Executive function processes mediate the impact of working memory impairment on intelligence in schizophrenia

European Psychiatry 30 (2015) 1–7

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Original article

Executive function processes mediate the impact of working memory impairment on intelligence in schizophrenia P. Wongupparaj a,*, V. Kumari a,b, R.G. Morris a a b

Department of Psychology, P078, Institute of Psychiatry, King’s College London, De Crespigny Park, London SE5 8AF, UK NIHR Biomedical Research Centre for Mental Health, South London and Maudsley NHS Foundation Trust, London, UK

A R T I C L E I N F O

A B S T R A C T

Article history: Received 12 March 2014 Received in revised form 3 June 2014 Accepted 4 June 2014 Available online 29 August 2014

Objective: The study investigated working memory, executive functions (conceptualized as response inhibition, updating, and shifting), and intelligence in schizophrenia, using structural equation modelling to determine the relationship between working memory and intelligence, testing whether specific executive functions act as a mediator for the association. Method: One hundred and twenty-five individuals diagnosed with schizophrenia and 64 healthy participants were included in the study, tested using measures of working memory, intelligence and executive functioning. Structural equation modelling (SEM) was used to estimate direct and indirect associations between main measures. Results: The schizophrenia group had significantly lower working memory, executive function and intelligence than the healthy group. The relationship between working memory and intelligence was significantly mediated by inhibition, updating and shifting functions. Conclusion: The study indicates a mediating role of executive functions in determining the association between working memory and intellectual function in schizophrenia. It is further proposed that in people with schizophrenia, cognitive remediation approaches targeting working memory through executive functioning may in turn improve intellectual function generally. ß 2014 Elsevier Masson SAS. All rights reserved.

Keywords: Working memory Executive functions Intelligence Schizophrenia Structural equation modelling

1. Introduction Numerous studies suggest impairment in intellectual functioning in early and adult onset schizophrenia, but also traversing the lifespan of schizophrenia [3,16,70,80]. Meta-analysis demonstrates consistently and markedly lower intelligence (g) in people with schizophrenia as a group, with conversely higher intelligence appearing to act as a protective factor against development of symptoms [51]. Furthermore, global cognitive abilities (i.e. performance on intelligence tests) in schizophrenia decline over time, when compared to healthy populations [36]. Connected to this issue is the evidence that working memory (WM) plays a pivotal role in high-cognitive abilities (e.g. reasoning ability) associated with intelligence [1,62,71]. WM impairment is also prominent in schizophrenia and regarded as a core deficit [9,32,55,66,74]. This has led to the suggestion that WM impairment may be causative in reducing overall intellectual functioning in schizophrenia [11,28], and this is further supported by

* Corresponding author. Tel.: +00 44 207 848 5716. E-mail addresses: [email protected], [email protected] (P. Wongupparaj). http://dx.doi.org/10.1016/j.eurpsy.2014.06.001 0924-9338/ß 2014 Elsevier Masson SAS. All rights reserved.

functional neuroimaging studies of schizophrenia patients that indicate WM task related activation abnormalities in frontoparietal regions implicated in both WM and g [77]. When considering this issue, the structure of WM and how the different WM components interface with other cognitive processes is of particular importance. For example, in the Baddeley and Hitch WM Model [5], WM is assumed to be a set of temporary storage systems under attentional control and coordinated by a central executive system (CE), the whole system underpinning the capacity for complex thought in humans (i.e. language comprehension, learning, reasoning, higher-cognitive abilities) [6]. The CE directs attentional focus to tasks at hand, dividing and switching between concurrent tasks or prioritising particular target information, and integrating WM and long-term memory (LTM). Additional multi-component WM models have been developed, specifying fine-grained concepts of central executive systems or common attentional control mechanisms including from an individual difference research perspective that focuses on assessment of variation in WM capacity of normal individuals [20,49,61,63]. In term of executive function, Miyake et al. [26,58,75] provide a framework in which a general set of executive functions (EFs)

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support WM, characterised as correlated but separable components, namely inhibition of prepotent responses, ‘‘inhibition’’, updating WM representations, ‘‘updating’’, and shifting between tasks or mental sets, ‘‘shifting.’’ Recently, Coolidge et al. [19] also suggested that the Baddeley and Hitch WM model contains elements similar to the key EFs investigated by Miyake et al. [58] – specifically Baddeley’s attentional control mechanisms are similar to Miyake et al’s specified shifting functions, in which attention is directed and redirecting mental representations are held in WM, maintained and manipulated by the CE, information is updated and there is the ability to focus on and block automatic and dominant responses or behaviours. Taken together, modelling of WM suggests WM-EF-g covariation as indicated by the above theoretical frameworks. Cognitive neuroscience provide further support for this notion, beginning with the neural circuitries of WM and intelligence sharing common neural processes in the frontal lobes, including dorsolateral prefrontal cortex (dlPFC) [7] and lateral prefrontal cortex (LPFC) [17,23], and also parietal brain regions [12,27,33,45,47,53]. In addition, restricted damage to frontoparietal area is often conceived of affecting the common area of cognitive control functions (a core part of WM) and g, causing a reduction in fluid intelligence [18,85]. Hence, a model of executive functioning, corroborated by extensive findings, suggests that fluid intelligence is mediated by specific fronto-parietal networks [23,29,68]. In schizophrenia, the importance of EFs has been emphasised, with deficits in EFs that are strongly implicated in WM control processing, such as response inhibition and cognitive flexibility, being observed [78]. In contrast, the storage mechanisms associated with WM, such as the phonological loop and visuospatial sketchpad mechanisms identified in the Baddeley and Hitch Working Memory model are relatively preserved [8,67,81]. Consequently, it has been suggested, for example by Weiss et al. [84], that impairment in the EF control mechanisms rather than the storage mechanisms that is responsible for the reduction found in working memory. In summary, there is substantial evidence for both reduced intelligence in schizophrenia and WM weakness being key features [9,32,55,66,74]. There is also evidence from healthy participants that WM is heavily linked to constructs concerning EFs. This latter observation can be combined with further evidence for impairment in EFs [46,50], raising the question as to how all three factors might be linked causally in relation to schizophrenia? Nevertheless, only a few studies have simultaneously focused on WM, EFs and/or intellectual deficits [34,69] and such studies may not have sufficient samples sizes to support the multivariate analyses needed to explore associations between constructs of interest. In this study, we analyse a relatively large sample of people with schizophrenia, specifically:  comparing people with schizophrenia and healthy participants to establish the level of deficit in our schizophrenia sample;  using SEM on the schizophrenia data to explore and estimate causal WM-EF-IQ relations, in particular whether components of EFs act as a mediator in a hypothesised causative relationship between WM and intellectual functioning.

2. Methods 2.1. Participants The participants were 125 outpatients with a DSM-IV diagnosis of schizophrenia or schizoaffective disorder, stable on their current medication for six or more weeks, and 64 healthy participants, matched on average to patients’ age, education and gender.

Patients were recruited from outpatient services in and around South London. Healthy participants were recruited via local advertisements. The research procedures were approved by the joint research ethics committee of the Institute of Psychiatry and Maudsley Hospital, London. 2.2. Tasks 2.2.1. WM was measured using the Letter – Number task WM was measured using the Letter – Number task [30]. The participant reads strings of alternating numbers and letters (e.g. ‘C7G4Q1S’) and has to repeat them immediately, arranging them in ascending order with the numbers in order first, followed by the letters in alphabetical order. Using a span technique, the test starts with two items, the difficulty level increasing by one item if the participant is correct in at least one attempt out of three at each level, with a maximum level of seven items. The measure used was the number of total correct responses. 2.2.2. Executive functions Executive functions were tested measuring the main EFs that support WM, according to the Miyake et al. [58]. This includes measuring:  responses inhibition tested using the Stroop Colour Word test [31], the measured used being an interference score;  updating using the computerised N – Back task, composed of 0-, 1- and 2- back conditions, with the percentage of correct responses used as the measure;  set shifting using the Wisconsin Card Sorting Test (WCST) [35], with the percentage of conceptual-level response taken as the measure. 2.2.3. Intelligence Intelligence was measured using the Wechsler Abbreviated Scale of Intelligence (WASI) [82], designed to be a short test of general intellectual ability. The abbreviated two-subtest version of the WASI was used, consisting of the Vocabulary and Matrix Reasoning subtests, measuring respectively, crystallized and fluid intelligence [39,40]. T-score measures were used. 2.3. Statistical analyses Independent t-tests and Chi-squared tests ascertained whether there were differences between the schizophrenia and control groups for the demographics and main neuropsychological measures. Cohen’s d was employed to compute the effect sizes when comparing the two groups. For the SEM analysis, applied to the schizophrenia group, it was necessary, firstly, to explore the dimensional structure of executive dysfunction. Inhibition, updating and shifting measures were subjected to a principal factor analysis (PCA) to determine the underlying factor-structure pertaining to these variables. The result of this analysis fed into the structural equation modelling, which was then used to analyze direct effects of WM on intelligence, and indirect effects via EFs. Furthermore, a bootstrapping method was employed to calculate 95% bias-corrected confidence intervals for parameters in the model. Together with the Maximum Likelihood (ML) estimation, which is the default estimation technique in AMOS, it is mathematically able to partial out the error variance from observed variables or capture only true score (latent variables), yielding more reliable and valid parameter estimation than comparable statistical methods, such as ordinary regression analysis or correlation analysis [15]. It also simultaneously computes direct, indirect, and total effects among variables in the model and so appropriately reflecting the true nature of the relationships between variables via

P. Wongupparaj et al. / European Psychiatry 30 (2015) 1–7

mediator and moderator analysis. Also, the goodness-of-fit indices from SEM are informative and robust in testing of fit between empirical data and theoretical models [38] (see Table 4 for criterion values). The Little’s Chi-Square statistic was employed for treatment of missing values, testing whether or not such values were Missing Completely At Random (MCAR). The t-tests and PCA were done using SPSS (Version 21) [2] and the SEM was supported by AMOS software (version 21) [2].

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N-Back condition measures were aggregated. The Kaiser-MeyerOlkin measure indicated that the sampling adequacy was acceptable for the analysis (KMO = 0.637) [42,48] and Barlett’s Test of Sphericity also showed that the correlation matrix was an identify matrix (x2 (3) = 60.276, P < 0.001), which would be suitable for factor-structure detection [79]. The three variables produced only a single factor, possibly reflecting more unitary construct of EFs in this clinical population, and in combination explained 60.79% of the overall variance. Given this initial analysis, a unitary construct of EFs was incorporated into the final SEM model.

3. Results 3.3. The causal relation between WM-EF-g 3.1. Sample description and group differences The schizophrenia group had the following demographics: mean age was 39.91 years (standard deviation: 10.67 years; range 18 to 61 years); mean years of education was 14.10 years (standard deviation: 3.25 years; range 7 to 25 years) and gender distribution was 69.6% male and 30.4% female. For healthy participants, mean age was 36.72 years (standard deviation 12.26 years; 20 to 65 years); mean years of education was 13.45 years (standard deviation: 3.46 years; range 5 to 22 years) and gender distribution was 64.1% male and 35.9% female. There were no statistical differences in age, education and gender between patient and controls; respectively: t (187) = 1.85, P = 0.066; t (187) = –1.293, P = 0.198 and x2 (1) = 0.594, P = 0.441). Ten percent of the total measurement data were missing across the sample. A missing value analysis indicated that data were missing at random (Little’s MCAR test: Chi-Square = 70.826, df = 64, P = 0.261). Consequently, for the SEM, the full ML method was used to compute unbiased estimates and substitute for the missing values. As shown in Table 1 and Fig. 1, the schizophrenia group had significantly lower mean scores than healthy participants on all variables, except for 0-back on the n-back task (t (187) = –1.316, P = 0.190). Also, effect sizes ranged from 0.400 to 1.071, which represented medium to large-sized effects. 3.2. Dimensionality of executive function tests A PCA was conducted on the main variables from the EF tests (inhibition, updating and shifting). For this analysis, the three

Table 2 shows the mean score and standard deviation of all variables used in the model. The tests of normality, skewnesses, kurtoses, Mardia’s multivariate kurtosis and Mahalanobis distance, were used to ascertain the distribution of variables’ value. It was found that all variables are normally distributed. The univariate skewnesses and kurtoses were within the range between –1 and +1 indicating normal distributed data. Mardia’s multivariate kurtosis was less than 3 assuming that the multivariate normality was basically met. Also, the multivariate outlier was detected by Mahalanobis distance and it was shown that no outlier under assumption of normality. As shown in Table 3, all observed variables were positively and significantly correlated with each other and the correlation coefficients ranged from 0.321 to 0.643. The SEM model was constructed as presented in Fig. 2. As shown in Table 4, all goodness-of-fit indices, except one (which was marginally acceptable), provided statistical evidence (i.e. met or exceeded criteria) that the model fits with empirical data [21]. As per our prediction, WM had direct and indirect associations with g, mediated by EFs. Also, linkages in the model include EFs supported by inhibition, updating and shifting; and g supported by Crystallized and Fluid intelligence. The model was then tested (Fig. 2; Table 5) using the statistical techniques further described above. The analysis revealed the statistical significance of indirect, direct and total relationships between WM and g. Moreover, the indirect effects between WM and g suggested that the given covariation could be explained by the role of EFs, also reflecting the underlying EF functions (inhibition, updating, and shifting), or significantly mediated WM – EF associations.

Table 1 Working memory, executive function, and intelligence means for schizophrenia (n = 125) and healthy groups (n = 64). Measures

WM (Letter-Number Task; total correct) Inhibition (Stroop Colour Word Test; interference score) 0-Back (percentage of correct responses) 1-Back (percentage of correct responses) 2-back (percentage of correct responses) Shifting (Wisconsin Card Sorting Test; the percentage of conceptual-level responses) WASI –Vocabulary score WASI-Matrix reasoning score

Groups Schizophrenia Mean (SD)

Healthy Mean (SD)

12.667 (4.260) 34.148 (10.300) 82.787 (18.304) 63.658 (27.660) 44.800 (26.128) 49.227 (29.789) 50.730 (11.682) 50.340 (12.398)

16.295 (3.470) 47.548 (14.395) 86.330 (15.864) 76.152 (32.090) 55.888 (26.156) 66.259 (18.151) 65.210 (12.379) 56.500 (9.229)

* = P < 0.05, ** = P < 0.01, *** = P < 0.001. Standard deviations appear in parentheses below means. a Equal variances not assumed.

t

P

df

Cohen’s d

5.883***

<0.001

187

0.934

6.629***

<0.001

96.977a

1.071

1.316

0.190

187

0.207

2.781**

0.006

187

0.616

2.760**

0.006

187

0.424 a

4.867***

<0.001

181.350

0.400

7.898***

<0.001

187

0.832

–3.849***

<0.001

162.655a

0.564

P. Wongupparaj et al. / European Psychiatry 30 (2015) 1–7

4

100 82.787

90

86.330 76.152

80

55.888

60

47.548

50

20

44.800

49.227

65.210

50.730

56.500 50.340

34.148

40 30

66.259

63.658

70

Schizophrenia Healthy

16.295 12.667

10 0

Fig. 1. Mean values (error bars display the 95% confidence interval, CI) on neuropsychological measure for the schizophrenia and healthy control groups.

Table 5 shows the direct, indirect, and total effects between SEM variables. When considering the effect of WM on g subcomponents via EFs, it was found that WM had a strongest effect via EFs on crystallized intelligence (standardised coefficient = 0.815, P < 0.05) than fluid intelligence (standardised coefficient = 0.788, P < 0.05). When considering the influence of EFs as mediators between WM and g, the analysis suggested that the inhibition function exerted the strongest effect, followed by the updating and shifting functions (see Fig. 2). Overall, WM explained 45.6% variance of g, and EFs additionally explained 36.6% variance, in total 82.2% of the variance.

4. Discussion To our knowledge, this is the first study that explored the association between WM and IQ using a multivariate SEM approach in people with schizophrenia. The analysis supported specifically the hypothesis that variations in EFs mediates the WM and g connection and also helps clarify which aspects of EFs are important in this regard. Key findings were that the association between WM and g was robust, and was stronger for crystallized than fluid intelligence and that the associations were explained by all three EF functions, the inhibition function exerting the strongest effect, followed by updating and shifting ability.

This pattern of associations can be compared to previous findings with normal participants indicating a tendency in the current sample of people with schizophrenia for the associations to involve a greater number of executive functions systems. For example, in comparison Burgess et al. [14] found common neural mechanism between WM and fluid intelligence in healthy participants in which the shared variance was significantly explained by interference control. Friedman et al. [26] found only the updating function could significantly predict intelligence. This general pattern of stronger associations between constructs mirrors the result of a factor analytical study of schizophrenia by Dickinson et al. [22] in which it was found, using a hierarchical six factor model, that there was a more generalized latent structure of cognitive ability in schizophrenia with higher correlations between cognitive domains than in a normal participant group. Such findings may suggest cognitive functioning is more unitary in schizophrenia. A notable finding was that crystallized intelligence was found to show the strongest association with WM and EFs. A feature of crystallized intelligence is that it draws on previously acquired knowledge consolidated in longer term storage mechanisms, characterised as semantic memory [10]. In the case of the Vocabulary test, used to measure crystallized intelligence, it is known that word knowledge and understanding accumulated developmentally and across the adult lifespan. It is of interest to

Table 2 Descriptive statistics for variables used in the structural equation model for schizophrenia patients (n = 125). Constructs of interest – Observed variables (tests)

Mean (SD)

Skewness

Kurtosis

Mardia’s Multivariate Kurtosis

Working Memory (Letter-Number Task; total correct)

12.667 (4.260) 34.148 (10.300) 82.787 (18.304) 63.658 (27.660) 44.800 (26.128) –0.002 (0.988) 49.227 (29.789) 50.730 (11.682) 50.340 (12.398)

–0.377

–0.157

0.675

0.429

0.567

–2.738

8.638

–0.828

–0.319

0.612

–0.452

–0.319

0.099

0.077

–0.517

–0.234

0.029

–0.595

–0.218

Executive functions - Inhibition (Stroop Colour Word Test; interference score) Executive functions – Updating (0-Back Task; percentage of correct responses) Executive functions – Updating (1-Back Task; percentage of correct responses) Executive functions – Updating (2-Back Task; percentage of correct responses) Executive functions – Updating (N-Back Task; aggregated score)a Executive functions – Shifting (Wisconsin Card Sorting Test; the percentage of conceptual-level responses) Intelligence – Crystallized (WASI – Vocabulary)b Intelligence – Fluid (WASI – Matrix reasoning)b a b

Factor score, computed by exploratory factor analysis and aggregated using regression method for 0-, 1-, and 2-back condition measures. T-scores shown here; mean and standard deviation of the General IQ Score derived from Vocabulary and Matrix Reasoning were respectively 100.53 and 21.36.

P. Wongupparaj et al. / European Psychiatry 30 (2015) 1–7 Table 3 Correlation coefficients between measures using in structural equation modelling. Observed variables

WM Inhibition Updating Shifting Crystallized Fluid

Pearson correlation coefficient WM

Inhibition

Updating

Shifting

Crystallized

Fluid

1 0.391** 0.365** 0.370** 0.643** 0.620**

1 0.504** 0.321** 0.435** 0.418**

1 0.403** 0.436** 0.335**

1 0.415** 0.514**

1 0.638**

1

**Correlation is significant at the 0.01 level (2-tailed).

speculate as why the associations were stronger in this regard, since WM and EFs are fluid and dynamic processes, such that one might have predicted the associations to be stronger, for example, than with fluid intelligence. One possibility is that the neurocognitive development, including the establishment of semantic knowledge, is affected in schizophrenia by weaknesses in WM and EFs, given the known effects, for example, of executive function impairments on memory acquisition [5]. Furthermore, inhibition ability had the highest loading (standardised coefficient = 0.676) on EFs, indicating the strongest effect on executive impairment. This may be related to the sensitivity of this particular function to the effects of schizophrenia. For example, previous studies have suggested that schizophrenia patients show inhibitory impairment in response suppression and selective attention, and furthermore they exhibit critical impairments in inhibitory mechanism involved in memory retrieval (retrieval-induced forgetting, RIF) [64,73,83]. Our results illustrate the potential use of multivariate statistics with path and mediation analysis to explore the structure of cognition in people with schizophrenia. The results should be considered as in need of replication in an even larger sample of schizophrenia patients exploring the reliability of our findings and also enabling an increased number of variables that could be considered in modelling. For example, the SEM results suggested that there is more than 17.8% of unexplained variance in g that could not be predicted solely by WM and EFs and such a study

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Table 4 The goodness-of-fit indices for maximum likelihood estimation. Goodness-of-fit indicesa

Observed values

Criterion values

Chi-square (df) GFI AGFI NFI IFI TLI CFI RMSEA

14.089(8), P = 0.079 0.965 0.908 0.949 0.977 0.956 0.977 0.078

P > 0.05 >0.90 >0.90 >0.90 >0.90 >0.90 >0.90 P < 0.05

GFI: goodness-of-fit statistic; AGFI: the adjusted goodness-of-fit statistic: NFI: Normed-fit index; IFI: incremental fit indices; TLI: the Tucker-Lewis index; CFI: comparative fit index; RMSEA: root mean square error of approximation. a Goodness-of-fit statistic.

could be expanded to include various WM storage components as outlined by comprehensive models of WM function [5]. The structure of cognition in schizophrenia, in particular pertaining to WM, has implications for management and treatment. In normal participants it has been shown that WM training may lead to the improvement of cognitive functioning [45,57,60,72,76]. Cognitive training in schizophrenia focusing specifically on executive functioning has been shown to increase everyday function [86]. Recently, pilot research has suggested that WM training in chronic schizophrenia may produce cognitive benefits [41,65], a finding perhaps predicted by the current results. In terms of the implications of our results for the treatments of schizophrenia, it has already been shown that activation of EFrelevant regions (dlLPFC) within the WM neural network in fully/ partially antipsychotic medication resistant patients prior to them receiving cognitive behaviour therapy for psychosis predicts the degree of responsiveness to this therapy [54]. A recent review by Lett and colleagues [56] illustrated that the benefits of pharmaceutical interventions and cognitive remediation therapy on WM of schizophrenia patients. The current results, taken together with such findings, highlight the need to develop effective means to target WM deficits, focussing on EFs in schizophrenia.

Fig. 2. SEM model concerning the relationship between WM and intelligence, mediated by Executive functions (EFs) (maximum likelihood estimation) R2Executive function = 0.363 and R2General intelligence = 0.822. (Note that in this model, General intelligence is a latent construct and is not based on the full scale intelligence calculation as provided in Table 2, footnote 2.) (*** = P < 0.001.).

P. Wongupparaj et al. / European Psychiatry 30 (2015) 1–7

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Table 5 The direct, indirect, and total effect between structural equation modelling variables (standardised regression weights). Observed variables and latent variables

Direct effect (95% bias-corrected confidence interval)

Indirect effect (95% bias-corrected confidence interval)

Total effect (95% bias-corrected confidence interval)

WM! Executive function

–

WM! Inhibition

0.602*** (0.500 – 0.748) –

WM! Updating

–

WM! Shifting

–

WM! General intelligence WM! Crystallized intelligence

0.465*** (0.216–0.652) –

WM! Fluid intelligence

–

Executive function ! General intelligence Executive function ! Crystallized intelligence

0.547*** (0.338–0.812) –

Executive function ! Fluid intelligence

–

0.602*** (0.500–0.748) 0.407*** (0.360–0.461) 0.402*** (0.313–0.509) 0.368*** (0.253–0.498) 0.795*** (0.695–0.882) 0.648*** (0.552–0.730) 0.626*** (0.522–0.710) 0.547*** (0.338–0.812) 0.446*** (0.271–0.652) 0.431*** (0.266–0.648)

0.407*** (0.360–0.461) 0.402*** (0.313–0.509) 0.368*** (0.253–0.498) 0.330*** (0.191–0.606) 0.648*** (0.552–0.730) 0.626*** (0.522–0.710) – 0.446*** (0.271–0.652) 0.431*** (0.266–0.648)

*** = P < 0.001.

5. Conclusions Significant differences were found in WM, EFs and g in between schizophrenia and healthy groups, with schizophrenia groups being significantly lower on WM, EFs and g measures. Moreover, there is a significant relationship between WM and intelligence in schizophrenia, and this association is mediated by executive functions, in order of strength, inhibition, updating and shifting, respectively. Our observations, taken together with relevant finding in the literature, suggest that WM deficit with a focus on EFs needs to be considered a potential treatment target in order to improve every day function and maximise clinical benefits of psychosocial interventions for this clinical population. Disclosure of interest The authors declare that they have no conflicts of interest concerning this article. Acknowledgements This research was supported by a Thailand Government PhD Scholarship to Peera Wongupparaj and funds from the Biomedical Research Centre for Mental Health at the Institute of Psychiatry, King’s College London, and the South London and Maudsley NHS Foundation Trust for some of the time of Professor Veena Kumari. References [1] Ackerman PL, Beier ME, Boyle MO. Working memory and intelligence: the same or different constructs? Psychol Bull 2005;131:30–60. [2] Arbuckle JL. IBM SPSS AMOS 21: user’s guide. Armonk, NY: IBM Corp; 2012. [3] Aylward E, Walker E, Bettes B. Intelligence and schizophrenia: meta-analysis of the research. Schizophrenia Bull 1984;10:430–59. [5] Baddeley A. Working memory, thought and action. Oxford: Oxford University Press; 2007. [6] Baddeley A. Working memory: theories, models, and controversies. Annu Rev Psychol 2012;63:1–29. [7] Barbey AK, Colom R, Grafman J. Dorsolateral prefrontal contributions to human intelligence. Neuropsychologia 2013;51:1361–9. [8] Barch D. What can research on schizophrenia tell us about the cognitive neuroscience of working memory? Neuroscience 2006;139:73–84. [9] Barch DM, Ceaser A. Cognition in schizophrenia: core psychological and neural mechanisms. Trends Cogn Sci 2012;16:27–34.

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