Neuro-cognition and social cognition elements of social functioning and social quality of life

Neuro-cognition and social cognition elements of social functioning and social quality of life

Author’s Accepted Manuscript Neuro-cognition and social cognition elements of social functioning and social quality of life Ilanit Hasson-Ohayon, Mich...

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Author’s Accepted Manuscript Neuro-cognition and social cognition elements of social functioning and social quality of life Ilanit Hasson-Ohayon, Michal Mashiach-Eizenberg, Nitzan Arnon-Ribenfeld, Shlomo Kravetz, David Roe www.elsevier.com/locate/psychres

PII: DOI: Reference:

S0165-1781(17)30031-8 http://dx.doi.org/10.1016/j.psychres.2017.09.004 PSY10814

To appear in: Psychiatry Research Received date: 7 January 2017 Revised date: 13 July 2017 Accepted date: 5 September 2017 Cite this article as: Ilanit Hasson-Ohayon, Michal Mashiach-Eizenberg, Nitzan Arnon-Ribenfeld, Shlomo Kravetz and David Roe, Neuro-cognition and social cognition elements of social functioning and social quality of life, Psychiatry Research, http://dx.doi.org/10.1016/j.psychres.2017.09.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Neuro-cognition and social cognition elements of social functioning and social quality of life Ilanit Hasson-Ohayona*, Michal Mashiach-Eizenbergb, Nitzan Arnon-Ribenfelda, Shlomo Kravetza, David Roec3 a

Department of Psychology, Bar Ilan University, Ramat Gan, Israel

b

Department of Health System Management, Max Stern Academic College of Emek

Yezreel, Israel c

Department of Community Mental Health, Faculty of Social Welfare and Health

Sciences University of Haifa, Haifa, Israel *Corresponding author: Ilanit Hasson-Ohayon, Department of Psychology, Bar-Ilan University, Ramat-Gan, Israel, [email protected] Abstract Previous studies have shown that deficits in social cognition mediate the association between neuro-cognition and functional outcome. Based on these findings, the current study presents an examination of the mediating role of social cognition and includes two different outcomes: social functioning assessed by objective observer and social quality of life assessed by subjective self-report. Instruments measuring different aspects of social cognition, cognitive ability, social functioning and social quality of life were administered to 131 participants who had a diagnosis of a serious mental illness. Results showed that emotion recognition and attributional bias were significant mediators such that cognitive assessment was positively related to both, which in turn, were negatively related to SQoL. While one interpretation of the data suggests that deficits in emotion recognition may serve as a possible defense mechanism, future studies should re-assess this idea. Key words: serious mental illness; social-cognition; neuro-cognition; social functioning; social quality of life

1. Introduction 1

Research has shown that persons with serious mental illness (SMI) experience a decreased social quality of life (SQoL) and social functioning compared to people without SMI (e.g. Ulas et al., 2008). This lower SQoL and functioning are attributed to deficits in social cognition that includes attribution errors, theory of mind (TOM), and emotion recognition abilities (Brune et al., 2011; Penn et al., 2008). These deficits impede their ability to accurately identify the emotions of others, leading them to make faulty attributions regarding other people's behaviors as well as faulty inferences about others' thinking and intentions (Augoustinos et al., 2006; Penn et al., 2008). In addition, deficits in their neuro-cognitive abilities to plan and perform goalbased activities (e.g. verbal and visual memory as well as executive functions) are also thought to account for the low SQoL and social functioning (e.g. Schmidt et al., 2011; Sergi et al., 2007; Green et al, 2000). Empirical studies as well as research in neuro-imaging suggests that neuro-cognition and social cognition are related yet distinct in their contribution to outcome (Allen et al., 2007; Brunet-Gouet and Decety, 2006; Pinkham et al., 2008). In addition, social cognition has been found to mediate the association between neuro-cognition and functional outcomes (Bell et al., 2009; and reviews of Couture et al., 2006 and Schmidt et al., 2011). According to this mediation, cognitive ability increases social cognition which in turn increases functional outcomes (Schmidt et al., 2011). Notably, the abovementioned studies measured general functional outcomes such as GAF (e.g. Schmidt et al., 2011) or more specific ones such as vocational performance (e.g. Bell et al., 2009), which reflects relatively objective measures of functioning. To gain a more complete understanding of the complex patterns between neurocognition, social cognition and outcome, however, it is necessary to study also subjective outcomes which are considered distinct yet related and complementary to the often assessed objective ones (Silverstein and Bellack, 2008; Roe et al., 2011). Thus, while social functioning refers to the social skills one has in order to perform a variety of social roles (Priebe, 2007), SQoL refers to the level of satisfaction one has with regard to his or her social network and activity and is considered an important domain of the overall quality of life construct (Connell et al., 2014). Thus, SQoL presents a subjective appraisal of ones' social activity and status based on positive aspects such as feeling belongings, fitting in with society and negative aspects such as being in a relationship where constantly criticized and stigmatization (Connell et al, 2014). Studies on SQoL among persons with SMI revealed lower SQoL than people 2

without SMI and that this lower SQoL is related to aspects of metacognition and social cognition (Hasson-Ohayon et al., 2014; Ofir-Eyal et al., 2014). In addition to the significant illness burden (Nordstroem et al., 2017), it was suggested that deficits in understanding oneself and the other in an interpersonal context results in lower satisfaction with one's social life (Hasson-Ohayon et al., 2011; Ofir-Eyal et al., 2016). Of note, previous studies have rarely compared subjective and objective measures of social life domains, despite possibly serving complementary aspects. While past research supports the associations between specific aspects of social cognition (mostly emotion recognition and theory of mind; e.g. Bell et al., 2008) and neuro-cognition, the purpose of the current study was to test whether social cognition abilities – i.e., emotion recognition, theory of mind, and attributional bias – mediate the association between neuro-cognition and social functioning, and between neurocognition and SQoL. We hypothesized that social cognition abilities would mediate the associations between neuro-cognition and two outcomes: social functioning and SQoL. Method 2.1 Study design and procedure The current study was part of a larger intervention study that assessed the effectiveness of Social Cognition and Interaction Training (SCIT) versus therapeutic alliance-focused therapy in psychiatric community settings in Israel (Clinicaltrail.gov ID NCT02380885). The data presented in this paper were collected before the intervention took place, between 2014 and 2016. Approval for the study was obtained from the ethics committee of the Department of Psychology at Bar-Ilan University, as well as from two psychiatric hospital committees. After receiving a detailed explanation of the study, all research participants provided their written informed consent. Data were collected by an experienced mental health practitioner who was trained to administer the study measures. 2.2 Participants The current study included 131 participants whose ages ranged from 20-69 years (M=39.3, SD=10.7). These individuals had a case-record diagnosis of SMI and a psychiatric disability of at least 40% (determined by a medical committee, made up in part by a psychiatrist), and met the criteria for National Insurance Institute of Israel (NII) disability benefits (a roughly comparable process to attaining the designation of SMI in the U.S.). However, previous research showed that 86% of 16,000 people in 3

Israel who had a psychiatric disability of at least 40% had a diagnosis of a psychoticrelated disorder (Shtruch et al., 2009); it is therefore likely that most of the participants in our study sample had a psychotic disorder. In addition, the majority were men (06%), 65% had never been married, and most of them had completed at least a high school level of education (06%). Their mean duration of illness was 14.2 years (SD=9.1), and their mean number of previous hospitalizations was 2.1 (SD=2.8). Inclusion criteria were participants' fluency in Hebrew and their provision of informed consent. The inclusion of a sample of persons with a serious mental illness but different diagnoses is in accord with SAMHSA definition of SMI as presenting a category of disorders with low GAF score, and based on the idea that diverse diagnosis share similar social deficits (Iyer et al., 2005; Kessler et al., 2003) and therefore interventions aim at improving these deficits are provided across different diagnoses groups (Chan et al., 2010; Hasson-Ohayon et al., 2014; Lahera et al., 2013; Penn et al., 2005). Of note, SMI is regarded as including schizophrenia, bipolar disorder, severe forms of depression, and obsessive– compulsive disorder with predomination of schizophrenia spectrum disorders (Iyer et al, 2005).

2.3 Measurements 2.3.1 The Facial Emotion Identification Task (FEIT; Kerr and Neale, 1993) is a widely used measure of emotion perception and is indexed by the number of correctly identified emotions out of a total of nineteen pictured faces. Emotions include happiness, anger, sadness, fear, surprise, and shame. The FEIT has demonstrated good reliability in studies on schizophrenia (Kerr and Neale, 1993; Mueser et al., 1996; Penn et al., 2000). In the current study we examined reliability by Test-Retest reliability among a sub-sample of twenty-five of the respondents participated in the reliability test. The correlation between the two measurements was 0.76. 2.3.2 The Hebrew-language version (Shamay-Tsoory et al., 2005) of the Faux Pas Recognition Test (Stone et al., 1998) was used in this study to assess theory of mind (TOM). This measure consists of 10 stories in which a faux pas has occurred and 10 stories in which no faux pas has occurred (control stories). A faux pas is considered to have occurred when the speaker says something without taking into account that the listener might not want to hear this story or might be hurt by it. After each story, the participants are asked seven questions regarding their recognition of the occurrence of a faux pas (e.g., their understanding of the mental state of the speaker and listener, or 4

their understanding of the emotional state of the listener). This task assesses emotional and cognitive attributions, and the score for each story ranges between 0 and 7 (task range: 0-70). Cronbach’s alpha in our previous study was high (0.91 in Hasson-Ohayon et al, 2014), and in the current study it was found to be satisfactory at 0.71. 2.3.3 The Hebrew-language version (Hasson-Ohayon et al., 2014), of the Ambiguous Intentions Hostility Questionnaire (AIHQ; Combs et al., 2007), was administered to participants. The AIHQ is a measure of attributional style for situations with negative outcomes and ambiguous causality. Participants are asked to read each of five vignettes, to imagine that the scenario is happening to her or him (e.g., “You walk past a group of teenagers at a mall and you hear them start to laugh”), and to write down the reason why the other person (or persons) acted the way he/she/they did toward the participant. Two independent raters subsequently code this written response on a 5-point Likert scale for the purpose of computing a “hostility bias.” The participant then rates the degree to which he or she thinks the other person (or persons) performed the action on purpose, how angry this action would make the participant feel, and how much the participant would blame the other person (or persons). Finally, the participant is asked to write down how she or he would respond to the situation, a response which is later coded by two independent raters in order to compute an “aggression index.” For this study, we chose one variable to present attributional biases (to avoid too many analyses) and therefore we focused on the primary outcome of the hostility bias for ambiguous situations (ranged 5-25, with a higher number indicating a greater hostility bias) as it reflects a bias in perception of the other (and not a possible behavioral response). The AIHQ has been shown to have very good levels of reliability and inter-rater agreement (ICC = 0.80+) and to be correlated with other measures of paranoia and hostility (Combs et al., 2007; 2009). Our previous study showed high inter-rater reliability (ICC = 0.85) of the Hebrew version used with SMI (Hasson-Ohayon et al, 2014). Cronbach's alpha in the current study was 0.71. 2.3.4 The Hebrew-language version (Lifshitz et al., 2012) of the Montreal Cognitive Assessment (MoCA, Nasreddine et al., 2005) was used as a screening test for global cognitive function. This tool assesses memory, visuospatial ability, executive function, attention, concentration, working memory, and orientation. Administering this measure takes approximately 15 minutes and has a maximum score of 31, with 5

lower scores representing poorer performance. It has been previously used with persons who have schizophrenia and has been shown to be sensitive in detecting cognitive deficits (Wu et al., 2014). 2.3.5 The Social Skills Performance Assessment (SSPA; Patterson et al., 2001) was used to assess social functioning. In this task, the subject and the assessor engage in two 3-minute role-play conversations (“scenes”) on pre-determined topics (e.g. “Your landlord has not fixed a leak that you told him about last week, and now you are calling him on the phone to follow up”). Role-plays are tape-recorded and rated by independent coders. The SSPA has good face validity as a social skills measure, and among individuals with schizophrenia it has shown excellent inter-rater reliability, good test-retest reliability, and good convergent validity with a measure of activities of daily living (Patterson et al., 2001). Each domain is rated on a 5-point Likert-type scale with higher scores signifying greater social skills. Domains are summed to yield total scores for each scene. Permission to translate and use the scale was obtained from the developers. Cronbach's alpha in the current study was 0.96. In addition, agreement between raters on the SSPA total score was measured by the mean IntraClass Correlation (ICC), with a high and satisfactory mean score of 0.89. 2.3.6 The Wisconsin Social Quality of Life Scale (Becker et al., 1995). Participants' SQoL was assessed via the social subscale of the Wisconsin Quality of Life Client Questionnaire that was developed by Becker et al (1993) and translated into Hebrew (Kravetzet al., 2002). The comprehensive scale of the Wisconsin Quality of life scale Social includes seven domains and the social sub-scale used in the current study measure the client’s social relations and social skills via self-report. Accordingly, clients are asked to rate their experience of amount of social support and their satisfaction with social relations. Items for example: "How satisfied or dissatisfied are you with the number of friends you have?", "How satisfied or dissatisfied are you with how you get along with other people?" This scale consists of 10 self-report items which reflect social QoL. The scores range from 0 to 7 with higher scores indicating higher social QoL. Both the English-language and Hebrew-language versions of the scale have acceptable reliability and validity (Becker et al., 1993; Kravetz et al., 2002; Van Nieuwenhuizen et al., 1997). In the current study, Cronbach's alpha was 0.78. 3. Results 3.1 Descriptive statistic and correlational analysis

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Table 1 presents descriptive statistics of all study measures and correlations between them. As can be seen, mean scores of emotion recognition, SQoL, AIHQ and faux pas were similar to the ones found in a previous studies with similar population (HassonOhayon et al, 2014; 2015; note that previous studies used a short version of faux-pas so results are only apparent lower due to sum scores). The SSPA mean was lower than reported in other samples of schizophrenia (e.g. see Roberts and Penn, 2009) and the MocA mean score is higher than in other studies on schizophrenia (e.g. Fisekovic et al, 2012) possibly due to the current study's heterogeneity of sample. Opposed to hypotheses, a significant negative correlation was found between social quality of life (SQoL) and: a. the facial emotion identification task (FEIT) (r=-.32, p<.001); b. the cognitive assessment (MoCA) (r=-.37, p<.001); and a significant positive correlation was found between the cognitive assessment (MoCA) and the Ambiguous Intentions Hostility Questionnaire (AIHQ) (r=.24, p<.01). In addition and in accord with hypotheses, a significant positive correlation was found between: a. the cognitive assessment (MoCA) and the facial emotion identification task (FEIT) (r=.41, p<.001); b. the cognitive assessment (MoCA) and the Faux Pas Recognition Test (r=.40, p<.001); c. the Faux Pas Recognition Test and the facial emotion identification task (FEIT) (r=.44, p<.001); and a significant negative correlation was found between: a. social quality of life (SQoL) and the Ambiguous Intentions Hostility Questionnaire (AIHQ) (r=-.29, p<.01); b. the Social Skills Performance Assessment (SSPA) and the Ambiguous Intentions Hostility Questionnaire (AIHQ) (r=-.34, p<.001). 3.2 Multiple regression analyses To examine whether the variables of social cognition (FEIT, Faux Pas Recognition Test and AIHQ) were associated with SQoL and the Social Skills Performance Assessment (SSPA), we conducted two hierarchical multiple regression analyses. Age and cognitive assessment (MoCA) were entered into the regression at Step 1 in order to control for these variables. The variables of social cognition (FEIT, Faux Pas Recognition Test and AIHQ) were entered into the regression at Step 2. Results of this analysis are summarized in Table 2. The results of Step 1 show a negative association between cognitive assessment (MoCA) and SQoL (β=-0.42, p<0.001), and a positive association between age and the Social Skills Performance Assessment (β=0.19, p<0.05).

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In the second step, the facial emotion identification task (FEIT) was a significant predictor of SQoL (β=-0.24, p<0.01). A lower score on the FEIT was associated with a better SQoL. In addition, the Ambiguous Intentions Hostility Questionnaire (AIHQ) was a significant predictor of SQoL and the Social Skills Performance Assessment (β=-0.24, p<.01, β=-0.32, p<.001, respectively). Lower hostility was associated with a better SQoL and a better Social Skills Performance Assessment. 3.3 Mediation analyses We conducted an analysis using a multiple mediation approach (Preacher and Hayes, 2008). Doing so provides unstandardized direct effects, as well as the unique indirect effect of each mediating variable and the combined overall effect of the mediating variables. Three mediators (FEIT, Faux Pas Recognition Test and AIHQ) were entered into the model simultaneously. The multiple mediation approach utilizes a bootstrap test, for which we generated 1000 samples, to produce 95% confidence intervals which indicate a significant indirect effect if they do not include zero. 3.4 A multiple mediation model of cognitive assessment (MoCA) and social quality of life through social cognition (FEIT, Faux Pas Recognition Test and AIHQ): The multiple mediation analyses indicated a significant direct effect (B= -0.08, SE=0.03, β=-0.25, p<0.01), and total effect (B= -0.12, SE=0.03, β=-0.37, p<0.001), between cognitive assessment (MoCA) and SQoL. Table 3 contains the parameter estimates for the total and specific indirect effects on the relationship between cognitive assessment (MoCA) and SQoL as mediated by social cognition (FEIT, Faux Pas Recognition Test and AIHQ). As can be seen in Table 3, the total indirect effect of all of the mediators was significant, mediation effect = -0.04, CI = [-0.086, -0.012]. An examination of specific indirect effects indicates that, among the mediating variables, FEIT and AIHQ are mediators of the relationship between cognitive assessment (MoCA) and SQoL (mediation effect = 03, CI = [-0.059, -0.007] for FEIT and mediation effect = -02, CI = [-0.048, -0.003] for AIHQ). Thus, FEIT and AIHQ were significant mediators such that cognitive assessment (MoCA) was positively related to FEIT and AIHQ which, in turn, were negatively related to SQoL (see Table 3). 3.5 A multiple mediation model of cognitive assessment (MoCA) and the Social Skills Performance Assessment (SSPA) through social cognition (FEIT, Faux Pas Recognition Test and AIHQ):

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Results of multiple mediation analyses indicated that the total effect of cognitive assessment (MoCA) and the Social Skills Performance Assessment (SSPA) was not significant (B= -0.07, SE=0.31, β=-0.02, p>0.05). According to Hayes (2013), there is a growing consensus among quantitative methodologists that a total effect should not be a prerequisite for searching for evidence of indirect effects (Hayes, 2013; p. 169). In addition, the direct effect of cognitive assessment (MoCA) and the Social Skills Performance Assessment (SSPA) was not significant (B= -0.13, SE=0.34, β=-0.04, p>0.05). Table 3 contains the parameter estimates for the total and specific indirect effects on the relationship between cognitive assessment (MoCA) and the Social Skills Performance Assessment (SSPA) as mediated by social cognition (FEIT, Faux Pas Recognition Test and AIHQ). With zero in the confidence interval, the total indirect effect of these three variables of social cognition was not significant, mediation effect = 0.06, CI = [-0.388, 0.539]. However, Preacher and Hayes (2008) argued that specific indirect effects should still be examined even in the presence of a nonsignificant total indirect effect. Thus, we also examined the specific indirect effect of each of the mediating variables on the relationship between cognitive assessment (MoCA) and the Social Skills Performance Assessment (SSPA). Only AIHQ was a mediator of the relationship between cognitive assessment (MoCA) and SQoL, mediation effect = -28, CI = [-0.630, -0.092]. Thus, AIHQ was a significant mediator such that cognitive assessment (MoCA) was positively related to AIHQ, which, in turn, was negatively related to the Social Skills Performance Assessment (SSPA) (see Table 3). 4. Discussion Based on previous research assessing the association between neuro-cognition, social cognition and functional outcome, the current study tested a mediation model in which three aspects of social cognition (emotion recognition, theory of mind, and attributional biases) would mediate the association between neuro-cognition and two outcomes – social functioning and SQoL – among persons with SMI. Results support the mediation of hostile attribution bias between neuro-cognition and both social functioning and SQoL. Accordingly, among this population, cognitive ability seems to increase one's perception of the other as hostile, which in turn to lead to lower social functioning and SQoL. Thus, there is an indication that cognitive ability is related to the development of perception of the other as hostile, which leads to 9

reduced SQoL and social functioning. Notably, while previous studies showed that cognitive ability is beneficial for such aspects of social cognition as TOM (e.g. TOM) (Bell et al., 2009; Schmidt et al, 2011) it is not beneficial for reducing attributional bias according to current study results. The current findings portray a more complex picture as greater cognitive ability increases both attributional bias which is not a desirable outcome and emotion recognition which is a desirable outcome. This calls for further exploration of possible moderators that may "turn" the cognitive ability to produce positive results. It may be that flexibility in thinking interacts with cognitive abilities to enable one to preform shifts in attributing hostility to others. Thus, one might perceive one as hostile due to cognitive process of working memory and visual-spatial ability that are needed in order to develop an inner representation of the other. Yet, this biased representation can be re-checked via cognitive flexibility, which is indeed an important component of social cognition training (e.g. SCIT, Penn et al, 2005). The finding that increase in emotion recognition seems to reduce SQoL calls for a cautious re-interpretation of the implications of emotion recognition among persons with SMI. Although emotion recognition is considered an important positive ability and it is a well-documented deficit among persons with SMI (Feingold et al., 2016; Kohler et al., 2009), there are inconsistencies with regard to its association with subjective quality of life. Some studies have shown no association between emotion recognition and subjective outcome (e.g. Maat et al., 2012) whereas others have shown a positive association between a few dimensions of emotion recognition and quality of life (e.g. Hofer et al., 2009). The negative association found in the current study between emotion recognition and SQoL implies that a relatively accurate perception of emotions based on the other person's facial expression induces negative feelings among persons with SMI. It is possible that people with SMI are actually exposed to more facial expressions of disgust and fear due to the public stigma towards people with SMI which is well documented. If this is the case it is not surprising that the price of accurate emotion recognition is increased distress. In addition, this finding in consistent with the extensive literature that shows that cognitive abilities are positively related to insight into the illness (e.g. Alman et al., 2006), which is negatively related to self-stigma (e.g. Hasson-Ohayon et al., 2012) and therefore may posse negative implications.

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Evidently, there is extensive evidence regarding positive associations between emotion recognition and functional outcomes (Couture et al., 2006; Kee et al., 2003; Kee et al., 2004), pinpointing the adaptive functional outcome of accurate recognition of emotions. However, adaptive functioning is not equivalent to subjective SQoL. One might function according to social norms and still not feel satisfied with his or her social life. Therefore, social cognition aspects (in the current study, the aspect of emotion recognition) may be related differently to either objective or subjective outcomes. Of note, additional explanations might account for these results such as the heterogeneous sample and celling effect. To conclude, the current study provides evidence that social cognition in the form of attributional bias mediates the association between neuro-cognition and both SQoL and social functioning. Cognitive ability was found to be associated with increased attributional biases, and this relation was found to be associated with decreased subjective outcomes in the social domain. In addition, the unexpected finding that cognitive ability is associated with increased emotion recognition which subsequently decreases SQoL – a subjective outcome – calls for further research. While this interpretation suggests that deficits in emotion recognition may serve as a defense mechanism, future studies should re-assess this idea and trace potential moderators in the association between emotion recognition and subjective outcomes, with the use of neuro-cognition as an independent variable. At the clinical level, when attempts are made to increase an individual's social cognition, attention should be paid to the possible abovementioned negative effects resulting from accurate emotion recognition. For example, since improving a person's emotion recognition is an essential part of SCIT that is designed to improve his/her social functioning (Roberts et al., 2016), decreasing his/her potential social distress may require the addition of narrative or metacognitive interventions that address the subjective experience of the self (Hasson-Ohayon et al., 2016; Lysaker et al., 2014). Accordingly, adding these interventions to training that fosters specific social skills may help individuals integrate emotion recognition into a broader representation of the self and others. This integration might in turn help them cope with actual social events when they are confronted with others' negative attitudes towards them. Also, more emphasis on flexibility of attribution may result in higher benefits resulting for cognitive abilities.

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A few limitations of the current study should be noted. First, previous research has indicated that the deficits in emotion recognition among persons with schizophrenia may be associated with impairment in their recognition of specific emotions (e.g., negative emotions, Chen et al., 2012; Huang et al., 2011); accordingly, further research which would enable a differentiation between their recognition of various emotions with regard to the current research questions is needed. Second, while people with an SMI share serious impairments in functioning (Iyer et al., 2005; Kessler et al., 2003), the lack of a more diagnostically homogenous sample makes it hard to generalize from these findings to specific groups of populations. In addition, the current study is cross-sectional and therefore causality cannot be inferred. These limitations, together with previous literature on the positive association between emotion recognition and outcome, require special caution with the interpretation of the finding on negative association between emotion recognition and outcome. Third, results regarding the AIHQ should be interpreted with caution due to recent studies that commented on the limitations of the psychometric qualities of the instrument (Pinkham et al, 2015; Buck et al, 2017). Finally, the current study used a social functioning instrument that assesses functioning via roleplay coding. This mode of assessment, although responsive to possible biases of self-report scales, differs from more traditional self-report scales (Patterson et al., 2001), thus making it difficult to draw comparisons.

Acknowledgment: This study was funded by the Israel Science Foundation (grant 329/13) of authors IHO, SK and DR (authors 1,4,5).

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Table 1: Pearson correlations, Means, SDs, and possible ranges of the variables. 1

1. Social Quality of life

2

3

1

18

4

5

M (SD)

Range

4.62 (1.45)

1-7

2. FEIT

-0.32**

1

3. Faux-Pas task

-0.14

0.44**

1

4. MoCa

-0.37**

0.41**

0.40**

1

5. SSPA

0.11

0.15

0.16

-0.02

6. AIHQ - Hostility Bias

-0.29*

0.06

-0.02

0.24*

12.01 (2.94)

0-19

30.11 (7.76)

0-70

25.21 (4.39)

0-31

1

50.73 (15.40)

17-85

-0.34**

11.23 (4.05)

5-25

FEIT = Face Emotion Identification Task MoCa = Montreal Cognitive Assessment SSPA = Social Skill Performance Assessment AIHQ = Ambiguous Intentions Hostility Questionnaire * p<.01, ** p<.001

Table 2: Hierarchical Multiple Regression for Predicted Social Quality of life and Social Skill Performance Assessment (SSPA) (N=129) Social Skill Performance Assessment (SSPA)

Social Quality of life Predictor Variable Step 1: Constant Age MoCa Step 2: Constant Age MoCa FEIT Faux-Pas task AIHQ - Hostility Bias

B 8.67 -0.02 -0.14 9.74 -0.02 -0.1 -0.12 0.01 -0.08

SE 0.98 0.01 0.03 0.98 0.01 0.03 0.04 0.02 0.03

* p<.05, **p<.01, ***p<.001

19

β

R2 0.15

-0.13 -0.42*** 0.25 -0.15 -0.29** -0.24** 0.07 -0.24**

B 36.10 0.28 0.15 40.50 0.22 0.04 0.73 0.19 -1.22

SE 11.27 0.14 0.33 11.15 0.13 0.36 0.50 0.19 0.33

β

R2 0.03

0.19* 0.04 0.17 0.15 0.01 0.14 0.09 -0.32***

Table 3: Summary of parallel multiple mediator model analyses (Independent variable: cognitive assessment – MoCA) Dependen Effect of IV on M Mediati t (a) ng variable variable (DV) B SE β (M)

Total

Effect of M on DV (b) B

AIHQ Hostilit y

Total

FEIT

β

Social Quality of life

Effe ct

SE

95% CI

0.04

0.0 2

[0.08 6, 0.01 2] [0.05 9, 0.00 7] [0.01 1, 0.03 8] [0.04 8, 0.00 3]

0.2 7

0.0 5

0.41* **

0.1 1

0.0 5

-0.22*

0.03

0.0 1

0.7 1

0.1 4

0.40* **

0.0 1

0.0 2

0.06

0.01

0.0 1

0.2 2

0.0 8

0.24* *

0.0 8

0.0 3

0.22* *

0.02

0.0 1

0.06

0.2 3

0.20

0.1 3

FEIT

FauxPas task

SE

Indirect Effect (a×b)

Social Skill Performa nce Assessme nt (SSPA) 0.2 7

0.0 5

0.41* **

0.7 2 20

0.5 0

0.14

[0.38 8, 0.53 9] [0.03

FauxPas task AIHQ Hostilit y

0.7 1

0.1 4

0.40* **

0.2 0

0.1 9

0.10

0.14

0.1 5

0.2 2

0.0 8

0.24* *

1.2 7

0.3 3

0.33* **

0.28

0.1 3

5, 0.49 4] [0.09 4, 0.51 8] [0.63 0, 0.09 2]

* p<.05, ** p<.01, *** p<.001 Boldface type highlights a significant effect as determined by the 95% bias corrected confidence interval (95% CI).

Highlights    

People with serious mental illness experience significant impairments in social functioning and social quality of life. In previous literature both neuro-cognition and social cognition have been found to be related to these social outcomes. Current study showed attributional bias to mediate the association between neuro-cognition and both outcomes. Emotion recognition was found to mediate neuro-cognition and social quality of life with the unexpected finding of negative association between emotion recognition and outcome.

21