also displayed fewer genuine smiles (i.e., frequency) when compared to controls, t(82) =-5.00, p = <.001. There was no difference in the frequency of non-genuine smiles, t(82) =-0.84, p = 0.41.

2

The relationship between frequency of genuine smiles and bullying in the CHR group was not significant, r = 0.07, p = 0.74.

Conflict of Interests

The authors declare no conflict of interest.

Contributor Information

Jordyn R. Ricard, School of Education and Social Policy, Northwestern University.

Tina Gupta, Department of Psychology, Northwestern University.

Teresa Vargas, Department of Psychology, Northwestern University.

Claudia M. Haase, School of Education and Social Policy and (by courtesy) Department of Psychology, Northwestern University

Vijay A. Mittal, Department of Psychology, Department of Psychiatry, Institute for Policy Research, Department of Medical Social Sciences, and Institute for Innovations in Developmental Sciences, Northwestern University

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon request.

References

  1. Addington J, Cadenhead KS, Cornblatt BA, Mathalon DH, McGlashan TH, Perkins DO, … & Cannon TD (2012). North American prodrome longitudinal study (NAPLS 2): overview and recruitment. Schizophrenia Research, 142(1-3), 77–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Addington J, Liu L, Buchy L, Cadenhead KS, Cannon TD, Cornblatt BA, … & McGlashan TH (2015). North American prodrome longitudinal study (NAPLS 2): the prodromal symptoms. The Journal of Nervous and Mental Disease, 203(5), 328. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Addington J, Penn D, Woods SW, Addington D, & Perkins DO (2008). Facial affect recognition in individuals at clinical high risk for psychosis. The British Journal of Psychiatry, 192(1), 67–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Ambady N, & Rosenthal R (1992). Thin slices of expressive behavior as predictors of interpersonal consequences: A meta-analysis. Psychological bulletin, 111(2), 256. [Google Scholar]
  5. Amminger GP, Schäfer MR, Papageorgiou K, Klier CM, Schlögelhofer M, Mossaheb N, … McGorry PD (2011). Emotion Recognition in Individuals at Clinical High-Risk for Schizophrenia. Schizophrenia Bulletin, 38(5), 1030–1039. doi: 10.1093/schbul/sbr015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Andreasen NC (1982). Negative symptoms in schizophrenia. Definition and reliability. Arch Gen Psychiatry, 39(7), 784–788. doi: 10.1001/archpsyc.1982.04290070020005 [DOI] [PubMed] [Google Scholar]
  7. Barger PB, & Grandey AA (2006). Service with a Smile and Encounter Satisfaction: Emotional Contagion and Appraisal Mechanisms. Academy of Management Journal, 49(6), 1229–1238. doi: 10.5465/AMJ.2006.23478695 [DOI] [Google Scholar]
  8. Barlett C, & Coyne SM (2014). A meta-analysis of sex differences in cyber-bullying behavior: The moderating role of age. Aggressive behavior, 40(5), 474–488. [DOI] [PubMed] [Google Scholar]
  9. Beck AT, Steer RA, & Brown GK (1996). Beck Depression Inventory. San Antonio, TX: Psychological Corporation. [Google Scholar]
  10. Burns EE, Jackson JL, & Harding HG (2010). Child maltreatment, emotion regulation, and posttraumatic stress: The impact of emotional abuse. Journal of Aggression, Maltreatment & Trauma, 19(8), 801–819. [Google Scholar]
  11. Cannon TD, Cadenhead K, Cornblatt B, Woods SW, Addington J, Walker E, … Heinssen R (2008). Prediction of psychosis in youth at high clinical risk: a multisite longitudinal study in North America. Arch Gen Psychiatry, 65(1), 28–37. doi: 10.1001/archgenpsychiatry.2007.3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Carvalho Fernando S, Beblo T, Schlosser N, Terfehr K, Otte C, Löwe B, … Wingenfeld K (2014). The impact of self-reported childhood trauma on emotion regulation in borderline personality disorder and major depression. Journal of Trauma & Dissociation, 15(4), 384–401. [DOI] [PubMed] [Google Scholar]
  13. Catone G, Marwaha S, Kuipers E, Lennox B, Freeman D, Bebbington P, & Broome M (2015). Bullying victimisation and risk of psychotic phenomena: analyses of British national survey data. Lancet Psychiatry, 2(7), 618–624. doi: 10.1016/s2215-0366(15)00055-3 [DOI] [PubMed] [Google Scholar]
  14. Cicchetti D, & Rogosch FA (2009). Adaptive coping under conditions of extreme stress: Multilevel influences on the determinants of resilience in maltreated children. New directions for child and adolescent development, 2009(124), 47–59. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Cohn JF, Ambadar Z, & Ekman P (2007). Observer-based measurement of facial expression with the Facial Action Coding System. The handbook of emotion elicitation and assessment, 1(3), 203–221. [Google Scholar]
  16. Constantino JN, Davis SA, Todd RD, Schindler MK, Gross MM, Brophy SL, … Reich W (2003). Validation of a brief quantitative measure of autistic traits: comparison of the social responsiveness scale with the autism diagnostic interview-revised. Journal of autism and developmental disorders, 33(4), 427–433. [DOI] [PubMed] [Google Scholar]
  17. Corcoran C, Walker E, Huot R, Mittal V, Tessner K, Kestler L, & Malaspina D (2003). The stress cascade and schizophrenia: etiology and onset. Schizophr Bull, 29(4), 671–692. doi: 10.1093/oxfordjournals.schbul.a007038 [DOI] [PubMed] [Google Scholar]
  18. Cornell D, Gregory A, Huang F, & Fan X (2013). Perceived prevalence of teasing and bullying predicts high school dropout rates. Journal of educational psychology, 105(1), 138. [Google Scholar]
  19. Davidson RJ, Ekman P, Saron CD, Senulis JA, & Friesen WV (1990). Approach-withdrawal and cerebral asymmetry: Emotional expression and brain physiology: I. Journal of Personality and Social Psychology, 58(2), 330–341. doi: 10.1037/0022-3514.58.2.330 [DOI] [PubMed] [Google Scholar]
  20. Davies H, Wolz I, Leppanen J, Fernandez-Aranda F, Schmidt U, & Tchanturia K (2016). Facial expression to emotional stimuli in non-psychotic disorders: A systematic review and meta-analysis. Neuroscience & Biobehavioral Reviews, 64, 252–271. [DOI] [PubMed] [Google Scholar]
  21. Ekman P, Davidson RJ, & Friesen WV (1990). The Duchenne smile: Emotional expression and brain physiology: II. Journal of Personality and Social Psychology, 58(2), 342–353. doi: 10.1037/0022-3514.58.2.342 [DOI] [PubMed] [Google Scholar]
  22. Ekman P, & Friesen W (1978). Manual of the facial action coding system (FACS). Trans. ed. Vol. Consulting Psychologists Press, Palo Alto. [Google Scholar]
  23. Ekman P, & Friesen W (1982). Felt, false, and miserable smiles. Journal of Nonverbal Behavior, 6, 238–252. [Google Scholar]
  24. Ekman P, Hager JC, & Friesen WV (1981). The symmetry of emotional and deliberate facial actions. Psychophysiology, 18(2), 101–106. doi: 10.1111/j.1469-8986.1981.tb02919.x [DOI] [PubMed] [Google Scholar]
  25. First MB, Gibbon M, Spitzer RL, Benjamin LS, & Williams JB (1997). Structured clinical interview for DSM-IV® axis ii personality disorders SCID-II: American Psychiatric Pub. [Google Scholar]
  26. Frank MG, Ekman P, & Friesen WV (1993). Behavioral markers and recognizability of the smile of enjoyment. Journal of Personality and Social Psychology, 64(1), 83–93. doi: 10.1037/0022-3514.64.1.83 [DOI] [PubMed] [Google Scholar]
  27. Fuhrmann D, Knoll LJ, & Blakemore S-J (2015). Adolescence as a sensitive period of brain development. Trends in cognitive sciences, 19(10), 558–566. [DOI] [PubMed] [Google Scholar]
  28. Fusar-Poli P, Bonoldi I, Yung AR, Borgwardt S, Kempton MJ, Valmaggia L, … McGuire P (2012). Predicting psychosis: meta-analysis of transition outcomes in individuals at high clinical risk. Arch Gen Psychiatry, 69(3), 220–229. doi: 10.1001/archgenpsychiatry.2011.1472 [DOI] [PubMed] [Google Scholar]
  29. Fusar-Poli P, Borgwardt S, Bechdolf A, Addington J, Riecher-Rössler A, Schultze-Lutter F, … Yung A (2013). The psychosis high-risk state: a comprehensive state-of-the-art review. JAMA Psychiatry, 70(1), 107–120. doi: 10.1001/jamapsychiatry.2013.269 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Fusar-Poli P, Cappucciati M, Borgwardt S, Woods SW, Addington J, Nelson B, … Riecher-Rössler A (2016). Heterogeneity of psychosis risk within individuals at clinical high risk: a meta-analytical stratification. JAMA Psychiatry, 73(2), 113–120. [DOI] [PubMed] [Google Scholar]
  31. Fusar-Poli P, Nelson B, Valmaggia L, Yung AR, & McGuire PK (2014). Comorbid depressive and anxiety disorders in 509 individuals with an at-risk mental state: impact on psychopathology and transition to psychosis. Schizophrenia Bulletin, 40(1), 120–131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Gray HM, Ishii K, & Ambady N (2011). Misery loves company: When sadness increases the desire for social connectedness. Personality and Social Psychology Bulletin, 37(11), 1438–1448. doi: 10.1177/0146167211420167 [DOI] [PubMed] [Google Scholar]
  33. Gupta T, Cowan HR, Strauss GP, Walker EF, & Mittal VA (2021). Deconstructing negative symptoms in individuals at clinical high-risk for psychosis: evidence for volitional and diminished emotionality subgroups that predict clinical presentation and functional outcome. Schizophrenia Bulletin, 47(1), 54–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Gupta T, Haase CM, Strauss GP, Cohen AS, & Mittal VA (2019). Alterations in facial expressivity in youth at clinical high-risk for psychosis. Journal of Abnormal Psychology, 128(4), 341–351. doi: 10.1037/abn0000413 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Gupta T, Haase CM, Strauss GP, Cohen AS, Ricard JR, & Mittal VA (2020). Alterations in facial expressions of emotion: Determining the promise of ultrathin slicing approaches and comparing human and automated coding methods in psychosis risk [Press release] [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Harker L, & Keltner D (2001). Expressions of positive emotion in women’s college yearbook pictures and their relationship to personality and life outcomes across adulthood. Journal of Personality and Social Psychology, 80(1), 112–124. doi: 10.1037/0022-3514.80.1.112 [DOI] [PubMed] [Google Scholar]
  37. James W (1884). What is an emotion? Mind, os-IX(34), 188–205. doi: 10.1093/mind/os-IX.34.188 [DOI] [Google Scholar]
  38. Johnson SL, Haase CM, Beermann U, Sanchez AH, Tharp JA, Lwi SJ, … Nguyen NK (2017). Positive urgency and emotional reactivity: Evidence for altered responding to positive stimuli. Emotion, 17(3), 442. [DOI] [PubMed] [Google Scholar]
  39. Keltner D (2009). Born to be good: The science of a meaningful life. New York, NY, US: W W Norton & Co. [Google Scholar]
  40. Keltner D, & Bonanno GA (1997). A study of laughter and dissociation: Distinct correlates of laughter and smiling during bereavement. Journal of Personality and Social Psychology, 73(4), 687–702. doi: 10.1037/0022-3514.73.4.687 [DOI] [PubMed] [Google Scholar]
  41. Keltner D, & Gross JJ (1999). Functional accounts of emotions. Cognition and Emotion, 13(5), 467–480. doi: 10.1080/026999399379140 [DOI] [Google Scholar]
  42. Keltner D, & Kring AM (1998). Emotion, social function, and psychopathology. Review of General Psychology, 2(3), 320–342. doi: 10.1037/1089-2680.2.3.320 [DOI] [Google Scholar]
  43. Kirkpatrick B, Fenton WS, Carpenter WT Jr., & Marder SR (2006). The NIMH-MATRICS consensus statement on negative symptoms. Schizophr Bull, 32(2), 214–219. doi: 10.1093/schbul/sbj053 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Kohler CG, Richard JA, Brensinger CM, Borgmann-Winter KE, Conroy CG, Moberg PJ, … Calkins ME (2014). Facial emotion perception differs in young persons at genetic and clinical high-risk for psychosis. Psychiatry research, 216(2), 206–212. [DOI] [PubMed] [Google Scholar]
  45. Konrad K, Firk C, & Uhlhaas PJ (2013). Brain development during adolescence: neuroscientific insights into this developmental period. Dtsch Arztebl Int, 110(25), 425–431. doi: 10.3238/arztebl.2013.0425 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Krause R, Steimer E, Sänger-Alt C, & Wagner G (1989). Facial expression of schizophrenic patients and their interaction partners. Psychiatry, 52(1), 1–12. doi: 10.1080/00332747.1989.11024424 [DOI] [PubMed] [Google Scholar]
  47. Kring AM, & Caponigro JM (2010). Emotion in Schizophrenia: Where Feeling Meets Thinking. Curr Dir Psychol Sci, 19(4), 255–259. doi: 10.1177/0963721410377599 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Kring AM, & Elis O (2013). Emotion deficits in people with schizophrenia. Annu Rev Clin Psychol, 9, 409–433. doi: 10.1146/annurev-clinpsy-050212-185538 [DOI] [PubMed] [Google Scholar]
  49. Larson T, Lundström S, Nilsson T, Selinus EN, Råstam M, Lichtenstein P, … Kerekes N (2013). Predictive properties of the A-TAC inventory when screening for childhood-onset neurodevelopmental problems in a population-based sample. BMC psychiatry, 13(1), 233. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Levenson RW (1999). The intrapersonal functions of emotion. Cognition and Emotion, 13(5), 481–504. doi: 10.1080/026999399379159 [DOI] [Google Scholar]
  51. Lwi SJ, Casey JJ, Verstaen A, Connelly DE, Merrilees J, & Levenson RW (2019). Genuine smiles by patients during marital interactions are associated with better caregiver mental health. The Journals of Gerontology: Series B, 74(6), 975–987. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Mayo D, Corey S, Kelly LH, Yohannes S, Youngquist AL, Stuart BK, … Loewy RL (2017). The role of trauma and stressful life events among individuals at clinical high risk for psychosis: a review. Frontiers in psychiatry, 8, 55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. McGlashan T, Miller T, Woods S, Rosen J, Hoffman R, & Davidson L (2001). Structured interview for prodromal syndromes. New Haven, CT: PRIME Research Clinic, Yale School of Medicine. [Google Scholar]
  54. Miller TJ, McGlashan TH, Rosen JL, Cadenhead K, Cannon T, Ventura J, … Woods SW (2003). Prodromal assessment with the structured interview for prodromal syndromes and the scale of prodromal symptoms: predictive validity, interrater reliability, and training to reliability. Schizophr Bull, 29(4), 703–715. doi: 10.1093/oxfordjournals.schbul.a007040 [DOI] [PubMed] [Google Scholar]
  55. Noah T, Schul Y, & Mayo R (2018). When both the original study and its failed replication are correct: Feeling observed eliminates the facial-feedback effect. Journal of Personality and Social Psychology, 114(5), 657–664. doi: 10.1037/pspa0000121 [DOI] [PubMed] [Google Scholar]
  56. Papa A, & Bonanno GA (2008). Smiling in the face of adversity: the interpersonal and intrapersonal functions of smiling. Emotion, 8(1), 1–12. doi: 10.1037/1528-3542.8.1.1 [DOI] [PubMed] [Google Scholar]
  57. Pechtel P, & Pizzagalli DA (2011). Effects of early life stress on cognitive and affective function: an integrated review of human literature. Psychopharmacology (Berl), 214(1), 55–70. doi: 10.1007/s00213-010-2009-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Rigby K, & Slee PT (1993). Dimensions of interpersonal relation among Australian children and implications for psychological well-being. The Journal of social psychology, 133(1), 33–42. [DOI] [PubMed] [Google Scholar]
  59. Shore DM, & Heerey EA (2011). The value of genuine and polite smiles. Emotion, 11(1), 169. [DOI] [PubMed] [Google Scholar]
  60. Slee PT, & Rigby K (1993). Australian school children’s self appraisal of interpersonal relations: The bullying experience. Child psychiatry and human development, 23(4), 273–282. [DOI] [PubMed] [Google Scholar]
  61. Sobin C, & Sackeim HA (1997). Psychomotor symptoms of depression. American Journal of Psychiatry, 154(1), 4–17. [DOI] [PubMed] [Google Scholar]
  62. Soussignan R (2002). Duchenne smile, emotional experience, and autonomic reactivity: A test of the facial feedback hypothesis. Emotion, 2(1), 52–74. doi: 10.1037/1528-3542.2.1.52 [DOI] [PubMed] [Google Scholar]
  63. Strauss GP, Pelletier-Baldelli A, Visser KF, Walker EF, & Mittal VA (2020). A review of negative symptom assessment strategies in youth at clinical high-risk for psychosis. Schizophrenia research. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Tognin S, Catalan A, Modinos G, Kempton MJ, Bilbao A, Nelson B, … Valmaggia LR (2020). Emotion Recognition and Adverse Childhood Experiences in Individuals at Clinical High Risk of Psychosis. Schizophr Bull, 46(4), 823–833. doi: 10.1093/schbul/sbz128 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Turner MG, Exum ML, Brame R, & Holt TJ (2013). Bullying victimization and adolescent mental health: General and typological effects across sex. Journal of Criminal Justice, 41(1), 53–59. [Google Scholar]
  66. Valmaggia LR, Day FL, Kroll J, Laing J, Byrne M, Fusar-Poli P, & McGuire P (2015). Bullying victimisation and paranoid ideation in people at ultra high risk for psychosis. Schizophr Res, 168(1–2), 68–73. doi: 10.1016/j.schres.2015.08.029 [DOI] [PubMed] [Google Scholar]
  67. Vargas T, Damme KSF, & Mittal VA (2019). Bullying victimization in typically developing and clinical high risk (CHR) adolescents: A multimodal imaging study. Schizophrenia research, 213, 40–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Vargas T, Zou DS, Conley RE, & Mittal VA (2019). Assessing Developmental Environmental Risk Factor Exposure in Clinical High Risk for Psychosis Individuals: Preliminary Results Using the Individual and Structural Exposure to Stress in Psychosis-Risk States Scale. Journal of clinical medicine, 8(7), 994. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Yee CI, Gupta T, Mittal VA, & Haase CM (2020). Coping with family stress in individuals at clinical high-risk for psychosis. Schizophrenia Research, 216, 222–228. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Yildirim MH, Yildirim EA, Kaser M, Guduk M, Fistikci N, Cinar O, & Yuksel S (2014). The r+elationship between adulthood traumatic experiences and psychotic symptoms in female patients with schizophrenia. Compr Psychiatry, 55(8), 1847–1854. doi: 10.1016/j.comppsych.2014.08.049 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon request.

RESOURCES