Psychological Medicine Relationship between jumping to conclusions cambridge.org/psm and clinical outcomes in people at clinical high-risk for psychosis Ana Catalan1,2 , Stefania Tognin1,3, Matthew J. Kempton1,4, Daniel Stahl5, Original Article Gonzalo Salazar de Pablo6,7, Barnaby Nelson8,9, Christos Pantelis10, *EU-GEI High Risk Study Group Author: See appendix for author list. Anita Riecher-Rössler11, Rodrigo Bressan12, Neus Barrantes-Vidal13, Marie- Cite this article: Catalan A et al (2022). Odile Krebs14,15, Merete Nordentoft16, Stephan Ruhrmann17, Gabriele Sachs18, Relationship between jumping to conclusions and clinical outcomes in people at clinical Bart P. F. Rutten19, Jim van Os1,19,20, Lieuwe de Haan21, Mark van der Gaag22,23, high-risk for psychosis. Psychological Medicine EU-GEI High Risk Study*, Lucia R. Valmaggia4,24 and Philip McGuire1,3,4 52, 1569–1577. https://doi.org/10.1017/ S0033291720003396 1 Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, Received: 15 June 2020 London, UK; 2Deparment of Mental Health, Biocruces Bizkaia Health Research Institute. Basurto University Revised: 18 August 2020 Hospital. Facultad de Medicina y Odontología, Campus de Leioa, University of the Basque Country, UPV/EHU. Accepted: 1 September 2020 Plaza de Cruces 12. 48903, Barakaldo, Bizkaia, Spain; 3Outreach and Support in South London (OASIS) service, First published online: 6 October 2020 South London and Maudsley NHS Foundation Trust, Likondon, UK; 4National Institute for Health Research (NIHR) Biomedical Research Centre (BRC), London, UK; 5Department of Biostatistics and Health Informatics, King´s Key words: College London, Institute of Psychiatry, Psychology, and Neuroscience, London, UK; 6Department of Child and Functioning; psychosis; transition to psychosis; Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK; ultra high-risk 7 Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Author for correspondence: Universitario Gregorio Marañón School of Medicine, Universidad Complutense, Instituto de Investigación Sanitaria Ana Catalan, Gregorio Marañón (IiSGM), CIBERSAM, Madrid, Spain; 8Orygen, Parkville, Victoria, Australia; 9Centre for Youth E-mail: ana.catalan@kcl.ac.uk Mental Health, University of Melbourne, Parkville, Victoria, Australia; 10Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Carlton South, Victoria, Australia; 11 Medical Faculty, University of Basel, Basel, Switzerland; 12Depto Psiquiatria, LiNC – Lab Integrative Neuroscience, Escola Paulista de Medicina, Universidade Federal de São Paulo – UNIFESP, São Paulo, Brazil; 13Departament de Psicologia Clínica i de la Salut (Universitat Autònoma de Barcelona), Fundació Sanitària Sant Pere Claver, Spanish Mental Health Research Network (CIBERSAM), Madrid, Spain; 14INSERM, IPNP UMR S1266, Laboratoire de Physiopathologie des Maladies Psychiatriques, Université de Paris, Institut de Psychiatrie (CNRS GDR 3557), Paris, France; 15Faculté de Médecine Paris Descartes, GHU Paris – Sainte-Anne, Pôle Hospitalo Universitaire PEPIT C’JAAD, Paris, France; 16Mental Health Center Copenhagen and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, Mental Health Center Glostrup, Mental Health Services in the Capital Region of Copenhagen, University of Copenhagen, Copenhagen, Denmark; 17Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany; 18 Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; 19Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, The Netherlands; 20Department of Psychiatry, UMC Utrecht Brain Center, Utrecht University Medical Centre, Utrecht, The Netherlands; 21Early Psychosis Department, Amsterdam UMC, Amsterdam, The Netherlands; 22VU University, Faculty of Behavioural and Movement Sciences, Department of Clinical Psychology and Amsterdam Public Mental Health research institute, Amsterdam, The Netherlands; 23Department of Psychosis Research, Parnassia Psychiatric Institute, The Hague, The Netherlands and 24Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK Abstract Background. Psychosis is associated with a reasoning bias, which manifests as a tendency to ‘jump to conclusions’. We examined this bias in people at clinical high-risk for psychosis © The Author(s), 2020. Published by (CHR) and investigated its relationship with their clinical outcomes. Cambridge University Press. This is an Open Methods. In total, 303 CHR subjects and 57 healthy controls (HC) were included. Both Access article, distributed under the terms of the Creative Commons Attribution- groups were assessed at baseline, and after 1 and 2 years. A ‘beads’ task was used to assess NonCommercial-NoDerivatives licence (http:// reasoning bias. Symptoms and level of functioning were assessed using the Comprehensive creativecommons.org/licenses/by-nc-nd/4.0/), Assessment of At-Risk Mental States scale (CAARMS) and the Global Assessment of which permits non-commercial re-use, Functioning (GAF), respectively. During follow up, 58 (16.1%) of the CHR group developed distribution, and reproduction in any medium, psychosis (CHR-T), and 245 did not (CHR-NT). Logistic regressions, multilevel mixed provided the original work is unaltered and is properly cited. The written permission of models, and Cox regression were used to analyse the relationship between reasoning bias Cambridge University Press must be obtained and transition to psychosis and level of functioning, at each time point. for commercial re-use or in order to create a Results. There was no association between reasoning bias at baseline and the subsequent onset derivative work. of psychosis. However, when assessed after the transition to psychosis, CHR-T participants showed a greater tendency to jump to conclusions than CHR-NT and HC participants (55, 17, 17%; χ2 = 8.13, p = 0.012). There was a significant association between jumping to conclu- sions (JTC) at baseline and a reduced level of functioning at 2-year follow-up in the CHR group after adjusting for transition, gender, ethnicity, age, and IQ. 1570 Ana Catalan et al. Conclusions. In CHR participants, JTC at baseline was associated with adverse functioning at the follow-up. Interventions designed to improve JTC could be beneficial in the CHR population. Introduction Jumping to conclusions (JTC) is a reasoning bias characterised by were recruited from the same geographical areas. Inclusion criteria a tendency to seek less information before making a decision for all participants were: being aged 15–35; being able to consent, (Fine, Gardner, Craigie, & Gold, 2007). It has been reported in and having adequate language skills local to each center. In add- patients with a first episode of psychosis (FEP) (Langdon, Still, ition, CHR participants had to meet high risk for psychosis criteria Connors, Ward, & Catts, 2014), in chronic psychosis (Moritz, defined according to the Comprehensive Assessment of At-Risk Van Quaquebeke, & Lincoln, 2012), in relatives of patients with Mental States scale (CAARMS) (Yung et al., 2005). The exclusion psychosis (Van Dael et al., 2006), in people at clinical high-risk criteria for CHR participants were: having had prior experience for psychosis (CHR) (Rausch et al., 2016), and in healthy volun- of a psychotic episode of more than 1-week as determined by the teers with delusional beliefs (Linney, Peters, & Ayton, 1998). CAARMS (Yung et al., 2005) and Structural Clinical Interview Some studies have found JTC to be particularly associated with for DSM Disorders (SCID) (Wittchen, Zauding, & Fydrich, delusions (Freeman, Pugh, & Garety, 2008; Jolley et al., 2014) 1997); previous treatment with an antipsychotic for a psychotic or proneness to delusions (Broome et al., 2007), but this has episode; and IQ < 60. Exclusion criteria for HC included meeting not been reported consistently in all studies (Catalan et al., criteria for CHR, and report a personal or (first-degree) family 2015; So et al., 2012). Among people at CHR, there is a diversity history of psychosis. of potential clinical outcomes. While a minority of individuals will go on to transition to psychosis, among those that do not, Procedures the severity of psychotic symptoms may either increase or decrease, and level of functioning may worsen or improve All procedures contributing to this work comply with the ethical (Fusar-Poli et al., 2012; Michel, Ruhrmann, Schimmelmann, standards of the relevant national and institutional committees Klosterkötter, & Schultze-Lutter, 2018). The presence of a reason- on human experimentation and with the Helsinki Declaration of ing bias in CHR participants is of particular interest because it 1975, as revised in 2008. Medical Ethics Committees approved all might influenceany of these outcomes that may occur. In particu- procedures involving human subjects at each participating site lar, because JTC has been associated with psychosis and delusions and approved the study protocol. Written informed consent was (So, Siu, Wong, Chan, and Garety, 2016), JTC might increase the obtained from all participants. All participants were assessed at likelihood of attenuated psychotic symptoms persisting or pro- baseline and after 12 and 24 months. If a CHR subject transitioned gressing into a psychotic disorder. Only one previous study has to psychosis, a follow-up assessment was made as soon as possible investigated the relationship between JTC and clinical outcomes after transition, 1 year after transition, and 2-years after transition. in CHR participants. Winton-Brown et al. (2015) followed up a relatively small CHR sample (N = 23) and did not find a relation- Instruments ship between JTC and later transition to psychosis. However, JTC has been associated with adverse clinical outcomes in patients Detailed socio-demographic characteristics were assessed using with psychosis. Rodriguez et al. (2018) reported that JTC at base- the modified Medical Research Council socio-demographic line was associated with a higher risk of compulsory admissions to Schedule (European Network of National Networks studying hospital and police interventions at follow-up. Gene-Environment Interactions in et al., 2014; Mallett, 1997). The present study aimed to examine the JTC bias at baseline The CAARMS was used to measure subclinical psychotic symp- in a large CHR sample, and to investigate its relationship with toms (Yung et al., 2005). CAARMS is a semi-structured interview clinical outcomes. JTC was assessed in CHR participants and con- that assesses psychotic symptoms and a range of other psycho- trols at baseline and after 1 and 2 years. Outcomes were assessed pathological symptoms occurring in emerging psychotic disorder. in terms of transition to psychosis, the severity of psychotic symp- Individuals were classified as CHR for psychosis if they met at toms, and level of functioning. We tested the hypothesis that least one of the following risk criteria: (i) Vulnerability Group (a within the CHR sample, the presence of a JTC bias would be asso- first-degree relative with a psychotic disorder or personal diagnosis ciated with adverse clinical outcomes. of schizotypal personality disorder, in combination with a signifi- cant drop in functioning); (ii) Attenuated Psychotic Symptoms (psychotic symptoms sub-threshold either in intensity or fre- Methods quency); (iii) Brief Limited Psychotic Symptoms (a recent brief psychotic episode that resolved spontaneously within 1 week). Sample The Scale for Assessment of Negative Symptoms (SANS) Participants were recruited from 11 centers from July 2010 to (Andreasen, 1982), the Montgomery-Asberg Depression Scale August 2015, as part of the European Union Gene–Environment (MADRS) (Montgomery & Asberg, 1979), and the Young Interactions (EU–GEI) study (European Network of National Mania Rating Scale (Young, Biggs, Ziegler, & Meyer, 1978) were Networks studying Gene-Environment Interactions in et al., used to assess negative and affective symptoms. 2014). The overall design of the study was naturalistic, longitudinal, The Structured Clinical Interview for DSM-IV Disorders and prospective, consisting of a baseline and two follow-up time (SCID) (First, Spitzer, Gibbon, & Williams, 1995) was used to points. CHR participants (N = 303) were recruited from local clin- establish the presence of other psychiatric disorders and to ical early detection services, while healthy controls (HC; N = 57) exclude the presence of current psychotic disorders. Psychological Medicine 1571 The disability subscale of the Global Assessment of sites (level 3). Consequently, we used a three-level mixed model Functioning (GAF disability) scale (APA, 2000) was used to assess with fixed and random effects to analyse and control for the functional outcome. The GAF scoring system is based on the within-subject level of clustering and clustering within-sites. An severity of symptoms and level of functioning for individuals unstructured covariance pattern model was used to model the under mental health care. As the GAF score decreases, symptoms dependency of the repeated observations of the same subject and the severity of a mental health illness are more severe. The while study site was included as a random factor to account for total GAF score was not used because it can reflect symptom the dependency of the subjects within site (Brown & Prescott, severity as well as the level of functioning. 2015). The GAF disability scale at follow-up was used as the CAARMS, SCID, and GAF measures were repeated at each dependent variable while the JTC (as categorical variable yes/ follow-up time point. Transition to psychosis was defined accord- no) was used as independent variable. Subsequently, we estimated ing to the CAARMS criteria (Yung et al., 2005). A short version of the effect of JTC on the GAF scale. The following a priori-selected the WAIS-III test (Velthorst et al., 2013) was used to assess IQ. confounders were included in the model: transition, age, gender, A computerised version of the ‘beads’ task (Garety, Hemsley, IQ, and ethnicity. The model included the interaction between & Wessely, 1991) was used to assess probabilistic reasoning. JTC at baseline and its evolution across different time points. Participants were shown two jars of coloured beads in equal but Finally, we assessed the accuracy of the model to predict unseen opposite ratios (60 red: 40 blue; 60 blue: 40 red). The jars were cases of the same underlying population (internal validation) using hidden and, at the start of the task, a sequence of beads from repeated 10-fold cross-validation (Hastie, Tibshirani, & Friedman, one of the two jars was displayed to the participants. After each 2009). In 10-fold cross-validation, the single available dataset is bead, participants were asked if they were ready to decide which randomly divided into 10 folds of equal size. In turn, each fold is jar the beads were being drawn from or if they would like to used as the unseen data (test set) with the remaining n-1 folds see another bead before making a decision. In the present pooled together as the training set (Stahl & Pickles, 2018). study, we examined two variables: (i) ‘Draws to decision’ Prediction accuracy is estimated by comparing observed and pre- (DTD), the number of draws made before making a decision; cited GAF scores and quantified as explained variance (r 2). We and (ii) ‘JTC/no JTC’, where JTC is operationally defined as repeated this procedure 200 times to get a stable estimate and reaching a decision after two or fewer beads (Garety et al., report the average over the 200 10-folds runs. 2005; Rodriguez et al., 2018; So et al., 2012; So & Kwok, 2015). Because mixed-effects models with incomplete cases are diffi- This threshold is thought to reflect a clear expression of a reason- cult to assess in repeated cross-validation, we estimated the pre- ing bias and is useful in discriminating between clinical and non- diction accuracy for time 2 and 3 separately and included study clinical groups (Garety et al., 2005; Van Dael et al., 2006). We site as a categorical factor. Because the number of missing predic- used the most difficult version of the task (60:40), as this has tors was relatively small (N = 11 out of 201 available observations been found to be sensitive to detecting effects in relation to vari- for each time point), a complete case analysis was performed. ation in levels of psychotic symptoms (Lincoln, Ziegler, Mehl, & Because of the large number of study sites relative to sample W, 2010; So et al., 2012). size, we rerun the cross-validation without study site. All the analyses were performed with STATA 15 package (StataCorp., 2017). Except for the cross-validation, which was per- Statistical analyses formed in R using the package glmnet (Friedman, Hastie, & One-way analyses of variance (ANOVA) were used to examine Tibshirani, 2010). differences in continuous variables at baseline, 1-year, and 2-year follow up. For categorical variables, chi-square tests and Results Fisher´s exact test, when indicated, were performed. To analyse the stability of JTC rates across follow-ups, we performed a In total, 411 individuals were assessed at baseline (i.e. 344 CHR McNemar´s test. and 67 HC). A total of 10 HC and 41 CHR participants were Cross-sectional associations between JTC and the psychotic excluded, as they did not complete the ‘beads’ task at baseline. symptoms at baseline were analysed using hierarchical logistic The non-included sample did not differ significantly from the regression models. The number of beads requested by a partici- included sample in terms of gender, age, IQ, ethnicity, or GAF pant yielded a continuous outcome variable with a range of 0– scores (online Supplementary Table S1). The demographic and 20. This variable was positively skewed, and we followed standard clinical features of the groups are shown in Table 1. At baseline, procedure to categorize JTC reasoning bias in a binary category IQ and GAF disability scores were higher in the HC than the requesting two or fewer beads before making a decision or CHR group. Relative to the total sample at baseline (N = 360), more than two (JTC/no JTC). In order to assess whether any asso- 173 participants (45.3%) completed the task at 1-year follow-up, ciation with delusional symptoms was independent of other posi- and 134 (37.2%) did so at 2-year follow-up. At follow up, there tive, negative or affective symptoms, subsequent analyses were were no significant differences between the remaining partici- performed in which all symptom domains assessed with the pants and drop-outs in terms of ethnicity, gender, IQ, or GAF dis- CAARMS were entered simultaneously in the model. ability score. However, they differed in age at both, 1 year The relationship between JTC and transition to psychosis [non-missing 23.8 (S.D. = 4.8) v. missing 21.8 (S.D. = 4.7), p < within 2 years was analysed using the Cox regression method 0.05], and 2 years [non-missing 23.2 (S.D. = 4.6), and missing adjusting by gender, age, ethnicity (White, Black, Asian, Mixed, 21.7 (S.D. = 4.8), p < 0.05]. North African, and others), site, and IQ. In total, 8 CHR participants (5.9%) were taking antipsychotic Examining the relationship between JTC at baseline and func- medication at baseline, 69 were taking antidepressants (22.8%) tional outcome, at follow-up in the CHR group involved data with and 17 sedatives (5.6%, including benzodiazepines). Only one a multilevel structure because multiple observations were nested HC (1.7%) used antidepressants and another one (1.7%) used within participants (level 2), and participants were nested within sedatives. 1572 Ana Catalan et al. Table 1. Socio-demographic and clinical characteristics of the sample HC (N = 57) CHR (N = 303) Mean age in years (S.D.) 23.0 (3.9) 22.5 (4.6) Gender male, N (%) 30 (52.6%) 157 (51.8%) Mean IQ (S.D.) 112.2 (18.4)** 98.5 (16.8) Ethnicity White, N (%) 36 (63.2%)* 218 (72.2%) Ever employed, N (%) 53 (93%)* 222 (73.3%) CHR group, N (%) Vulnerability group 49 (16.2%) APS 252 (83.2%) BLIPS 21 (6.9%) GAF disability mean (S.D.) 85.6 (9.1)** 55.2 (12.4) THC dependence (last 12 months), N (%) 3 (5.3%) 31 (10.2%) HC, healthly controls; CHR, clinical high-risk for psychosis; IQ, intelligence quotient; THC cannabis. *p < 0.05;**p < 0.001. To analyse longitudinal changes in JTC status within the CHR group. This difference was not present at baseline. At both time group subsequent to baseline, we used the last available ‘JTC/no points, the tendency to JTC was associated with a lower level of JTC’ measure for the dropouts cases [average follow-up time = functioning (Fig. 2). 21.2 months (S.D. = 0.5)]. A total of 15 CHR (23.4%) who were There was a significant association between JTC and IQ in the classified as ‘JTC’ at baseline changed to ‘no-JTC’, at 1-year whole sample ( p < 0.001); however, the effect size of this associ- follow-up, while 4 (1.7%) classified as ‘no-JTC’ at baseline were ation was small (R 2 = 0.06). classed as ‘JTC’ at 1-year follow up ( p = 0.02). There were no sig- In the CHR group, there was an association between JTC at nificant changes in ‘JTC/no JTC’ status between 1-year and 2-year baseline and the GAF disability score at follow-up. At 2-year follow-up ( p = 0.14). There were no significant changes in ‘JTC/ follow-up, the CHR sub-group with ‘JTC’ reasoning bias at base- no JTC’ status in HC group. line scored 5.3 points less [95% CI (−8.5 to −2.1), p = 0.001, adjusted] than the CHR sub-group with ‘no-JTC’ at baseline (Table 2). Relationship between JTC and transition to psychosis Repeated 10-fold cross-validation of two linear regressions pre- After 2-years, 58 CHR individuals (16.1%) had made a transition dicting GAF disability, including site as a fixed factor, showed that to psychosis (CHR-T), while 245 had not (CHR-NT). There were the ‘JTC’ at baseline model predicted 26% of the variance of GAF no statistically significant differences between CHR-T and score at time 2 and 24.7% at time 3. Removing site as predictor CHR-NT or between these subgroups and HC in the percentage further increased the prediction accuracy to 30.5% (1-year of JTC rates at baseline, or 1-year follow-up. However, the rate follow-up) and 30.6% (2-years follow-up). of JTC in the CHR-T group was significantly higher than in the other groups at 2-year follow-up (χ2 = 8.13, df = 2; p = 0.012) (Fig. 1). At this time point, the CHR-T participants had already Discussion developed psychosis; the mean days between the transition and This study sought to examine reasoning bias in the CHR popula- the last ‘beads’ task assessment was 795 days (S.D. = 85.3). The tion and its relationship with clinical outcomes. To our knowl- number of DTD by the group is detailed in online edge, this is the largest study of JTC in a CHR sample to date. Supplementary Table S2. We found that there were no differences between CHR and HC The Cox regression revealed no significant association between at baseline, but that a significant reasoning bias emerged in JTC and transition to psychosis in the CHR group (all p’s > 0.05). CHR-T participants when they were reassessed 2 years after tran- Within the CHR group, there were no significant or trend-level sition. Within the CHR sample, there were no differences at base- associations between ‘beads’ task performance and scores on the line between participants who did or did not transition to CAARMS subscales (positive, negative and general psychopath- psychosis, but those who developed psychosis showed a greater ology), the SANS, the Young Rating Mania score or the reasoning bias at 2-year follow-up. Similarly, within the CHR MADRS. A descriptive summary of the symptoms, according to sample, the tendency to JTC was not associated with the level JTC group, is detailed in online Supplementary Table S3. of functioning at baseline, but it was significantly associated with lower functioning at 1-year and 2-year follow-up. However, we did not find a significant relationship between JTC Relationship between JTC and functional outcome and positive psychotic symptomatology in the CHR sample. Within the CHR group, there was a significant difference in GAF Previous studies have reported a higher prevalence of JTC in disability scores at both 1-year (Z = −2.3, p = 0.02), and 2-year (Z CHR participants than in HC (Broome et al., 2007; Rausch = −2.0, p = 0.04) follow-up between the ‘JTC’ group and ‘no-JTC’ et al., 2016). However, in the present study, this difference was Psychological Medicine 1573 Fig. 1. Level of JTC bias at baseline and 1- and 2-year follow-up. *significant level p < 0.05. JTC, jumping to conclu- sions; HC, healthy controls; CHR-T, clinical high-risk subjects who made a transition to psychosis; CHR-NT, clinical high- risk subjects who did not make transition to psychosis. Fig. 2. Relationship between JTC and level of functioning in CHR subjects. GAF means during the follow-up period. JTC, jumping to conclusions; GAF, Global Assessment of Functioning. Table 2. Relation between JTC at baseline and GAF disability evolution at follow-up after adjusting by other variables in CHR population GAF disability N = 275 ß S.E. z p 95% CI JTC baselinea −5.3 1.6 −3.3 0.001 (−8.5 to −2.1) a Gender 0.05 1.2 0.04 1.0 (−2.4 to 2.5) a Ethnicity 1.6 2.1 0.7 0.4 (−3.0 to 5.8) Age −0.3 0.1 −2.3 0.03 (−0.6 to −0.05) IQ 0.03 0.04 0.9 0.4 (−0.04 to 0.1) CHR-T −6.7 1.5 −4.5 <0.001 (−9.6 to −3.7) _cons 61.2 5.5 11.2 <0.001 (50.4–71.9) IQ, intelligence quotient; CHR-T, CHR subjects who made a transition to psychosis; S.E., standard error; 95% CI, 95% confidence interval of Beta. a Reference group: ‘no JTC’ at baseline. White ethnicity. Male gender. 1574 Ana Catalan et al. only present at 2-year follow-up and only in those who made is possible that people in the early stages of psychosis are more transition to psychosis. Methodological differences can partially likely to improve this trait (Catalan et al., 2015). explain this discrepancy. Rausch et al. (2016) used the SIPS and CHR participants with a JTC bias failed to show an improve- SPI-A to define the CHR state in a sample of 188 subjects, analys- ment in the level of functioning over the 2 years subsequent to ing the differences between CHR subgroups (basic symptoms v. baseline that was evident in the subgroup who did not have a at-risk mental state status). The SIPS semi-structured interview JTC bias at baseline (Fig. 2). This suggests that in this population, (McGlashan, Walsh, & Woods, 2010) and the CAARMS (Yung the presence of a reasoning bias is associated with a relatively poor et al., 2005) present some differences in the measures of frequency prognosis, in terms of functional outcome. Moreover, the cross- and severity of the psychotic symptoms. The SIP-A (Schultze- validation demonstrated that baseline JTC reasoning bias pre- Lutter, Addington, Ruhrmann, & Klosterkötter, 2007) specifically dicted GAF disability (30.6% explained variance) at 2-year focuses on the detection of basic symptoms. We cannot exclude follow-up (predicted values correlate by 0.55). Although the pre- that these differences might have an impact on the comparability diction finding is modest for it to be used in a prediction tool, the of the results. In addition, although Broome et al. (2007) internal validation shows that the model is useful for understand- used the CAARMS to define CHR status, the sample analysed ing the factors that influence the evolution of the GAF disability was smaller (N = 35) compared to the present one. The larger scale. This finding could be used in future research to develop a sample in the current study might have increased the statistical productive tool. power to detect the possible existing differences. None of these Previous studies described a link between JTC and functional studies analysed the relation between JTC and the transition to outcomes. For example, Rodriguez et al. (2018) showed that, in a psychosis. FEP sample, JTC was related to a higher risk of subsequent com- In the present study, the presence of a reasoning bias in pulsory admission and police intervention, both related to a worse CHR-T participants at follow up but not at baseline (prior tran- social functioning. Dudley et al. (2013) found that patients with sition to psychosis), suggests that JTC may be linked to change in psychosis whose JTC bias improved over 2-year follow up had a clinical status subsequent to baseline. This would be consistent better clinical outcome than patients with a persistent JTC bias. with our finding that within the CHR sample, JTC was associated Accordingly, a prospective study in a general population sample with a low level of functioning at follow up, and with the onset of with affective dysregulation reported that the subsequent onset psychosis. We did not find evidence that JTC was linked to the of psychosis was more likely when a JTC bias was present severity of psychotic symptoms, as has been reported in some pre- (Rauschenberg et al., 2020). vious studies in the CHR population (Winton-Brown et al., 2015) It has been suggested that JTC bias is related to the decision- and schizophrenia (Dudley, Taylor, Wickham, & Hutton, 2016). making process (Turner et al., 2018), and the decision-making However, in a recent meta-analysis, a link between JTC and the process based on weak evidence might be linked with a worse psychotic symptom was not found (So et al., 2016). functioning outcome in the long term. However, the formation To the best of our knowledge, only one previous study has of delusions needs other concurrent factors, such as the necessity examined whether, in CHR individuals, JTC is associated with of closure (McKay, Langdon, & Coltheart, 2007) and overconfi- subsequent transition to psychosis (Winton-Brown et al., 2015). dence in errors (Moritz et al., 2017). This study reported no relationship between JTC at baseline In our sample, the level of positive psychotic symptoms was and subsequent transition to psychosis. While this negative result not associated with the rate of JTC reasoning bias, suggesting might have been related to the small CHR sample in that study that JTC could reflect a more general vulnerability to disorders (n = 23), this is much less likely to have been a factor in the pre- with psychosis proneness rather than to specific psychotic symp- sent study, which had a large CHR sample at baseline, of whom toms. The low psychotic symptoms level in our sample could, at 58 CHR made a subsequent transition to psychosis. In the current least in part, explain the lack of association between JTC and work, the presence of a reasoning bias after the onset of psychosis psychotic symptoms. Similarly, in a recent meta-analysis So suggests that this may be specific to the emergence of frank psych- et al. (2016) could not demonstrate a definitive relationship osis, as opposed to the CHR state. However, we also found an between JTC and psychotic symptoms. Nevertheless, as Bentall association with poor functioning, a measure that is independent (1999) stated, the assumption behind the dichotomous trait-state of transition status. An association with transition might also distinction is that abnormalities are either present before the reflect a relationship with the severity of psychotic symptoms. emergence of symptoms (in which case they might play a causal However, our analyses found no evidence that JTC was linked role) or covary with symptoms (in which case they may be either to symptom severity. Finally, because the final follow-up assess- part of the symptom picture or epiphenomena). Thus, this is not ment in the CHR-T participants was performed after they had the only possible relationship between JTC and psychotic symp- transitioned to psychosis, it is possible that the findings were toms. It might be that cognitive performance underpinning JTC related to the effects of treatment initiated after psychosis onset. in psychotic patients and in psychosis-proneness populations However, So et al. (So, Garety, Peters, & Kapur, 2010) did not can be normal under optimal environmental circumstances find that antipsychotic treatment influences JTC bias. and become pathological if these circumstances become In our sample, the proportion of the CHR participants for unfavourable. whom the status changed from `JTC´ to `no-JTC´ after 1-year Our data highlight the relationship between the presence of follow-up was 23.4%. This suggests that this group is prone to JTC and worse functioning outcomes. Since some therapeutic change. Ormrod et al. (2012) investigated the progression of strategies have demonstrated to be useful in the treatment of JTC in FEP patients. When tested over two-time points around JTC (Moritz et al., 2014; Roberts et al., 2014; Waller, Freeman, 8 months apart from baseline, there was a degree of instability Jolley, Dunn, & Garety, 2011), it would be recommendable in JTC over time. However, in people with more long-standing incorporating these strategies in the early treatment of CHR psychosis, the decision-making style, in terms of JTC rates, does population. For example, recently Turner et al. (2018) reported not seem to change (Peters & Garety, 2006; So et al., 2012). It the benefits of a specific intervention derived from the meta- Psychological Medicine 1575 cognitive treatment manual developed by Moritz et al. (Moritz, References Woodward, & Burlon, 2007) in reducing JTC bias. American Psychiatric Association. (2000). Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR). Washington, DC: American Psychiatric Association. Strengths and limitations Andreasen, N. C. (1982). Negative symptoms in schizophrenia. Definition and Strengths of the present study include the large size of the CHR reliability. 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Applied mixed models in medicine (3rd ed.). ment. Because of evidence that the association between psychotic Chichester, West Sussex: Hoboken: John Wiley & Sons Inc. symptoms and JTC might no longer be significant when general Catalan, A., Simons, C. J., Bustamante, S., Olazabal, N., Ruiz, E., Gonzalez de intelligence is taken into account (European Network of National Artaza, M., … Gonzalez-Torres, M. A. (2015). Data gathering bias: Trait Networks studying Gene-Environment Interactions in et al., vulnerability to psychotic symptoms? PLoS One, 10(7), e0132442. doi: 2014; So et al., 2012; Tripoli et al., 2020), we included a measure 10.1371/journal.pone.0132442 Dudley, R., Daley, K., Nicholson, M., Shaftoe, D., Spencer, H., Cavanagh, K., & of IQ in our analyses. This indicated that the associations between Freeston, M. (2013). Jumping to conclusions’ in first-episode psychosis: A JTC and transition and a poor level of functioning at follow-up longitudinal study. British Journal of Clinical Psychology, 52, 380–393. were not attributable to an effect of IQ, or other potentially con- doi: 10.1111/bjc.12023 founding variables such as age, gender, and ethnicity. Dudley, R., Taylor, P., Wickham, S., & Hutton, P. (2016). Psychosis, delusions This study presents also some limitations. The size of the HC and the “Jumping to Conclusions” reasoning bias: A systematic review and group did not match that of the CHR sample, which might have meta-analysis. Schizophrenia Bulletin, 42(3), 652–665. doi: 10.1093/schbul/ reduced our power to detect statistically significant associations sbv150 within the HC group. In addition, the follow-up dropout rate European Network of National Networks studying Gene-Environment could have influenced the strength of the conclusions, since Interactions in Schizophrenia (EU-GEI), van Os, J., Rutten, B. P., Myin-Germeys, I., Delespaul, P., Viechtbauer, W., … Mirjanic, T. (2014). patients that chose to participate in the study might have pre- Identifying gene-environment interactions in schizophrenia: sented with a less severe psychotic symptomatology. Contemporary challenges for integrated, large-scale investigations. Schizophrenia Bulletin, 40(4), 729–736. doi: 10.1093/schbul/sbu069 Fine, C., Gardner, M., Craigie, J., & Gold, I. (2007). Hopping, skipping or Conclusions jumping to conclusions? Clarifying the role of the JTC bias in delusions. This study suggests that the presence of a reasoning bias in people Cogntive Neuropsychiatry, 12, 46–77. at CHR for psychosis is associated with adverse clinical outcomes, First, M., Spitzer, R., Gibbon, M., & Williams, J. B. (1995). Structured clinical such as a low level of functioning. This finding together with find- interview for DSM-IV axis I disorders (SCID). New York, NY: New York ings around the efficacy of treatment approaches targeting JTC State Psychiatric Institute Biometrics Research. Freeman, D., Pugh, K., & Garety, P. (2008). Jumping to conclusions and para- (Garety et al., 2015; So et al., 2015) may show promise in enhan- noid ideation in the general population. Schizophrenia Research, 102(1–3), cing treatment responses in CHR population. 254–260. doi: 10.1016/j.schres.2008.03.020 Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for gen- Supplementary material. The supplementary material for this article can eralized linear models via coordinate descent. Journal of Statistical Software, be found at https://doi.org/10.1017/S0033291720003396 33, 1–22. Retrieved from http://www.jstatsoft.org/v33/i01/. Acknowledgements. We would like to thank all participants who took part Fusar-Poli, P., Bonoldi, I., Yung, A. R., Borgwardt, S., Kempton, M. J., in the study. This work was supported by the European Network of National Valmaggia, L., … McGuire, P. (2012). Predicting psychosis: Meta-analysis Schizophrenia Networks Studying Gene–Environment Interactions (EU-GEI) of transition outcomes in individuals at high clinical risk. Archives of Project is funded by grant agreement HEALTH-F2-20010–241909; [Project General Psychiatry, 69(3), 220–229. doi: 10.1001/archgenpsychiatry.2011. EU-GEI] from the European Community´s Seventh Framework Programme. 1472 Additional support was provided by a Medical Research Council Fellowship Garety, P., Freeman, D., Jolley, S., Dunn, G., Bebbington, P. E., Fowler, D. G., to M Kempton (grant MR/J008915/1). N Barrantes-Vidal received additional … Dudley, R. (2005). Reasoning, emotions, and delusional conviction in support from the Ministerio de Ciencia, Innovación e Universidades psychosis. Journal of Abnormal Psychology, 114, 373–384. (PSI2017-87512-C2-1-R) and the Generalitat de Catalunya (2017SGR1612 Garety, P., Hemsley, D. R., & Wessely, S. (1991). Reasoning in deluded schizo- and ICREA Academia Award). The study received financial support by phrenic and paranoid patients. Biases in performance on a probabilistic French Health Ministry (PHRC, AOM-07-118, ‘Influence of cannabis psycho- inference task. The Journal of Nervous and Mental Disease, 179(4), 194– pathological outcome in At-risk mental state’ (ICAAR study)) et de la 201. doi: 10.1097/00005053-199104000-00003 Fondation pour la Recherche Médicale (fellowship OG). Sainte-Anne Garety, P., Waller, H., Emsley, R., Jolley, S., Kuipers, E., Bebbington, P., … Hospital promoted the study. D Stahl was part-funded by the National Freeman, D. (2015). Cognitive mechanisms of change in delusions: An Institute for Health Research (NIHR) Maudsley Biomedical Research Centre experimental investigation targeting reasoning to effect change in paranoia. at South London Maudsley Foundation Trust and King’s College Schizophrenia Bulletin, 41(2), 400–410. doi: 10.1093/schbul/sbu103 London. 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Jumping to conclusions at first onset of psych- osis predicts longer admissions, more compulsory admissions and police involvement over the next 4 years: The GAP study. Psychological Medicine, 49(13), 2256–2266. doi: 10.1017/S0033291718003197. Appendix Schultze-Lutter, F., Addington, J., Ruhrmann, S., & Klosterkötter, J. (2007). EU-GEI High Risk Study Group Author list Schizophrenia proneness instrument, adult version (SPI-A). Rome: Giovanni Fioriti. Philip McGuire1, Lucia R. Valmaggia2, Mathilde Antoniades1, Maria Calem1, So, S., Chan, A. P., Chong, C. S., Wong, M. H., Lo, W. T., Chung, D. W., & Matilda Azis1, Sara Pisani1, Lieuwe de Haan3, Mark van der Gaag4,5, Eva Chan, S. S. (2015). Metacognitive training for delusions (MCTd): Velthorst3,6, Tamar C. Kraan3, Daniella S. van Dam3, Nadine Burger5, Psychological Medicine 1577 Barnaby Nelson7, Patrick McGorry7, G Paul Amminger7, Christos Pantelis8, University of Melbourne & Melbourne Health, Carlton South, Victoria, Athena Politis7, Joanne Goodall7, Anita Riecher-Rössler9, Stefan Borgwardt9, Australia; 9Medical Faculty, University of Basel, Basel, Switzerland; 10LiNC - Charlotte Rapp9, Sarah Ittig9, Erich Studerus9, Renata Smieskova9, Rodrigo Lab Interdisciplinar Neurociências Clínicas, Depto Psiquiatria, Escola Bressan10, Ary Gadelha10, Elisa Brietzke11, Graccielle Asevedo10, Elson Paulista de Medicina, Universidade Federal de São Paulo – UNIFESP; Asevedo10, Andre Zugman10, Neus Barrantes-Vidal12, Tecelli Domínguez- 11 Depto Psiquiatria, Escola Paulista de Medicina, Universidade Federal de Martínez13, Anna Racioppi14, Thomas R. Kwapil15, Manel Monsonet14, Lídia São Paulo – UNIFESP; 12Departament de Psicologia Clínica i de la Salut Hinojosa14, Mathilde Kazes16,17, Claire Daban16,17, Julie Bourgin16,17, Marion (Universitat Autònoma de Barcelona), Fundació Sanitària Sant Pere Claver Plaze16,17, Célia Jantac16,17, Marie-Odile Krebs16,17, Dorte Nordholm18, Lasse (Spain), Spanish Mental Health Research Network (CIBERSAM); Randers18, Kristine Krakauer18, Louise Birkedal Glenthøj18, Birte Glenthøj18, 13 CONACYT-Dirección de Investigaciones Epidemiológicas y Psicosociales, Merete Nordentoft18, Stephan Ruhrmann19, Dominika Gebhard19, Julia Instituto Nacional de Psiquiatría Ramón de la Fuente Muñiz (México); Arnhold20, Joachim Klosterkötter19, Gabriele Sachs21, Iris Lasser21, 14 Departament de Psicologia Clínica i de la Salut (Universitat Autònoma de Bernadette Winklbaur21, Philippe A Delespaul,22,23, Bart P. Rutten22, Jim Barcelona); 15Department of Psychology, University of Illinois at Urbana- van Os1,24 Champaign (USA); 16INSERM, IPNP UMR S1266, Laboratoire de Department of Psychosis Studies 1Department of Psychosis Studies, Physiopathologie des Maladies Psychiatriques, Université Paris Descartes, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, Université de Paris, CNRS, GDR3557-Institut de Psychiatrie Paris, France; 17 De Crespigny Park, Denmark 458 Hill, London, United Kingdom SE5 8AF; Faculté de Médecine Paris Descartes, GHU Paris - Sainte-Anne, Service 2 Department of Psychology, Institute of Psychiatry, Psychology & Hospitalo-Universitaire, Paris, France; 18Mental Health Center Copenhagen Neuroscience, King’s College London, De Crespigny Park, Denmark Hill, and Center for Clinical Intervention and Neuropsychiatric Schizophrenia 456 London, United Kingdom SE5 8AF; 3AMC, Academic Psychiatric Research, CINS, Mental Health Center Glostrup, Mental Health Services in Centre, Department Early Psychosis, Meibergdreef 5, 1105 AZ Amsterdam, the Capital Region of Copenhagen, University of Copenhagen; 19Department The Netherlands; 4VU University, Faculty of Behavioural and Movement of Psychiatry and Psychotherapy, Faculty of Medicine and University Sciences, Department of Clinical Psychology and EMGO+ Institute for Hospital, University of Cologne, Cologne, Germany; 20Psyberlin, Berlin, Health and Care Research, van der Boechorststraat 1, 1081 BT Amsterdam, Germany; 21Department of Psychiatry and Psychotherapy, Medical The Netherlands; 5Parnassia Psychiatric Institute, Department of Psychosis University of Vienna, Vienna, Austria; 22Department of Psychiatry and Research, Zoutkeetsingel 40, 2512 HN The Hague, The Netherlands; 6Icahn Neuropsychology, School for Mental Health and Neuroscience, South School of Medicine at Mount Sinai, department of Psychiatry, 1425 Limburg 463 Mental Health Research and Teaching Network, Maastricht Madison Ave, New York, NY 10029; 7Centre for Youth Mental Health, University Medical Centre, P.O. Box 616, 6200 MD 464 Maastricht, The University of Melbourne, 35 Poplar Road (Locked Bag 10), Parkville, Netherlands; 23Mondriaan Mental health Trust, P.O. Box 4436 CX Heerlen, Victoria 485 3052, Australia; Orygen, Parkville, Victoria, Australia; The Netherlands; 24Department of Psychiatry, UMC Utrecht Brain Center, 8 Melbourne Neuropsychiatry Centre, Department of Psychiatry, The Utrecht University Medical Centre, 3584 CG Utrecht, The Netherlands.