Contributions of Neurocognition and Social Cognition

PATHWAYS TO FUNCTIONAL IMPAIRMENT IN SCHIZOPHRENIA: CONTRIBUTIONS OF NEUROCOGNITION AND SOCIAL COGNITION

A dissertation submitted to Kent State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Amanda McCleery

August, 2012

Dissertation written by Amanda McCleery B.S., University of Toronto, Canada, 2004 M.A., Kent State University, USA, 2009 Ph.D., Kent State University, USA, 2012

Approved by

_Nancy M Docherty______, Chair, Doctoral Dissertation Committee

_John Gunstad______, Members, Doctoral Dissertation Committee

_Manfred van Dulmen______

_Colleen Novak______

_Vera Camden______

Accepted by

_Maria Zaragoza______, Chair, Department of Psychology

_Timothy Moerland______, Dean, College of Arts and Sciences

TABLE OF CONTENTS

LIST OF FIGURES ...... V

LIST OF TABLES ...... VI

ACKNOWLEDGEMENTS ...... VII

CHAPTER 1 INTRODUCTION ...... 1

1.1 Social Functioning Deficits in Schizophrenia ...... 2

1.2 Social Cognitive Deficits in Schizophrenia...... 3

1.3 Neurocognitive Impairment in Schizophrenia...... 15

1.4 Are Social Cognitive Deficits Fully Accounted for by Impaired Neurocognition in

Schizophrenia?...... 18

1.5 Summary and Aims of Present Study...... 24

CHAPTER 2 METHOD...... 27

2.1 Participants ...... 27

2.2 Procedure...... 29

2.3 Measures...... 29

2.4 Data Analysis ...... 36

CHAPTER 3 RESULTS...... 46

3.1 Descriptive Statistics ...... 46

3.2 Bivariate Correlations Between Study Variables ...... 50

3.3 Confirmatory Factor Analysis ...... 55

3.4 Structural Regression Analyses...... 61

CHAPTER 4 DISCUSSION...... 71

REFERENCES...... 79

APPENDIX A . SHIPLEY INSTITUTE OF LIVING SCALE ...... 100

iii

APPENDIX B . WISCONSIN CARD SORT TASK-64 ...... 101

APPENDIX C . TRAILMAKING TEST B ...... 102

APPENDIX D . CONTINUOUS PERFORMANCE TASK – IDENTICAL PAIRS ...... 105

APPENDIX E . DIGIT SPAN...... 106

APPENDIX F . TRAILMAKING TEST A ...... 107

APPENDIX G . FACIAL EMOTION IDENTIFICATION TEST ...... 110

APPENDIX H . BELL-LYSAKER EMOTION RECOGNITION TEST ...... 111

APPENDIX I . PROFILE OF NONVERBAL SENSITIVITY...... 112

APPENDIX J . HINTING TASK ...... 113

APPENDIX K . BRUNE TASK...... 114

APPENDIX L . ASSESSMENT OF INTERPERSONAL PROBLEM SOLVING SKILLS

...... 126

APPENDIX M . SOCIAL FUNCTIONING SCALE...... 140

APPENDIX N . RESULTS: MULTIPLY IMPUTED DATASETS (M=5)...... 154

LIST OF FIGURES

Figure 1 Proposed a) one-factor and b) two-factor models of neurocognition and social

cognition ...... 37

Figure 2. Proposed structural equation models...... 39

Figure 3. Two-factor confirmatory factor analysis model, standardized solution (AIPSS

subsample, n=91)...... 57

Figure 4. One-factor confirmatory factor analysis model, standardized solution (AIPSS

subsample, n=91)...... 58

Figure 5. Two-factor confirmatory factor analysis model, standardized solution (total

sample, n=120)...... 59

Figure 6. One-factor confirmatory factor analysis model, standardized solution (total

sample, n=120)...... 60

Figure 7. Unique effects model, standardized solution (AIPSS subsample, n=91)...... 63

Figure 8. Indirect effects model, standardized solution (AIPSS subsample, n=91)...... 65

Figure 9. Distribution of parameter estimates for direct (path c) and indirect (path a x b)

effects (AIPSS subsample, n=1000 converged simulations)...... 66

Figure 10. Unique effects model, standardized solution (total sample, n=120)...... 68

Figure 11. Indirect effects model, standardized solution (total sample, n=120)...... 69

Figure 12. Distribution of parameter estimates for direct (path c) and indirect (path a x b)

effects (total sample, n=881 converged simulations)...... 70

LIST OF TABLES

Table 1. Sample characteristics (n=123)...... 28

Table 2. Descriptive statistics (total sample, n=123)...... 47

Table 3. Descriptive statistics (AIPSS subsample, n=94)...... 49

Table 4. Bivariate correlations between study variables (n=123)...... 52

ACKNOWLEDGEMENTS

My deepest thanks to my mentor, Dr. Nancy Docherty – I feel incredibly fortunate to have had the privilege to train with her. Her dedication to research, mentorship, and professional stewardship is inspiring, and I am grateful for all the encouragement, guidance, support, and generosity she provided throughout my time at Kent. Thank you to Jennifer Aakre, Marielle

Divilbiss, Kristen Cimera, and the Docherty lab members for their help and support. Thanks for all the thoughtful comments and insight from my committee members: Drs. John Gunstad,

Manfred van Dulmen, Colleen Novak, Vera Camden, and Susan Roxburgh. I also would like to acknowledge the research participants who dedicated their time and effort to be part of the project, as well as the staff at Community Support Services for facilitating data collection for Dr.

Docherty’s grant, from which this dissertation is derived.

Completing a Ph.D. program is pretty arduous and I cannot express how lucky I feel to have had the love, support, and understanding of my family and friends throughout this process.

Keith Romanuik: you’re pretty awesome, bud. I couldn’t ask for a better friend or partner in life.

Big thanks to my mum and dad for encouraging me to take risks and for their unconditional support. I also want to thank my dad for always responding to my constant stream of questions when I was a kid with “well, let’s look it up” – although I found it super annoying back then, I will (begrudgingly) admit that it started me off on the right foot for a career in research!

Amanda McCleery

May 9, Kent, Ohio

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CHAPTER 1

Introduction

Schizophrenia is marked by impairment across a variety of domains including neurocognition, social cognition, and social functioning. Although robust associations between neurocognition (e.g., executive functions, attention, memory) and functional outcome have been reported in the literature (e.g., Addington & Addington, 1999; Brekke, Hoe, Long, & Green,

2007; Green, Kern, Braff, & Mintz, 2000), there is evidence that social cognition, that is, information processing of social stimuli such as emotion recognition, social cue perception, and inferring the intentions of others, contributes to social functioning above-and-beyond variance accounted for by neurocognition (e.g., Pinkham & Penn, 2006), possibly mediating the relationship between neurocognition and functional outcome (Addington, Saeedi, & Addington,

2006; Sergi, Rassovky, Nuechterlein, & Green, 2006; Vauth, Rüsch, Wirtz, & Corrigan, 2004).

However, previous investigations have been methodologically limited, for example employing small sample sizes, limited neurocognitive test batteries, or considering only a single facet of social cognitive abilities (e.g., facial emotion recognition). Moreover, relatively few studies have utilized powerful data analytic techniques such as structural equation modeling (SEM), a method that combines confirmatory factor analysis and path analysis within in a single model.

Identification of mediating variables in the relationship between neurocognition and functional outcome in schizophrenia is important, as these variables may serve as potential targets for psychosocial intervention. The purpose of the proposed study was to utilize SEM to investigate the relationships between neurocognition, social cognition, and social functioning in a large sample of clinically-stable outpatients with a diagnosis of schizophrenia or schizoaffective disorder. It was expected that neurocognition and social cognition would emerge as separate, but 1 2

closely related, constructs, and that social cognition would mediate the relationship between neurocognition and social functioning in this sample. What follows is a brief review of the literature surrounding functional, social cognitive, and neurocognitive impairment in schizophrenia.

1.1 Social Functioning Deficits in Schizophrenia

Profound social functioning impairment is a cardinal feature of schizophrenia (Englehardt

& Rosen, 1976). Indeed, this is reflected in the diagnostic criteria outlined in the Diagnostic and

Statistical Manual of Mental Disorders (DSM-IV-TR; APA, 2000). Individuals with schizophrenia demonstrate marked social functioning impairment relative to non-psychiatric (e.g.,

Blanchard, Mueser, & Bellack, 1998) and psychiatric comparison samples (e.g., Bystirnsky, et al., 2001; Schooler & Paykel, 1966). Impaired social functioning in schizophrenia can manifest in a number of ways such as high rates of unemployment (Schmid, Stassen, Gross, Huber, & Angst,

1991; Shepard, Watt, Falloon, & Smeeton, 1989), low average income (Melle, Friis, Hauff, &

Vaglum, 2000), poor educational or occupational achievement (Lenior, Dingemans, Linzen, de

Haan, & Schene, 2001), social isolation (Melle et al., 2000; Schooler & Paykel, 1966), poor adjustment to community living (Wallace, 1984), and unstable housing (Lenior et al., 2001). In a comprehensive review of functional outcome over the course of the illness, Wallace (1984) noted that schizophrenia is marked by impairment across community (i.e., instrumental role functioning, daily living skills) and interpersonal functioning (i.e., number and quality of social contacts, interpersonal skills), and these impairments may precede illness onset.

Green and colleagues (2000) cite that assessment of functional outcome in schizophrenia

typically falls into three domains: 1) skill acquisition within rehabilitation programs, 2)

laboratory-based assessment of social problem-solving or interpersonal skills, and 3) assessment of community role functioning and competency performance of daily living skills. Considering the broad scope of social functioning, for the purpose of the current study social functioning will

be narrowly defined as interpersonal skill competency and community role functioning. Common laboratory measures of interpersonal skill involve asking the participant to generate and role-play solutions to common social problems (e.g., Assessment of Interpersonal Problem Solving Skills;

AIPSS; Donohoe, Carter, Bloem, Hirsch, Laasi, & Wallace, 1990). Self-report measures of

community role functioning (e.g., Social Functioning Scale; SFS; Birchwood, Smith, Wetton, &

Copestake, 1990) are also used frequently in the literature, as are clinician-rated indices (e.g.,

Multnomah Community Ability Scale; MCAS; Barker, Barron, Bentson, McFarland, & Bigelow,

1994; Quality of Life Scale; QLS; Heinrichs, Hanlon, & Carpenter, 1984).

1.2 Social Cognitive Deficits in Schizophrenia

Studies of social cognition in schizophrenia samples generally involve assessing patients’

abilities and comparing their performance to a non-psychiatric control sample, and occasionally,

to a psychiatric control sample such as individuals with depression or bipolar affective disorder.

Research has consistently demonstrated that schizophrenia patients have difficulty with social

cognition tasks such as recognition and discrimination of facial affect and affective prosody, and

ToM abilities (Brüne & Bodenstein, 2005; Craig, Hatton, Craig, & Bentall, 2004; Herold, Tenyi,

Lenard, & Trixler, 2002; Langdon, Coltheart, Ward, & Catts, 2002; Pickup & Frith, 2001; Sarfati,

Hardy-Bayle, Brunet, & Widocher, 1999).

Facial Affect Recognition. To assess facial affect recognition, participants are typically

shown a standardized set of still photos of faces (e.g., Eckman & Friesen, 1976) where target

actors display posed facial expressions of emotion (e.g., happy, sad, angry, disgusted, surprised, fearful) or a neutral expression. Participants are then asked to select which response option from an array best describes the emotion that the target is feeling. For facial affect discrimination, participants are shown pairs of photos and are asked whether the targets are displaying the same or different emotional expression. Numerous studies of facial affect perception have been conducted with schizophrenia samples, and results consistently indicate that patients are significantly less accurate in their appraisals of facial emotional expressions than healthy control samples (e.g., Addington & Addington, 1998; Leitman, Foxe, Butler, Saperstein, Revheim, &

Javitt, 2005; Scholten, Aleman, Montagne, & Kahn, 2005) or psychiatric control patients (bipolar affective disorder, Addington & Addington, 1998; depressive disorder, Weniger, Lange, Rüther,

& Irle, 2004; but see Zuroff & Colussy, 1986), where male schizophrenia patients appear to show greater impairment than female patients (Scholten et al., 2005). Moreover, individuals with schizophrenia appear to have particular difficulty processing facial expressions associated with negative emotions such as fear and disgust (Doughtery, Bartlett, & Izard, 1974; Edwards,

Jackson, & Pattison, 2002; Mandal, Pandey, & Prasad, 1998; Muzekari & Bates, 1977; Scholten et al., 2005; Weniger et al., 2004), and non-paranoid subtype patients tend to show greater deficits than paranoid subtype patients (Lewis & Garver, 1995; Phillips, Williams, Senior, et al., 1999;

Weniger et al., 2004).

Facial affect recognition impairment is present early in the illness, as demonstrated by poor performance among first episode patients (Addington et al., 2006; Kuscharska-Pietura,

Davis, Masiak, & Phillips, 2005) and among individuals considered to be at high risk for developing schizophrenia (i.e., putatively prodromal; Addington, Penn, Woods, Addington, &

Perkins, 2008), and hence impairment cannot be fully attributed to long-term use of neuroleptics

or non-specific illness factors such as institutionalization or chronicity of severe mental illness.

There is conflicting evidence regarding the temporal stability of facial affect recognition deficits

(Edwards et al., 2002). In a cross-sectional study, Kuscharska-Pietura and colleagues (2005)

found facial affect recognition impairment to be present among first-episode and multi-episode

(i.e., chronic) schizophrenia patients, with multi-episode patients showing greatest impairment,

suggesting that facial affect recognition deficits are a trait-like feature with a possible worsening

course. Addington and Addington (1998) tested patients during an acute episode and 3 months later during a period of relative clinical remission and found no difference in performance, lending additional support for the assertion that facial affect recognition deficits are a stable feature of the illness. In contrast, using a cross-sectional design, Weniger et al. (2004) found remitted patients to be relatively unimpaired on tests of facial affect recognition, indicating the deficit is state-dependent. It is possible that facial affect recognition improves over longer durations of recovery. Longitudinal studies may clarify the stability of these deficits.

One potential explanation for facial affect recognition deficit is that poor performance is due to a generalized impairment in visual perception (or face perception specifically), rather than a specific impairment for processing emotional facial stimuli. To address this, tasks assessing non-affective face recognition (e.g., the Test of Facial Recognition; Benton, Van Allen, Hamser,

& Levin, 1978) and object recognition (e.g.,. the Visual Form Discrimination Task; Benton,

Hamsher, Varney, & Spreen, 1983) can be employed. When performance on facial affect recognition tasks are compared to tasks controlling for general face processing and/or object perception abilities, the results have been mixed. Weniger et al. (2004) found facial affect recognition impairment in disorganized and paranoid patients was not fully accounted for by face processing deficits. Schneider and colleagues (2006) compared schizophrenia patients to non-

psychiatric control participants on facial affect recognition and discrimination tasks, as well as face recognition and age discrimination control tasks. The patients showed differential impairment on the facial affect tasks, suggesting specific impairment for affective stimuli. In contrast, Addington and Addington (1998) found that performance on a facial affect recognition task was associated with performance on a non-affective face recognition task, lending support for a generalized perceptual deficit. Similarly, Kerr and Neale (1993) found evidence for a generalized deficit among their sample of unmedicated schizophrenia patients, as patients showed similar levels of impairment on tests of facial affect identification, facial affect discrimination, and face recognition.

Following the method of Kerr and Neale (1993), Mueser et al. (1996) replicated these results with a sample of chronically ill, yet medicated patients. Mueser and colleagues did find that duration of illness and severity of negative symptoms correlated with performance on some of the face measures, and also found an association between face perception and ward behaviour, signaling that face processing deficits (affective and non-affective) are predictive of social functioning. Salem, Kring and Kerr (1996) found that medicated schizophrenia outpatients performed significantly worse on tests of facial affect recognition, facial affect discrimination, and face recognition than non-psychiatric controls, but the group by task interaction was not significant, again suggesting a generalized deficit. Contrary to Mueser et al. (1996), Salem et al. did not find that task performance was negatively impacted by symptomatology or chronicity of illness.

Likewise, Nelson et al. (2007) compared performance on facial affect recognition, face recognition, and visual form discrimination to test if facial affect recognition deficits in

schizophrenia can be attributed to a generalized perceptual impairment. Cluster analysis yielded two groups of patients: one group that was severely impaired on all perceptual tasks and exhibited greater levels of disorganized symptoms, and a second group exhibiting mild impairments and consisting mainly of patients with the paranoid subtype of schizophrenia. Nelson and colleagues argue that their findings support the generalized deficit hypothesis since no group emerged with relatively intact perception for objects and faces in the presence of facial affect recognition impairment.

Utilizing facial affect, face processing, and object recognition tasks, Penn and colleagues

(2000) were interested to determine whether facial affect recognition impairment is specific and varies as a function of illness phase. Participants were acutely ill schizophrenia inpatients, extended care schizophrenia inpatients, and a non-psychiatric control sample. Not surprisingly, acutely ill patients performed significantly worse on both facial affect tests when compared to the

non-psychiatric control participants; however, the extended care patients showed intermediate

performance. For object recognition, both patient groups performed significantly worse than non-

psychiatric controls; for the acutely ill patients, facial affect recognition and discrimination

performance was not fully accounted for by performance on the general perception tasks, suggesting a specific impairment for emotional stimuli. For the extended care patients, facial affect recognition and discrimination were associated on a trend level, and face identification was associated with facial affect discrimination and object recognition. In sum, both patient groups were impaired on all tasks, affective and neutral, face and object based. However, for the acutely ill patients, performance on the control tasks did not fully account for performance on the affective tasks, suggesting a specific deficit. Penn and colleagues surmised that for extended care patients, facial affect discrimination impairment may be attributable to a generalized deficit,

while affect recognition impairment may reflect a specific deficit for processing emotional stimuli.

Hooker and Park (2002) reported that schizophrenia patients were impaired on facial

affect and face processing tasks relative to non-psychiatric control participants, and that

performance on these tasks was associated at a trend level within patients but not for the control

group. Hooker and Park noted that the differential performance between patients and control

samples was greater for emotion perception tasks, and offered that although patients appear to

show poor performance on perceptual tasks in general, perhaps individual studies have

insufficient power to detect a specific deficit for emotional stimuli in schizophrenia, an issue

which could be resolved via meta-analysis.

Affective Prosody. In addition to poor recognition of facial affect, there is recent interest

in the ability of schizophrenia patients to glean affective information from speech cues such as

tone of voice (i.e., affective prosody). For these studies, participants are presented with audio

recordings of actors saying phases that are affectively neutral in their content (e.g., “Fish can

jump out of water”), but demonstrate affective prosody (e.g., fear, anger, happiness, surprise,

etc.), and are asked to identify what emotion the target is feeling from an array of response

options. For affective prosody discrimination, participants are presented with paired audio

recordings and are asked to determine whether the affective prosody of the pair is the same or

different.

In addition to comparing unmedicated schizophrenia patients to non-psychiatric controls

on facial affect perception, Kerr and Neale (1993) included tests of affective prosody recognition

and discrimination, as well as a general auditory processing control task. Similar to their results for facial affect perception, Kerr and Neale found that patients evidenced poor performance on all auditory tasks relative to controls, and performance on the auditory processing task was significantly related to affective prosody discrimination performance within patients, once again supporting the generalized deficit hypothesis.

Similarly, Leitman and colleagues (2005) investigated the relationships between affective prosody recognition and discrimination, and facial affect recognition and discrimination in a sample of schizophrenia patients and non-psychiatric controls. Leitman et al. found that patients

were significantly impaired relative to controls on all tasks. Moreover, patients showed equivalent impairment, regardless of whether the task was visual or auditory. To explore whether deficits in affective prosody were related to auditory processing impairment, the patients also completed tasks assessing ability to match tones and to identify distorted melodies. Using principal components analysis, the researchers found that for the patients, all auditory tasks (i.e., affective and non-affective) loaded onto a single factor and facial affect tasks loaded onto a separate,

orthogonal factor. Performance on vocal affect tasks was not significantly related to facial affect

task performance; however performance on the auditory processing tasks was associated with

affective prosody performance for patient group. Leitman and colleagues interpreted these

findings as supportive of the generalized deficit hypothesis, and offer that perhaps information

processing dysfunction at the sensory level impacts social cognition in a “bottom-up manner” by

which higher-level cognitive functions, such as affective prosody recognition and discrimination,

are impaired due to insufficient sensory input.

Dynamic Expression of Emotion and Nonverbal Social Perception. Tasks combining both dynamic facial affect and affective prosody have also been used with schizophrenia samples. For example, the Bell-Lysaker Emotion Recognition Task (BLERT; Bell, Bryson, & Lysaker, 1997) consists of a videotaped sequence of a Caucasian male actor repeating affectively neutral statements while exhibiting facial expressions and affective prosody consistent with emotions such as sadness, anger, fear, disgust, happiness, or surprise. Patients with schizophrenia are less accurate on the BLERT than non-psychiatric controls (Nienow, Docherty, Cohen & Dinzeo,

2006; Pinkham & Penn, 2006). Performance on the BLERT is associated with facial affect recognition performance (Pinkham & Penn, 2006), and also to neurocognitive domains such as vigilance and executive functions (Bryson, Bell, & Lysaker, 1997; Nienow et al., 2006).

Another dynamic measure of social perception is the Profile of Nonverbal Sensitivity

(PONS; Rosenthal, Hall, Archer, DiMatteo, & Rogers, 1979), which consists of short videotaped scenes of a Caucasian female actor engaged in a variety of social behaviours. Verbal content was eliminated from voice information via content-filtered (i.e., high frequencies filtered out) or random-splicing (i.e., scrambled audio information), forcing the viewer to make judgments based on affective prosody. For each scene, limited audio and/or visual information is available to the participant, allowing for investigation of social cue decoding ability along a variety of non-verbal channels (i.e., face, body, and affective prosody), in isolation or in concert. After each scene, the participant must choose between two options which they believe best describes the situation occurring in the scene.

Using the PONS, Toomey and colleagues (2002) found that schizophrenia patients were less sensitive to non-verbal social cues compared to non-psychiatric controls. Moreover, the

patients failed to improve their social perception accuracy when additional cues were made available; Toomey and colleagues suggested that perhaps the patients were unable to effectively process the additional cues and were experiencing “information overload”. For the patients,

PONS performance was significantly associated with disorganization symptoms and sustained

attention ability, suggesting that cognitive impairment may underlie poor social perception

abilities in schizophrenia. Supporting this interpretation, paranoid subtype patients performed

significantly better on the social perception task than non-paranoid patients; previous research has

suggested that the paranoid subtype is marked by less severe cognitive impairment (Goldstein,

Shemansky, & Allen, 2005). Similarly, our own study of PONS performance in an outpatient

sample suggested that nonverbal sensitivity was associated with disorganization symptoms and

neurocognitive impairment in domains such as executive functioning and working memory

(McCleery, St-Hilaire, Aakre, et al., 2008).

Theory of Mind. Theory of mind (ToM) is an aspect of social cognition which refers to

one's ability to attribute mental states to others; that is, the ability to make inferences about the

thoughts, feelings, and intentions of others. Accurate ToM requires one to hold representations of

the beliefs others hold about their environment, and to understand that the behaviour of others is

influenced by these beliefs, regardless of whether the beliefs are correct. ToM has been assessed

in a variety of ways in the schizophrenia literature, including picture sequencing tasks (e.g.,

Brüne Picture Sequencing Task; Brüne, 2005), inferring complex mental states from pictures of

eyes (Reading the Mind in the Eyes Task; Baron-Cohen, Wheelwright, Hill, Raste, & Plumb,

2001), and answering questions about the false beliefs or intentions of story characters (e.g.,

Strange Stories Task; Fletcher, Happe, Frith, et al., 1995; Irony Sensitivity Task; Langdon &

Coltheart, 2004).

Schizophrenia patients show inaccuracies in their judgments of the mental states of others using traditional ToM tasks (e.g., Brüne & Bodenstein, 2005; Corcoran, Mercer, & Frith, 1995;

Craig et al., 2004; Greig, Bryson, & Bell, 2004; Herold et al., 2002; Langdon et al., 2002; Pickup

& Frith, 2001; Pinkham & Penn, 2006; Sarfati et al., 1999). A recent meta-analysis of 29 ToM studies (Sprong, Schothorst, Vos, Hox, & van Engeland, 2007) suggests that ToM impairment in schizophrenia is a relatively robust (overall effect size d =-1.24) and stable feature of the disorder.

Regarding clinical correlates, Sprong and colleagues found that disorganized patients appeared to show the greatest impairment in ToM, and deficits were present among patients in remission

(effect size d = -0.69).

One criticism of the aforementioned ToM tasks is their relatively high cognitive loading, particularly for working memory, executive functions, and verbal abilities, hence making it difficult to ascertain whether ToM impairment is secondary to general cognitive dysfunction or a specific social cognitive deficit (Abell, Happé, & Frith, 2000; Horan, Nuechterlein, Wynn, Lee,

Castelli, & Green, 2008). Moreover, the ecological validity of these tasks has been questioned, since “online” or spontaneous, real-world ToM is invoked in response to rapidly changing, dynamic social stimuli (Russell, Reynaud, Herba, Morris, & Corcoran, 2006). Based on the work of Heider and Simmell (1944), which demonstrated that adults will attribute intentions and goals to simple, moving geometric shapes, Frith and colleagues (e.g., Castelli, Happé, Frith, & Frith,

2000; Abell et al., 2000) introduced a task (“Animation Task”) which appears to have minimal cognitive loading and prompts relatively automatic inferences of mental states in non-psychiatric adults and children. In this task, short animated sequences involving two triangles moving about a rectangular enclosure are presented, and the participant is simply asked to report what they think

occurred in the animation. The animations fall into one of three categories: random movement

(e.g., triangles are drifting or bouncing around the enclosure), goal-directed movement (e.g., one triangle is chasing the other, or the triangles are dancing with each other), or ToM movement

(e.g., one triangle is deceiving the other, or one triangle is coaxing the other to a different area of the enclosure). Responses are scored for accuracy of the description and use of mental state language, while considering length of response.

To date, two studies have employed the Animations Task with schizophrenia samples.

Russell and colleagues (2006) compared performance between schizophrenia inpatients and non-

psychiatric control participants on the task. Overall, patients were less accurate across all

animation sequences, and used less mentalizing language in their descriptions of the ToM

sequences compared to non-psychiatric control participants. When patients were divided into

subgroups based on their clinical presentation, Russell et al. found that patients with prominent

negative symptoms (e.g., poverty of speech, flat affect) combined with disorganization symptoms

exhibited the worst performance across all conditions (i.e., random movement, goal-directed

movement, and ToM movement), followed by patients with paranoid symptoms (i.e.,

hallucinations, delusions of reference), while patients with passivity phenomena (i.e., delusions of

control, thought insertion, thought broadcasting) and remitted patients demonstrated relatively

intact ToM, underscoring the importance of considering the clinical heterogeneity of

schizophrenia samples in social cognition research. Interestingly, Russell and colleagues noted

that inaccuracies in the random movement condition for paranoid and passivity patients typically

resulted from patients reading “too much” into the sequences; these particular patients were

inappropriately inferring intention to non-intentional movement, suggesting possible presence of

“excessive” ToM among patients vulnerable to delusions and hallucinations.

Using the Animations Task, Horan and colleagues (2009) found that within both schizophrenia outpatients and non-psychiatric controls, the most mental state attributions were made for ToM animations, and the fewest for random movement sequences, supporting the validity of the task. Between groups, patients made less appropriate responses across conditions, and made fewer mental state attributions for both the goal-directed and ToM sequences compared to the control sample, with between-group differences being largest for the ToM condition.

Contrary to Russell et al.’s (2006) findings, Horan and colleagues did not find evidence for excessive ToM for random animations in the patient group, nor did significant clinical correlates for task performance emerge, possibly related to reduced variability in symptom severity in

Horan et al.’s outpatient sample.

In summary, compared to non-psychiatric samples, individuals with schizophrenia exhibit discernible social cognitive deficits in domains such as facial affect and affective prosody perception, non-verbal social perception, and ToM. When schizophrenia samples have been compared to psychiatric control samples, individuals with schizophrenia often have demonstrated higher levels of social cognitive impairment. Social cognitive deficits appear to be relatively durable and stable features of the illness, are present upon diagnosis, and may precede the onset of illness. There is some evidence to suggest that there is heterogeneity in performance on social cognitive tasks related to illness subtype (i.e., disorganized, paranoid, etc.); however this may be a proxy indicator of neurocognitive status since the paranoid subtype is associated with milder cognitive impairment, while the disorganized subtype is marked by relatively impaired cognitive functioning. When tasks controlling for general perceptual abilities have been included in

investigations of social cognition in schizophrenia, the results have been mixed. Next, neurocognitive impairment in schizophrenia will be reviewed briefly.

1.3 Neurocognitive Impairment in Schizophrenia

Currently, schizophrenia is conceptualized as primarily a disorder of neurocognition

(Green & Nuechterlein, 1999; Rund, 1998). Neurocognitive impairment in schizophrenia is diffuse and pervasive, with deficits on the order of approximately 1 to 2 standard deviations below non-psychiatric control samples across most cognitive domains, including memory, attention, processing speed, vigilance, and executive functions (Gold, 2004; Green, 2006;

Heinrichs & Zakzanis, 1998). Although there are reports of individuals with schizophrenia demonstrating neurocognitive performance within the normal range, these high-functioning individuals do appear to show impairment relative to their estimated premorbid abilities

(Reichenberg, Weiser, Rapp, et al., 2005), particularly for speeded tasks and working memory tests (Wilk, Gold, McMahon, Humber, Iannone, & Buchanon, 2005). This assertion is bolstered by studies of monozygotic twins discordant for schizophrenia, where the affected twin evidences significant and generalized cognitive impairment relative to the non-affected twin (Cannon,

Huttunen, Lonnqvist, et al., 2000; Goldberg, Torrey, Gold, et al., 1995).

Cognitive impairment appears to be a relatively stable feature of the disorder, distinct from clinical status (i.e., acute vs. remitted state) and fairly unresponsive to neuroleptic treatment

(Keefe, Bilder, Davis, et al., 2007; Rund, 1998). Recent studies assessing patients in the early phase of the disorder demonstrate that marked cognitive impairment is present at the first episode of psychosis (Addington & Addington, 2002; Addington, Brooks, & Addington, 2003; Gold,

Arndt, Nopoulos, O’Leary, & Andreasen, 1999; Hoff, Saksuma, Wieneke, Horon, Kushner, &

DeLisi, 1999; Mohamed, Paulsen, O’Leary, Arndt, & Andreasen, 1999), and likely precedes the

onset of illness (Hawkins, Addington, Keefe, et al., 2004; Lencz, Smith, McLaughlin, et al., 2006;

Simon, Cattapan-Ludewig, Zmilacher, et al., 2007). There is some evidence to suggest that cognition may improve slightly after the acute episode has resolved (Addington, Saeedi,

Addington, 2005; Gold et al., 1999), although Bilder and colleagues (2000) note marked cognitive impairments were still apparent six months after clinical stabilization in their first episode sample. It is unclear whether the apparent cognitive “improvement” reported in some studies is related to practice effects associated with repeated testing; for example, Addington et al.

(2005) report cognitive improvement over follow-up in both the patient group and the non-

psychiatric control sample, and note that alternative forms of neuropsychological tests were not

used. The presence of attenuated cognitive impairment among first degree relatives of

schizophrenia patients, and among individuals who endorse sub threshold psychotic-like

symptoms (i.e., schizotypal traits) suggests that cognitive impairment may represent a

vulnerability marker for the disorder (reviewed in Claridge, 1997; Egan, Goldberg, Gscheidle, et

al., 2001; Farone, Seidman, Kremen, Toomey, Pepple, & Tsuang, 2000; Gooding, Kwapil, &

Tallent, 1999; Lenzenweger & Korfine, 1994; Sitskorn, Aleman, Ebisch, Appels, & Kahn, 2004).

There is some debate in the literature regarding whether cognitive impairment in

schizophrenia is best explained by a generalized cognitive deficit, or whether there is differential

impairment for specific cognitive domains (e.g., Andreason, 1999; Blanchard & Neale, 1994;

Dickinson, Iannone, Wilk, & Gold, 2004; Nuechterlein, Barch, Gold, Goldberg, Green, &

Heaton, 2004); however, evidence for a generalized cognitive impairment is compelling.

Blanchard and Neale (1994) compared performance of unmedicated schizophrenia patients with

age, gender, and education-matched healthy control participants on a comprehensive

neuropsychological test battery and found no evidence for differential impairment of specific

cognitive domains in schizophrenia; rather, the patients demonstrated diffuse, bilateral cognitive dysfunction suggestive of a generalized cognitive deficit. Andreason (1999) posits that “cognitive dysmetria”, or disrupted connectivity and functional coordination or synchrony between subcortical and cortical regions mediated by the cerebellum (cortico-cerebellar-thalamic-cortical circuit), results in widespread, generalized dysfunction of higher-order cognitive domains such as

executive function, attention, memory, language, and emotion.

Dickinson and colleagues (2004; 2008) argue that although specific impairment in

domains such as memory, processing speed, and executive functions are often reported in

schizophrenia samples, performance in these domains is often highly intercorrelated, likely

reflecting the contribution of general ability, or “psychometric g,” to performance on these tasks.

To explore this hypothesis further, Dickinson et al. (2004) used single common factor analysis, a

variant of structural equation modeling, to model performance on Wechsler scales (WAIS-III;

Wechsler, 1997a; WMS-III; Wechsler, 1997b) in schizophrenia patients and non-psychiatric

control participants. Relative to control participants, the patients demonstrated significantly

impaired performance across Wechsler scales. Their analysis suggested that approximately 65%

of overall diagnosis-related cognitive impairment could be explained by a single cognitive factor

shared across all tests, suggesting pervasive, generalized cognitive impairment in schizophrenia,

which Dickenson et al. (2008) coined “deficit g.” In 2008, Dickenson et al. replicated and

extended their findings using a more comprehensive neuropsychological test battery. Once again,

a large proportion (63%) of diagnosis-related variance in test performance was mediated by a

single, common factor. However, contrary to their previous findings, small, direct effects of

diagnosis on processing speed and verbal memory were observed, suggesting that patients were

differentially impaired in these particular cognitive domains. It is possible that differential deficits

for processing speed and verbal memory are reflective of illness-related structural changes to the temporal lobe and hippocampal formation frequently reported in imaging studies (Gur, Turetsky,

Cowell, et al., 2000; Honea, Crow, Passingham, & Mackay, 2005; Steen, Mull, McClure, Hamer,

& Lieberman, 2006; Wright, Rabe-Hesketh, Woodruff, David, Murray, & Bullmore, 2000).

1.4 Are Social Cognitive Deficits Fully Accounted for by Impaired Neurocognition in

Schizophrenia?

As discussed above, pervasive neurocognitive and social cognitive deficits have been reported in schizophrenia samples. When neurocognitive variables have been included in studies of social cognition in schizophrenia, some variability in performance can often be attributed to neurocognitive functioning. Attention, vigilance, executive functions, verbal and spatial memory, and language abilities have been associated with static and dynamic perception of affective cues, and verbal and non-verbal social cues (Addington & Addington, 1998; Bryson et al., 1997; Kee,

Kern, & Green, 1998; Kohler, Bilker, Hagendoorn, Gur, & Gur, 2000; Nienow et al., 2006;

Pinkham & Penn, 2006; Toomey et al., 2002). When cognitive correlates of ToM have been investigated, associations with domains such as verbal memory, executive function, and vigilance have been reported (Bryson et al., 1997; Greig et al., 2004; Pinkham & Penn, 2006). Indeed, using structural equation modeling, Vauth and colleagues (2004) estimated that 83% of the variance in social cognition in schizophrenia can be explained by neurocognition. However,

Bryson and colleagues (1997) report a much smaller proportion of variance (34%) in performance on the BLERT was accounted for by neurocognition. Sergi and colleagues (2007) note that while neurocognition and social cognition are highly related, statistically modeling neurocognition and social cognition separately provided a better fit for their data than a single factor which

encompassed both constructs, suggesting that social cognition and neurocognition are distinct, but associated constructs.

Considering the pervasive cognitive impairment present in schizophrenia, detection of a specific social cognitive deficit is likely to be difficult, since performance on social and non- social tasks is likely saturated by “deficit g.” However, outside of the schizophrenia literature, there is evidence for dissociation between social cognition and neurocognition (Adolphs, 2001).

For example, Williams Syndrome, a rare genetic disorder, is marked by intact social cognition in the presence of pervasive neurocognitive deficits, particularly in visuospatial abilities and diminished intellectual ability (Jones, Bellugi, Lai, & Chiles, 2000; Reiss, Eckert, Rose, et al.,

2004). Mentally retarded children are able to pass the same ToM tests which high functioning

(i.e., average or above average IQ) autistic children routinely fail (Baron-Cohen, Leslie, & Frith,

1985). Lesion studies have demonstrated that damage to the orbitofrontal cortex results in decreased ToM abilities in the presence of intact verbal and memory abilities (Cicerone &

Tanenbaum, 1997), while focal bilateral lesions to the amygdala render impairment in facial affect and affective prosody recognition for negative emotions (Adolphs, Damasio, Tranel, &

Damasio, 1996; Adolphs, Tranel, Damasio, & Damasio, 1995; Scott, Young, Calder, Hellawell,

Aggleton, & Johnson, 1997). These dissociations between neurocognition and social cognition point to the existence of brain regions which are relatively specialized for processing social stimuli, such as the amygdala, mirror neuron system, and superior temporal sulcus.

Structural and Functional Abnormalities of the “Social Brain” in Schizophrenia. Further

evidence for a specific social cognitive impairment in schizophrenia has emerged from several

imaging studies indicating structural and functional abnormalities of the amygdala in

schizophrenia. The amygdala is highly likely to be related to social cognitive abilities such as

facial affect and affective prosody perception, given the role of this structure in assessing threat

and salience, and for emotion processing (Adolphs, 2001; LeDoux, 2000). Structural imaging

studies suggest that the volume of the amygdala is significantly reduced in schizophrenia patients

(reviewed in Aleman & Kahn, 2005; Wright et al., 2000). Moreover, functional imaging studies

indicate that amygdala activation is dysregulated in schizophrenia; compared to non-psychiatric

control samples, patients show increased tonic activation of the amygdala, accompanied by

failure to show normal activation in response to affectively negative stimuli (e.g., sad mood

induction or angry faces), and, in some studies, enhanced activation in response to affectively

neutral stimuli (reviewed in Aleman & Kahn, 2005; Gur, McGrath, Chan, et al., 2002; Phillips et

al., 1999; Schneider, Weiss, Kessler, et al., 1998).

The mirror neuron system (MNS), a collection of neurons in the primary motor and

inferior parietal cortices, preferentially fire when an individual engages in a particular action, and

when one observes others engaging in a similar action (Gallese & Goldman, 1998; Rizzolatti &

Craighero, 2004). The MNS is hypothesized to the be the neural basis of ToM, since the MNS

appears to be involved in mentally simulating the actions of others, which may facilitate

perspective-taking and determination of intentions or mental states of others (Gallese &

Goldman, 1998).

Motor cortex MNS functioning is assessed by stimulating the primary motor cortex using

transcranial magnetic stimulation (TMS) and measuring motor evoked potential (MEP) amplitude

of a target muscle while the participant watches video of the target muscle region engaging in a

particular activity (e.g., measuring MEP amplitude of the participant’s hand muscle while the

participant views video of a hand grasping a pen). Increased MEP amplitude while watching the action over-and-above baseline levels indicates activation of the MNS (Enticott, Hoy, Herring,

Johnston, Daskalakis, & Fitzgerald, 2008; Rizzolatti & Craighero, 2004). To date, only a single study has investigated MNS functioning in schizophrenia. Using the paradigm outlined above,

Enticott and colleagues (2008) compared target muscle MEP amplitude between schizophrenia and non-psychiatric control participants while participants watched videos displaying random movement of the target muscle, goal-directed movement, and continuing movement. Despite absence of MEP amplitude differences between the two groups at baseline, the patients showed less MEP amplitude during observation of goal-directed and continuing action within the stimulated muscle compared with controls, indicating reduced MNS activity. Enticott et al. note that their study cannot determine whether reduced MNS activity in the patients is related to primary dysfunction of the MNS, or if the MNS fails to become activated due to insufficient input from networks earlier in the information processing stream.

Functionally related to the MNS, the superior temporal sulcus (STS) receives and integrates input from the dorsal (i.e., “where”) and ventral (“what”) visual streams (Allison, Puce,

& McCarthy, 2006). Cells in the STS respond preferentially to biological or “animate” movement such as intended action, body movement, hand movement, head movement, mouth movements, lip reading, and gaze direction (Adolphs, 2001; Allison et al., 2006; Blakemore & Decety, 2001;

Pelphry, Mitchell, McKeown, Goldstein, Allison, & McCarthy, 2003). Moreover, static images conveying dynamic movement have also been shown to activate the STS (Reviewed in

Blakemore & Decety, 2001). Researchers have hypothesized that ToM is partially mediated by this region, since biological movement provides cues for intention (e.g., gaze direction, reaching for an object, moving towards a goal; Allison et al., 2006; Blakemore & Decety, 2001). Indeed,

the Animations Task discussed above illustrates that attribution of mental states can be based solely upon information gleaned from movement. Using the Animations Task, Castelli and colleagues (2000; 2002) demonstrated that healthy adults show enhanced STS activation while

watching ToM sequences compared to random movement sequences, lending support for the role of the STS in ToM abilities. Significant bilateral volume reductions in the superior temporal regions have been reported in schizophrenia samples, suggesting that STS functionality may be

compromised (Honea et al., 2005; Wright et al., 2000). Taken together, the above evidence

indicates that in addition to compromised cognitive abilities, brain regions specialized for automatic processing of social stimuli (e.g., amygdala, MNS, and STS) may be dysfunctional in schizophrenia resulting in social cognitive impairment.

Social Cognition as a Potential Mediator between Neurocognitive Impairment and

Functional Outcome. As discussed above, neurocognition and social cognition may be best

conceptualized as related, but distinct constructs. Neurocognitive functioning has been

demonstrated to be a reliable predictor of social functioning (e.g., Addington & Addington, 1999;

2000; 2008; Addington, McCleary, & Munroe-Blum, 1998; Cohen, Forbes, Mann, & Blanchard,

2006; Green, Kern, & Heaton, 2004; McGurk & Meltzer, 2000; Milev, Ho, Arndt, & Andreasen,

2005; Smith, Hull, Huppert, & Silverstein, 2002). Likewise, social cognition has emerged as a

robust predictor of functional outcome (e.g., Kee, Green, Mintz, & Brekke, 2003; Pinkham &

Penn, 1996; Mueser et al., 1996). Interestingly, cognitive interventions have yielded little

improvement in social functioning (Green et al., 2004), whereas social cognition training has

yielded promising results (Roberts & Penn, 2009). Therefore, recent investigations have turned to

whether social cognition may mediate the relationship between neurocognition and social

functioning. To date, the evidence is favorable for this hypothesis although many studies have

been methodologically limited due to sparse cognitive or social cognitive assessment batteries and/or small sample sizes.

Addington and colleagues (2006) tested a mediation model for non-psychiatric controls, first episode psychosis patients, and multi-episode psychosis patients. Regression analyses demonstrated that social cognition (i.e., facial affect recognition) partially mediated the relationship between neurocognition and social functioning (i.e., QLS scale). No evidence for mediation was found for the non-patients. A weakness of the study was that assessment of social cognition was limited to facial affect recognition, and social functioning was limited to a single measure. Similarly, using regression and a more comprehensive social cognitive battery (i.e.,

BLERT, FEIT, and ToM tasks), Pinkham and Penn (2006) reported that social cognition contributed to interpersonal skill over-and-above the contribution of neurocognition in a sample of outpatients with schizophrenia. However, mediation was not formally tested in this sample.

Recent studies utilizing larger samples and structural equation modeling (SEM) or related techniques also support the mediating role of social cognition. Using SEM, Vauth and colleagues

(2004) tested the relationship between neurocognition (i.e., executive functions, attention, and verbal memory), social cognition (i.e., understanding of social cues and use of social schemas) and vocational functioning in 133 inpatients with schizophrenia enrolled in a vocational training program. The resulting model demonstrated a high association between social cognition and non- social cognition; approximately 83% of the variance in social cognition could be explained by non-social cognition. Moreover, both social and non-social cognition were associated with vocational outcome, explaining approximately 25% of the variance in work skills. Social cognition exerted greater influence on vocational outcome than non-social cognition, suggesting

that social cognition may be a mediator in the relationship between non-social cognition and vocational functioning; however mediation was not formally tested in the model.

Sergi, Rassovsky, Nuechterlein, and Green (2006) tested mediation directly using SEM

and found support for their assertion that social cognition is a mediating variable. However, their assessments were limited; neurocognition was limited to a single domain (i.e., early visual processing), and single indicators of social cognition (i.e., the PONS) and social functioning (i.e.,

Role Functioning Scale) were included in the model. Likewise, Bowie and colleagues (2008) demonstrated that social cognition mediated the relationship between specific aspects of neurocognition (processing speed, attention, working memory) and functional outcome (i.e., vocational skills and community activities) in a large outpatient sample using SEM. Utilizing path

analysis, Bell et al. (2009) demonstrated that social cognition and social discomfort partially

mediated the relationship between neurocognition and vocational outcome in their sample of

outpatients. Finally, Addington et al. (2010) compared non-psychiatric control participants to

first-episode and multi-episode psychosis patients and found that social cognition mediated the relationship between neurocognition and social functioning (i.e., AIPSS, QLS, and SFS) in the patient sample. A limitation of the study was that only a single aspect of social cognition was assessed (i.e., facial affect recognition).

1.5 Summary and Aims of Present Study

In sum, schizophrenia is marked by significant functional, social cognitive, and neurocognitive deficits. In addition, neurocognition and social cognition have emerged as robust

predictors of social dysfunction. There is conflicting evidence in the literature regarding whether

social cognitive deficits can be fully accounted for by neurocognition or not. The presence of

pervasive cognitive dysfunction renders the task of detecting evidence for specific social cognitive deficits difficult, since performance on social and non-social tasks is saturated by a generalized cognitive deficit. However, research does suggest that although a large proportion of the variance in social cognition can be accounted for by neurocognition, they are best modeled as related, yet distinct constructs. In addition, structural and functional changes to brain regions specialized for social stimuli have been identified, and these regions may be compromised by the illness. Thus, it seems plausible that in schizophrenia, a specific impairment for processing social information is superimposed upon a generalized cognitive deficit. Furthermore, although neurocognitive impairment is undoubtedly related to both social cognition and social functioning, social cognitive impairment appears to be a more proximal predictor of social dysfunction, likely mediating the relationship between neurocognition and social functioning.

The purpose of the present study was to thoroughly examine the relationships between neurocognitive impairment, social cognition, and social functioning in a clinically-stable, outpatient sample of individuals diagnosed with schizophrenia or schizoaffective disorder. This study aimed to address limitations of the existing literature by utilizing a) a large sample, b) a comprehensive neurocognitive test battery, and c) multiple facets of social cognition (i.e., static and dynamic facial affect recognition, affective prosody, and theory of mind). In addition, structural equation modeling was be used to test these associations. The following hypotheses were tested:

1. Neurocognition, social cognition, and social functioning are significantly associated with

each other.

2. Utilizing confirmatory factor analysis, neurocognition and social cognition are closely

related, yet distinct constructs, thus replicating the results of Sergi and colleagues (2007).

Specifically, it was hypothesized that a two-factor model, with neurocognition and social

cognition modeled as separate latent constructs, would provide better fit for the data than

a single-factor model where neurocognition and social cognition are combined.

3. When neurocognition and social cognition are modeled as separate latent variables within

a structural regression model, social cognition would mediate the association between

neurocognition and social functioning. Specifically, it was hypothesized that a model

considering the indirect effect of neurocognition on social functioning via social

cognition would provide better fit for the data than an alternative model where only

unique, direct effects of neurocognition and social cognition on social functioning are

considered (i.e., where social cognition was not denoted as a mediating variable). In

addition, to support presence of significant mediation, the 95% confidence interval for

parameter estimate for the direct effect of neurocognition on social functioning was

predicted to overlap with zero, while the 95% confidence interval for the parameter

estimate for the indirect effect of neurocognition on social functioning via social

cognition was predicted to be non-zero.

CHAPTER 2

Method

2.1 Participants

Clinically stable outpatients with a DSM-IV-TR diagnosis of schizophrenia or schizoaffective disorder (n=123) who participated in a larger, ongoing study conducted at a community mental health center in Akron, Ohio (Cognitive Bases of Language Disturbance in

Schizophrenia; P.I.: Nancy M. Docherty, Ph.D.) were included in the sample. Exclusion criteria were minimal and included current substance abuse, presence of organic brain syndrome or mental retardation, or history of seizure disorder, head injury with loss of consciousness, inhalant use, or alcohol dependence requiring detoxification. Sample characteristics are presented in Table

1. Demographically, the sample was mostly male (n=68, 55.3%), African American (n=79,

64.2%), with a mean age of 40.35 years (s.d.=7.37 years), and age at first psychiatric hospitalization of 22.63 years (s.d.=7.74 years). Nearly all patients reported holding a current prescription for neuroleptic medication (97.5%). The majority of participants were unemployed

(n=101, 82.1%).

Diagnoses and study eligibility were verified via structured clinical interview (Schedule of Affective Disorders and Schizophrenia; SADS; Endicott & Spitzer, 1978) and review of treatment records. SADS interviews were conducted by psychology graduate students, and consensus research diagnoses were reached under the supervision of a Clinical Psychologist

(NMD).

27 28

Table 1. Sample characteristics (n=123).

Variable n (%)

Sex

Male 68 (55.3)

Female 55 (44.7)

Diagnosis

Schizophrenia 60 (48.8)

Schizoaffective Disorder 63 (51.2)

Ethnicity

African American 79 (64.2)

Caucasian 39 (31.7)

Other 5 (4.1)

Years of Education

<9 7 (5.7)

9 12 (9.8)

10 9 (7.3)

11 16 (13.0)

12 49 (39.8)

13-16 30 (24.4)

Employment Status

Unemployed 101 (82.1)

Employed 22 (17.9)

Living Situation

Independent, alone 54 (43.9)

Independent, with other 39 (31.7)

With family 12 (9.8)

Group facility 15 (12.2)

Other (e.g., homeless) 3 (2.4)

2.2 Procedure

Participants were assessed individually over four 2-hour testing sessions. Testing sessions took place once per week, with the examiner, day of the week, and time of day held constant for each participant whenever possible, and tests were completed in a fixed order. Informed consent was obtained from each participant and the protocol was approved by the Kent State University

Institutional Review Board. Participants received $50 per session to compensate for time spent in each session and the cost of travel to the community mental health center for testing.

2.3 Measures

Neurocognition. Premorbid intellectual ability was estimated using the verbal subscale of the Shipley Institute of Living Scale (SILS; Shipley, 1940; Appendix A). The verbal subtest of

SILS contains 40 multiple-choice vocabulary items. Scores range from 0-40, where higher scores are indicative of better performance. Split-half reliability for the SILS verbal subtest is 0.87, and test-retest reliability is approximately r=0.60 (Lezak, Howieson, & Loring, 2004; Zachary, 1986).

Executive functioning was assessed using the computerized version of the Wisconsin

Card Sort Task-64 Card Version (WCST-64; Kongs, Thompson, Iverson, & Heaton, 2000;

Appendix B), and Trail Making Task B (Trails B; Army Individual Test Battery, 1944; Appendix

C). For the WCST-64, participants are presented with four stimulus cards (one red triangle, two green stars, three yellow crosses, four blue circles) and are asked to sort each of 64 response cards to the four stimulus cards based on one of three dimensions: colour (red, green, yellow, or blue), form (triangles, stars, crosses, or circles), or number (one, two, three, or four symbols).

Participants are not told how the cards are to be sorted, but rather, the participant must identify the rewarded sorting dimension through trial and error, utilizing visual and auditory feedback given after each response (i.e., “RIGHT” or “WRONG” is displayed on the computer screen and said out loud to the participant after each choice). After 10 consecutive correct responses, the rewarded dimension shifts, requiring the participant to use feedback to identify the new sorting rule (e.g., rewarded dimension shifts from colour to form). The variables of interest for the

WCST-64 are number of categories completed (overall success index, with a maximum of 6 sets

of 10 consecutive correct responses), total number of errors, number of perseverative errors

(persisting with a sorting rule which is no longer rewarded), and non-perseverative errors. Aside

from number of categories completed, the WCST-64 variables are expressed as T-scores based on

age-corrected data from the normative sample. The variable of interest is total number of errors.

The WCST is sensitive to frontal lobe dysfunction (Heaton, 1981) and the full-length WCST has

demonstrated good reliability and validity in the literature (Greve, 2001). Correlations between

score indices for the WCST and the shortened 64-card version ranged from r=0.70-0.91 in a

schizophrenia sample (Robinson, Kester, Saykin, Kaplan, & Gur, 1991). Among schizophrenia

patients, WCST performance has demonstrated predictive utility for vocational functioning

(Lysaker, Bell, & Beam-Goulet, 1995). For the Trail-making Task B (TMTB; Army Individual

Test Battery, 1944), participants are asked to complete a “connect the dots” style timed test where

dots are to be joined in a specific order (alternating numbers and letters). The variable of interest

for TMTB is time to completion (in seconds). Test-retest reliability for a schizophrenic sample over a 1 year interval was r=0.81 (Matarazzo, Matarazzo, Wiens, Gallo, & Klonoff, 1976).

Regarding discriminant validity, schizophrenia patients evidence more errors and longer mean

completion time than healthy populations and patients with major depressive disorder (Mahurin,

Velligan, Hazleton, Davis, Eckert, & Miller, 2006).

Sustained attention was assessed using the Continuous Performance Task – Identical

Pairs (CPT-IP; Cornblatt, Risch, Faris, Friedman, & Erlenmeyer-Kimling, 1988; Appendix D), a

computerized test assessing the subject’s ability to correctly identify targets (i.e., identical pairs of

numbers) amidst distracters across three conditions (2-digit, 3-digit, and 4-digit numbers). The

variable of interest is the sensitivity score, d’. Test-retest reliability for the CPT-IP over a two-

year period range from r=0.56-0.73 (Cornblatt et al., 1988). Attentional deficits assessed by the

CPT-IP appear to be fairly specific to schizophrenia-spectrum conditions (Cornblatt et al., 1988;

2001).

Working Memory was assessed using the Digit Span (DS) from the Wechsler Adult

Intelligence Scale – III (WAIS-III; Wechsler, 1997a; Appendix E), where participants are asked

to repeat sequences of numbers forwards and backwards. Scores range from 0-24, where higher

scores are indicative of better performance. Among non-clinical samples, test-retest reliability for

the Digit Span ranges from r=0.66-0.89. Individuals with psychosis evidence impairment on the

Digit Span on the order of 0.21 to 0.34 s.d. below that of non-psychiatric controls, and

impairment on Digit Span Backward is stable over a 12-30 month interval (de Mello Ayres et al.,

2010). Likewise, schizophrenia patients demonstrate significant impairment on the Digit Span

Backward relative to patients with bipolar affective disorder (Sanchez-Morla et al., 2009).

Psychomotor speed was assessed using the Trail-making Task A (TMTA; Army

Individual Test Battery, 1944; Appendix F), where participants are asked to connect-the-dots in numeric order. The variable of interest for TMTA is time to completion (in seconds). Test –retest reliability over a 1 year interval for a schizophrenic sample was r=0.84, and schizophrenia patients demonstrate significantly worse performance on TMTA relative to healthy control participants (Matarazzo et al., 1976).

Social cognition. Static facial affect recognition was assessed using the Facial Emotion

Identification Test (FEIT; Kerr & Neale, 1993; Appendix G). Participants are shown 36 black and white photographs of human faces where the target is expressing one of seven emotions (happy, sad, fear, anger, surprise, disgust, or neutral), and are asked to select which emotion from the array they believe the target is expressing. Accuracy scores range from 0-36 (chance performance=5), where higher scores are indicative of better emotion identification abilities.

Internal consistency for the FEIT in a schizophrenia sample was α=0.74 (Kerr & Neale, 1993), and performance on the FEIT is associated with performance on related tasks of emotion perception in a schizophrenia sample (Pinkham & Penn, 2006). For the current sample, internal consistency for the FEIT was α=0.78.

Dynamic facial affect and affective prosody recognition was assessed using the Bell-

Lysaker Emotion Recognition Test (BLERT; Bell et al., 1997; Appendix H). Participants are shown 21 short video clips of a male actor expressing vocally, facially, and gesturally one of seven emotions (happiness, sadness, fear, anger, surprise, disgust, or neutrality) while repeating content-neutral statements. As with the FEIT, participants are asked to select from an array which

affective label best describes the emotion that the target actor is expressing. Accuracy scores range from 0-21 (chance performance=3), where higher scores reflect better emotion recognition abilities. The BLERT has demonstrated stability and test-retest reliability over a 5-month period

(weighted kappa=0.94, r=0.76), as well as discriminant validity (i.e., schizophrenia patients demonstrate significantly poorer performance than substance abusing and healthy comparison samples; Bell et al., 1997). For the current sample, internal consistency for the BLERT was

α=0.76.

Perception of nonverbal social cues and affective prosody were assessed using a shortened version of the Profile of Nonverbal Sensitivity (PONS; Rosenthal et al., 1979;

Appendix I), the Half-PONS. For the Half-PONS, participants are shown 110 short video clips of a Caucasian female actor engaged in a variety of social behaviours. For each scene, limited audio and/or visual information is available to the participant, allowing for investigation of social cue decoding ability along a variety of non-verbal channels (i.e., face, body, and affective prosody), in isolation or in concert. After each scene, the participant must select between two options which they believe best describes the situation occurring in the scene. Overall accuracy score ranges

from 0-110 (chance performance=55), where higher scores are indicative of better performance.

For the full PONS, internal consistency for non-clinical samples ranges from α=0.86 to 0.92

(Ambady et al., 1995). Internal consistency for the Half-PONS with a schizophrenia sample was

α=0.78 (Sergi et al., 2006). Compared to healthy control participants, a schizophrenia sample demonstrated significant impairment on the PONS (Toomey et al., 2002). For the current sample, internal consistency for the Half-PONS was α=0.78.

Theory of mind was assessed using two tasks: 1) the Hinting Task (HT; Corcoran et al.,

1995; Appendix J), and 2) the Brune Task (BT; Brune, 2005; Appendix K). For the HT, participants are read 10 short vignettes about two story characters and are asked to infer a character’s intentions. Scores range from 0 to 20, where higher scores reflect better ToM abilities.

For the BT, participants are asked to sequence pictures in order to tell a coherent story. In order to complete the sequence correctly, the participant must infer intention, thoughts, and feelings of illustrated story characters. Participants are then asked to answer questions about the beliefs and intentions of story characters. Scores range from 0 to 59, where higher scores reflect better ToM abilities. For the current sample, internal consistency for the BT was α=0.76. Psychometric data assessing internal consistency and test-retest reliability of these measures in other samples are not available. This has been noted as a limitation in the literature (Couture, Penn, & Roberts, 2006;

Harrington, Siegert, McClure, 2005). Regarding validity, a recent meta-analysis (Bora, Yucel, &

Panteris, 2009) found effect sizes of d=1.06 for the HT and d=1.08 for the BT for differences between schizophrenia patients and healthy control samples. Pinkham and Penn (2006) found performance on the HT to be related to ratings of overall social skill in a schizophrenia sample.

Social functioning. Social functioning was assessed in two ways: 1) a performance

measure, the Assessment of Interpersonal Problem Solving Skills (AIPSS; Donahoe et al., 1990;

Appendix L), and 2) the Social Functioning Scale (SFS; Birchwood et al., 1990; Appendix M), a

self-report measure of engagement in social activities and level of independence. For the AIPSS,

participants are asked to identify social problems from videotaped vignettes (e.g., a property

manager fails to attend to a tenant’s complaint regarding a faulty apartment utility), generate a

solution to each social problem identified, and enact each solution via role-play with the

experimenter. There are thirteen vignettes in total, 10 “problem” vignettes and 3 control (i.e., no

problem) vignettes. Participants receive one-point for each social problem correctly identified.

Generated solutions to the social problems are scored on a scale of 0 (inappropriate solution) to 2

(solution that will obtain desired goal). Role-plays are audio-recorded and scored for response content (0-2), performance (0-2), and overall level of social competency (0-2). Total AIPSS scores range from 0 to 33, with higher scores indicative of better interpersonal problem solving skills. The variable of interest for the AIPSS was total percent correct. The AIPSS has demonstrated adequate psychometric properties with schizophrenia samples; test-retest reliability ranges from r=0.46-0.77, Cronbach’s α=0.64-0.74, ecological validity, and sensitivity to

treatment effects (Spaulding et al., 1999; Vogler, Spaulding, Kleinlein, & Johnson, 2010). For the

current sample, AIPSS responses were consensus rated by two advanced graduate students.

Internal consistency for the AIPSS was α=0.82.

The SFS was developed for use with outpatients with schizophrenia. It assesses how frequently respondents engage in various social activities, as well as their ability to complete daily living activities independently. Specifically, the SFS provides detailed assessment of 7

social functioning domains: 1) social engagement/withdrawal, 2) interpersonal behavior, 3) prosocial activities, 4) recreation, 5) independence-competence, 6) independence-performance, and

7) employment/occupation. Participants are asked to rate how frequently (i.e., “never”, “rarely”,

“sometimes”, or “often”) they have engaged in specific social activities over a time period (e.g.,

past 3 months). In addition, participants are asked to rate their ability to perform specific daily

living skills (i.e., “can do without help”, “needs help”, “unable to do with help”). Total SFS score

ranges from 0 to 223, and is intended to provide an indication of overall social functioning

(Birchwood, et al., 1990). The SFS has demonstrated good reliability and validity in a

schizophrenia sample (Birchwood et al., 1990). Internal consistency for subscales ranges from

Cronbach’s α=0.69-0.87, with good correspondence between self-ratings made by patients and ratings of patient functioning by family members across subscales (r=0.63-0.99; Birchwood et al.,

1990). Regarding construct validity, for a combined schizophrenia and healthy control sample,

SFS items load onto a single factor accounting for 57% of the variance, which Birchwood et al.

(1990) deemed a “social adjustment” or “psychosocial functioning” factor. Factor loadings remained relatively consistent when analyses were conducted separately for each group. The ability of the SFS to distinguish between groups (i.e., schizophrenia, healthy community sample,

non-affected siblings of patients) demonstrates criterion validity of the measure (Birchwood et al.,

1990).

Clinical Symptoms. Positive and negative symptoms of schizophrenia were rated using the Positive and Negative Syndrome Scale (PANSS; Kay, Fiszbein, & Opler, 1987). Ratings were based on the SADS clinical interview conducted at the first study session. Internal consistency for the Positive, Negative, and General Psychopathology subscales range from Cronbach’s α=0.73-

0.81, and test-retest correlations for subscales over a 3 to 6 month period ranges from r=0.60 to

0.80 (Kay et al., 1987). Significant relationships between PANSS subscales and related clinical

and demographic correlates indicate good criterion-related validity (Kay et al., 1987). The

PANSS has been used extensively in previous research in our laboratory, and good interrater

reliability has been obtained for the current sample using a subset of interviews (ICC > .80).

2.4 Data Analysis

Bivariate correlations were used to quantify linear associations between study variables.

Confirmatory factor analysis (CFA) models were used to test the factor structure of neurocognition and social cognition. Structural equation modeling (SEM) was employed to test

the relationships between neurocognition, social cognition, and functional outcome in the study sample. Models were estimated using EQS Version 6.2 software (Bentler, 2012). SEM is an ideal analytical method to test these relationships because 1) indicators will be subject to CFA to construct latent variables, 2) all relationships between latent variables can be tested simultaneously rather than in a step-wise fashion (e.g., multiple regression), and 3) both direct and indirect pathways between latent variables can be modeled and quantified.

First, competing confirmatory factor models were tested to verify the independence of

neurocognition and social cognition. Fit indices between one-factor (Figure 1a) and two-factor

(Figure 1b) models were compared using the chi-square difference statistic (Δχ2), where a

significant statistic indicates that the model with the lower model chi-square (Model χ2) value is

the better fitting model (Kline, 2005). Based on theory and prior research with a schizophrenia

sample (Sergi et al., 2007), it was expected that a two-factor model would provide better fit for

the data.

Figure 1 Proposed a) one-factor and b) two-factor models of neurocognition and social cognition

CPT FEIT

DS BLERT

Combined WCST Neurocognition and PONS Social Cognition

TMTA HT

TMTB BT

CPT FEIT

DS BLERT

WCST PONS Neurocognition Social Cognition

TMTA HT

TMTB BT

Note: CPT=Continuous Performance Task Identical Pairs, DS=Digit Span,

WCST=Wisconsin Card Sorting Test, TMTA=Trail Making Test A, TMTB=Trail Making Test

B, FEIT=Facial Affect Identification Test, BLERT=Bell-Lysaker Emotion Recognition Test,

PONS=Profile of Nonverbal Sensitivity, HT=Hinting Task, BT=Brune Task

Second, a model was fit to the data testing unique, direct effects of neurocognition and social cognition on social functioning (“direct effects model”, Figure 2a). Next, an alternative model testing the indirect effect of neurocognition on social functioning via social cognition (i.e.,

“indirect effect model”, where social cognition is denoted as a mediating variable, Figure 2b) was fit to the data to test the hypothesis that social cognition mediates the relationship between neurocognition and social cognition. It was hypothesized that the indirect effect model would provide better fit for the data, supporting the hypothesis that social cognition mediates the association between neurocognition and social functioning.

Figure 2. Proposed structural equation models.

a) Direct effects model

FEIT BLERT PONS HT BT

AIPSS

Social Cognition

Social Functioning

SFS

Neurocognition

CPT DS WCST TMTA TMTB

b) Indirect effect model

FEIT BLERT PONS HT BT

AIPSS

Social Cognition b

Social a Functioning

SFS c’ Neurocognition

CPT DS WCST TMTA TMTB

Note: CPT=Continuous Performance Task Identical Pairs, DS=Digit Span,

WCST=Wisconsin Card Sorting Test, TMTA=Trail Making Test A, TMTB=Trail Making Test

B, FEIT=Facial Affect Identification Test, BLERT=Bell-Lysaker Emotion Recognition Test,

PONS=Profile of Nonverbal Sensitivity, HT=Hinting Task, BT=Brune Task, AIPSS=Assessment of Interpersonal Social Skills, SFS=Social Functioning Scale.

Model fit was assessed using three fit indices: 1) model chi-square (Model χ2) where non- significant values indicate good fit for the data (Kline, 2005), 2) comparative fit index (CFI) compares the research model to the independence model, where values >0.90 indicate good fit

(Kline, 2005), and 3) root mean square error of approximation (RMSEA) where values less than

0.05 indicate excellent fit, 0.06-0.10 indicate acceptable fit, and >0.10 indicate poor fit (Kline,

2005). Comparison between the direct effects and indirect effect models were made using chi- square difference statistic (Δχ2). Mediation was quantified through bootstrapping (sampling with replacement, n=1000 replications), a powerful technique for assessing indirect effects, where 95% confidence intervals (95%CI) for the indirect effect parameter estimate (i.e., path a x b in Figure

2b) and direct effect parameter estimate of neurocognition on social functioning (i.e., path c’ in

Figure 2b) were generated. Mediation is supported when the 95%CI for path c’ overlaps with a value of zero, and the 95%CI for the indirect effect (path a x b) is non-zero (Hayes, 2009;

Preacher & Hayes, 2004; Shrout & Bolger, 2002). Assessing indirect effects through bootstrapping parameter estimates is preferable to Sobel’s test, a more traditional test of mediation, as Sobel’s test is a large sample test with relatively high risk of Type II error

(MacKinnon & Fairchild, 2009; Shrout & Bolger, 2002). Moreover, bootstrapping procedures generate confidence intervals that accurately reflect the often asymmetric or skewed bootstrap distribution (i.e., “bias corrected” confidence intervals) of the indirect effect (i.e., path a x b in

Figure 2b), whereas confidence intervals for parameter estimates derived from the normal distribution (i.e., “normal theory” confidence intervals) assume distributions are symmetrical

(Shrout & Bolger, 2002).

Data Considerations for SEM. 1) Sample size. Various rules of thumb exist regarding ideal sample size for SEM. One frequently cited guideline is a minimum sample size of n=100

(Kline, 2005). Other rules of thumb include ratios such as 10 participants: 1 indicator, 15 participants: 1 indicator, and 5 participants: 1 parameter (Kline, 2005). With twelve indicators in the SEM model, the sample of size of n=123 meets the n>100 and 10 participants: 1 indicator guidelines outlined above. Moreover, the proposed sample size of this study is not atypical for schizophrenia research, where SEM models have been estimated for samples as small as n=75.

2) Missing Data. SEM methods require complete data. Various methods for handling missing data exist, such as listwise deletion (e.g., for all study variables or for the dependent variable only), data-based methods such as single imputation using the expectation-maximization

(EM) algorithm and multiple imputation (i.e., creating multiple datasets of imputed data, akin to bootstrapping simulation methods, and combining results across imputed datasets), and full- information maximum likelihood (FIML) a robust model-based procedure where missing data is not imputed, but rather the means, variances, and covariances of study variables are estimated based on the sum of casewise likelihood functions based on available data (Enders, 2001).

Listwise deletion, which may be a viable option when the amount of missing data is negligible and are missing at random (i.e., probability of missingness is unrelated to variable for which data is missing, but may be associated with other variables in the dataset; Donders, van der Heijden,

Stinjen, & Moons, 2006; Rubin, 1976) or missing completely at random (i.e., probability of missingness is not related to any variables in the dataset; Donders et al., 2006; Rubin, 1976), results in loss of power due to reduced sample size. Single imputation methods using the EM algorithm are robust when the proportion of missing data is small and data are missing at random

(Schafer, 1999). Multiple imputation methods have been demonstrated to be robust even in the face of high percentage of missing data (e.g., 50%; Graham & Schafer, 1999), typically requiring five or fewer imputed datasets to yield unbiased parameter estimates (Schafer, 1999; Schafer &

Olsen, 1998). Unfortunately, although parameter estimates can be pooled across imputed datasets

(Rubin, 1996), there is no established manner in which to combine fit indices from SEM analyses

(Carter, 2006). SEM program packages such as EQS (Bentler, 2012) offer FIML for handling missing data, which is the method recommended by Bentler for SEM (Bentler, 2006).

For the AIPSS, n=29 participants were missing data as the measure was not available for administration due to delay transferring the video from video home system (VHS) format to digital video disc (DVD). For the SFS, n=19 participants were missing data for the Performance subscale as a page of the measure was missing from these test packets. These data were assumed to be at least missing at random, as probability for missingness for the AIPSS and SFS

Performance was due to factors other than social functioning of the participants (i.e., administrative factors, order of entry into the study).

Due to large amounts of missing data on the AIPSS, a dependent variable, missing data was handled two ways. First, due to concerns regarding imputation of dependent variables (e.g., see Young & Johnson, 2010), a subsample was created consisting of participants who had complete data for the AIPSS (i.e., listwise deletion for dependent variable; hereafter referred to as

“AIPSS subsample”), and for the CFA and SEM analyses, missing data for the remaining variables was imputed using the EM algorithm in NORM version 2.03 software (Schafer, 2000).

Second, the entire sample (hereafter referred to as “total sample”) was subjected to FIML methods in EQS for the CFA and SEM analyses. In addition, multiple imputation (m=5 datasets across 1000 simulations retained for analysis) were also conducted using NORM version 2.03 software (Schafer, 2000) and the imputed datasets were analyzed separately. As noted previously, as fit indices could not be pooled across datasets, these data were interpreted in a more qualitative manner to explore whether handling missing data using MI yields similar results to FIML methods. These data are presented in Appendix N.

3) Multicollinearity. Indicators for latent variables are expected to be moderately associated with each other if they are indeed measuring the same underlying construct. However,

correlations between indicators that are excessively high (e.g., r>0.90) may lead to empirical underidentification of the model (Kline, 2005; Tabachnick & Fidell, 2007). Thus, the correlation matrix was examined carefully to screen for multicollinearity. Mechanisms for dealing with multicollinearity include either 1) creating a summary score of the multicollinear variables to create a new indicator variable (e.g., summing standard scores), or 2) selecting the most reliable of the multicollinear indicators. Selection between these methods is based on conceptual considerations (e.g., desire to retain a conceptually-relevant indicator in the model) and methodological considerations (e.g., indicator properties such as reliability and validity).

4) Normality and Absence of Outliers. SEM techniques assume univariate and multivariate normality. Prior to analysis, the data were screened for univariate (e.g., inspection of skewness and kurtosis values and inspection of histograms) and multivariate normality (e.g.,

Mardia’s statistic <3). Similarly, the data were screened for univariate (e.g., using boxplots and examination of distribution of standard scores) and multivariate (e.g., examination of

Mahalanobis distance statistic) outliers. Univariate outliers were retained but altered so that the extreme score had less influence on the analyses (Tabachnick & Fidell, 2007). Specifically, univariate outliers were “taken-in” to a value equivalent to 3.25 standard deviations away from the mean when the outlier was omitted from the dataset. Multivariate outliers were removed from the dataset.

5) Linearity. Correlations and scatterplots were examined to ensure linear relationships

between variables.

6) Normality of Residuals. Normal-quantile (N-Q) and normal-probability (P-P) plots of standardized residuals were examined to detect deviations from normality.

CHAPTER 3

Results

3.1 Descriptive Statistics

Descriptive statistics for study variables for the total sample are presented in Table 2, and for the AIPSS subsample in Table 3. Distributions of all continuous study variables met normality assumptions. Outlying scores on the following variables were taken-in to 3.25 standard deviations from the mean in order to reduce the impact of extreme scores on subsequent analyses

(Tabachnick & Fidell, 2007): TMTA (n=2), DS (n=1), FEIT (n=1), HT (n=1), SFS Interpersonal

Relations (n=2), SFS Performance (n=2), and SFS Competence (n=6). Gender effects were evident for SILS Vocabulary [t (118) = -3.12, p=0.02] and the BT [t (109) =-2.85, p<0.01] with females attaining lower scores than males. Due to sample size restrictions on power, separate analyses by gender were not conducted. These variables were standardized by gender prior to further analysis. For ease of interpretation, scores for TMTA and TMTB were recoded so that higher scores were indicative of better performance.

46 47

Table 2. Descriptive statistics (total sample, n=123).

Variable n Mean (s.d.) Range

PANSS Positive 122 17.45 (6.00) 7.00-34.00

PANSS Negative 122 14.67 (4.87) 7.00-31.00

SILS Vocabulary 121 23.33 (6.11) 6.00-36.00

TMTA† 123 20.48 (9.24) 7.00-47.00

TMTB† 121 131.43 (75.75) 35.00-360.00

CPT d’ 119 1.80 (0.79) -0.60-3.64

WCST Total Errors 118 37.86 (9.48) 20.00-65.00

DS Total 121 12.16 (3.38) 3.00-24.00

BLERT 121 13.41 (3.43) 3.00-20.00

FEIT 123 22.64 (4.50) 9.00-32.00

HT 120 15.35 (3.43) 5.00-20.00

BT 112 44.33 (10.20) 20.00-59.00

PONS 114 73.82 (9.46) 50.00-91.00

SFS Social Engagement 121 9.43 (2.90) 3.00-15.00

SFS Interpersonal 121 7.16 (1.63) 2.00-9.00

SFS Performance 104 29.63 (5.57) 13.00-39.00

SFS Recreation 121 21.14 (6.72) 5.00-40.00

SFS Pro-social 121 18.74 (11.44) 0.00-53.00

SFS Competence 121 35.60 (3.45) 27.00-39.00

SFS Employment 120 4.08 (2.66) 0.00-10.00

SFS Total 102 126.03 (25.81) 54.00-194.00

AIPSS (% correct) 94 48.41 (13.96) 19.27-83.63

Note: †Raw score, PANSS=Positive and Negative Syndrome Scale, SILS=Shipley

Institute of Living Scale, TMTA=Trail Making Test A, TMTB=Trail Making Test B,

CPT=Continuous Performance Task Identical Pairs, DS=Digit Span, WCST=Wisconsin Card

Sorting Test, BLERT=Bell-Lysaker Emotion Recognition Test, FEIT=Facial Affect Identification

Test, HT=Hinting Task, BT=Brune Task, PONS=Profile of Nonverbal Sensitivity, SFS=Social

Functioning Scale, AIPSS=Assessment of Interpersonal Social Skills.

Table 3. Descriptive statistics (AIPSS subsample, n=94).

Variable n Mean (s.d.) Range

PANSS Positive 93 17.98 (5.91) 7.00-34.00

PANSS Negative 93 14.76 (5.06) 7.00-31.00

SILS Vocabulary 92 23.20 (6.07) 6.00-35.00

TMTA† 94 20.21 (8.40) 7.00-44.27

TMTB† 92 131.51 (76.35) 41.78-360.00

CPT-IP d’ 88 1.75 (0.78) -0.60-3.64

WCST Total Errors 90 38.13 (9.83) 20.00-65.00

DS Total 92 12.02 (3.18) 6.00-23.00

BLERT 92 13.34 (3.49) 3.00-20.00

FEIT 94 22.43 (4.56) 8.00-30.00

HT 85 15.07 (3.56) 4.00-20.00

BT 85 43.99 (10.20) 20.00-59.00

PONS 85 72.68 (9.63) 51.00-91.00

SFS Social Engagement 92 9.48 (3.07) 3.00-15.00

SFS Interpersonal 92 7.11 (1.60) 3.00-9.00

SFS Performance 90 29.76 (5.69) 14.00-39.00

SFS Recreation 92 21.39 (7.20) 5.00-40.00

SFS Pro-social 92 19.69 (12.18) 2.00-53.00

SFS Competence 92 35.53 (3.82) 26.00-39.00

SFS Employment 91 3.87 (2.46) 0.00-10.00

SFS Total 88 127.21 (26.07) 61.00-194.00

AIPSS (% correct) 94 48.41 (13.96) 19.27-83.63

Note: †Raw score, PANSS=Positive and Negative Syndrome Scale, SILS=Shipley

Institute of Living Scale, TMTA=Trail Making Test A, TMTB=Trail Making Test B,

CPT=Continuous Performance Task Identical Pairs, DS=Digit Span, WCST=Wisconsin Card

Sorting Test, BLERT=Bell-Lysaker Emotion Recognition Test, FEIT=Facial Affect Identification

Test, HT=Hinting Task, BT=Brune Task, PONS=Profile of Nonverbal Sensitivity, SFS=Social

Functioning Scale, AIPSS=Assessment of Interpersonal Social Skills.

3.2 Bivariate Correlations Between Study Variables

Bivariate correlations between study variables are presented in Table 4. In hypothesis 1, it was predicted that neurocognition, social cognition, and social functioning would be significantly associated with each other. Hypothesis 1 was partially supported; several social cognitive variables showed significant associations with neurocognitive variables, and several social cognitive variables showed significant associations with the AIPSS, one of the social functioning variables. However, social functioning showed minimal association with neurocognition. Moreover, one of the social functioning measures, the SFS, showed few associations with any study variables, including the AIPSS (r=-0.11, p>0.05). The SFS was negatively associated with severity of PANSS negative symptoms (r=-0.22, p<0.05) and positively associated with SILS Vocabulary (r=0.31, p<0.01). The SFS also showed an unexpected relationship with psychomotor speed (TMTA), where better performance on TMTA was associated with lower scores on the SFS (r=-0.23, p<0.05). In contrast, the AIPSS showed no significant associations with negative symptoms, SILS Vocabulary, or neurocognitive variables, but was significantly associated with all social cognitive variables with the exception of the FEIT

and PONS. Notably, most neurocognitive and social cognitive variables were significantly associated with SILS Vocabulary, and some social cognitive variables were associated with

PANSS negative symptoms. Within domains, aside from the DS which showed minimal associations with study variables, neurocognitive variables were largely associated with each other in expected ways. Similarly, the social cognitive variables were significantly correlated with each other with the exception of the BT and HT (r=0.18, p>0.05). As noted above, the two social

functioning measures, the AIPSS and SFS, were not significantly associated with each other.

Table 4. Bivariate correlations between study variables (n=123).

1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

1. PANSS Pos.

2. PANSS Neg. .23*

3. SILS Vocab. -.16 .01

4. TMTA† .10 -.17 .09

5. TMTB† .03 -.21* .33** -.53**

6. CPT-IP d’ -.14 -.06 .51** .17 .40**

7.WCST Total -.15 -.11 .37** .14 .37** .25** Errors

8. DS -.05 -.04 -.09 .01 .04 .06 -.05

9. BLERT .07 -.30** .38** .30** .39** .25** .33** -.07

10. FEIT .00 -.27** .30** .17 .37** .23* .25** -.03 .52**

11.HT -.02 -.14 .07 .02 .16 .20* .04 .07 .22* .42**

12.BT .02 .30-.18** .22* .57** .37** .38** -.17 .42** .46**

13.PONS -.12 -.25** .30** .13 .25** .34** .32** .04 .50** .46**

14. SFS Social -.23* -.18 .03 -.19* -.05 -.05 .03 .12 -.14 -.01 Engagement

15. SFS -.12 -.20* .05 -.09 -.06 -.12 .09 .07 -.03 .08 Interpersonal

16. SFS -.10 -.13 .21* -.16 -.07 -.05 .15 -.05 .04 .09 Performance

17. SFS -.13 -.21* .28** -.11 -.06 -.07 .07 -.05 -.04 -.02 Recreation

18. SFS Pro- -.10 -.12 .19* -.21* -.04 -.08 .07 -.08 -.09 -.10 social

19.SFS -.13 -.17 .19* -.12 -.07 .01 .05 -.18* .06 -.05 Competence

20. SFS .02 -.10 .14 .00 .17 .06 .04 -.05 .01 .06 Employment

21. SFS Total -.18 -.22* .31** -.23* -.05 -.07 .14 -.09 -.03 -.05

22. AIPSS .08 .02 .02 .14 .12 .19 -.13 -.06 .31** .16

Table 4 cont’d.

11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21.

1. PANSS Pos.

2. PANSS Neg.

3. SILS Vocab.

4. TMTA†

5. TMTB†

6. CPT-IP d’

7. WCST Total

Errors

8. DS

9. BLERT

10. FEIT

11. HT

12. BT .18

13. PONS .37** .40**

14. SFS Social -.11 -.20* -.03

Engagement

15. SFS .05 -.08 -.09 .30**

Interpersonal

16. SFS .19 .04 .00 .11 .25**

Performance

17. SFS -.02 .02 .02 .28** .27** .55**

Recreation

18. SFS Pro- -.12 -.03 -.11 .40** .27** .38** .67** social

19. SFS -.01 .03 -.09 .02 .37** .38** .12 .11

Competence

20. SFS -.04 .20* -.11 .07 .16 .31** .21* .27** .32**

Employment

21. SFS Total -.03 -.03 -.09 .45** .47** .68** .84** .88** .39** .46**

22. AIPSS .22* .23* .03 -.18 .07 -.12 -.08 -.09 .02 -.04 -.11

Note: †Reverse coded score, *p<0.05, **p<0.01, PANSS=Positive and Negative

Syndrome Scale, SILS=Shipley Institute of Living Scale, TMTA=Trail Making Test A,

TMTB=Trail Making Test B, CPT=Continuous Performance Task Identical Pairs, DS=Digit

Span, WCST=Wisconsin Card Sorting Test, BLERT=Bell-Lysaker Emotion Recognition Test,

FEIT=Facial Affect Identification Test, HT=Hinting Task, BT=Brune Task, PONS=Profile of

Nonverbal Sensitivity, SFS=Social Functioning Scale, AIPSS=Assessment of Interpersonal

Social Skills.

3.3 Confirmatory Factor Analysis

In hypothesis 2, it was predicted that utilizing confirmatory factor analysis (CFA), neurocognition and social cognition would emerge as closely related, yet distinct constructs, thus replicating the findings of Sergi and colleagues (2007). Specifically, it was hypothesized that a two-factor model, where neurocognition and social cognition are modeled as separate latent

factors, would provide better fit for the data than a one-factor model where neurocognition and social cognition are combined.

Prior to analysis, multivariate outliers were identified using Mahalanobis distances and removed (n=3). Per Kline (2005), study variables were rescaled prior to CFA and SEM analysis

(i.e., multiplied by a constant) in order to bring their relative variances within a ratio of 1:10 to ease convergence of the models. In the initial run, the two-factor model proved to be better fit for the data than the one-factor model. However, examination of the standardized residuals matrix revealed several large standardized residuals (i.e., >0.10) for the WCST and BT, suggesting that these variables were not well-explained by the model (Bentler, 2006). Thus, the models were respecified, where the WCST and BT were removed form the model. Indeed, removing these variables improved model fit and resulted in few large standardized residuals.

For the AIPSS subsample, the two-factor model [Model χ2(19)=28.64, p=0.07, CFI=0.95,

RMSEA=0.08 (90% CI: 0.00, 0.13); Figure 3] proved to be a superior fit for the data compared

with the one-factor model [Model χ2(20)=60.99, p<0.01, CFI=0.77, RMSEA=0.15 (90% CI: 0.11,

0.19); Figure 4; Δχ2(1)=31.36, p<0.001]. For the two-factor model, the latent factors were

significantly correlated at r=0.58.

Figure 3. Two-factor confirmatory factor analysis model, standardized solution (AIPSS subsample, n=91).

Model χ2(19)=28.64, p=0.07, CFI=0.95, RMSEA=0.08 TMTA BLERT

0.49* 0.65*

TMTB 0.94† 0.58* HT Social Neurocognition Cognition

0.78* 0.58*

CPT 0.50* 0.58* 0.57† FEIT

DS PONS

Note: †factor loading fixed, *p<0.05, TMTA=Trail Making Test A, TMTB=Trail Making

Test B, CPT=Continuous Performance Task Identical Pairs, DS=Digit Span, FEIT=Facial Affect

Identification Test, BLERT=Bell-Lysaker Emotion Recognition Test, HT=Hinting Task,

PONS=Profile of Nonverbal Sensitivity.

Figure 4. One-factor confirmatory factor analysis model, standardized solution (AIPSS subsample, n=91).

Model χ2(20)=60.00, p<0.01, CFI=0.77, RMSEA=0.15 TMTA BLERT

0.41* 0.56*

TMTB 0.76† Combined 0.46* HT Neurocognition and Social Cognition

0.56* 0.63*

CPT FEIT

0.56† 0.53*

DS PONS

Note: †factor loading fixed, *p<0.05, TMTA=Trail Making Test A, TMTB=Trail Making

Test B, CPT=Continuous Performance Task Identical Pairs, DS=Digit Span, FEIT=Facial Affect

Identification Test, BLERT=Bell-Lysaker Emotion Recognition Test, HT=Hinting Task,

PONS=Profile of Nonverbal Sensitivity.

Likewise, for the total sample the two-factor model [Model χ2(19)=34.74, p=0.02,

CFI=0.93, RMSEA=0.08 (90%CI: 0.04, 0.13); Figure 5] was a superior fit for the data compared

with the one-factor model [Model χ2(20)=66.40, p<0.01, CFI=0.78, RMSEA=0.14 (90% CI: 0.10,

0.17); Figure 6; Δχ2(1)=31.66, p<0.001]. For the two-factor model, the latent factors were

significantly correlated at r=0.60. Similar results were obtained using the five multiple imputation

datasets (presented in Appendix N sections a through c).

Figure 5. Two-factor confirmatory factor analysis model, standardized solution (total sample,

n=120).

Model χ2(19)=34.74, p=0.02, CFI=0.93, RMSEA=0.08 TMTA BLERT

0.55† 0.70†

TMTB 0.84* 0.46* HT Social Neurocognition Cognition

0.75* 0.51*

CPT 0.52* 0.60* 0.68* FEIT

DS PONS

Note: †factor loading fixed, *p<0.05, TMTA=Trail Making Test A, TMTB=Trail Making

Test B, CPT=Continuous Performance Task Identical Pairs, DS=Digit Span, FEIT=Facial Affect

Identification Test, BLERT=Bell-Lysaker Emotion Recognition Test, HT=Hinting Task,

PONS=Profile of Nonverbal Sensitivity.

Figure 6. One-factor confirmatory factor analysis model, standardized solution (total sample, n=120).

Model χ2(20)=66.40, p<0.01, CFI=0.78, RMSEA=0.14 TMTA BLERT

0.39† 0.67†

TMTB 0.61* Combined 0.42* HT Neurocognition and Social Cognition

0.49* 0.70*

CPT 0.65* FEIT

0.48*

DS PONS

Note: †factor loading fixed, *p<0.05, TMTA=Trail Making Test A, TMTB=Trail Making

Test B, CPT=Continuous Performance Task Identical Pairs, DS=Digit Span, FEIT=Facial Affect

Identification Test, BLERT=Bell-Lysaker Emotion Recognition Test, HT=Hinting Task,

PONS=Profile of Nonverbal Sensitivity.

3.4 Structural Regression Analyses

For hypothesis 3, it was posited that when neurocognition and social cognition were modeled as separate latent variables within a structural regression model, social cognition would mediate the association between neurocognition and social functioning. Specifically, it was hypothesized that a model considering the indirect effect of neurocognition on social functioning via social cognition would provide better fit for the data than an alternative model where only unique, direct effects of neurocognition and social cognition on social functioning were considered (i.e., where social cognition was not denoted as a mediating variable). In addition, it was hypothesized that the 95% confidence interval (95%CI) for the bootstrapped path estimate for the direct effect between neurocognition and social functioning would overlap with zero, while the 95%CI for the bootstrapped path estimate for the indirect effect between neurocognition and social functioning (i.e., mediated via social cognition) would not overlap with zero, providing evidence for a significant mediation effect. Hypothesis 3 was tested, despite lack of evidence for significant association between neurocognition and social functioning, as Zhao and colleagues

(2010) argue that lack of zero-order effect of the independent variable (i.e., neurocognition) on the dependent variable (i.e., social functioning) does not preclude possibility of a significant mediation effect (i.e., via social cognition).

In the initial run, inclusion of the SFS in the “social functioning” latent variable yielded large standardized residuals (i.e., >0.10), suggesting that the SFS variable was not well-explained by the model (Bentler, 2006). Moreover, the SFS had a negative loading value for the “social functioning” factor. These results were not surprising, as the SFS showed minimal associations with the remaining study variables in the aforementioned bivariate correlation matrix. Therefore,

the model was respecified without the SFS, and the AIPSS variable was included in the model as an observed indicator.

For the AIPSS subsample, when neurocognition and social cognition were modeled as unique, independent predictors of AIPSS performance the model did not fit the data well [Model

χ2(25)=62.25, p<0.01, CFI=0.80, RMSEA=0.12 (90%CI: 0.09, 0.16); see Figure 7]. When the

model was respecified to include a path from neurocognition to social cognition (i.e., “indirect

effect” model), the model fit the data well [Model χ2(26)=38.58, p=0.04, CFI=0.93,

RMSEA=0.08 (90%CI: 0.02, 0.12); see Figure 8] and proved to be a superior fit than the unique

effects model (Δχ2(1)=23.67, p<0.001).

For the indirect effect model, the path estimate between the neurocognition factor and the

AIPSS was non-significant and near-zero. As per the two-factor CFA model, the neurocognition and social cognition latent factors were significantly associated. Interestingly, the path between social cognition and the AIPSS failed to reach significance. The 95%CI for the bootstrapped parameter estimate for the direct effect path between neurocognition and AIPSS performance overlapped with zero [n=1000 converged simulations, Normal Theory 95%CI: -0.28, 0.29; Bias

Corrected 95%CI: -0.26, 0.31]. However, the 95%CI for the indirect effect also slightly overlapped with zero [n=1000 converged simulations, Normal Theory 95%CI: -0.03, 0.46, Bias

Corrected 95%CI: -0.03, 0.46], indicating absence of a significant mediation effect (see Figure 9 for distribution of path estimates). Notably, mean fit indices for the bootstrapped indirect effect model were less favorable than for the non-bootstrapped model [mean Model χ2=63.30, mean

CFI=0.83, mean RMSEA=0.13].

The correlation matrix suggested SILS Vocabulary and PANSS negative symptoms as potential covariates for neurocognition and social cognition. Thus the indirect effects model was respecified to include these potential covariates. However, their inclusion did not result in improved model fit, nor did their inclusion markedly alter the relationships between neurocognition, social cognition, and AIPSS performance. Considering that the current sample size is relatively small for SEM analysis, the simpler model (i.e., model without covariates) was preferred.

Figure 7. Direct effects model, standardized solution (AIPSS subsample, n=91).

Model χ2(25)=62.25, p<0.01, CFI=0.80, RMSEA=0.12 TMTA BLERT

0.49* 0.66*

TMTB 0.96† 0.59* HT Social Neurocognition Cognition

0.57* 0.78*

CPT FEIT 0.07ns 0.27*

0.49* 0.54†

DS PONS

AIPSS

Note: †factor loading fixed, *p<0.05, nsp>0.05, TMTA=Trail Making Test A,

TMTB=Trail Making Test B, CPT=Continuous Performance Task Identical Pairs, DS=Digit

Span, FEIT=Facial Affect Identification Test, BLERT=Bell-Lysaker Emotion Recognition Test,

HT=Hinting Task, PONS=Profile of Nonverbal Sensitivity, AIPSS=Assessment of Interpersonal

Problem Solving Skills.

Figure 8. Indirect effect model, standardized solution (AIPSS subsample, n=91).

Model χ2(26)=38.58, p=0.04, CFI=0.93, RMSEA=0.08 TMTA BLERT

0.67* 0.49*

TMTB 0.95† 0.57* HT 0.58* Social Neurocognition Cognition

0.58* 0.77*

CPT FEIT

0.02ns 0.30ns 0.50* 0.56†

DS PONS

AIPSS

Note: †factor loading fixed, *p<0.05, nsp>0.05, TMTA=Trail Making Test A,

TMTB=Trail Making Test B, CPT=Continuous Performance Task Identical Pairs, DS=Digit

Span, FEIT=Facial Affect Identification Test, BLERT=Bell-Lysaker Emotion Recognition Test,

HT=Hinting Task, PONS=Profile of Nonverbal Sensitivity, AIPSS=Assessment of Interpersonal

Problem Solving Skills.

Figure 9. Distribution of parameter estimates for direct (path c) and indirect (path a x b) effects

(AIPSS subsample, n=1000 converged simulations).

Similarly, for the total sample, when neurocognition and social cognition were modeled as independent predictors of AIPSS performance (i.e., direct effects model) the model did not fit the data well [Model χ2(26)=77.61, p<0.01, CFI=0.75, RMSEA=0.14 (90%CI: 0.10-0.16); see

Figure 10]. When the model was respecified to include a path from neurocognition to social

cognition (i.e., indirect effect model), the model fit was borderline [Model χ2(25)=49.88, p<0.01,

CFI=0.87, RMSEA=0.10 (90%CI: 0.06-0.14); see Figure 11], but proved to be a superior fit than

the unique effects model (Δχ2(1)=27.73, p<0.001).

For the indirect effect model, the path estimate between the neurocognition factor and the

AIPSS was non-significant and near-zero. The path between the neurocognition and social cognition latent factors was significant. As above, the path between social cognition and the

AIPSS failed to reach significance. The 95%CI for the bootstrapped parameter estimate for the path between neurocognition and AIPSS performance overlapped with zero [n=881 converged

simulations, Normal Theory 95%CI: -0.320, 0.443; Bias Corrected 95%CI: -0.281, 0.476]. The

95%CI for the indirect effect also overlapped with zero [n=881 converged simulations, Normal

Theory 95%CI: -0.136, 0.423; Bias Corrected 95%CI: -0.129, 0.428], indicating absence of a

significant mediation effect (see Figure 12 for distribution of path estimates). Similar to the

AIPSS subsample, mean fit indices for the bootstrapped data were less favorable than for the non-

bootstrapped data [mean Model χ2=74.94, mean CFI=0.78, mean RMSEA=0.14]. Similar results

were obtained from the five multiple imputation datasets, although notably, the 95%CI for the

indirect effect in two of the five multiply imputed datasets did not overlap with zero, indicating

presence of a mediation effect (see Appendix N sections d through h).

Figure 10. Direct effects model, standardized solution (total sample, n=120).

Model χ2(26)=77.61, p<0.01, CFI=0.75, RMSEA=0.14 TMTA BLERT

0.56† 0.69†

TMTB 0.90* 0.49* HT Social Neurocognition Cognition

0.46* 0.76*

CPT FEIT 0.09ns 0.19ns

0.47* 0.68*

DS PONS

AIPSS

Note: †factor loading fixed, *p<0.05, nsp>0.05, TMTA=Trail Making Test A,

TMTB=Trail Making Test B, CPT=Continuous Performance Task Identical Pairs, DS=Digit

Span, FEIT=Facial Affect Identification Test, BLERT=Bell-Lysaker Emotion Recognition Test,

HT=Hinting Task, PONS=Profile of Nonverbal Sensitivity, AIPSS=Assessment of Interpersonal

Problem Solving Skills.

Figure 11. Indirect effect model, standardized solution (total sample, n=120).

Model χ2(25)=49.88, p<0.01, CFI=0.87, RMSEA=0.10 TMTA BLERT

0.71† 0.55†

TMTB 0.83* 0.47* HT 0.60* Social Neurocognition Cognition

0.51* 0.75*

CPT FEIT

0.07ns 0.20ns 0.52* 0.67*

DS PONS

AIPSS

Note: †factor loading fixed, *p<0.05, nsp>0.05, TMTA=Trail Making Test A,

TMTB=Trail Making Test B, CPT=Continuous Performance Task Identical Pairs, DS=Digit

Span, FEIT=Facial Affect Identification Test, BLERT=Bell-Lysaker Emotion Recognition Test,

HT=Hinting Task, PONS=Profile of Nonverbal Sensitivity, AIPSS=Assessment of Interpersonal

Problem Solving Skills.

Figure 12. Distribution of parameter estimates for direct (path c) and indirect (path a x b) effects

(total sample, n=881 converged simulations).

CHAPTER 4

Discussion

Utilizing confirmatory factor analysis (CFA) and structural equation modeling (SEM), the current study investigated the relationships between neurocognition, social cognition, and social functioning in a sample of outpatients diagnosed with schizophrenia or schizoaffective disorder. It was hypothesized that 1) neurocognition, social cognition, and social functioning would be significantly associated with each other, 2) that a two-factor CFA model, with neurocognition and social cognition modeled as separate latent constructs, would provide better fit for the data than a single-factor CFA model where neurocognition and social cognition are combined, and 3) that a SEM model considering the indirect effect of neurocognition on social functioning via social cognition would provide better fit for the data than an alternative model where only unique, direct effects of neurocognition and social cognition on social functioning are considered (i.e., where social cognition is not denoted as a mediating variable).

Hypothesis 1 (i.e., significant associations between neurocognition, social cognition, and social functioning) was partially supported. Aspects of neurocognition and social cognition were significantly associated with each other, suggesting possible overlap between social and non- social cognitive abilities. Likewise, aspects of social cognition were significantly associated with the Assessment of Interpersonal Problem Solving Skills (AIPSS), a performance-based measure of social functioning which requires the respondent to attend to social cues in order to decode interpersonal problems and to effectively deploy social skills in an attempt to resolve the interpersonal problem. This finding was not surprising given that performance on the AIPSS

71 72

would undoubtedly depend upon social cognitive abilities such as identification of the emotional and mental states of others. Contrary to expectations, the AIPSS was not significantly associated with any of the neurocognitive variables. However, Nuechterlein and colleagues (2008) note that neurocognition appears to be more strongly associated with some aspects of social functioning

(e.g., occupational and/or academic functioning) than others (e.g., interpersonal functioning).

Notably, the Social Functioning Scale (SFS), a self-report measure of engagement and competency in various aspects of social and independent daily functioning was not significantly associated with the AIPSS. Moreover, the SFS was not associated with any social cognitive variable or neurocognitive variables beyond a positive association with verbal intellectual abilities and an unexpected negative association with psychomotor speed. These findings regarding the SFS were unexpected and raise the specter that perhaps the SFS may not have been a valid index of social functioning for the current sample.

The SFS was validated on a younger and less chronic sample of schizophrenia patients, most of whom were living with their family of origin (Birchwood et al., 1990). In addition, approximately one-quarter of the SFS validation sample was gainfully employed. In contrast, the current sample was considerably older, had a more chronic course of illness, a slightly higher level of unemployment, and very few participants lived with their family of origin. Several SFS items require some degree of disposable income (e.g., going to a concert, movie, or sporting event, eating out at a restaurant, taking a class), which, for an individual with chronic and severe mental illness living independently on a fixed disability income, may be somewhat cost prohibitive. Thus, perhaps for some individuals in the current sample, engagement in some social activities assessed by the SFS was associated with factors other than level of social functioning,

neurocognitive impairment, or social cognitive impairment. Several other SFS items assess fairly low-level independent living skills (e.g., basic hygiene, basic housekeeping) which may only be sensitive to relatively severe levels of impairment (Addington & Addington, 1999). Previous research involving the SFS has yielded mixed results; although associations between the SFS and neurocognition and social cognition have been reported in the literature (e.g., Addington et al.,

2010), two studies noted absence of associations between the SFS and neurocognition (Addington

& Addington, 1999; Nuechterlein et al., 2008) and social cognition (Nuechterlein et al., 2008) in schizophrenia. Another possibility is that some patients in the current sample were not reliable historians of their own level of social functioning on the SFS. However, in their validation study

Birchwood and colleagues (1990) noted excellent congruence between patient-report and parent- report on the SFS. Further research is needed to assess the reliability and validity of the SFS for schizophrenia patients with a more chronic course of illness.

Hypothesis 2, which posited that a two-factor model of neurocognition and social cognition would provide better fit for the data than a one-factor model, was supported thus replicating the findings of Sergi and colleagues (2007). As per previous research (e.g., Addington et al., 2010; Sergi et al., 2007; Vauth et al., 2004), a moderately-high association between the neurocognition and social cognition latent variables was observed, indicating that there is considerable overlap between the constructs, thus lending credence to the assertion that neurocognition and social cognition are distinct, yet associated domains in schizophrenia. This overlap may reflect that neurocognitive abilities in domains such as psychomotor speed, sustained attention, working memory, and executive functions are used in conjunction with more specialized social cognitive abilities (e.g., affect recognition, theory of mind) to process socio- emotional stimuli. The overlap may also reflect saturation of neurocognition and social cognition

with “deficit g”, as suggested by the significant associations between verbal intellectual abilities and the majority of social cognitive and neurocognitive variables. Another possibility is that the overlap represents the shared impact of an unmeasured third variable (e.g., sensory or perceptual deficits) on neurocognition and social cognition. Finally, although the neurocognitive and social cognitive test batteries used in the current study were fairly comprehensive, it is conceivable that additional overlap (or possibly total overlap, with social cognition being fully explained by neurocognition) between the constructs would be identified if additional measures of each domain were included. Future studies which employ neuroimaging techniques such as functional magnetic resonance imaging (fMRI) and psychophysiological methods such as electroencephalographic (EEG) recordings of event-related potentials (ERP) may help further elucidate the nature of the relationship between neurocognition and social cognition in schizophrenia (e.g., shared and distinct neural substrates), as well as the potential direct and indirect effects of potential third variables such as perceptual and sensory deficits on downstream neurocognition and social cognition.

The initial run of the two-factor CFA indicated that the Wisconsin Card Sort Task

(WCST) and the Brune Task (BT) were poorly explained by the model. Laws (1999) criticized the WCST as being more reflective of a generalized intellectual deficit than executive dysfunction in schizophrenia. Thus, it is possible that for the current sample the WCST was a poor fit for the CFA model due to excessive saturation with “deficit g.” For the BT, participants are required to correctly sequence a story and infer mental states of illustrated characters in a comic strip-style task. Given the sequencing and reasoning requirements of the task, as well as the sparse psychometric data available in the literature regarding the BT (Couture, Penn, &

Roberts, 2006; Harrington, Siegert, McClure, 2005), it seems plausible that the BT was a poor fit

for the model due to poor discriminant validity. That is, perhaps the BT, in addition to measuring theory of mind, also has a relatively high loading on neurocognitive abilities. Removing the BT from the CFA model yielded improved model fit, but may have biased subsequent analyses toward confirming a two-factor solution. However, if the BT does indeed have poor discriminant validity, allowing the task to remain in the model may have biased subsequent analyses toward confirming a one-factor solution. Further research regarding the validity of the BT in schizophrenia samples is needed.

The finding that neurocognition and social cognition are separable, but associated constructs has implications for psychosocial rehabilitation strategies for individuals with schizophrenia. The medium-high correlation between the neurocognitive and social cognitive factors observed in the current study suggest that social cognitive impairments may be impacted to some degree by treatments geared toward improving neurocognition in schizophrenia such as cognitive remediation interventions (e.g., Medalia & Choi, 2009). However, sizeable proportion of social cognition is not accounted for by neurocognition in schizophrenia, indicating that interventions specifically targeting social cognition may be a fruitful line of research. Indeed, recent studies which have employed social cognitive skills training interventions have yielded promising results regarding remediation of social cognitive deficits in schizophrenia (e.g., Horan,

Kern, Shokat-Fadai, Sergi, Wynn, & Green, 2009; Horan et al., 2011; Roberts & Penn, 2009).

There was mixed evidence for Hypothesis 3, which posited that a significant indirect effect between neurocognition, social cognition, and social functioning would be supported by the data. In comparison with the direct effects model, the indirect effect model provided significantly better fit for the data, and the 95% confidence interval (95%CI) for the bootstrapped parameter

estimate for the direct effect path between neurocognition and social functioning squarely overlapped with zero. However, the 95%CI for the bootstrapped parameter estimate for the indirect effect path slightly overlapped with zero for the AIPSS subsample and total sample, indicating absence of a significant mediation effect. Examination of the distribution of the bootstrapped parameter estimate for the indirect pathway (Figures 9 & 12) suggests the possibility that the current study may have been underpowered to detect mediation, as the mean and bulk of the distribution of the bootstrapped indirect effect parameter estimates lie above zero.

Moreover, two of five multiply imputed datasets yielded 95%CI for the indirect effect that did not overlap with zero, lending further support for the hypothesis that a weak mediation effect exists.

A larger sample size would likely provide the additional power needed to reliably detect

mediation.

Due to the lack of association between the SFS and other study variables and indication

that the SFS was poorly explained by the SEM model, the current study’s assessment of social

functioning was restricted to the AIPSS. Addition of other reliable and valid measures of social

functioning to the model may serve to amplify the “signal-to-noise” ratio in order to facilitate

detection of a mediation effect. As noted above, further research is warranted to determine the

reliability and validity of the SFS for chronic schizophrenia. Utilization of social functioning

measures that focus on occupational functioning was not a viable option due to the low rate of

employment in the current sample. Future studies could employ informant-rated (e.g., family

member, friend, significant other, case manager) social functioning measures in addition to

performance-based and self-report social functioning measures.

The current study adds to the schizophrenia literature by replicating the findings of Sergi and colleagues (2007) regarding the distinction between neurocognition and social cognition in schizophrenia. Moreover, given the relative dearth of information regarding the psychometric properties of some social cognition measures, theory of mind tests in particular, and the relationships between such measures and other tests of social cognition (e.g., facial affect identification) and neurocognition, findings from the current study regarding the reliability and correlates of these measures may inform future research. Strengths of the current study include assessment of multiple domains of neurocognition and social cognition. As noted above, a weakness of the current study is that the sample size was small for SEM analysis and hence may not have been of sufficient power to permit detection of a mediation effect. Another weakness is that only one measure of social functioning was included in the SEM models, which likely negatively impacted the likelihood of detecting mediation. Finally, the amount of missing data on the AIPSS, the outcome variable, was large. However, missing data were handled in a variety of ways (i.e., listwise deletion for the dependent variable, full-information maximum likelihood method, and multiple imputation) and the main findings were consistent across these techniques.

In summary, the current study replicated and bolstered previous research surrounding the nature of neurocognition and social cognition in schizophrenia. Specifically, findings of the current study provided support to a two-factor model, with neurocognition and social cognition modeled as separate but associated factors. Regarding the mediator role of social cognition in the relationship between neurocognition and social functioning, the results suggested the possible presence of a mediation relationship, as evidenced by the superior fit of the indirect effect model in comparison to the direct effects model. However, a formal test of mediation did not support the presence of a significant indirect effect, possibly attributable to insufficient power. The results of

the current study also illuminated areas for further research, such as exploration of the validity and reliability of social functioning measures for individuals with chronic schizophrenia.

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Zuroff, D.C., & Colussy, S.A. (1986). Emotion recognition in schizophrenic and depressed

inpatients. Journal of Clinical Psychology, 42, 411-417.

APPENDIX A. Shipley Institute of Living Scale

Instructions: In the test below, the first word in each line is printed in capital letters. Opposite it are four other words. Circle the one word which means the same thing, or most nearly the same thing as the first word. If you don’t know, guess. Be sure to circle the one word in each line that means the same thing as the first word.

Example: LARGE red big silent wet

Instructions: Complete the following by filling in either a number or a letter for each dash (___). Do the items in order, but don’t spend too much time on any one item.

Example: A B C D ____

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APPENDIX B. Wisconsin Card Sort Task-64

Administration

 Click on the WCST-64 icon on the desktop and ensure the speakers are turned on

WCST-64 Computer Version 2.lnk  Select “New Client File”  Enter subject’s information into the appropriate boxes – be sure to include the participant’s age – this is needed to generate the score report.  Select “Add Protocol”  Select “Administer” to start the task  Read these instructions to the participant:

“This test is a little unusual because I am not allowed to tell you very much about how to do it. You will be asked to match each of the cards that appear here” (point to the first response card at the bottom center of the screen) “to one of these four key cards” (point to each of the stimulus cards at the top of the screen).

“On the keyboard in front of you are four symbols which resemble the key cards” (point to each of the Keytops on the keyboard followed by they stimulus card which it represents). “To make a match, simply press the key with the symbol that you believe matches the card at the bottom of the screen” (point to the first response card at the bottom center of the screen).

“The computer will place your card under the key card that you select, and a new card will appear at the bottom of the screen. If you wish to change your answer before the card stops moving, immediately press the Escape Key. The Escape Key is the default cancel command. You may elect to change this via View/Options. You will then be permitted to select again. However, you may not change your answer after the card stops moving. If this happens don’t try to hit another key, just go on to the next card.

“I cannot tell you how to match the cards, but the computer screen will show you each time whether you are right (correct) or wrong (incorrect). I will also say the same word the computer shows on the screen ‘right (correct)’ or ‘wrong (incorrect),’” or “The computer will also say the same word it shows on the screen, ‘right (correct)’ or ‘wrong (incorrect).’”

“If you were wrong, simply try to match the next card correctly, and then continue matching the cards correctly until the test is over. There is no time limit on this test. Are you ready? Let’s begin.”

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APPENDIX C. Trailmaking Test B

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APPENDIX D. Continuous Performance Task – Identical Pairs

Administration

 Open the CPT-IP program from the desktop “MATRICS CPT” shortcut MATRICS CPT.lnk  Click the “Login” button, enter username and password  On main menu, click “New Experiment”  Date should be filled in by the computer – double-check that the computer’s calendar & clock are correct  Sequence file: click “Browse” and select “MATRICS.sav” and click “Open”  Fill out remaining fields on the Experiment Information Screen to specify respondent information (subject ID, session number, age, sex) and click “OK” and the start screen will appear. Skip the “Practice file” option – a practice will be offered by the program so you don’t need to select it  Read aloud the instructions that appear on the screen: “For the next 10 minutes we’re going to measure attention and memory. You are going to see numbers flash on the screen.” The instructions go on to explain the task, which is basically for the participant to press down on the left mouse button whenever a number appears on the screen twice in a row. * * * Progress through the next several screens, reading all instructions out loud. When the screen says “Would you like a practice block?” type “y” for yes.

 Hand the mouse to the participant. The participant’s eyes should be 2 feet from the screen.  Press any key (except “Enter”) to begin the practice block. After the practice block look at the scores to make sure the participant understands the task (the maximum score of responses is 2 hits, 0 false alarms, and 0 randoms for the practice block). Once you’ve looked at the scores click “Close”.  If the participant understands the task, type “n” (for no) to the on-screen question “Would you like another practice block?” (type “y” for another practice block if necessary)  When the participant is ready to start the test, press any key (except “Enter”) to start the 2- digit task.  When the 2-digit task ends a set of instructions will appear for the 3-digit task (and likewise between the 3-digit and 4-digit tasks) – take a 1-minute break between the tasks and press any key (except “Enter”) to start the next task.  At the end of the test, the following screen will appear: “Experiment Complete Thank-you for participating” Press “ESC” to complete the administration and return to the main menu. Data will be stored in C:\MATRICSCPT\Results.

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APPENDIX E. Digit Span

Instructions:

Digits Forward: I’m going to say some series of numbers. After each set of numbers, please repeat them back to me, in the same order.

Digits Backward: Now I’m going to say some more numbers, but this time please say them back in the reverse order.

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APPENDIX F. Trailmaking Test A

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APPENDIX G. Facial Emotion Identification Test

Instructions: I’m going to show you some pictures of faces expressing one of seven different emotions: Happiness, anger, fear, sadness, disgust, surprise, or neutrality. By neutrality, I mean no emotion. For each face, say which of these emotions is being expressed. If you aren’t sure, guess. Here are the seven choices (give subject the BLERT emotions card, and show the faces one at a time. Note each response on the answer sheet and move to the next face. No time limit).

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APPENDIX H. Bell-Lysaker Emotion Recognition Test

Instructions: I’m going to show you video clips of a man saying sentences and expressing one of seven different emotions: Happiness, anger, fear, sadness, disgust, surprise, or neutrality. By neutrality, I mean no emotion. For each video clip, say which of these emotions is being expressed. If you aren’t sure, guess. Here are the seven choices (give subject the BLERT emotions card, and show the faces one at a time. Note each response on the answer sheet and move to the next face. No time limit).

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APPENDIX I. Profile of Nonverbal Sensitivity

Instructions: The film and sound track you are about to witness was designed so that we may learn how well people can match facial expressions, body movements, and tone of voice to the actual situation in which the expressions, movements, and tones originally occurred. You will see and hear a series of audio and video segments, and for each one you are to judge which of two real-life situations is represented by the segment you have just seen or heard. After each segment you will have a short period of time in which to make your judgment.

Some of the visual segments will have no sound track. Some of the visual segments will have a sound track, but you will not be able to understand the words. Instead, you will hear speech that has been changed in various ways, so that you will be able to judge only the tone of voice in which something was said. Some of the segments will be made up of only these speech altered portions of the sound track, and for these there will be no film to watch at all. In fact, the very first segment is like this.

After each segment, I will list two brief descriptions of everyday life situations. One of these descriptions correctly describes the actual situation you will see and/or hear, while the other description does not describe situation accurately. For each segment, please indicate your response out loud.

Many of the choices will be difficult, but you should choose one of the descriptions even though you may feel quite uncertain about the correct answer. Choose the more likely description for each segment even if you feel you might be guessing. Your guesses may be much more accurate than you imagine.

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APPENDIX J. Hinting Task

Instructions: Read the stories to the participant followed by the prompt. If a correct response is not given for the first hint, then introduce the next hint.

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APPENDIX K. Brune Task

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theory of mind – picture stories

Sequencing score:

1./4. card correct=2 points each 2./3. card correct=1 point each

1st picture story correct sequence 1 2 3 4 P E A R patient’s sequence points (max 6)

Sequencing Time:

Questionnaire:

a) What does the red person believe the blue one intends to do? (2nd order belief)

b) What does the red person expect from the blue person? (Reciprocity)

Sum of points (max 8):

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Subject# ______Date ______

2nd picture story correct sequence 1 2 3 4 H U R T patient’s sequence points (max 6)

Sequencing Time:

Questionnaire:

a) What does the blue person believe is in the bag? (false belief)

b) What’s in the bag? (reality)

c) What does the blue person believe the red person intends to do? (2nd order false belief)

d) What does the red person expect, the blue person believes, what he (the red one) intends to do? (3rd order false belief)

e) What do you think the red person intended to do? (deception)

Sum of points (max 11):

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Subject# ______Date ______

3rd picture story correct sequence 1 2 3 4 H O L E patient’s sequence points (max 6)

Sequencing Time:

Questionnaire:

a) What does the red person believe the others intend to do? (2nd order false belief)

b) What do the two characters want the red person to believe they intend to do? (cheating)

c) What do they intend to do? (deception)

d) What does the red person now think the others intended to do? (cheating detection)

Sum of points (max 10):

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Subject# ______Date ______

4th picture story correct sequence 1 2 3 4 J A I L patient’s sequence points (max 6)

Sequencing Time:

Questionnaire:

a) What does the bald person think the other person intends to do? (2nd order belief)

b) What does the bald person expect from the other person? (reciprocity)

Sum of points (max 8):

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Subject# ______Date ______

5th picture story correct sequence 1 2 3 4 J A C K patient’s sequence points (max 6)

Sequencing Time:

Questionnaire:

a) What does the blond person belief is in the box? (false belief)

b) What’s in the box? (reality)

c) What does the blond person belief the other person intends to do? (2nd order false belief)

d) What does the person with the dark hair expect the blond person to believe he (the dark person) intends to do? (3rd order false belief)

e) What do you think the dark haired person intended to do? (deception)

Sum of points (max 11):

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Subject# ______Date ______

6th picture story correct sequence 1 2 3 4 B I K E patient’s sequence points (max 6)

Sequencing Time:

Questionnaire:

a) What does the blue person intend to do? (intention)

b) What does the shopgirl beliefe has happened? (false belief)

c) What do the blue and the red person intend to do? (cheating)

d) What does the red person expect from the blue person? (reciprocity)

e) What does the shopgirl now think the boys intended to do? (cheating detection)

Sum of points (max 11):

APPENDIX L. Assessment of Interpersonal Problem Solving Skills

ASSESSMENT OF

INTERPERSONAL PROBLEM SOLVING SKILLS

ADMINISTRATION MANUAL

Clyde P. Donahoe Michael J. Carter William D. Bloem Gaye L. Leff

ACKNOWLEDGEMENTS

This manual and the associated videotape were made possible, in part, by grants from the National Institute of Handicapped Research and the Veterans Administration Rehabilitation Research and Development Service.

The authors express appreciation to Robert P. Liberman, M.D., for his assistance in obtaining these grants and making possible the work accomplished in this project.

Janice K. Artzer participated in the early stages of the project, and her clinical and creative ideas are very much appreciated.

We thank David Foy and Steven Boone for their contributions in the early stages of the project.

Very special thanks are due to Charles Wallace, whose work in interpersonal problem solving preceded this project, and whose conceptualization of receiving, processing, and sending skills provides the basis of the methodology.

Special acknowledgements are given to the staff of the Social and Independent Living Skills Program, Department of Veterans Affairs Medical Center - West Los Angeles, whose assistance made the development of these materials possible.

FOR ADDITIONAL INFORMATION

Inquiries about this manual and the associated videotape should be addressed to

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Clyde P. Donahoe, Ph.D. Psychology Service (116B) Audie L. Murphy Memorial Veterans Hospital 7400 Merton Minter Blvd. San Antonio, Texas 78284 (210) 617-5121 [email protected]

The AIPSS videotape and administration manual were developed with U.S. Federal grants and may be reproduced without permission.

REFERENCES

Addington J. & Addington D. (1999) Neurocognitive and social functioning in schizophrenia. Schizophrenia Bulletin, 2, 173-182.

Addington, J. & Addington, D. (1998). Facial affect recognition and information processing in schizophrenia and bipolar disorder. Schizophrenia Research, 32, 171-181.

Addington J., McCleary, L. & Munroe-Blum, H. (1998) Cognitive and social Dysfunction in Schizophrenia. Schizophrenia Research, 34, 59-66.

Donahoe, C.P., Carter, M.J., Bloem, W.D., Hirsch, G.L., Laasi, N., & Wallace, C.J. (1990). Assessment of interpersonal problem-solving skills. Psychiatry, 53, 329-339.

Grant, C., Addington, J., Addington, D. & Konnert, C. (2001) Social functioning in first and multi-episode schizohrenia. Canadian Journal of Psychiatry, 46, 669-672

Spaulding, W. D., Reed, D. Sullivan, M., Richardson, C., & Weiler, M. (1999). Effects of cognitive treatment in psychiatric rehabilitation. Schizophrenia Bulletin, 25, 657-676.

Spaulding, W., Fleming, S., Reed, D. R., Sullivan, M. Storzbach, D., & Lam, M. (1999). Cognitive functioning in rehabilitation of schizophrenia. Schizophrenia Bulletin, 25, 275-289.

Spaulding, W. D. & Poland, J. S. (2001). Cognitive rehabilitation for schizophrenia: Enhancing social cognition by strengthening neurocognitive functioning. In P. W. Corrigan & D. L. Penn (Eds.), Social Cognition and Schizophrenia (pp. 217-247). Washington D.C.: American Psychological Association.

Sullivan, G. Marder, S. R., Liberman, R. P., Donahoe, C. P., & Mintz, J. Social skills and relapse history in outpatient schizophrenics. Psychiatry, 53, 340-345.

INTRODUCTION

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Assessment of Interpersonal Problem Solving Skills (AIPSS) is a method for determining particular cognitive and behavioral performance deficits individuals might have in difficult interpersonal situations. These are situations between two people in which one person hinders the second person from obtaining a desired goal. The second person must determine the nature of the problem, decide on some appropriate solution, and then perform the solution in a socially appropriate and effective manner. An example of such a situation might be:

You arrive for a job interview at the appointed time. You tell the receptionist, "Hello, my name is Mr. Jones, and I'm here for an interview with Mr. Smith." The receptionist replies, "I'm sorry, but Mr. Smith has gone home for the day."

The problem, of course, is that you want a job interview, but the receptionist has thrown an obstacle in the way: you learn that your interviewer is not available. You consider some solutions: you might leave the office and call back later; you might ask if there is someone else you could talk with; you might leave a message for Mr. Smith to call you; you might get angry with the receptionist and let her know what an inconsiderate employer Mr. Smith is. All of these solutions lead to some positive or negative consequences. You must decide which alternative you think is best, and then enact the solution. Most of these alternatives require that you say something to the receptionist, and the outcome will be influenced both by what you say and how you say it. That is, both the content of the solution and the performance of it are important factors in how well you realize your desired goal.

This analysis implies a problem solving model of social skills. First, you must recognize the existence of a problem. This calls for skills of problem identification. You also must conceptualize the problem by understanding what is the goal and what is the obstacle. The ability to describe the goal and obstacle is problem description. Together, problem identification and problem description are called receiving skills. The next step involves some complicated cognitive processing in which you must consider various alternatives, identify and weigh the consequences, and choose which alternative you think is best; these skills are called processing skills. Finally, you must be able to enact a solution. Sending skills consist of content skills (choosing the right thing to say or do), and performance skills, (how you say or do it). Performance skills include appropriate eye contact, voice volume, body posture, gestures, facial affect, speech timing, etc.

This manual describes the scoring and administration of the AIPSS, an instrument for assessing social skills, using a problem-solving model. Using the terminology proposed by Wallace et al. (1980), the constructs measured by the instrument are operationally defined as Receiving-Processing-Sending (RPS) skills. It is hypothesized that the RPS model is sequential: competent performance at one stage depends on competent performance at previous stages.

AIPSS uses videotaped presentations of social interactions to assess receiving, processing, and sending skills. The assessment consists of 13 videotaped scenes and a demonstration scene, each showing two characters engaged in a social interaction. Three scenes present no particular problem, and ten scenes present a problem in which the primary character is faced with an obstacle presented by the other person. The examiner pauses the videotape after each scene and asks the examinee a series of questions directed at assessing receiving skills (problem identification and problem description); processing skills; and sending skills (content, performance, and overall). Receiving and processing skills are assessed through simple questions

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requiring relatively brief verbal responses. Sending skills require the examiner to engage the examinee in a role play of a response to the situation. Specific criteria are used for scoring the examinee's responses.

The AIPSS was developed based primarily on research with schizophrenic outpatients. See Donahoe, et al. (1990) for a description of its development and for information concerning the reliability and validity of the instrument.

Receiving Skills

Identification: Description: Is there a problem? What is the goal and obstacle? Processing Skills: What do I think I should do? Sending Skills: Content What do I actually say or do? Performance How do I perform the response? Overall How competent is my response?

ADMINISTRATION SUMMARY

Set up testing site, materials, and equipment. Give initial instructions to the examinee. Present demonstration scene and practice until the examinee understands the procedure. Present and score 13 assessment scenes: Give instructions. Ask receiving skills questions. Ask processing questions. Enact sending skills role play. SET-UP MATERIALS AND EQUIPMENT Videocassette recorder (VCR) and television monitor Videotape: Assessment of Interpersonal Problem Solving Skills Blank audiocassette tape (30 min or more) AIPSS Administration Manual and Scoring Sheet Pen or Pencil TESTING SITE A quiet testing room free of distractions is required. It should be large enough to allow room for the video and audio equipment and two chairs. There should also be ample room for conducting role plays in which some movement is required.

The two chairs are placed facing each other in front of the video monitor. That is, the examiner and examinee are facing each other, but are both able to view the monitor. The audiocassette recorder is within convenient reach of the examiner. The examiner has the administration manual and scoring sheet in hand.

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Before the examination begins, the examiner loads the assessment tape into the VCR, performs whatever color adjustments are required and cues the tape to the demonstration scene. The sound level is adjusted during this process. The audiotape is loaded and the audiocassette recorder is readied. The examinee is then invited into the examining room and asked to be seated in the examinee's chair.

Note on the AIPSS videotape The AIPSS videotape, which is actually titled Assessment and Training of Interpersonal Problem Solving Skills consists of "Assessment Scenes" and "Training Scenes." Use the Assessment Scenes in the administration of AIPSS. The Training Scenes were developed for a pilot project to train problem solving skills. These scenes are very experimental, and most users of AIPSS should ignore this section of the videotape. For purposes of training, use the more recently developed module, Training of Interpersonal Problem Solving Skills.

INITIAL INSTRUCTIONS After the examinee sits down, is relaxed, and is ready to begin, the examiner instructs the examinee as follows: You will watch a videotape with brief scenes that show two people talking to each other. At some point in their conversation a problem may or may not occur.

You are to watch each scene very carefully and put yourself in the place of one of the people that I will ask you to identify with. The scenes are very short, and I can play each one only once, so be sure to pay close attention to the videotape. After each scene, I will stop the tape and ask you a few questions. First, I will ask you if there is a problem in the scene. If there is a problem, then I will ask you to explain the problem to me. Then I will ask you to tell me exactly what you would say or do if you were in that situation. Then, I will ask you to show me what you would say or do if we were really in the situation on the videotape. Before we start, we will practice the entire procedure on a demonstration scene, so just relax and pay close attention to the videotape.

INSTRUCTIONS FOR EACH SCENE All scenes are administered with the same procedure, with some exceptions for the demonstration scene. Following is the step by step procedure; details pertaining to each scene are given in the pages for each scene.

If this is your first reading of this section, some of the details regarding scoring may not be clear; these should become clear after you have had a chance to read SCORING INSTRUCTIONS.

The novice examiner may have difficulty in scoring during the assessment. For that reason, the audiotape recorder is used to record the examinee's verbal responses and may be scored after the examination is complete.

At the beginning of each scene:

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Say: Pay close attention to the next scene. Play the videotape. When the scene freezes, say: Please identify with (indicate person in scene). When the scene is finished, pause the videotape. Release the pause button on the audiotape. RECEIVING SKILLS Ask: Is there a problem in this scene? If the examinee says "Yes" or "No," then score Identification. If the examinee is vague or uncertain, say: From what you have seen from the videotape, please decide whether or not there is a problem in this scene, and answer yes or no. After the examinee responds, score Identification.

INSTRUCTIONS FOR EACH SCENE For scenes with no problems, if the examinee said there was a problem, continue with the assessment procedure, but do not score Description, Processing, Content, Performance, and then Overall. For a problem scene, if the examinee said there was no problem, do not continue the assessment procedure for that scene. Description, Processing, Content, Performance, and Overall are scored as 0 (see SCORING INSTRUCTIONS). Skip to the next scene.

For problem scenes for which the examinee said there was a problem, continue with the assessment procedure. Say: Please explain the problem to me as if I have never seen the videotape before. If examinee responds with a response clearly scoreable as a 2, then score Description and continue with PROCESSING SKILLS. If examinee responds with a response scoreable as a 0 or 1, or you are not certain the response could be scored 2, say: Is there anything else I need to know in order to understand there problem in this situation? If the examinee is still vague or nonspecific, say: Could you be more specific about that? and after the examinee responds, score Description (this may be scored later from audiotape).

PROCESSING SKILLS For no-problem scenes, if the examinee said there was a problem, administer the assessment procedure, but do not score Processing. This section is not administered if the examinee said there was no problem. Score Processing as a 0. Otherwise, say: If you were in this situation, what would you say or do now? If the examinee gives a clear, specific response, then score Processing (may be scored later from audiotape). If the examinee gives a vague, nonspecific response, then say:

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Could you be more specific about what you say or do in this situation? and score Processing (may be scored later from audiotape). SENDING SKILLS For no-problem scenes, if the examinee said there was a problem, administer the assessment procedure, but do not score Content, Performance, or Overall. This section is not administered for problem scenes in which the examinee said there was no problem. Score Content, Performance, and Overall as 0. Say: Now I'd like you to show me what you would say or do in this situation. I'll be the person that was talking and I'll say the last few words that he/she said. Then, you go right into what you would say or do. Arrange yourself according to the Cues for Examinee described for each scene and do the role play. When the examinee has finished responding, stop the audiotape, and verbally reinforce the examinee's involvement. If the examiner believes that the examinee has given a response scoreable as at least 1, and believes that if the role play were to continue, the examinee has a chance to improve his score, then briefly continue the role play according to Continuation given for many scenes. In the real situation, responses are not necessarily one sentence retorts; sometimes a response consists of a brief dialogue. Score Content and Overall (may be scored later from audiotape). Score Performance (must be scored now). Go to next scene.

SCORING INSTRUCTIONS Scoring criteria for each scene are given in this manual on the pages associated with each scene. The examiner should follow these criteria as closely as possible. If the examiner has difficulty scoring the examinee's responses based on the specified criteria, the following guidelines may be of assistance.

RECEIVING SKILLS

IDENTIFICATION

The examinee must give a yes or no response, and the examiner may instruct the examinee to give either a yes or a no response. If the examinee cannot decide, the examiner should encourage the examinee to respond with either yes or no, whichever the examinee thinks is most likely.

In the ten scenes in which a problem is present, "Yes" responses are scored 1, and "No" responses are scored 0. For these scenes, if the examinee indicates that there is no problem, (but in fact, there is), do not administer Description, Processing, Content, Performance, or Overall. However, these scales are all scored as 0.

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In the three no problem scenes, a yes is scored 0, a no is scored 1. For these scenes, if the examinee indicates that there is a problem, (when, in fact, there is not), administer the remainder of the questions for that scene, but do not score.

DESCRIPTION

Nine of the problem scenes contain problems with both a goal and an obstacle. The examinee gets one point for describing the obstacle and one point for describing the goal. Specifying both a goal and the obstacle gets two points. The only exception to this scoring procedure is Scene 2 in which 2 points are given for describing the obstacle (we have found that it is very rare for an examinee to describe the use of the telephone as the very obvious goal in this scene).

In some cases, the goal may seem so obvious that the examinee will describe only the obstacle; the examiner should ask the probe questions described in INSTRUCTIONS FOR EACH SCENE. Sometimes, the examinee will still not describe the goal, although the examiner may believe that the examinee does, in fact, know what the goal is. However, the examiner must remember that what is being assessed is the ability to verbally describe the goal and obstacle; therefore, unless the examiner has clear evidence that the examinee knows the goal, the point for articulating the goal should not be scored. If the examinee states neither the goal nor the obstacle, the score is 0.

PROCESSING SKILLS

PROCESSING A Processing response is scored 2 if it is the best response in the sense that it is more likely than other responses to get the goal and it minimizes the likelihood of negative consequences. A response is scored 1 if it is not the best response, but it still has a significant chance of reaching the goal and does not produce serious negative consequences. If a response is unlikely to obtain the goal, or it is likely to cause a serious negative consequence, it should be scored 0.

In addition, a response which corresponds to an incorrect problem description response should be scored 0. Despite the fact that it may be an appropriate response for the stated problem, it is nevertheless inappropriate for the correct problem. Occasionally, an examinee will indicate that he or she would not do or say anything to respond to a scene. This response should be scored 0. Bizarre responses are always scored 0.

SENDING SKILLS

The scoring must be based on how well the examinee actually performs the role played response, not on what the examinee says he or she will do or say. The scoring is essentially a three point scale (0, 1, 2), with .5 increments if the examiner cannot decide, for example, if the response merits a 1 or 2.

CONTENT Rate the effectiveness of the examinee's words in getting the goal independently of how they are spoken, or what nonverbal behavior the examinee demonstrates. In other words, rate the response as if it were written. For problem scenes in which the examinee said there was no problem, or if the examinee says or does nothing during the role play, score Content as 0.0.

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PERFORMANCE Rate the effectiveness of the manner in which the examinee responds, considering such characteristics as voice volume and fluency and clarity, eye contact, appropriateness of affect, posture, gestures, etc. These behaviors should be rated in terms of their social appropriateness in the situation and how effective they make the delivery of the content. For no-problem scenes, do not score Performance. For problem scenes in which the examinee said there was no problem, or if the examinee says or does nothing during the role play, score Performance as 0.0.

OVERALL

Rate the overall effectiveness of the examinee's responses in achieving the goal. Consider both the content of the response and the performance of the response. However, the Overall score is score is not necessarily an average of content and performance; for some responses, content is extremely important and performance is less critical; in other situations, content plays a vital role in determining effectiveness. For no-problem scenes, do not score Overall. For problem scenes in which the examinee said there was no problem, or if the examinee says or does nothing during the role play, score Overall as 0.0. Be sure to rate the response on the basis of how effective it is, not on a basis of a mismatch with the Processing response.

SCORING CRITERIA FOR SENDING SKILLS CONTENT AND OVERALL 0.0 Extremely unlikely to get goal; likely to produce significant negative consequences. 0.5 Not likely to get goal, but no really severe negative consequences. 1.0 May get goal, but clearly not the best response; no really bad conse¬quences. 1.5 Likely to get goal; a good response; could be improved; lacks polish. 2.0 Very effective; minimizes negative consequences; very likely to get goal; a smooth, polished response.

PERFORMANCE 0.0 Extremely inappropriate; bizarre; highly offensive. 0.5 Clearly less than adequate; substantial omission of important nonverbal components. 1.0 Barely adequate; room for considerable improvement; but not really inappropriate. 1.5 Appropriate; adequate; but not polished. 2.0 Very appropriate; polished; smooth delivery.

TOTAL SCORES Because of the sequential nature of the RPS social skills model, there are several ways in which the total scores using the AIPSS can be computed. We describe two methods in this section.

GENERAL SCORING One scheme that we have found useful is to compute each subject's percentage mean score on each of the AIPSS subscales. This can be accomplished easily by adding the subject's scores for all items on the scale, dividing by the total possible points, and multiplying by 100 to get a percentage.

For example, suppose a subject received the following scores on the Identification scale: Item: 1 2 3 4 5 6 7 8 9 10 11 12 13 Score: 1 1 1 0 1 1 0 0 1 1 1 1 0

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To compute the total score, first sum the points earned across all items 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 0 = 9. Then, divide by the maximum score possible (13) and multiply by 100. So, in this case the examinee's total score on Identification is (9/13) x 100, or 69%. A simple variation of this scoring method would be to use only scores from problem scenes, omitting scenes 3,7, and 9 (which would give a score of 70%).

Suppose the same examinee received the following scores on the Description scale: Item: 1 2 3 4 5 6 7 8 9 10 11 12 13 Score: 2 1 - 0 2 2 - 0 - 2 1 1 0 (Remember that items 3, 7, and 9, the no-problem scenes, are not scored for any scale except Identification).

Examining these scores, we observe that this examinee received a 2 on responses to four scenes, a 1 on responses to three scenes, and that three responses were unscoreable (because the examinee had indicated that there was no problem in three scenes that actually contained problems during the Identification part of the test). To compute the total score for Description, therefore, first compute the sum 2 + 1 + 2 + 2 + 2 + 1 + 1 = 11. Next, divide by the a maximum possible score of 20 for Description, which has 10 items, each worth a possible two points. The examinee's total score for Description is (11/20) x 100 = 55%.

Notice that when using this scoring scheme, the scores for items depend on responses from a previous stage of the RPS model. For example, if an examinee says there is no problem in a scene for which there actually is a problem, the Description score for that scene is 0, even though the examinee's ability to describe a goal and obstacle was not actually measured. This method yields a "general" score that is based on a sequential model of problem solving. In other words, each step relies on information from a previous step. This General Scoring procedure is what is used in the administration instructions described earlier in this manual.

SPECIFIC SCORING You may wish to treat each scale as a measure of a separate ability, and as such, score only the items that you have an opportunity to observe. If this is the case, the maximum possible points for each scale depends on each examinee. For example, if an examinee only had a chance to respond to seven Description questions (because the examinee missed three Identification items), then the maximum score possible is 14. Thus, percentage scores using this "specific" method may be based on fewer items than the "general" method. Scores obtained via the "specific method" will necessarily be greater than or equal to those obtained via the "general" method.

In other words, with this specific scoring method, do not score Description, Processing, Content, or Performance if Identification was scored 0. Do not score Processing, Content, or Performance if Processing was scored 0. Do not score Content or Performance if Processing was scored 0. Overall is always scored because it represents the overall social response to the situation; if the examinee would not perform a response in this situation, then this would rate a 0.0.

To obtain the total score for a particular scale, compute a sum across items (scenes) that have been scored.

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COMPOSITE SCORES Whichever scoring system you choose, you may also wish to obtain composite scores, such as a "cognitive" score and a "behavioral" score. For example, you may obtain a cognitive problem solving score by combining Description with Processing, dividing by the maximum possible points (40), and multiplying by 100. To obtain a composite Sending Skills score, you might combine the Content and Performance scales. Many other composites are also possible.

We consider the AIPSS to be primarily a research instrument. The user should carefully use the general, specific, or some other scoring system that best fits the particular application of the test.

LEARNING THE AIPSS If approached in an organized, systematic manner, the AIPSS is no more difficult to master than any other standardized test. We have provided some guidelines to facilitate your mastery of the assessment procedure and to anticipate any questions you may have about the AIPSS. We do recommend, however, given the experimental nature of the test and the scoring criteria, that when you begin using the AIPSS, you do so under the supervision of a psychologist who is familiar with test construction.

When learning the AIPSS, the first thing you should do is to view the videotape, Assessment of Interpersonal Problem Solving Skills, and read this manual several times. In this way, you will be familiar with the materials before you try to implement the procedure.

Once you have a basic knowledge of the assessment procedure, you are ready to begin getting some "hands-on" experience. You may find it helpful to recruit a colleague to act as an examinee for the first administrations of the assessment, because a majority of your time will probably be spent on logistics. The first few times that you practice the assessment, you should be concerned merely with getting through the procedure smoothly. In fact, you may wish to skip scoring altogether. The issues you should concentrate on during these sessions are placement of the furniture and equipment in the testing site, knowing the instructions for the examinee, remembering to turn on the audiotape recorder for each item, etc. You should continue to practice this way until the assessment procedure flows smoothly and you do not need to stop for a length of time to figure out what to do next.

When you are comfortable with the mechanics and logistics of the AIPSS, you are ready to begin concentrating on scoring. You may wish to have another person role play the examinee's part while you run through the procedure several times. You should administer the AIPSS under testing conditions to your assistant, and when you are through, score the entire test (but remember, Performance must be scored during the administration of the test). The scoring criteria are fairly specific, and generally, examinee's responses fall into the given categories. Occasionally, examinees will give responses that don't fit neatly into the criteria. Don't panic! Just use your best judgment to rate the response according to its chances of solving the problem.

After you have administered the test to nonpatient examinees and you have practiced the scoring, the logical step is to practice the entire procedure on actual patients. When you feel comfortable with both the administration and the scoring of the test with patients, it is advisable that you determine how reliable you are as an examiner by computing interrater reliability.

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Interrater reliability is a measure of how much your ratings of an examinee's responding agrees with another examiner who has independently rated the responses. High measures of interrater reliability are indicators that your ratings are not idiosyncratic -- it is important that different examiners arrive at similar scores for an examinee.

The criterion that is to be used for interrater reliability is that, for a given examinee's response, two independent raters are in agreement if their ratings match perfectly on Identification, Description, and Processing. For Content, Performance, and Overall, two raters are in agreement if their ratings are within 0.5 of each other. For each scale, you should be in agreement at least 80% (preferably higher) of the time across items. The psychologist who is supervising the use of this instrument can advise you on the procedures for determining interrater reliability.

There are numerous measures of interrater reliability other than percentage agreement, and a psychologist familiar with psychometrics may wish to use an alternative measure.

Once interrater reliability has been established, you are ready to begin using the AIPSS

TIPS ON ADMINISTRATION

In this section we address some common questions and pitfalls we have learned from helping others learn to administer the AIPSS. After you have learned to administer the AIPSS, you may wish to return to this section for assistance and tips on how to avoid common problems.

It is not uncommon for examinees to jump to Problem Description after they have viewed the video tape before you have had a chance to ask them the Problem Identification question. That is, immediately after viewing a scene on the videotape, they may say "The problem in this scene is ..." before you've had a chance to ask them if there is a problem in this scene. If this arises, you should interrupt the examinee, ask him or her not to get ahead, and remind the examinee that you first need a "Yes" or "No" to the question "Is there a problem in this scene?" Because Processing is similar to SENDING SKILLS, examinees may wish to jump right into a role play during the Processing part of the assessment. Remind the examinees to only tell you about what they would say or do, not show you. Do not give the examinee any hints about the correctness or incorrectness of his or her response. Some examiners may be prone to give a "minimal verbal reinforcer" such as "Good!" after the examinee's response. Only reinforce the examinee's participation in the assessment or the examinee's correct following of the assessment procedure. This problem occurs frequently during the role-play part of SENDING SKILLS. The examiner should not follow-through with a response that indicates that the examinee has solved the problem. For example, in the scene involving the return of a defective sweater, the examinee may request to speak to the supervisor during the role-play. Do not respond "OK, I'll go get him." You may say "Thank you for showing me what you would do in this situation." In setting up the role-play, do not say, "Let's pretend you were in this situation." You do not want examinees to "pretend." You want examinees to show you what they would actually do if they were in the situation. Follow the administration instructions!

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You are not allowed to replay any assessment scene (except, of course, the Demonstration Scene). This also means you should not verbally review what was on the videotape (e.g., "OK, you've walked into the store, complained about the defective sweater, and the clerk told you they don't make refunds or exchanges). All you should do is repeat what the other person the scene said as you begin the role-play ("All I know is that we don't make refunds or exchanges.") In the assessment procedure, some probing is allowed if the examinee is vague or uncertain. Follow these instructions carefully. If you don't probe at all, you will miss instances in which the examinee actually knows the correct response. If you probe more than is allowed by the administration procedure, you increase the chance that you will subtlety coach the examinee into making the correct response. A good principle to remember that the AIPSS procedures are designed to assess whether the examinee can respond quickly to the examiner's question, not whether they have the correct response stored somewhere in their brain's memory and can retrieve it or can construct a correct response and can retrieve it given enough time or assistance. In a real situation, one must respond fairly quickly to be effective. Sometimes an examinee will respond during Processing that they would not say or do anything. You must still assess the examinee's sending skills. The examinee may respond differently. Can some scenes be skipped to save administration time? If you want to rely on the psychometric properties established by Donahoe, et al. (1990), the answer is no. If you are using the AIPSS as part of a research project, you may modify the procedures any way you wish! In Problem Identification examinees will sometimes give a detailed verbal replay of the scene in response to being asked to describe the problem. If this is the case, do not assume the examinee understands the problem; ask the examinee to describe the problem.

DEMONSTRATION SCENE The purpose of the demonstration scene is to ensure that the examinee fully understands the assessment procedures and how to participate appropriately in the assessment. The examinee must understand that close attention to the videotape is required in order to answer the questions that follow. To facilitate this, the examiner may replay the Demonstration Scene.

It is important to be sure that the examinee responds realistically during all phases of the assessment. The examinee must try as hard as possible to put himself or herself into the scene and respond as he or she would in the actual situation.

The demonstration scene should be used to coach appropriate participation, and the examiner may model appropriate participation, but must avoid giving clues to the examinee regarding what constitutes correct responses, such as giving an explicit response for the goal of the scene, or requesting that better eye contact be made. Be sure that the examinee is being trained to understand the procedures of the assessment, but not being trained to give better responses.

(Job Interview) IDENTIFY with the man on the left. RECEIVING IDENTIFICATION

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No scoring. Be sure the examinee answers yes or no and understands a problem exists. If the examinee does not see that a problem exists, then use prompts and coaching to get him or her to recognize the existence of a problem.

DESCRIPTION No scoring. The examiner should follow the instructions described in INSTRUCTIONS FOR EACH SCENE. If after the two prompts, the examinee has not articulated both the goal the man needs an interview and the obstacle the interviewer is not there -- then the examiner may need to prompt the examinee to be sure that he or she understands the need to describe all parts of the problem as if the examiner had never seen this scene before. If necessary, have the examinee describe exactly what happened in the scene. As the examinee gives the description, the examiner may wish to point out the problem, but do not coach what is a goal and what is an obstacle. If necessary, the examiner may also replay the scene, encouraging the examinee to pay close attention to the details.

PROCESSING No scoring. The examiner should follow the instructions described in INSTRUCTIONS FOR EACH SCENE. If after the two prompts, the examinee has not been specific about what he or she would say or do in the situation, then the examiner may need to give additional prompts to be more specific.

SENDING Cues for the Examinee Examinee stands. Examiner, sitting in front of examinee, at a ninety degree angle, as if sitting at a desk, turns to Examinee and says: I'm sorry, but Mr. Smith has gone home for the day. Continuation None. Be sure to verbally reinforce the examinee's involvement in the role play (I really felt as if you were in the scene). If the examinee did not seem very involved in the scene, then coach the examinee to perform a realistic and involved role play (Really try to put yourself into the scene and show me exactly what you would say or do if you were really in the situation). If the examinee appears to understand the procedures, then the examiner should say, If you have no questions, we will begin. Begin the first assessment scene, SCENE 1. Assessment scenes may be played only once. If the examinee asks for a replay, then the examiner should remind the examinee that scenes cannot be replayed.

PERFORMANCE No scoring.

CONTENT AND OVERALL No scoring.

APPENDIX M. Social Functioning Scale

THE SOCIAL FUNCTIONING SCALE

INDIVIDUALS VERSION

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NAME: ______

This questionnaire helps us to learn how you have been getting on since you became ill. This questionnaire takes about 20 minutes to complete- before getting started could you please answer the following: Where do you live? Answer: ______Who do you live with? Answer: ______FOR INTERVIEWER’S USE ONLY: Raw Score Scaled Score Withdrawal/Social Engagement (W) Interpersonal Communication (Inter) Independence-Performance (Ip) Independence-Competence (Ic) Recreation (R) Prosocial (P) Employment/Occupation (E/O)

What time do you get up each day? Average weekday ______Average weekend (if different) ______

On average how many waking hours do you spend alone in one day? e.g., alone in a room, walking out alone, listening to radio or watching TV alone etc. Please tick one of the boxes:

0-3 hours Very little spent alone 3-6 hours Some of time 6-9 hours Quite a lot of the time 9-12 hours A great deal of time 12 hours Practically all the time

How often will you start a conversation at home?

Almost never Rarely Sometimes Often

How often do you leave the house (for any reason)?

Almost never Rarely Sometimes Often

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How do you react to the presence of strangers/people that you don’t know?

Avoid them Feel nervous Accept them Like them

How many friends do you have at the moment? (people who you see regularly, do activities with etc.)

Do you have a partner?

How often are you able to carry out a sensible or rational conversation? Please tick a box

Almost never Rarely Sometimes Often

How easy or difficult do you find it talking to people at the moment?

Very easy Quite easy Average Quite difficult Very difficult

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Please place a tick against each item to show how often you have done the following over the past 3 months.

Never Rarely Sometimes Often Buying items from the shops (without help) Washing pots, tidying up etc. Regular washing, bathing etc. Washing own clothes Looking for a job/working Doing the food shopping Prepare and cook a meal Leaving the house alone Using buses, trains etc. Using money Budgeting Choosing and buying clothes for self Take care of personal appearance.

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Please place a tick in the appropriate column to indicate how often you have done any of the following activities over the past 3 months.

` Never Rarely Sometimes Often Playing musical instruments Sewing, knitting Gardening Reading things Watching television Listening to records or radio Cooking D.I.Y activities (e.g. putting up shelves) Fixing things (car, bike, household etc). Walking, rambling Driving\cycling (as a recreation) Swimming Hobby (e.g. collecting things) Shopping Artistic activity (painting, crafts etc.)

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Please place a tick in the appropriate column to indicate how often you have done any of the following activities over the past 3 months.

Never Rarely Sometimes Often Cinema Theatre\Concert Watching an indoor sport (squash, table- tennis). Watching an outdoor sport (football, rugby). Art gallery\ museum. Exhibition. Visiting places of interest. Meetings, talks etc. Evening Class. Visiting relatives in their homes. Being visited by relatives. Visiting friends (including boy/girlfriends). Parties. Formal occasions. Disco etc. Nightclub\ Social club Playing an indoor sport. Playing an outdoor sport. Club\ Society. Pub. Eating Out. Church Activity.

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Please place a tick against each item to show how able you are at doing or using the following.

Adequately Needs Help Unable Don’t know Public transport Handling money. Budgeting. Cooking. Weekly shopping. Looking for a job/ in employment Washing own clothes. Personal hygiene. Washing, tidying etc. Purchasing from shops. Leaving the house alone. Choosing and buying clothes. Caring for personal appearance.

Are you in regular employment? (This includes industrial therapy, rehabilitation or retraining courses).

Yes No

1 IF YES: What sort of job . How many hours do You work per week? . How long have you had this job? .

2 IF NO: When were you last in employment? . What sort of job was it? . How many hours per week? .

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Are you registered disabled?

Yes No

Do you attend hospital as a day patient?

Yes No

If not employed (do not answer if working)

Do you think you are capable of some sort of employment?

Definitely yes Would have difficulty Definitely no

How often do you make attempts to find a new job? (e.g. go to the Job Centre, look in the newspaper.) Almost never Rarely Sometimes Often

Scoring Key: SOCIAL ENGAGEMENT/WITHDRAWAL (minimum = 0 maximum = 15)

What time do you get up each day? Score Before 9am 3 9-11am 2 11am-1pm 1 AFTER 1pm 0

Average weekday ______Average weekend (if different) ______(if different, take highest score)

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On average, how many hours do you spend alone in one day? Please tick one of the boxes:

Score Very little spent alone 3 -3 Some of time 2 -6 Quite a lot of the time 1 -9 A great deal of time 0 -12 Practically all the time 0 2

How often will you start a conversation at home?

Almost never Rarely Sometimes Often 0 1 2 3

How often do you leave the house (for any reason)?

Almost never Rarely Sometimes Often 0 1 2 3

How do you react to the presence of strangers/people you don’t know?

Avoid them 0 Feel nervous 1 Accept them 2 Like them 3

Total for section:

INTERPERSONAL COMMUNICATION (minimum = 0 maximum = 9)

How many friends do you have at the moment? (people who you see regularly, do activities with etc.)

Do you have a partner?

Yes No

Note: Items 1 and 2 are summed – total score of 1 & 2 =

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Score total as: 0=0, 1=1, 2=2, 3+=3 score of summed items =

How often are you able to carry out a sensible or rational conversation? Please tick a box

Almost never 0 Rarely 1 Sometimes 2 Often 3

How easy or difficult do you find it talking to people at the moment? Very easy 3 Quite easy 3 Average 2 Quite difficult 1 Very difficult 0

Total for section:

INDEPENDENCE - PERFORMANCE (minimum = 0, maximum = 39) Please place a tick against each item to show how often you have done the following over the past 3 months. Never Rarely Sometimes Often 0 1 2 3

Buying items from the shops (without help) Washing pots, tidying up etc. Regular washing, bathing etc. Washing own clothes Looking for a job/working Doing the food shopping Prepare and cook a meal Leaving the house alone Using buses, trains etc. Using money Budgeting Choosing and buying clothes for self Take care of personal appearance.

Total for section:

150

RECREATION

(minimum = 0, maximum = 45) Please place a tick in the appropriate column to indicate how often you have done any of the following activities over the past 3 months.

Never Rarely Sometimes Often 0 1 2 3 Playing musical instruments Sewing, knitting Gardening Reading things Watching television Listening to records or radio Cooking D.I.Y activities (e.g. putting up shelves) Fixing things (car, bike, household etc). Walking, rambling Driving\cycling (as a reaction) Swimming Hobby (e.g. collecting things) Shopping Artistic activity (painting, crafts etc.)

Total for section:

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PROSOCIAL (minimum = 0, maximum = 66) Please place a tick in the appropriate column to indicate how often you have done any of the following activities over the past 3 months.

Never Rarely Sometimes Often 0 1 2 3 Cinema Theatre\Concert Watching an indoor sport (squash, table- tennis). Watching an outdoor sport (football, rugby). Art gallery\ museum. Exhibition. Visiting places of interest. Meetings, talks etc. Evening Class. Visiting relatives in their homes. Being visited by relatives. Visiting friends (including boy/girlfriends). Parties. Formal occasions. Disco etc. Nightclub\ Social club Playing an indoor sport. Playing an outdoor sport. Club\ Society. Pub. Eating Out. Church Activity.

Total for section

152

INDEPENDENCE – COMPETENCE

(minimum = 0, maximum = 39) Please place a tick against each item to show how able you are at doing or using the following.

3 2 1 0 Adequately Needs Help Unable (needs) Not known Public transport Handling money. Budgeting. Cooking for shopping. Weekly shopping. Looking for a job/working. Washing own clothes. Personal hygiene. Washing, tidying etc. Purchasing from shops. Leaving the house alone. Choosing and buying clothes. Caring for personal appearance.

Total for section:

OCCUPATION\EMPLOYMENT (minimum = 0 maximum = 10) Are you in regular employment? (This includes industrial therapy, rehabilitation or retraining courses).

Yes No

1 IF YES: What sort of job . How many hours do You work per week? . How long have you had this job? .

2 IF NO: When were you last in employment? .

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What sort of job was it? . How many hours per week? .

Are you registered disabled?

Yes No

Do you attend hospital as a day patient?

Yes No

If employed, please assign one of the following scores on the basis of the above information: (possible scores 7-10).

Score 10 if full time gainful earnings or full time student Score 9 if part time gaining earnings or housewife or mother Score 8 if employed until recently e.g. in the last 6 months, and actively pursuing work e.g. redundancy. Score 7 if industrial therapy or rehabilitation

If unemployed or none of the above, add scores on asterisked (*) questions over page (possible scores 0-6). If not employed (do not answer if working) Do you think you are capable of some sort of employment? * Definitely yes Would have difficulty Definitely no 3 2 0

How often do you make attempts to find a new job? (e.g. go to the Job Centre, look in the newspaper.) * Almost never Rarely Sometimes Often 0 1 2 3

Total score for section :

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APPENDIX N. Results: Multiply Imputed Datasets (m=5) a) Two-factor confirmatory factor analysis model, standardized solution (multiply imputed datasets m=5).

TMTA BLERT

TMTB †c HT Social g Neurocognition Cognition

h d

CPT a †i FEIT

DS PONS

Note: †factor loading fixed, TMTA=Trail Making Test A, TMTB=Trail Making Test B,

CPT=Continuous Performance Task Identical Pairs, DS=Digit Span, FEIT=Facial Affect

Identification Test, BLERT=Bell-Lysaker Emotion Recognition Test, HT=Hinting Task,

PONS=Profile of Nonverbal Sensitivity.

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Two-factor confirmatory factor analysis model, standardized solution (multiply imputed datasets m=5).

Dataset 1 Dataset 2 Dataset 3 Dataset 4 Dataset 5 Mean (s.d.)

Model χ2 41.09 32.87 33.46 45.46 37.16 38.01 (5.31)

CFI 0.90 0.94 0.93 0.89 0.92 0.92 (0.02)

RMSEA 0.10 0.08 0.08 0.11 0.09 0.09 (0.01) a 0.54 0.57 0.59 0.55 0.54 0.56 (0.02) b 0.57 0.61 0.56 0.61 0.63 0.60 (0.03) c 0.88 0.91 0.88 0.90 0.90 0.89 (0.01) d 0.52 0.49 0.53 0.48 0.47 0.50 (0.03) e 0.51 0.47 0.48 0.49 0.48 0.49 (0.02) f 0.70 0.71 0.74 0.70 0.74 0.72 (0.02) g 0.46 0.42 0.36 0.49 0.40 0.43 (0.05) h 0.74 0.73 0.70 0.75 0.71 0.73 (0.02) i 0.73 0.77 0.70 0.71 0.72 0.73 (0.03)

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b) One-factor confirmatory factor analysis model, standardized solution (multiply imputed datasets, m=5).

TMTA BLERT

b f

TMTB c† Combined g HT

Neurocognition

and Social d h

CPT FEIT

i e*

DS PONS

Note: †factor loading fixed, TMTA=Trail Making Test A, TMTB=Trail Making Test B,

CPT=Continuous Performance Task Identical Pairs, DS=Digit Span, FEIT=Facial Affect

Identification Test, BLERT=Bell-Lysaker Emotion Recognition Test, HT=Hinting Task,

PONS=Profile of Nonverbal Sensitivity.

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One-factor confirmatory factor analysis model, standardized solution (multiply imputed datasets, m=5).

Dataset 1 Dataset 2 Dataset 3 Dataset 4 Dataset 5 Mean (s.d.)

Model χ2 82.16 71.31 67.46 84.91 78.75 76.92 (7.34)

CFI 0.73 0.78 0.78 0.73 0.74 0.75 (0.03)

RMSEA 0.17 0.15 0.14 0.17 0.16 0.16 (0.01) b 0.36 0.38 0.40 0.37 0.41 0.38 (0.02)

c 0.60 0.63 0.66 0.62 0.62 0.63 (0.02)

d 0.51 0.50 0.53 0.51 0.50 0.51 (0.01)

e 0.48 0.49 0.48 0.51 0.49 0.49 (0.01)

f 0.67 0.67 0.68 0.66 0.68 0.67 (0.01)

g 0.41 0.38 0.31 0.44 0.37 0.38 (0.05)

h 0.68 0.68 0.64 0.69 0.66 0.67 (0.02)

i 0.69 0.74 0.65 0.67 0.66 0.68 (0.04)

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c) Confirmatory factor analysis models χ2 difference test (Δχ2; multiply imputed datasets, m=5)

Dataset 1 Dataset 2 Dataset 3 Dataset 4 Dataset 5

one-factor χ2(20) 82.16 71.31 67.46 84.91 78.75 two-factor χ2(19) 41.09 32.87 33.46 45.46 37.16

Δχ2(1) 41.07* 38.44* 34.00* 39.45* 41.59*

*p<0.05

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d) Direct effects model, standardized solution (multiply imputed datasets, m=5).

TMTA BLERT

b f

TMTB c† g HT Social Neurocognition Cognition

d h

CPT FEIT j k

e i†

DS PONS

AIPSS

Note: †factor loading fixed, TMTA=Trail Making Test A, TMTB=Trail Making Test B, CPT=Continuous Performance Task Identical Pairs, DS=Digit Span, FEIT=Facial Affect Identification Test, BLERT=Bell-Lysaker Emotion Recognition Test, HT=Hinting Task, PONS=Profile of Nonverbal Sensitivity, AIPSS=Assessment of Interpersonal Problem Solving Skills.

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Direct effects model, standardized solution (multiply imputed datasets, m=5).

Dataset 1 Dataset 2 Dataset 3 Dataset 4 Dataset 5 Mean (s.d.)

Model χ2 78.99 77.03 74.75 87.86 75.43 78.81 (5.32)

CFI 0.78 0.80 0.78 0.77 0.80 0.79 (0.01)

RMSEA 0.13 0.13 0.13 0.14 0.13 0.13 (0.00) b 0.57 0.60 0.57 0.61 0.63 0.60 (0.03)

c 0.94 0.97 0.94 0.95 0.94 0.95 (0.01)

d 0.47 0.45 0.49 0.43 0.43 0.45 (0.03)

e 0.47 0.43 0.43 0.45 0.44 0.44 (0.02)

f 0.69 0.71 0.73 0.70 0.75 0.72 (0.02)

g 0.48 0.43 0.39 0.51 0.41 0.44 (0.05)

h 0.74 0.73 0.69 0.73 0.68 0.71 (0.03)

i 0.73 0.75 0.70 0.71 0.73 0.72 (0.02)

j -0.02 -0.02 -0.05 -0.07 -0.07 -0.05 (0.03) k 0.27 0.28 0.28 0.36 0.34 0.31 (0.04)

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e) Indirect effect model, standardized solution (multiply imputed datasets, m=5).

TMTA BLERT

f b

TMTB c† g HT a Social Neurocognition Cognition

d h

CPT FEIT

j k e i

DS PONS

AIPSS

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Indirect effect model, standardized solution (multiply imputed datasets, m=5).

Dataset 1 Dataset 2 Dataset 3 Dataset 4 Dataset 5 Mean (s.d.)

Model χ2 56.28 48.89 47.07 62.60 51.40 53.25 (6.27)

CFI 0.87 0.91 0.91 0.86 0.89 0.89 (0.02)

RMSEA 0.10 0.09 0.09 0.11 0.10 0.10 (0.01) a 0.54 0.57 0.59 0.55 0.54 0.56 (0.02) b 0.57 0.61 0.56 0.61 0.63 0.60 (0.03) c 0.88 0.91 0.89 0.90 0.90 0.90 (0.01) d 0.52 0.49 0.53 0.47 0.46 0.49 (0.03)

e 0.51 0.47 0.48 0.49 0.47 0.48 (0.02)

f 0.72 0.73 0.76 0.72 0.76 0.74 (0.02) g 0.46 0.42 0.36 0.49 0.40 0.43 (0.05)

h 0.73 0.72 0.68 0.73 0.69 0.71 (0.02)

i 0.71 0.76 0.69 0.70 0.71 0.71 (0.03) j -0.06 -0.05 -0.12 -0.13 -0.13 -0.10 (0.04) k 0.31 0.31 0.33 0.40 0.38 0.35 (0.04)

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f) Structural regression models χ2 difference test (Δχ2; multiply imputed datasets, m=5).

Dataset 1 Dataset 2 Dataset 3 Dataset 4 Dataset 5

Direct effect model χ2(26) 78.99 77.03 74.75 87.86 75.43

Indirect effect model χ2(25) 56.28 48.89 47.07 62.60 51.40

Δχ2(1) 22.71* 28.14* 27.68* 25.26* 24.03*

*p<0.05

g) 95% Confidence intervals (95%CI) for direct (path c) and indirect (path a x b) effects

(multiply imputed datasets, m=5).

95% CI Dataset 1 Dataset 2 Dataset 3 Dataset 4 Dataset 5

Path c [-0.35, 0.19] [-0.34, 0.25] [-0.46, 0.15] [-0.39, 0.15] [-0.43, 0.15]

Normal theory

Path c [-0.33, 0.22] [-0.32, 0.29] [-0.46, 0.15] [-0.36, 0.18] [-0.39, 0.17]

Bias corrected

Path a x b [-0.01, 0.40] [-0.02, 0.43] [-0.01, 0.50] [0.05, 0.45] [0.03, 0.47]

Normal theory

Path a x b [0.00, 0.41] [0.00, 0.45] [0.00, 0.52] [0.05, 0.45] [0.04, 0.48]

Bias corrected

h) Distribution of parameter estimates for direct (path c) and indirect (path a x b) effects

(multiply imputed datasets, m=5).

Dataset 1 (n=1000 converged simulations).

164 165

Dataset 2 (n=1000 converged simulations).

166

Dataset 3 (n=999 converged simulations).

167

Dataset 4 (n=998 converged simulations).

168

Dataset 5 (n=1000 converged simulations).