Personality and Individual Differences 50 (2011) 717–722

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Personality and Individual Differences

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The influence of positive affect on jumping to conclusions in delusional thinking

Gary Lee a,⇑, Christine Barrowclough a, Fiona Lobban b

a School of Psychological Sciences, University of Manchester, Zochonis Building, Manchester M13 9PL, United Kingdomb Spectrum Centre for Mental Health Research, Division of Health Research, Lancaster University, Lancaster LA1 4YW, United Kingdom

a r t i c l e i n f o a b s t r a c t

Article history:Received 20 August 2010Received in revised form 5 December 2010Accepted 16 December 2010

Keywords:Jumping to conclusionsDelusionsPositive affectPsychosis

0191-8869/$ - see front matter � 2010 Elsevier Ltd. Adoi:10.1016/j.paid.2010.12.024

⇑ Corresponding author. Tel.: +44 1423 504690; faxE-mail address: gejlee@gmail.com (G. Lee).

This study examined relationships between positive affect and jumping to conclusions (JTC) in delusionalthinking. One hundred and eighty-nine non-clinical participants entered an internet experiment andwere randomized into one of two conditions. Those in a positive condition performed online creativitytasks and received bogus positive feedback as part of a positive affect induction procedure, whilst a neu-tral condition received neutral feedback. Both groups were subsequently assessed on a survey task forchanges in JTC. In line with hypotheses, participants in the positive condition requested significantlymore survey comments before drawing conclusions than those in the neutral condition. Results suggestthat increases in positive affect may be linked with a tendency to gather more information before makingdecisions (i.e. a reduction in JTC). The influence of positive affect on reasoning biases in social environ-ments is discussed.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

The relationship between delusional thinking and the jumping-to-conclusions (JTC) bias has become a focal point within psychosisresearch over the last 20 years. JTC describes a tendency for peoplewith delusions to seek less information before making decisions,when compared to non-clinical control groups (Fine, Gardner,Craigie, & Gold, 2007; Freeman, 2007).

JTC appears to be specifically related to delusional thinking inpsychosis samples (Fine et al., 2007; Moritz & Woodward, 2005;Peters, Thornton, Siksou, Linney, & MacCabe, 2008). However, thephenomenon has also been detected in ‘at-risk’ groups (Broomeet al., 2007) and in non-clinical populations scoring highly on mea-sures of delusion-proneness (Colbert & Peters, 2002; Warman &Martin, 2006; White & Mansell, 2009). This has led psychologiststo recognise the possible role of JTC in the development and main-tenance of psychotic symptoms where it is thought to contribute tohow readily delusional ideas are accepted (Freeman, Pugh, & Garety,2008; Garety, Kuipers, Fowler, Freeman, & Bebbington, 2001).

Despite its theoretical importance, it is unclear what factorsinfluence JTC. Garety, Hemsley, and Wessely (1991) have sug-gested that anxiety or depression could exacerbate the bias byincreasing hasty decision making. However, the evidence to sup-port this assertion is mixed. Correlational studies have generallyfailed to uncover any relationship between performance on JTCtasks and the presence of anxiety or depression (Freeman et al.,2008; Garety et al., 2005; Warman, Lysaker, Martin, Davis, &

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Haudenschield, 2007; Warman & Martin, 2006). On the other hand,two experimental studies, which involved the manipulation ofanxiety have produced conflicting results. Both Lincoln, Lange,Burau, Exner, and Moritz (2009) and So, Freeman, and Garety(2008) induced anxiety in an experimental group using evocativeimagery. High anxiety groups were then compared to controlgroups on a JTC task. Although So et al. (2008) did not find any dif-ference between the groups, Lincoln et al. (2009) observed thatparticipants in their high anxiety group requested less informationwhen making decisions. These authors concluded that anxietyaccentuates the JTC bias, leading to more paranoid thinking(Lincoln et al., 2009).

A limitation of this research is that it overlooks a large literatureon affect and its relationship to cognition. Affect is used here as abroad term that encompasses both emotions and moods, in linewith previous definitions (e.g., Gray & Watson, 2007; Schwarz &Clore, 2007). In this sense, affect is also open to self-report by theperson experiencing it (Gray & Watson, 2007). A dominant viewis that affect can be described along two dimensions, positiveand negative affect, reflecting the valence of underlying emotionsand moods (Watson, Clark, & Tellegen, 1988). Although JTC studieshave focussed on negative affect in the context of anxiety, theyhave not considered the significance of positive affect (such asjoy, enthusiasm or interest). Yet, there is substantial evidence tosuggest that positive affect influences thought and behavior in dif-ferent ways to negative affect (Fredrickson, 1998, 2001).

The wealth of research in this area has been reviewed elsewhere(see Isen, 2008; Schwarz & Clore, 2007). However, with regards topositive affect, there appear to be a number of key influences oncognition and behavior. At a basic level, positive affect is thought

Table 1Participants characteristics.

N %

GenderMale 49 25.9Female 140 74.1

EthnicityWhite 140 74.1Asian 7 3.7Black 3 1.6Chinese 2 1.1Other 8 4.2Not reported 29 15.3

Current level of studyUndergraduate 126 66.7Postgraduate 43 22.7Other/not reported 20 10.6

Current subject of studySciences 89 47.1Arts 63 33.3Other/Not reported 37 19.6

Mean (S.D.) Min Max

Age 22.8 (6.0) 18 55PDI-21 Total score 64.3 (39.8) 0 214

Abbreviations: PDI-21 = Peters delusions inventory (21-item)

718 G. Lee et al. / Personality and Individual Differences 50 (2011) 717–722

to broaden the scope of attention and to allow access to more di-verse information stored in memory (Fredrickson, 2001). This re-sults in flexible thinking and varied ways of engaging theenvironment (Fredrickson, 2001; Isen, 2008). Typically, these ef-fects have been observed within laboratory studies, where theinduction of positive affect has been associated with better creativeproblem-solving and more exploratory behavior (e.g., Estrada, Isen,& Young, 1997; Kahn & Isen, 1993).

Whether positive affect facilitates or impairs accuracy on deci-sion-making tasks remains controversial and appears heavilydependent on the nature and context of the task (for a brief sum-mary of this evidence, see Brand, Reimer, & Opwis, 2007; Isen,2008). Nevertheless, one theory suggests that individuals experi-encing positive affect will tend to behave in ways that are morelikely to maintain their positive state (Isen, 2008). Thus, investiga-tors have shown that under circumstances where decision-makinghas meaningful consequences to the individual, positive affect maypromote risk-averse behavior (Estrada et al., 1997; Isen, 2001,2008). These effects have been illustrated by findings that peoplein positive moods utilize a greater number of cues to inform theirjudgements (e.g., Bramesfeld & Gasper, 2008; Djamasbi, 2007) andare less likely to gamble when potential losses are at hand (Arkes,Herren, & Isen, 1988).

The results of these studies would seem directly relevant to theJTC bias. In the first place, JTC is characterized by restricted infor-mation-seeking, which contrasts to the observed influence of posi-tive affect on information use. Secondly, a feature of JTC tasks isthat requesting more information reduces the risk of making incor-rect decisions. Therefore, so long as the task is framed in a person-ally meaningful way, positive affect should predict moreinformation-seeking (i.e. less JTC).

The present study examined the association between positiveaffect and JTC in a student sample. The use of a non-clinical sampleis justified as JTC is related to levels of delusion-proneness in non-psychotic individuals (e.g., Colbert & Peters, 2002), as well as thosewith florid symptoms of psychosis. Whilst replication in clinicalsamples is clearly necessary, researching possible processes under-lying psychosis-like phenomena in non-clinical populations iswidely used to overcome many of the difficulties of recruitmentand potential confounds of medication and illness history in clinicalpopulations. This paradigm assumes a common, dimensional viewof psychosis, where psychotic symptoms are thought to lie at oneend of a continuum with normal experience (Claridge, 1990).

In this study, we recruited participants into an internet-basedexperiment to see whether positive affect could influence JTC. Spe-cifically, we attempted to induce positive affect with a meaningfulstimulus, in the form of performance feedback that might beencountered within social situations. We assessed subsequentperformance on a JTC task with the following hypotheses:(1) As areplication of earlier findings, we expected higher levels of delu-sion-proneness to be associated with fewer requests for informa-tion on the JTC task.(2) Participants exposed to the positive affectinduction (positive condition) would require more information be-fore making decisions than those receiving no mood induction(neutral condition). In other words, the former group would exhi-bit less JTC.(3) This second finding would be due to an increase ininformation-seeking and positive affect from baseline (pre-feed-back) to post-feedback for the positive condition.

2. Method

2.1. Participants

Advertisements were sent out to undergraduate and postgradu-ate students via the university email system. In response, the studywebsite registered over 380 hits, representing the number of times

the participant information page was accessed. In total, 189 stu-dents completed the study and were entered into a 50 pounds ster-ling prize draw. Participants’ characteristics are displayed inTable 1.

2.2. Materials

All materials in the following section were computerized anddisplayed on a website, which could be accessed over the univer-sity network using standard web-browsing software. The websitewas created using a programming script by Goritz and Birnbaum(2005), which allowed participants’ responses to be transferredfrom the web-pages into a secured database.

2.2.1. Jumping to conclusions (JTC) ‘Survey’ taskWe adapted a widely used probabilistic reasoning task to assess

JTC (Warman & Martin, 2006; Warman et al., 2007). Participantswere asked to imagine that two surveys had been conducted aboutthem. In both surveys, 100 people provided comments about theparticipant’s character. In survey A, there were 80 positive com-ments and 20 negative ones. In survey B, the proportions were re-versed. Participants were told that the computer had selected oneof the surveys and that their goal was to decide which survey hadbeen chosen, based on the comments extracted. Comments werepresented one at a time, but as many or as few comments couldbe requested before a decision was made. Participants were askednot to make a decision until they were ‘quite’ confident of beingcorrect. Unbeknown to participants, comments were always pre-sented in a fixed order, following Warman and Martin (2006).There were two sequences: a majority positive (JTCp) and a major-ity negative (JTCn) sequence of comments.. The key variable wasthe number of comments requested before a decision was made.A recent review concluded that this ‘draws-to-decision’ variablewas the most reliable indication of JTC, with delusional popula-tions consistently exhibiting fewer ‘draws’ (Fine et al., 2007).

2.2.2. Positive affect inductionA false-feedback paradigm was used to induce positive affect.

Similar procedures have been adopted to successfully induce af-fect (e.g., Nummenmaa & Niemi, 2004). At the beginning of the

1 To prevent individuals from participating more than once, the server wasprogrammed to reject email addresses that had already been used.

2 Scores from the PANAS, along with the JTC variable were non-normallydistributed. Therefore, additional nonparametric tests were conducted for groupcomparisons. Since these analyses yielded similar results, only parametric statisticsare reported for consistency.

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experiment participants were deceived into thinking they wouldbe assessed on their creative ability. This would involve complet-ing a battery of creativity tests followed by viewing feedbackabout their performance. In fact, no creative ability was mea-sured and all participants were shown a video containing eitherpositive or neutral feedback.

Online creativity tasks consisted of word games, similar to theAlternate Uses Tests (Guilford, Christensen, Merrifield, & Wilson,1978), as well as a picture game, inspired by the Rorschach test(Exner, 2002). The tasks were made deliberately ambiguous tomake it difficult for participants to evaluate their own perfor-mance. Tasks were also timed so that the next would appear whena limit had been reached.

The research team recorded two video clips, lasting one minuteeach. In a positive feedback clip, the researcher praises the partic-ipant enthusiastically and informs them they have done excel-lently (‘‘performed in the top 25% of the population’’). It was hopedthat the personal nature of the video clip would enhance positiveaffect. In the neutral feedback clip, the researcher’s manner ispleasant but neutral. Participants are told they have done ‘okay’and that they have out-performed 50% of the public. In total, theaffect induction procedure took approximately 6 min. A negativeinduction was not included, owing to an ethical concern regardingthe potential distress caused by negative feedback.

2.2.3. Questionnaires2.2.3.1. Positive and Negative Affect Schedule (Watson et al., 1988).The PANAS comprises twenty adjectives describing current affect.There are ten adjectives forming a positive affect scale (e.g., ‘‘inter-ested’’ and ‘‘proud’’) and ten for a negative affect scale (e.g., ‘‘nervous’’and ‘‘jittery’’). Each item is rated 1–5, reflecting the degree to whichthat adjective is felt (1 = ‘‘very slightly or not at all’’, 5 = ‘‘extremely’’).The positive affect scale is sensitive to subjective increases inpositive moods or emotions, whilst the negative scale capturessubjective levels of distress (Watson et al., 1988).

2.2.3.2. Peters Delusion Inventory (Peters, Joseph, Day, &Garety, 2004). The PDI-21 consists of 21 items measuringdelusional–proneness in non-clinical populations. The questionsaim to capture delusion-like thoughts within the realm of everydayexperience, rather than clinical symptoms (e.g. ‘‘do you ever feel asif there is a conspiracy against you?’’). Each item is answered yes/noand if endorsed, three subscales are rated: distress (1 = ‘‘notdistressing at all’’, 5 = ‘‘very distressing’’), preoccupation (1 = ‘‘hardlyever think about it’’, 5 = ‘‘think about it all the time’’) and conviction(1 = ‘‘don’t believe it’s true’’, 5 = ‘‘believe it’s absolutely true’’). A totalscore, which was used in this study, is computed by summing allitems. Higher total scores reflect greater delusion-proneness. ThePDI has good internal consistency (Cronbach’s a = 0.82) andtest–retest reliability (r > 0.78, p < 0.01 for all subscales; Peterset al., 2004).

2.2.3.3. Self-evaluation of performance/task difficulty. Two single-item Likert scales were measured how well participants thoughtthey had performed compared to other people, and how difficultthey had found the JTC and creativity tasks. Both items were scored0–10 (0 = ‘‘performed much worse than others/very easy’’, 10 = ‘‘per-formed much better than others/very difficult’’). These items were in-cluded on two occasions (t1 and t2), to control for the possibleconfound of perceived differences in task difficulty and self-evalu-ation with the experimental effect.

2.3. Procedure

Approval for the study was gained from the university ethicscommittee. Participants accessed the study website via campus

computers and were required to provide an email address1 to en-ter. Materials were presented in the order shown in Fig. 1. The com-puter randomized participants into one of two conditions (positiveor neutral condition), differing only in the type of feedback provided.A debriefing page was provided to explain the deceptions at the endof the study.

2.4. Statistical analysis

Statistical analyses were conducted using SPSS 14.0. All testswere two-tailed. ANCOVA was used to compare group differenceswith adjustment for covariates in the main analysis and affectmanipulation check2. These models were checked for homogeneityof variance and regression slopes. Neither assumption was violated(all p-values from homogeneity tests >0.10).

3. Results

3.1. Delusion-proneness and JTC

Bivariate correlations were conducted between PDI scores andthe number of comments requested in the two trials of the JTC taskcompleted prior to the feedback. As hypothesised, participantsscoring more highly on the PDI requested significantly fewer sur-vey comments, r = �0.15, p < 0.05, R2 = 0.023. None of the demo-graphic variables from age, gender, ethnicity or current level ofstudy related to the dependent variable (all p-values >0.1).

3.2. Affect induction procedure

To check the affect manipulation was successful, post-feedbackpositive affect scores between the two conditions were compared.A one-way ANCOVA, controlling for pre-feedback positive affect,confirmed that positive affect was significantly higher after the po-sitive feedback than after the neutral feedback F(1,186) = 18.28,p < 0.001, R2 = 0.086. Within-group comparisons revealed that po-sitive affect increased significantly from pre-feedback to post-feed-back t(92) = 2.04, p < 0.05 in the positive condition and decreased inthe neutral condition, t(95) = �3.94, p < 0.01. There were no signifi-cant differences in negative affect between the groups when con-trolling for pre-feedback negative affect F(1, 186) = 2.49, p = 0.12,R2 = 0.013 (see Table 2).

3.3. Main analysis

The dependent variable was the total number of comments re-quested over two trials of the JTC task after the feedback. A one-way ANCOVA was conducted, using feedback condition as theindependent variable and the following as covariates: the totalnumber of comments from the two pre-feedback trials, PDI score,self-evaluation of performance (at t2), task difficulty (at t2). Theinteraction term between PDI score and feedback condition wasalso included in the model.

The only covariate that significantly related to the dependentvariable was the number of comments requested over the twopre-feedback trials of the JTC task F(1, 179) = 485.80, p < 0.001,R2 = 0.718. Adjusting for all covariates in the model, people in thepositive condition made significantly more requests for informa-tion than those in the neutral condition, F(1, 179) = 8.10, p < 0.01,

Fig. 1. Order of presentation for study materials. Abbreviations: PANAS, positive and negative affect schedule; t1, time 1; t2, time 2; JTCn, JTC survey task with majoritynegative comments; JTCp, JTC survey task with majority positive comments; PDI-21, Peters Delusion Inventory.

Table 2Continuous variables arranged by experimental group.

Positive condition(N = 93)

Neutral condition(N = 96)

Mean (s.d.) Median Mean (s.d.) Median

Pre-feedbackPANAS positive affect 27.39 (7.90) 26.50 27.30 (7.73) 28.00PANAS negative affect 14.75 (5.26) 13.00 14.40 (4.87) 13.00JTCn (trial 1) 4.61 (4.06) 3.00 4.20 (4.85) 3.00JTCp (trial 2) 4.43 (3.99) 3.00 4.76 (4.85) 3.00Total commentsa 9.04 (7.44) 7.00 8.96 (8.75) 5.50

Post-feedbackPANAS positive affect 28.71 (8.64) 29.00 25.19 (8.01) 24.00PANAS negative affect 13.20 (3.96) 12.00 13.76 (4.70) 12.00JTCn (trial 3) 5.44 (4.96) 4.00 4.19 (4.12) 3.00JTCp (trial 4) 5.05 (4.71) 4.00 4.12 (4.45) 2.50Total commentsb 10.49 (9.34) 9.00 8.31 (8.28) 5.50

Self-eval. of performance (t2) 5.12 (1.76) 5.00 4.70 (1.29) 5.00Perceived task difficulty (t2) 3.75 (2.17) 4.00 3.97 (2.10) 4.00

Abbreviations: PANAS = Positive and negative affect schedule, JTCn = JTC task withmajority negative comments, JTCp = JTC task with majority positive comments.

a Total of trials 1 and 2.b Total of trials 3 and 4.

720 G. Lee et al. / Personality and Individual Differences 50 (2011) 717–722

R2 = 0.040. The interaction between PDI score and feedback condi-tion was not significant, F(1, 179) = 1.82, p = 0.18, R2 = 0.001 suggest-ing that delusion-proneness did not moderate the effect offeedback condition on requests for information.

We also compared changes in pre to post-feedback performancewithin each experimental condition. Paired t-tests revealed in-creases in requests for information in the positive condition frompre-feedback to post-feedback t(92) = 2.76, p < 0.01, but no changein the neutral condition t(95) = �1.47, p > 0.15 (see Table 2).

3.4. Test repetition

The effect of test repetition was evaluated by comparing thenumber of survey comments requested in trials 1 and 2 of theJTC task and between trials 3 and 4. The first comparison was madeby collapsing data across experimental groups. None of the pairedt-tests were significant (all p-values > 0.20), suggesting that infor-mation-seeking did not change on successive trials either side ofthe affect manipulation (see also Table 2).

3.5. Potential confounding variables

The experimental groups were compared on a number of possi-ble confounding variables: age, gender, ethnicity, current educa-

tion level, self-evaluation of performance at t1 and task difficultyat t1. There were no significant differences between groups onthese variables (all p-values > .10).

4. Discussion

To our knowledge, this is the first published study to considerlevels of positive affect in relation to JTC. The results confirm ourcentral hypotheses: firstly, higher levels of delusion-proneness re-lated to fewer requests for survey comments in the non-clinicalsample. Secondly, we found that participants who received a posi-tive affect induction asked for more survey comments before mak-ing decisions than those in a neutral condition. This finding wasnot due to a reduction in requests in the neutral condition, butrather, to an increase in the positive condition. The results suggestthat increased positive affect may influence reasoning biases bypromoting information-seeking, hence reducing JTC. This assertionis strengthened by analyses that controlled for possible confound-ing variables.

The link between delusion-proneness and JTC was replicatedwith a comparable effect size to past studies (Warman & Martin,2006; Warman et al., 2007). However, on average, the current sam-ple requested fewer comments over two trials of the JTC task thanthose previously reported: the mean number of comments re-quested in this study ranged from 4.12 to 5.44, compared with5.52–6.20 (Warman & Martin, 2006). This might have been dueto the computerization of the task, which departed from traditionalJTC studies. Furthermore, participants may have approached thetask in a different way to usual, as it was framed as a test of crea-tive thinking. Nevertheless, the extent of information-seekingseemed reasonable, given the ratios of comments used.

The finding that participants requested more survey commentsafter the positive affect induction seems consistent with reportsthat positive affect is associated with risk-averse behavior in per-sonally meaningful situations (Isen, 2008). In this case, participantsmay have increased their requests to reduce the risk of getting anincorrect answer that might attract negative feedback. Alterna-tively, as Djamasbi (2007) has suggested, increased informationuse may be due to a greater willingness to explore options withinthe environment. This latter conclusion would be consistent withthe theory that positive affect broadens people’s mindsets andfacilitates approach behavior (Fredrickson, 1998, 2001).

As PDI scores did not moderate changes in information-seeking,this suggests that the effect of the experimental manipulation wasconsistent across the spectrum of delusion-proneness and not

G. Lee et al. / Personality and Individual Differences 50 (2011) 717–722 721

limited to those at the lower (or higher) end. Interestingly, weobserved only mild elevations in positive affect following our socialstimulus, yet these were sufficient to impact on decision-makingbehavior, as others have found (e.g., Isen, 2008).

Mild fluctuations of affect are likely to be more commonplacethan extreme changes during daily life. Therefore, their influenceon cognition may be especially relevant. Some researchers havefound that positive affect during family interactions predicts goodadjustment in early psychosis (Halford, Steindl, Varghese, &Schweitzer, 1999). Our results suggest that the experience of posi-tive affect during social contact, may have a place in bufferingagainst JTC, a risk factor in the development and maintenance ofdelusional symptoms (Garety et al., 2001).

Several limitations of this study are identified. In the neutralcondition, levels of positive affect decreased from pre to post-feed-back, yet a decrease in requests for survey comments was not ob-served. Although this result seems inconsistent with the argumentthat positive affect is linked to information-seeking, one explana-tion could be the impact of positive affect is specific to increasesand not decreases in this domain.

An alternative position is that positive affect has no impact onJTC at all: rather, the neutral feedback operated against a naturalpractice effect, which would otherwise see information-seeking in-crease through subsequent trials. For example, participants in theneutral condition may have become de-motivated after viewingdisappointing feedback. The analysis, however, offers no evidencethat repetition of the test, in the absence of feedback, leads to in-creased information-seeking on the task.

Our affect induction procedure was chosen for its’ social rele-vance: we felt this would simulate fluctuations in affect withinnormal settings. The implicit procedure also helped guard againstexperimental demand effects. Despite this, an internet study is stillfar removed from the experience of face-to-face interactions. Rep-lication in real world settings would be critical to supporting theecological validity of present results.

It is acknowledged that further research is needed to examinethe reliability of these findings in psychosis samples. From thispoint of view, the following suggestions are presented specula-tively. However, a potential implication for people with psychosisis that levels of positive affect may relate to open styles of thinkingwithin therapy. Fostering mild positive affect may, for instance, al-low clients to approach therapy openly and to consider more infor-mation when generating arguments for and against unhelpfulbeliefs. This would be compatible with the approaches of special-ized early intervention programs for psychosis that emphasizeencouragement and support for clients (e.g., Jackson, Edwards,Hulbert, & McGorry, 1999) as well as the frequent observation thatpositive client–therapist relationships lead to better treatmentoutcomes (Martin, Garske, & Davis, 2000). However, people withpsychosis often experience many other related symptoms, whichmay also impact these processes and the potential of therapeuticinterventions to influence them. For example, common negativesymptoms of schizophrenia include a diminished capacity to ex-press and possibly experience positive affect. This may increasethe tendency to JTC and reduce the effectiveness of therapeuticinterventions to increase positive affect.

In summary, the present investigation focused on the influenceof positive affect on JTC within a non-clinical sample. It was foundthat changes in positive affect through experimental manipulationwere accompanied by changes in the number of comments thatparticipants requested during a ‘survey’ task. These results suggestthat increasing positive affect leads to more information-seekingwhen making decisions (i.e. less JTC). The findings highlight theneed for future work to consider both positive and negative affectin relation to reasoning biases within social situations. It is un-known at this stage how likely these results would generalize to

clinical populations. But beyond replication in similar samples,investigations using recent onset or ultra-high risk groups wouldhelp clarify the role of positive affect on reasoning biases in thedevelopment and maintenance of psychosis.

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