Misattributing speech and jumping to conclusions: A longitudinal study in people at high risk of psychosis

European Psychiatry 30 (2015) 32–37

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Original article

Misattributing speech and jumping to conclusions: A longitudinal study in people at high risk of psychosis T.T. Winton-Brown a,*, M.R. Broome a,b, P. Allen a, I. Valli a, O. Howes a, P.A. Garety c, L.C. Johns c,1, P. McGuire a,1 a

Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, De Crespigny Park, PO Box 67, London SE5 8AF, United Kingdom Department of Psychiatry, University of Oxford, Oxford, United Kingdom c Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 23 June 2014 Received in revised form 29 September 2014 Accepted 30 September 2014 Available online 12 December 2014

Biases in cognition such as Jumping to Conclusions (JTC) and Verbal Self-Monitoring (VSM) are thought to underlie the formation of psychotic symptoms. This prospective study in people with an At Risk Mental State (ARMS) for psychosis examined how these cognitive biases changed over time, and predicted clinical and functional outcomes. Twenty-three participants were assessed at clinical presentation and a mean of 31 months later. Performance on a JTC and VSM tasks were measured at both time points. Relationships to symptom severity, level of function and the incidence of psychotic disorder were then examined. The levels of symptoms, function and VSM all improved over time, while JTC was stable. Five participants (22%) developed a psychotic disorder during the follow-up period, but the risk of transition was not related to performance on either task at baseline, or to longitudinal changes in task performance. JTC performance correlated with symptom severity at baseline and follow-up. Similarly, performance on the two tasks was not related to the level of functioning at follow-up. Thus, while the ARMS is associated with both VSM and JTC biases, neither predict the onset of psychosis or the overall functional outcome. ß 2014 Published by Elsevier Masson SAS.

Keywords: Psychosis Cognitive biases Jumping to Conclusions Verbal Self-Monitoring At Risk Mental State

1. Introduction Cognitive models propose that psychotic symptoms arise from faulty appraisals of anomalous or ambiguous experiences, driven by emotional processes and cognitive biases [14,32]. A key contributing factor to the formation of these appraisals is a ‘‘data gathering’’ or Jumping to Conclusions (JTC) bias [13], a tendency to use less information to reach a decision. This can be studied using a probabilistic reasoning task (the ‘‘Beads’’ task, [15]), in which a participant guesses which of the two jars a series of coloured beads is drawn from. Compared with healthy controls, patients with psychotic disorders tend to make their decision after seeing fewer beads, demonstrating a so-called JTC bias [12,26,6], and this bias is related to the intensity and conviction of delusional ideation [7,28]. It is also present in the 1st degree relatives of patients with schizophrenia [31], in non-clinical delusion-prone participants [24], and in people with an at risk mental state for psychosis (ARMS, [2]).

* Corresponding author. Tel.: +44 20 7848 0970; fax: +44 20 7848 0976. E-mail address: [email protected] (T.T. Winton-Brown). 1 Dr Johns and Prof McGuire contributed equally to this work. http://dx.doi.org/10.1016/j.eurpsy.2014.09.416 0924-9338/ß 2014 Published by Elsevier Masson SAS.

A self-recognition deficit such as faulty appraisal of ambiguous auditory verbal experiences is thought to contribute to auditory verbal hallucinations [1,33], and can be studied using an on-line Verbal Self-Monitoring (VSM) paradigm. The VSM task requires participants to make source judgments (i.e. self/other) about externally presented distorted speech trials. Relative to healthy controls, individuals with schizophrenia, affective psychosis or an ARMS [17,19,20] tend to misidentify their own distorted speech as being non-self in origin, particularly if they experience auditory verbal hallucinations [16]. If JTC and impaired VSM contribute to the generation of psychotic symptoms, they may be expected to co-vary with the severity of these symptoms over time. Data from cross-sectional studies suggest that the severity of both of these cognitive biases is related to the intensity of psychotic symptoms at clinical presentation. However, the extent to which these relationships are maintained as the severity of symptoms varies within an individual over time is less clear. Peters and Garety [27] found the JTC bias remained stable over time in deluded individuals whose presenting symptoms had resolved, and was also stable in a later larger sample of people with established psychosis whose delusional conviction had reduced [30]. However, Menon et al.

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[25] found that improvement in psychotic symptoms was accompanied by improved performance on an ‘‘emotionally salient’’ version of the beads task, although these changes were not correlated. There are no studies we know of that have examined VSM impairments over time. According to cognitive models of psychosis [14] tendencies to jump to conclusions and to misattribute the source of selfgenerated material increase the likelihood that subclinical psychotic experiences will develop into a psychotic disorder. This hypothesis may be tested by studying these cognitive biases in people with an ARMS. These individuals present with ‘‘prodromal’’ psychotic experiences and approximately a third of them will develop a first episode of psychosis within 24 months with another third remaining stable and a further third showing a symptomatic improvement [34,35,10]. ARMS subjects also vary according to their functional outcome, with some making a good recovery but others having a low level of functioning at follow-up, even if they have not developed psychosis [5,23,37]. Clinical measures at baseline are often poor predictors of outcomes in the ARMS [10], and alternative indicators of course and prognosis would be of great clinical value. We have previously shown that at clinical presentation, ARMS subjects have both a JTC bias and VSM impairments relative to controls [2,20]. The aim of the present study was to investigate the longitudinal course of these biases, and examine their relationship with a) longitudinal changes in the severity of psychotic symptoms, and b) long term clinical and functional outcomes. We first tested the hypothesis that both the severity of psychotic symptoms would be temporally linked to the severity of these cognitive biases. We then tested the prediction that the severity of these biases at baseline would predict clinical and functional outcomes at follow-up.

2. Methods

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Depression (Ham-D) scales were also included as affective symptoms are common in ARMS participants, and may play a central role in the development of psychotic symptoms [14]. The impact of symptoms on social and occupational function was assessed at both timepoints using the Global Assessment of Function (GAF) scale [4], scored by consensus between two OASIS clinicians blinded to task performance or other clinical symptom scale scores. Verbal Self-Monitoring was assessed using a well-established computerised paradigm [19,20,18]. Participants wore headphones and read aloud words presented on a screen with speech digitally processed and fed back simultaneously through headphones. The words heard back were either ‘‘self’’ or ‘‘other’’ generated (simultaneously triggered pre-recorded words) with severe, moderate or no distortion. The participant indicated the perceived word source via button press: Self, Other or Unsure. The percentage of errors was calculated for each of the three levels of distortion (none, moderate, severe). The primary measure used in subsequent analyses was the percentage of externalizing misattribution errors (‘‘self’’ feedback attributed to ‘‘other’’: % VSM errors) combined across moderate and severe levels of distortion, and excluded unsure responses. Jumping to Conclusions (JTC) was tested using a computerised version of the ‘‘beads’’ task with varying levels of demand [13]. Participants were shown a series of coloured beads and asked to decide which jar they were being drawn from. The ratio of beads in each jar varied: ‘‘easy’’ involved two colours of beads in a ratio of 85:15; ‘‘intermediate’’ involved two colours in a ratio of 60:40; and ‘‘hard’’ involved three colours of bead in a ratio of 44:28:28. Participants were asked to judge, based on the sequence of beads observed, which of the jars the beads were drawn from, and to press a button when they were ‘‘as certain as possible’’. The primary measure used in subsequent analyses was the number of beads required to make a response (‘‘draws to decision’’, DTD) in the moderate difficulty condition (60:40 DTD); this measure separated the groups most clearly at baseline and avoids floor and ceiling performance effects [7,2].

2.1. Design 2.4. Clinical follow-up This was a prospective study, with participants assessed at clinical presentation and a mean of 31 (s.d. = 19.5) months later. At each stage, participants were assessed clinically and with cognitive tasks that engage verbal self-monitoring (VSM) and probabilistic reasoning. 2.2. Participants Participants with an ARMS were recruited when they presented to Outreach and Support in South London (OASIS, [11]). At that time, they were assessed by two experienced clinicians using the Comprehensive Assessment for At Risk Mental State (CAARMS, [35]) with ARMS status later confirmed at a consensus meeting with the clinical team. All participants were native English speakers. Participants with a history of neurological disorder, or who met DSM-IV criteria for a substance misuse or dependence disorder other than nicotine were excluded. The study was approved by the Institute of Psychiatry Research Ethics Committee. Participants gave written informed consent and were compensated for their time and travel expenses. 2.3. Clinical assessment Psychotic-like symptoms were assessed using the Comprehensive Assessment of At Risk Mental States (CAARMS), Positive and Negative Syndrome Scale (PANSS, [21]) and the Peters Delusional Inventory (PDI, [28]). The Hamilton Anxiety (Ham-A) and

All ARMS participants were seen monthly by OASIS for at least 2 years and monitored for signs of transition to psychosis. Treatment involved needs-focused case management in all participants, plus a period of Cognitive Behaviour Therapy (CBT) in all but two participants and a period of low-dose antipsychotic medication in a minority (nine participants, 39%) of the sample. 2.5. Statistics Two follow-up outcomes were tested against baseline measures. The primary outcome was transition to psychosis, defined according to PACE criteria [34]. The secondary outcome was overall function at follow-up as measured by GAF score. Pre-specified clinical and task predictor variables were compared for each outcome using independent sample 2-tailed t-tests. Where significant differences were found, these were entered as predictor variables in a stepwise logistic or linear regression. Due to the limited numbers participants, we were able to enter up to three predictor variables for each regression. VSM and JTC measures were also correlated with measures of psychotic symptoms at follow-up and over time using Pearson’s product-moment correlations for parametric data and Spearmans’ r for non-parametric data. In order to limit the number of tests, we used a single measure for each task (% VSM externalizing misattribution errors and DTD in the moderate condition for the

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JTC task) and four clinical scores (PANSS total and positive subscale and PANSS delusion and hallucination items). Finally, to test their interrelationship, VSM and JTC were correlated with each other at the two timepoints and over time.

3. Results 3.1. Participants Twenty-five participants had both VSM and JTC tasks assessed at baseline. Twenty-three were assessed at follow-up, a mean of 30.8 months (SD 19.6 months) later - two participants could not be contacted. Due to technical difficulties, baseline VSM data were incomplete in three participants and JTC incomplete in one; all data were complete at follow-up. Eleven of the 23 participants were male, the mean age was 24.8 (SD 4.9) and mean IQ 100.2 (SD 10.7). 3.2. Clinical progression At follow-up, participants had significantly lower ratings than at baseline on CAARMS positive and subscales, PANSS-positive and subscales and PDI total and subscales (Table 1, Fig. 1C). There was a significant improvement in GAF scores, and a trend to improvement in Hamilton Anxiety scales (Table 1).

Table 1 Clinical scales at baseline and at follow-up. Measure - mean (SD)

Baseline

Follow-up

t

P (2-tailed)

PANSS total

50.6 (11.1)

46.9 (11.5)

1.24

NS

PANSS-positive PANSS delusions PANSS hallucinations

12.7 (3.2) 2.5 (0.8) 2.0 (0.8)

11.0 (3.6) 1.9 (1.0) 1.8 (1.0)

2.08 2.96 0.78

0.049* 0.007** NS

CAARMS positive CAARMS tc CAARMS pa CAARMS sa

16.5 3.6 2.9 1.6

(4.9) (1.0) (1.3) (1.3)

10.2 2.2 2.0 1.0

(6.6) (1.5) (1.6) (1.2)

3.98 3.59 2.42 1.94

0.001** 0.002** 0.024* 0.065^

(63.0) (23.6) (24.3) (18.4)

76.2 25.3 23.4 27.8

(51.8) (18.8) (17.3) (16.7)

3.29 3.11 3.15 2.54

0.004** 0.006** 0.006** 0.021*

PDI -total PDI - preoccupation PDI - distress PDI - conviction

108.2 36.1 37.2 34.8

Hamilton - anxiety

12.3 (9.8)

10.9 (9.5)

0.51

NS

Hamilton - depression

15.6 (9.1)

9.2 (8.9)

2.10

0.054^

GAF

57.4 (11.6)

70.7 (11.5)

2.86

0.01*

PANSS: Positive and Negative Syndrome Scale; CAARMS: comprehensive assessment of at risk mental state; tc: thought content; pa: perceptual abnormalities; sa: speech abnormalities; PDI: Peters’ Delusional Index; GAF: global assessment of functioning; ^: trend P < 0.1. * P < 0.05. ** P < 0.01.

3.3. Task performance The baseline task performance has been previously reported [2,20]: at baseline ARMS participants differed on both the JTC and VSM tasks relative to matched control groups. Participants with an ARMS drew fewer beads before reaching a decision on the moderate difficulty (60:40) beads task and made more externalizing misattribution errors during distorted feedback in the VSM task than controls.

At follow-up there was a significant improvement in mean VSM task performance. ARMS participants made fewer externalising misattribution errors during distorted feedback at follow-up than at baseline (mean (SD) percentage errors at time 1 = 24.9 (23.3), time 2 = 10.1 (11.7), t = 2.98; P = 0.008, Fig. 1A). In contrast, mean performance on the beads task remained stable between baseline and follow-up on the moderate (60:40) and hard (44:28:28) versions of the task (Fig. 1B). There was,

Fig. 1. Top panel: changes in VSM and JTC performance between baseline and follow-up. Bottom panel: changes in clinical scales between baseline and follow-up and relationship between baseline Hamilton Anxiety scores and follow-up GAF score. PANSS: Positive and Negative Syndrome Scale; CAARMS: Comprehensive Assessment of At Risk Mental State GAF: Global Assessment of Functioning; ^ trend P < 0.1 *significant P < 0.05.

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however, a longitudinal change in mean performance on the easy (85:15) version of the task, with participants choosing after a mean of 7.6 beads at baseline, and after 5.1 beads at follow-up (t = 2.28; P = 0.037; Fig. 1B). 3.4. Relationship between task performance and clinical outcomes 3.4.1. Transition to psychosis Five of 23 ARMS participants made a transition to psychosis within the follow-up period (21.7%) as defined by the PACE criteria [35]. None were acutely psychotic at time of re-testing, all longitudinal analyses included these participants. There were no significant mean baseline differences between ARMS participants who later made a transition and those who did not, in terms of VSM (% misattribution errors with distorted feedback) or JTC (60:40 Draws to Decisions), demographic or clinical measures (age, gender, GAF, PANSS-positive, Ham-A, Ham-D; Table 2). Furthermore, there were no differences in longitudinal change in JTC or VSM measures between these two groups and no differences in GAF at follow-up (mean (SD) t2 GAF in transitions 77.0 (11.0), non-transitions 68.9 (11.3) t = 1.43; P = 0.17). 3.4.2. Functional outcome The mean GAF score significantly improved over time amongst participants with an ARMS (Table 1), and at follow-up 39% of participants had a score greater than 75 (‘‘no more than slight impairment in overall function’’). GAF at follow-up correlated significantly with baseline HAM-A and PANSS total scores and at trend level with baseline beads task and PANSS-positive scores (Table 2). We entered baseline PANSS total, HAM-A, and beads (DTD Mod) scores in a stepwise linear regression with follow-up GAF as the continuous dependent outcome variable. The final model explained 21.6% of the variance in the final GAF score (model F = 4.96; P = 0.039 r = 0.465) and retained baseline HAM-A score (standardized beta = 0.465; P = 0.039, Fig. 1D) as the only significant predictor. 3.5. Relationship between task performance and clinical symptoms 3.5.1. VSM task At follow-up, there were no significant correlations between the % VSM errors and PANSS-positive subscale or delusion/ hallucination items. Similarly, there were no significant correlations between changes in these symptoms scores and VSM task performance between baseline and follow-up, although both improved in this time.

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3.5.2. JTC task At follow-up, performance on the JTC task (DTD 60:40) was correlated with the PANSS delusion (r = 0.436; P = 0.042) and PANSS hallucination (r = 0.443; P = 0.039) items but not with overall PANSS-positive scores. Changes in Draws to Decision in the intermediate (60:40) condition correlated specifically with a change in PANSS delusion subscores at trend level (r = 0.406; P = 0.094): those developing a more conservative style also had lower delusion scores over time. 3.6. Relationship between tasks There were no significant correlations between VSM and JTC task performance at baseline or at follow-up, or between the changes in task performance. 4. Discussion This is the first time to our knowledge that participants at clinical high risk for psychosis have been followed over time measuring JTC and VSM biases, and so little is known regarding the course of these biases or their relation to symptoms and clinical outcome. Over the 31 months following baseline, participants with an ARMS showed, on average, a clinical and functional improvement, with a reduction in the mean PANSS and CAARMS scores and a rise in the mean GAF score. Nevertheless, as in previous studies [11], the sample was heterogenous with respect to clinical and functional outcomes: 22% of subjects developed psychosis during follow-up, whereas 39% made a ‘‘functional recovery’’, as indexed by a follow-up GAF score > 75. Interestingly functional status at follow-up was unrelated to whether full-blown psychosis had occurred during the follow-up period, supporting suggestions that measures of ‘‘outcome’’ in this group be broadened beyond just transition to psychosis [22]. Alongside this clinical improvement, we found that participants’ mean VSM performance also improved significantly, although these two changes were not correlated. Practice effects are unlikely to account for this improvement, as a study in controls demonstrated stable performance on the paradigm over three months [18], and our follow-up period was significantly longer. An improvement in VSM performance in participants whose mental state has improved is consistent with cross-sectional evidence that VSM performance is worse in acutely psychotic than remitted patients with schizophrenia [19], and contrasts with evidence that VSM performance is related to a trait vulnerability to psychosis, with impairments evident in patients’ non-psychotic siblings [3]. In contrast, mean beads task performance remained

Table 2 Clinical measures and task performance against later transition to psychosis. Transition vs Non-transition

Correlation with GAF t2

Transition (n = 5)

Non-transition (n = 18)

t

P

r

P

Baseline scores GAF baseline HAM-A HAM-D PANSS total PANSS-positive VSM % errors Beads DTD Mod

55.7 (14.3) 8.5 (6.9) 14.0 (11.5) 46.4 (11.3) 12.8 (2.3) 12.8 (15.9) 8.8 (4.1)

57.8 (8.7) 13.2 (10.3) 15.9 (9.1) 51.8 (11.0) 12.7 (3.5) 15.6 (22.0) 9.4 (4.3)

0.333 0.862 0.312 0.942 0.046 0.240 0.26

NS NS NS NS NS NS NS

0.042 0.486 0.158 0.438 0.386 0.169 0.380

NS 0.026* NS 0.036* 0.069^ NS 0.081^

Change scores D VSM % errors D DTD Mod

0.07 (0.17) 0.0 (5.72)

0.315 0.347

NS NS

0.114 0.176

0.11 (0.17) 1.0 (5.43)

NS NS

GAF: global assessment of functioning; HAM-A: Hamilton Anxiety Scale; HAM-D: Hamilton Depression Rating Scale; PANSS: Positive and Negative Syndrome Scale; VSM % errors: verbal self-monitoring % misattribution errors during distorted feedback; DTD: Draws to Decision; Mod: 60:40 condition; ^: trend P < 0.1. * P < 0.05.

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stable over time. This replicates findings in chronic patients, where performance on the beads task was unchanged despite longitudinal reductions in delusional scores and levels of conviction [27,30] and the conclusions of a review that found JTC biases remained stable despite antipsychotic treatment and clinical improvement [29]. We tested whether performance on these two tasks would be correlated with the level of psychotic symptoms cross-sectionally and longitudinally. We had previously found that at baseline, JTC task performance was related to the level of delusional ideation on the PDI [2], whereas the relationship of VSM impairments to symptoms was less clear [20]. In the present study, we found relationships between JTC performance and PANSS delusion and hallucination item scores at follow-up, and a trend for the longitudinal changes in JTC performance and PANSS delusion to be correlated. This supports increasing evidence that the JTC bias relates most specifically to delusions [7,8]. In contrast, VSM task performance did not relate to symptoms either at follow-up nor in terms of the respective longitudinal changes between baseline and follow-up. Our hypothesis was that task performance would predict subsequent clinical and functional outcomes, as measured by transition to psychosis and functional score at follow-up (GAF). We reasoned that the greater the tendency to make misattributions and jump to conclusions regarding their anomalous experiences, the more likely ARMS participants would be to develop psychosis and be functionally impaired by their experiences. However, neither task performance at baseline nor the change in performance over time was significantly related to the later onset of psychosis. Because the number of participants who developed psychosis was small (n = 5), we cannot exclude the possibility that this was related to limited statistical power. There was a trend for more conservative baseline (i.e. normal) JTC scores to relate to greater functional status at follow-up, and baseline PANSS scores also related to GAF at follow-up. However, these associations were not significant when considered in a multivariate regression model, which identified baseline anxiety ratings as a significant independent predictor: subjects who had low levels of anxiety at baseline were more likely to have a good functional outcome. As well as supporting the central role that anxiety plays in cognitive models of the development of psychosis [12], this finding highlights the importance of recognizing and treating anxiety disorders in ARMS samples to improve overall functioning [36,9]. Finally, as we had studied both tasks in the same individuals, we were able to test whether these biases may be accessing a common underlying cognitive vulnerability. However, we found no correlation between JTC and VSM tasks at either timepoint or longitudinally. This is consistent with the notion that these tasks are engaging separate cognitive processes that impact on psychosis in different ways [12,26,6,17,19,20]. In summary, our data indicate that psychotic-like symptoms are related to a JTC bias, and that this is evident in people at high risk of psychosis, but this bias does not predict longitudinal transition to psychosis or functional outcome. However, the small size of our sample limited the statistical power of the analyses, and the lack of relationship between altered JTC and VSM performance and the risk of later psychosis should therefore be interpreted with caution. A further consideration is the potential effect of treatment. The ARMS group was recruited from a clinical service, and although only a minority (n = 8) received antipsychotic medication at some point during follow-up, most participants (n = 21) had received case management and psychological therapy. The extent to which the findings reflect the natural history of the ARMS or the effects of treatment was thus unclear, with small subsample sizes precluding formal testing of this.

Disclosure of interest The authors declare that they have no conflicts of interest concerning this article. Acknowledgements The study was supported by the Guy’s and St. Thomas’ Charitable Foundation and the Mental Health Foundation. TWB was supported by the Wellcome Trust. We also thank those who took part in the study and the staff of the OASIS team, South London and Maudsley NHS trust. References [1] Allen P, Aleman A, McGuire PK. Inner speech models of auditory verbal hallucinations: evidence from behavioural and neuroimaging studies. Int Rev Psychiatry 2007;19:407–15. [2] Broome MR, Johns LC, Valli I, Woolley JB, Tabraham P, Brett C, et al. Delusion formation and reasoning biases in those at clinical high risk for psychosis. Br J Psychiatry 2007;51:s38–42. [3] Brunelin JT, d’Amato T, Brun P, Bediou B, Kallel L, Senn M, et al. Impaired verbal source monitoring in schizophrenia: an intermediate trait vulnerability marker? Schizophr Res 2007;89(1–3):287–92. [4] Caldecott-Hazard S, Hall RCW. A modified GAF Scale. Psychiatr Serv 1994;45: 611–2. [5] Carrio´n RE, McLaughlin D, Goldberg TE, Auther AM, Olsen RH, Olvet DM, et al. Prediction of functional outcome in individuals at clinical high risk for psychosis. JAMA Psychiatry 2013;70:1133–42. [6] Fear CF, Healy D. Probabilistic reasoning in obsessive-compulsive and delusional disorders. Psychol Med 1997;27(1):199–208. [7] Fine CM, Gardner M, Craigie J, Gold I. Hopping, skipping or jumping to conclusions? Clarifying the role of the JTC bias in delusions. Cognit Neuropsychiatry 2007;12(1):46–77. [8] Freeman D, Pugh K, Garety P. Jumping to conclusions and paranoid ideation in the general population. Schizophr Res 2008;102(1–3):254–60. [9] French P, Shryane N, Bentall RP, Lewis SW, Morrison AP. Effects of cognitive therapy on the longitudinal development of psychotic experiences in people at high risk of developing psychosis. Br J Psychiatry Suppl 2007;51:s82–7. [10] Fusar-poli P, Bonoldi I, Yung AR, Borgwardt S, Kempton MJ, Valmaggia L, et al. Predicting psychosis: meta-analysis of transition outcomes in individuals at high clinical risk. Arch Gen Psychiatry 2012;69:220–9. [11] Fusar-Poli P, Byrne M, Badger S, Valmaggia LR, McGuire PK. Outreach and support in south London (OASIS), 2001–2011: ten years of early diagnosis and treatment for young individuals at high clinical risk for psychosis. Eur Psychiatry 2013;28:315–26. [12] Garety PA, Freeman D. The past and future of delusions research: from the inexplicable to the treatable. Br J Psychiatry 2013 [In Press]. [13] Garety PA, Hemsley DR, Wessley S. Reasoning in deluded schizophrenic and paranoid patients. Biases in performance on a probabilistic inference task. J Nerv Ment Disord 1991;179(4):194–201. [14] Garety PA, Bebbington P, Fowler D, Freeman D, Kuipers E. Implications for neurobiological research of cognitive models of psychosis: a theoretical paper. Psychol Med 2007;37(10):1377–91. [15] Huq SF, Garety PA, Hemsley DR. Probabilistic judgements in deluded and nondeluded participants. Q J Exp Psychol 1988;A40(4):801–12. [16] Johns LC, McGuire PK. Verbal self-monitoring and auditory hallucinations in schizophrenia. The Lancet 1999;353(9151):469–70. [17] Johns LC, Rossell S, Frith C, Ahmad F, Hemsley D, Kuipers E, et al. Verbal selfmonitoring and auditory verbal hallucinations in patients with schizophrenia. Psychol Med 2001;31(4):705–15. [18] Johns LC, Gregg L, Vythelingum N, McGuire PK. Establishing the reliability of a verbal self-monitoring paradigm. Psychopathology 2003;36(6):299–303. [19] Johns LC, Gregg L, Allen P, McGuire PK. Impaired verbal self-monitoring in psychosis: effects of state, trait and diagnosis. Psychol Med 2006;36(4): 465–74. [20] Johns LC, Allen P, Valli I, Winton-Brown T, Broome M, Woolley J, et al. Impaired verbal self-monitoring in individuals at high risk of psychosis. Psychol Med 2010;40:1433–42. [21] Kay SR, Fiszbein A, Opfer LA. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull 1987;13(2):261–76. [22] Lin A, Nelson B, Yung AR. At-risk’’ for psychosis research: where are we heading? Epidemiol Psychiatr Sci 2012;21:329–34. [23] Lin A, Wood SJ, Yung AR. Measuring psychosocial outcome is good. Curr Opin Psychiatry 2013;26:138–43. [24] Linney YM, Peters ER, Ayton P. Reasoning biases in delusion-prone individuals. Br J Clin Psychol 1998;37(Pt 3):285–302, http://www.ncbi.nlm.nih.gov/pubmed/ 9784884. [25] Menon MR, Mizrahi R, Kapur S. Jumping to conclusions’ and delusions in psychosis: relationship and response to treatment. Schizophr Res 2008;98(1– 3):225–31.

T.T. Winton-Brown et al. / European Psychiatry 30 (2015) 32–37 [26] Moritz S, Woodward TS. Jumping to conclusions in delusional and nondelusional schizophrenic patients. Br J Clin Psychol 2005;44(Pt 2): 193–207. [27] Peters E, Garety P. Cognitive functioning in delusions: a longitudinal analysis. Behav Res Ther 2006;44(4):481–514. [28] Peters ER, Joseph SA, Garety PA. Measurement of delusional ideation in the normal population: introducing the PDI (Peters et al. Delusions inventory). Schizophr Bull 1999;25(3):553–76. [29] So SH, Garety PA, et al. ’’Do antipsychotics improve reasoning biases? A review‘‘ Psychosom Med 2010;72(7):681–93 [published by Lippincott Wiliams and Wilkins, Tampa Florida USA]. [30] So SH, Freeman D, Dunn G, Kapur S, Kuipers E, Bebbington P, et al. Jumping to conclusions, a lack of belief flexibility and delusional conviction in psychosis: a longitudinal investigation of the structure, frequency, and relatedness of reasoning biases. J Abnorm Psychol 2012;121:129–39. [31] Van Dael FD, Versmissen D, Janssen I, Myin-Germeys I, van Os J, Krabbendam L. Data gathering: biased in psychosis? Schizophr Bull 2006;32(2):341–51.

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[32] van der Gaag M. A neuropsychiatric model of biological and psychological processes in the remission of delusions and auditory hallucinations. Schizophrenia Bulletin 2006;32(Supplement 1):S113–22. [33] Waters F, Waters F, Woodward T, Woodward T, Allen P, Allen P, et al. Selfrecognition deficits in schizophrenia patients with auditory hallucinations: a meta-analysis of the literature. Schizophr Bull 2012;38:741–50. [34] Yung AR, Phillips LJ, McGorry PD, Jackson HJ, Francey S, McFarlane CA, et al. Prediction of psychosis. A step towards indicated prevention of schizophrenia. Br J Psychiatry 1998;172(33):14–20. [35] Yung AR, Phillips LJ, Francey SM, Yuen HP, McGorry PD, Hallgren M, et al. Psychosis prediction: 12-month follow-up of a high-risk (‘‘prodromal’’) group. Schizophr Res 2003;60:21–32. [36] Yung AR, Yuen HP, Berger G, Francey S, Hung TC, Nelson B, et al. Declining transition rate in ultra high risk (prodromal) services: dilution or reduction of risk? Schizophr Bull 2007;33(3):673–81. [37] Yung AR, Nelson B, Thompson A, Wood SJ. The psychosis threshold in Ultra High Risk (prodromal) research: is it valid? Schizophr Res 2010;120:1–6.