Emotional intelligence in schizophrenia
Introduction
Disturbances in social cognition are increasingly recognized as common, clinically important features of schizophrenia (Penn et al., 2006). Social cognition encompasses a wide range of processes that are required for adaptive functioning, including perceiving, interpreting, and responding to the behaviors, emotions, and intentions of others (Brothers, 1990, Fiske and Taylor, 1991, Adolphs, 2001). In basic behavioral science research, the domain of social cognition encompasses a broad array of social learning, perceptual, and cognitive phenomena, such as vicarious learning, perceived self-efficacy, locus of control, self-regulation (Mischel, 1973), and human agency (Bandura, 2006). Research on social cognition in schizophrenia has centered on five systems: emotion processing, social perception, social knowledge, theory of mind, and attributional style (Green et al., 2005). The clinical importance of disturbances in these areas is demonstrated by their significant associations with various aspects of poor functional outcome, such as success in psychosocial rehabilitation programs and functioning in the areas of social relationships, independent living, and work/school endeavors (Couture et al., 2006). Furthermore, there is emerging evidence that social cognitive deficits mediate the well-established link between basic (non-social) cognition and psychosocial functioning in schizophrenia (Addington et al., 2006, Brekke et al., 2005, Sergi et al., 2006, Vauth et al., 2004). Thus, a deeper understanding of the scope and clinical correlates of social cognitive deficits in schizophrenia can help identify rational targets for new treatments to improve emotion processing and functional outcome.
The most extensively studied aspect of social cognition in schizophrenia is emotion processing, which refers broadly to perceiving, understanding, and managing emotion in oneself and others (e.g., Green et al., in press). Schizophrenia research has focused almost exclusively on emotion perception (the ability to detect emotion information from others' facial expressions), vocal inflections, or a combination of these cues. This is most commonly assessed by asking research participants to identify prototypical emotions expressed in static pictures of faces or recordings of voices (e.g., happy, sad, angry, afraid, surprised, disgusted, ashamed). Schizophrenia patients have repeatedly shown poorer performance than healthy controls on these types of tasks (for a review see Edwards et al., 2002). Among patients, performance on these types of tasks has sometimes, but not consistently, shown significant relationships to certain clinical symptoms (Edwards et al., 2002, Bozikas et al., 2004). However, impairment on these tasks is consistently associated with poor community functioning, particularly in the areas of independent living and work functioning (Kee et al., 2003, Couture et al., 2006).
Although there is general agreement that disturbances in emotion processing are an important element of the social cognitive deficits of schizophrenia, research in this area is limited by two related issues, one conceptual and one methodological. First, although the concept of emotion processing is quite broad, schizophrenia research has narrowly focused on emotion perception. This is primarily attributable to the fact that until recently basic social cognitive and affective scientists have not developed or tested theoretical models of the key components of emotion processing. Second, schizophrenia researchers have often used idiosyncratic emotion processing measures that have substantial psychometric limitations (e.g., ceiling effects, low reliability) and questionable ecological validity. A theoretically-based, psychometrically sound measure that assesses the broader construct of emotion processing would greatly facilitate research in this area.
In recent years, social cognitive and affective scientists have made considerable advances in defining key emotion processing components and developing instruments to assess them. One particularly influential framework is emotional intelligence (EI) theory developed by Mayer et al. (Mayer et al., 2001, Salovey and Grewal, 2005, Salovey and Mayer, 1990). The EI model comprises four interrelated emotion processing abilities or skill “branches”: (a) Perceiving emotions: the ability to perceive accurately, appraise, and express emotion; (b) Using Emotions: the ability to access and/or generate feelings when they facilitate thought; (c) Understanding Emotions: the ability to understand emotion language and knowledge about emotions; (d) Managing Emotions: the ability to regulate emotions to promote emotional and intellectual growth in oneself or other people (Mayer and Salovey, 1997). Branches 1 and 3 involve reasoning about emotions, whereas branches 2 and 4 involve Using Emotions adaptively.
To evaluate this model, Mayer et al. developed a multidimensional, task-based, ability measure called the Mayer–Salovey–Caruso Emotional Intelligence Test 2.0 (MSCEIT; Mayer et al., 2002). The MSCEIT and its predecessors are based on the idea that emotional intelligence involves problem solving with and about emotions. The test yields total and branch scores, based on either consensus or expert scoring, and has a factor structure consistent with the four-branch model (Brackett and Mayer, 2001). The reliability and validity of the MSCEIT have been extensively documented in non-clinical populations (Mayer et al., 2003). For example, MSCEIT data collected from 2112 adults indicated that this test has very good internal consistency, with an alpha coefficient of .91 for the total scale, and short-term (two week) test–retest reliability (r = 0.86; Brackett and Mayer, 2001). It assesses abilities that are separable from traditional measures of general intellectual ability, personality traits, and self-report measures of emotional competence (e.g., Brackett and Mayer, 2003, Lopes et al., 2003). In addition, higher MSCEIT scores are associated with higher levels of adaptive functioning across a variety of domains, including relationships with parents, friends, and romantic partners (Lopes et al., 2004, Lopes et al., 2005), interpersonal effectiveness (Rode et al., 2007), success in the workplace (Lopes et al., 2006), academic success (Brackett et al., 2004), and various health-related outcomes (see Mayer et al., 2008 for a thorough summary). Importantly, the MSCEIT demonstrates good incremental validity, showing relationships with various indices of adaptive functioning above and beyond the effects of general IQ or personality measures (e.g., Brackett and Mayer, 2003, Mayer et al., 2003).
Although promising in its potential, we know almost nothing about the utility of the MSCEIT for assessing emotion processing in individuals with severe mental illnesses. One study by Eack et al. (2007) utilized the MSCEIT to examine the efficacy of Cognitive Enhancement Therapy on social cognition in schizophrenia. However, that study did not include healthy comparisons subjects or examine whether MSCEIT scores were related to clinically relevant features of schizophrenia. The current study was designed to address three research questions:
- 1.
Do community-dwelling schizophrenia outpatients differ from non-psychiatric controls in their level of performance across the broad scope of emotion processes as assessed using the MSCEIT?
- 2.
Are the MSCEIT scores associated with clinical symptoms?
- 3.
Are the MSCEIT scores associated with community functioning in patients?
Based on the existing literature on emotion perception, we expected patients to perform worse than controls on the Identifying Emotions branch of the MSCEIT. The emotional processes that are measured by the other MSCEIT branches have not previously been examined in schizophrenia. Thus, we viewed these examinations as exploratory.
Section snippets
Participants
Fifty chronic schizophrenia outpatients and 39 non-patient controls were part of a project within the UCLA Center for Neurocognition and Emotion in Schizophrenia (K. H. Nuechterlein, Center P.I. and M. F. Green, Project P.I.). All patients met criteria for schizophrenia disorder based on the Structured Clinical Interview for DSM-IV (SCID; First et al., 1996a).
Non-patient controls were screened with the SCID-I, relevant sections of the SCID-II (First et al., 1996b), and the positive symptom
Participant characteristics
Demographic data for schizophrenia patients and control participants are summarized in Table 1. The groups did not significantly differ in age, sex, education level, parental education, ethnicity, and marital status. The schizophrenia outpatients in this study exhibited mild levels of symptoms on the SANS total score (M = 0.95, SD = 0.79) and SAPS total score (M = 1.79, SD = 1.05). The patients' average length of illness was 10.20 years (SD = 5.30).
Test of reliability
The internal consistency for each of the MSCEIT branches
Discussion
In the present study, we tested a broad range of emotion processing abilities in individuals with schizophrenia and non-psychiatric controls using a theory-based multidimensional assessment of emotional intelligence, the Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT; Mayer et al., 2002). The MSCEIT demonstrated strong internal consistency in the schizophrenia sample and adequate consistency in a sample of control subjects, suggesting that this measure is suitable for use in
Role of funding source
Funding for this project was supported by the National Institute of Mental Health Center Grant P50 MH 66586 (K. Nuechterlein, P.I.). The NIMH had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.
Contributors
All authors contributed to and have approved the final manuscript.
Conflict of interest
All other authors declare that they have no conflicts of interest.
Acknowledgements
Funding for this project was supported by the National Institute of Mental Health Center Grant P50 MH 66586 (K. Nuechterlein, P.I.). The authors wish to thank Mike DeGroot, Robin Kite, Jeff Nishii, and Samantha Swain for their assistance in data collection and data management.
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