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Psychology and Psychotherapy: Theory, Research and Practice (2014), 87, 44–59 © 2012 The British Psychological Society www.wileyonlinelibrary.com

An exploratory investigation of real-world reasoning in paranoia ∗

V. Huddy1, , G.P. Brown2 , T. Boyd3 and T. Wykes4 1

Department of Psychosis Studies, Institute of Psychiatry, KCL, London, UK Department of Psychology, Royal Holloway, University of London, UK 3 Barnet Enfield and Haringey Mental Health Trust, London, UK 4 Department of Psychology, Institute of Psychiatry, KCL, London, UK 2

Objectives. Paranoid thinking has been linked to greater availability in memory of past threats to the self. However, remembered experiences may not always closely resemble events that trigger paranoia, so novel explanations must be elaborated for the likelihood of threat to be determined. We investigated the ability of paranoid individuals to construct explanations for everyday situations and whether these modulate their emotional impact. Methods. Twenty-one participants experiencing paranoia and 21 healthy controls completed a mental simulation task that yields a measure of the coherence of reasoning in everyday situations. Results. When responses featured positive content, clinical participants produced less coherent narratives in response to paranoid themed scenarios than healthy controls. There was no significant difference between the groups when responses featured negative content. Conclusions. The current study suggests that difficulty in scenario construction may exacerbate paranoia by reducing access to non-threatening explanations for everyday events, and this consequently increases distress.

Practitioner Points • When working with distress associated with paranoia, practitioners should devote attention to developing coherent narratives where others are viewed as helpful and caring. • Some cases of distress associated with paranoia may result from an uncertain and confused state of mind rather than threatening thought content alone.

∗

Correspondence should be addressed to Vyv C. Huddy, Department of Psychosis Studies, Kings College London (KCL), Institute of Psychiatry, De Crespigny Park, London SE5 8AF, UK (e-mail: [email protected]). DOI:10.1111/j.2044-8341.2012.02072.x

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Persecutory beliefs are common in psychosis (Sartorius et al., 1986) and are associated with distress and avoidant behaviours (Freeman et al., 2007). Recent psychological interventions for delusional thinking focus on cognitive biases (Moritz & Woodward, 2007). Potential biases implicated include difficulties with theory of mind and the attribution of negative events to other people (see Freeman et al. 2007 for a review). Another line of research has proposed a role for decision heuristics in paranoid thinking (Bentall et al., 2008, 2009; Corcoran, 2010; Corcoran et al., 2006). One such mechanism – the availability heuristic – proposes that the probability of events is estimated on the basis of the availability of similar events in memory (Tversky & Kahneman, 1973). Two studies (Bentall et al., 2008; Corcoran et al., 2006) report that anticipation of future negative events is significantly associated with the frequency of past negative experiences in paranoid individuals, which suggests the operation of the availability heuristic. However, Bentall et al. (2008) note that in their study inflated estimates of threat to the self remained even when frequency of past negative events was controlled, suggesting that the availability of past negative experiences alone does not account for the anticipation of threat in paranoia, and that this process may involve other factors. In their original paper on the availability heuristic, Tversky and Kahneman (1973) suggested that, in addition to retrieving instances from memory, participants ‘may evaluate likelihood by attempting to construct stories, or scenarios’ of particular outcomes. They went on to suggest that ‘the plausibility of such scenarios, or the ease with which they come to mind, can provide a basis for the judgment of likelihood’ (p. 228). Kahneman and Tversky (1982) developed the notion of scenario construction by proposing a simulation heuristic for situations where probability estimates are primarily based on mental simulation of a scenario that takes place in the future and the individual has never experienced. The underlying theory predicts that coherent simulations that ’run’ more easily in the imagination lead to a greater estimate of their likelihood. Brown, Macleod, Tata, and Goddard (2002) developed a methodology to operationalize this heuristic using a methodology that taps onto real-world thinking processes. Their initial study was of women expecting to give birth for the first time and who might be expected to experience anxiety about this event. Participants were asked to mentally simulate the forthcoming event of going into labour and travelling to hospital to give birth. These narratives were rated in terms of defining dimensions of the simulation heuristic (e.g., logical connectedness and temporal flow) as a measure of Goodness of Simulation (GOS). Higher scores on GOS were associated with a higher probability estimates and a lessening of worry about the outcome, thus providing support for the theory underlying the simulation heuristic (Kahneman & Tversky, 1982), which specifies that more coherent thinking is linked to a higher subjective likelihood of the positive outcome and less worry about the outcome. The GOS measure aims to tap onto the respondent’s ability to generate a coherent and temporally connected narrative that draws together all the salient features of a scenario that has never happened to them. Therefore, at the starting point of the exercise, the scenario appears undefined and ambiguous. This is relevant to paranoid thinking, as it has been suggested paranoid thoughts emerge in ambiguous situations (Combs et al., 2009; Combs, Penn, Wicher, & Waldheter, 2007; Freeman et al., 2007). Thus, the simulation method is a potentially useful way to examine how paranoid individuals construct coherent stories that either resolve, or fail to resolve, the ambiguity inherent in the scenarios. The focus of the current study was, therefore, to adapt the simulation task to paranoid thinking and gather preliminary data from both clinical and healthy control samples.

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The source material for the scenarios used in the current study was drawn from the personal descriptions of paranoia in a previous study of individuals who had experience of clinical paranoia (Boyd & Gumley, 2007). A major consideration was that any scenario only has potential to be paranoia evoking because, as noted above, scenarios that evoke paranoia frequently appear ambiguous, so that the intentions of others may be interpreted as either negative or otherwise. This interpretation may vary according to idiosyncratic personal fears so that not all scenarios will trigger paranoid thoughts for all respondents. For this reason, we presented several different scenarios to participants and then classified responses post hoc using a rating to indicate whether or not the scenario elicited evidence that others held negative intentions towards the respondent. Conversely, we also wished to determine if respondents elaborated a positive interpretation of others intentions in the scenario. This followed from a prior (Gumley, Braehler, Laithwaite, MacBeth, & Gilbert, 2010) suggestion that, as well as over activation of threat during social information processing, there could be underactivation of representations of others as helpful, supportive, and consequently safe. Such qualities presumably down regulate threat systems, lead to positive affect, and may be a factor in inhibiting paranoid thinking. Based on previous research (Combs et al., 2007, 2009), we expected that paranoid individuals would more frequently produce responses where other people held negative intentions towards them than individuals in the healthy control sample. We additionally expected that positive intent responses would be less frequent in the paranoid group. The main focus of the study was to examine the relative ability of paranoid and healthy control groups when constructing coherent responses to everyday scenarios. Previous work using this methodology in obsessive compulsive disorder (OCD) had indicated that when scenarios match the personal fears of respondents – for example, harming or contamination – responses were more coherent than when the content was not personally relevant (Keen, Brown, & Wheatley, 2008). Johnson-Laird, Mancini, & Gangemi (2006) have proposed a framework for understanding this effect, termed the Hyper Emotion Theory. This account interprets narrative as a means of making sense of intense emotional experience, so that ’individuals focus on an aberrant basic emotion, they reason about it and its causes, and as a result, they become well practiced in reasoning about the topic, and their reasoning can maintain and generalize the illness’ (p. 825). In a similar vein, we anticipated that paranoid individuals would produce more coherent responses when their responses featured the negative intentions of others, compared to healthy controls. Conversely, healthy controls might be expected to produce more coherent responses when they featured the positive intentions of others compared to the clinical group. As noted earlier, the theory underlying the simulation heuristic suggests that when scenarios are narrated more coherently they appear more likely. If paranoid individuals produced higher coherence ratings for negative intention scenarios they should be accompanied by higher likelihood ratings for this group. This would be consistent with previous research on the availability heuristic (Bentall et al., 2008, 2009). Conversely, if healthy comparison subjects produced more coherent responses featuring the positive intentions of others they should appear more likely for this group. The worry rating should align with the affective content of the scenario so that well-formed accounts of negative material lead to greater worry, and conversely, well-formed accounts of positive material should be accompanied by lower worry ratings. Therefore, since we expected paranoid individuals to narrate the negative material better than healthy controls did, and

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conversely narrate positive material less well than controls, this group should display higher worry following both types of content. The scenarios were drawn from experiences of paranoia drawn from a preceding study (Boyd & Gumley, 2007). Thus, to people other than the participants in the previous study, the outcome of the scenarios could be imagined but may not appear likely to actually occur in the everyday life of the respondent. We therefore included an additional ‘ease of imagining’ variable that would allow participants to rate a response as plausible without necessarily expecting it to happen in the future. We expected ratings on this variable to converge with the likelihood rating.

Method Design We adopted a mixed design with a between-group (paranoid versus healthy control) and within-group (scenarios that featured negative intent or positive intent vs. those that did not) independent variables. The simulation task scenarios were presented in a counterbalanced order. Participants The primary inclusion criterion for the clinical participants was the presence of persecutory delusional ideation, following the relevant (Freeman & Garety, 2000) criteria, which are that the individual believes (1) there is a persecutor and (2) the persecutor intends to harm them. The nature of persecutory paranoid ideation in the week preceding the interview was specifically rated using Item 9 of the Brief Psychiatric Rating Scale (BPRS) (see Moritz & Woodward, 2005). This item assesses the belief ‘that others have now, or have had in the past malicious or discriminatory intent towards the patient’. At the time of testing, all clinical participants displayed at least ‘mild’ paranoid symptoms overall as defined by the BPRS (BPRS score 3 or above). Additionally, the PSYRATS scale was used to characterize the paranoid symptoms (see Table 1). The principal exclusion criteria were (1) difficulties with language or communication abilities, (2) evidence of an organic/physical cause of illness, and (3) current severe drug or alcohol problems. Of the 30 participants approached, 5 refused to participate and 4 failed to meet the recruitment criteria leaving 21 who took part in the study. The sample was recruited from community mental health teams and an inpatient rehabilitation service. The healthy comparison group was recruited by various means including advertisements placed in local job centres, a major charity, and a newspaper intranet site. The exclusion criteria were (1) a history of psychiatric illness and (2) current severe drug or alcohol problems. The comparison group scored in the normal range on the Peter’s Delusion Inventory (Peters, Joseph, Day, & Garety, 2004) that was administered to screen for delusion-like experiences. Measures The structure of the task was taken from the Means-Ends Problem-Solving Approach (MEPS) (Platt & Spivak, 1977) and adapted by Brown et al. (2002). For each scenario, participants were presented with the beginning of the imaginary scenario and the end of the scenario (see below). They were asked to give a step-by-step account of what

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Table 1. Sample characteristics symptom scores for the clinical and comparison participants Paranoid group

Age Years of education HADS anxiety HADS depression PSYRATS ratings ‘Cognitive’ items (out of 12) ‘Emotion’ items (out of 8)

Healthy control group

M

SD

M

SD

41.0 12.6 8.3 6.0

10.5 2.5 5.0 3.8

40.3 13.1 6.2 2.9

9.4 1.9 2.7 1.7

8.0 4.2

3.4 2.6

-

-

N (%) Sex % Male Ethnicity % White British Employed∗ % Employed Marital status∗ % Married or cohabitating

N (%)

19 (90)

-

18 (86)

-

13 (62)

-

14 (71)

-

1 (5)

-

14 (67)

-

1 (5)

-

14 (67)

-

Note. N = 21 for the clinical participants and N = 21 for the control participants. ∗ p ⬍ .05.

might have happened in between. The participants’ responses were audio taped and transcribed verbatim. The first step in developing scenarios evocative of paranoia involved reviewing the transcripts of interviews taken during the Boyd and Gumley (2007) qualitative study of paranoia. The aim was to identify narratives of specific situations that had evoked paranoia as signified by the report of feeling under threat from a persecutor. These paranoia exemplars could then be used to construct the beginning and end of the scenarios for which respondents would be asked to provide an account of what happened in between. An example of one of the scenarios is: ‘At the beginning of the situation, you are at home and someone nearby has been making a lot of noise. A friend arrives and the noise stops. Take a moment to imagine that. At the end of the situation, your friend leaves and the noise immediately resumes’ (see Appendix for all scenarios). Pilot data from non-clinical participants indicated that the scenarios frequently led to well formed but non-threatening content. For this reason, we chose to present several paranoid themed scenarios and then rated responses post hoc to separate scenarios that either elicited evidence of negative or positive intent towards the respondent or did not. Negative intent was used to capture the two key features of paranoia – threat and the presence of a persecutor (Freeman & Garety, 2000) – in a single variable, consistent with the evidence for a hostility bias in paranoia (Combs et al., 2007). Participants gave verbal ratings following administration of the scenarios along three dimensions: ease of imagining, subjective probability, and worry on a scale ranging from 0 (not at all) to 100 (completely). The imaginability scale rating was made in response to the statement, ‘I can easily imagine (picture) what I just described in that scenario’. For the subjective probability scale, the relevant rating statement was, ’I can see something like that actually taking place’. For worry, the respondents rated, ‘The possibility of that taking place is something that would worry me’.

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GOS coding criteria The GOS coding system was based on the approach taken by Brown et al. (2002). The elements of the coding system are as follows: (1) Logically sequenced, (2) Temporally ordered, (3) Uncertainty is minimized, (4) Good level of detail, (5) Easy to imagine, and (6) Flows smoothly, a global judgement of how well the scenario flows. Each aspect of GOS was rated on a 3-point scale, with each point anchored by specific criteria and with higher ratings indicating better quality simulations. Similar criteria were found to converge on a single underlying factor in the Brown et al. (2002) study. Following training with example transcripts for each scenario, two raters – a clinical psychologist and an occupational therapist – assigned scores on each of the six GOS dimensions described above to each scenario provided by respondents in both groups. The transcripts were also separated into three categories in terms of the extent to which hostile content was present: no hostility, implied hostility, or clear hostility. Inter-rater reliability was evaluated by having raters blind to all information concerning participant group carry out a second set of ratings, and clinical and control transcripts were randomly intermixed. The intraclass correlation coefficient (ICC) between raters ranged between 0.71 and 0.86 for the individual GOS item scores. The internal consistency of the items was high (␣ = .94) and justified using a total GOS score by summing the six dimensions. The ICC between the raters for this overall score was .77 and for the intent ratings was .71. Hospital Anxiety and Depression Scale (HADS) (Zigmond & Snaith, 1983) is a brief measure of both anxiety and depression that has been shown to have well-established reliability and psychometric properties. The HADS is made up of 14 items – seven anxiety items alternating with seven depression items. The Psychotic Symptom Rating Scale delusions subscale (Haddock et al., 1999) is a semi-structured interview designed to assess six dimensions of delusions each rated on a 5-point scale (0–4). These dimensions load onto two factors (Drake et al. 2007): the cognitive factor that includes the frequency and duration of preoccupation with the delusion, the degree of conviction and the degree of disruption to life. The second factor emotion factor was made up of the frequency and degree of distress. Procedure Measures were administered in the following order: demographic data was collected initially followed by the simulation task, HADS, with the clinical group additionally completing the PSYRATS measure last.

Results Analyses We first sought to establish if the scenarios elicited more negative intent responses and fewer positive intent responses in the paranoid group compared to the control group. The range of these variables was limited, and the distributions positively skewed, so we used non-parametric tests for these comparisons. The first hypothesis was that individuals in the paranoid group would produce more coherent responses to scenarios that featured negative intent than those in the healthy control group. We expected they would view these scenarios as being more likely, easier to imagine, and – as a result of their negative content – more worrying. To test this

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hypothesis, we created mean scores for the GOS, likelihood, ease of imaging, and worry across scenarios that featured negative intent. Group differences on these negative intent variables were then determined using t tests. The secondary hypothesis was that individuals in the healthy control group would produce more coherent material when their response featured positive intent compared to those in the paranoid group. Once again, we expected these to be rated as more likely and easier to imagine. We again produced mean scores for the simulation task variables for those scenarios that featured positive intent and, as above, tested group differences using t tests. We finally examined correlations between the simulation task variables and the cognitive and emotion factors extracted from the PSYRATS dimensions to support construct validity. Non-parametric correlations were used where the range of the scores was restricted, which was the case for the positive and negative intent variables. Sample characteristics The demographic composition of the two groups and scores on symptom measures are shown in Table 1. The groups were balanced in terms of age (t(40) = 0.20, n.s.), ethnicity (␹ 2 (1) = 5.0, n.s.), gender (␹ 2 (1) = 0.23, n.s.), and years of education (t(40) = 0.78, n.s.). As expected significantly more non-clinical participants were employed (␹ 2 (1) = 17.5, p < .05) and married (␹ 2 (1) = 17.6, p < .05). On the HADS questionnaire, the clinical group was significantly more depressed t(40) = 3.4, p = .002, and there was a trend for them to be more anxious t(40) = 1.73, p = .09. The PSYRATS scores are additionally given in Table 1 – these demonstrate moderate levels of preoccupation, disruption, distress, and conviction levels. Differences on the frequency of negative and positive content Paranoid participants produced more responses that featured negative intent than healthy control participants (U = 139.5, p < .05; see Table 2). The number of responses featuring positive intent showed the opposite difference, with healthy controls producing more positive responses than those in the paranoid group (U = 101.5, p < .01; see Table 2). Table 2. Number out of n = 21 (%) of clinical and non-clinical participants who produced negative and positive hostile content in response to the four scenarios Negative intent towards the respondent

Positive intent towards the respondent

Scenario

Paranoid N (%)

Control N (%)

Paranoid N (%)

Control N (%)

Noise Herbs Public Messages

10 (53) 6 (29) 13 (62) 6 (29)

9 (43) 2 (10) 5 (23) 5 (24)

1 (5) 1 (5) 8 (38) 0 (0)

6 (29) 2 (10) 16 (78) 3 (15)

Mean (SD)

Mean (SD)

Mean (SD)

Mean (SD)

1.7 (0.9)

1.0 (1.0)

0.5 (0.5)

1.2 (0.7)

Total (out of 4)

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Differences on simulation task variables for responses featuring either positive or negative content The simulation task variables are given in Table 3 for scenarios that featured negative and positive content. Nineteen paranoid individuals and 12 controls produced negative content. There were no group differences on any of the simulation task variables for scenarios that featured negative intent. These findings indicate, against our expectations, that paranoid individuals did not narrate the scenarios featuring negative content more coherently than the healthy control group; they did not rate them as more plausible or more worrying. Ten paranoid individuals and 18 healthy controls produced content that featured the positive intentions of others directed towards them. For these scenarios, as expected, there was a significant difference for both GOS and worry indices with paranoid individuals narrating the scenarios less coherently and expressing more worry about them. However, there was no significant difference between the groups on the subjective probability variable. Participants in the healthy control group tended to rate scenarios featuring positive intent as easier to imagine than those in the paranoid group. Differences in GOS and worry variables are depicted in Figures 1 and 2, respectively. The absence of a group difference on GOS for scenarios that featured negative intent was not anticipated. An exploratory ANOVA was conducted for the subgroups of both paranoid and healthy control participants who generated both positive and negative content (N = 9 in the former case and N = 10 in the latter). This analysis revealed a significant interaction between content and group (F(1, 17) = 10.8, p < .01), which indicated that healthy controls produced significantly higher GOS scores in response to

Table 3. Mean scores for goodness of simulation (GOS), ease of imaging, subjective probability, and worry Paranoid group

Healthy control group

Mean

SD

N

Mean

SD

N

t

11.0 11.3 10.9

3.2 3.3 4.1

21 19 10

13.9 12.3 15.5

1.9 2.9 2.1

21 12 18

−3.6∗∗ −0.8 −4.0∗∗

Ease of imagining (0–100) All 61.1 Negative intent 65.8 Positive intent 59.0

25.8 28.5 35.1

20 18 10

79.9 74.5 80.3

13.6 22.4 20.3

21 12 18

−2.9∗∗ −0.9 −2.0†

Subjective probability (0–100) All 43.0 Negative intent 42.4 Positive intent 54.0

25.3 30.9 32.3

20 18 10

59.1 55.7 62.3

18.8 31.9 28.3

21 12 18

−2.3∗ −1.1 −0.7

Worry (0–100) All Negative intent Positive intent

27.7 34.1 41.1

20 18 10

26.5 41.0 14.6

24.8 29.4 20.7

21 12 18

2.2∗ 1.2 2.7∗

GOS (6–18) All Negative intent Positive intent

∗

44.9 55.2 46.5

p ⬍ .05; ∗∗ p ⬍ .01; † p ⬍ .10. All = scores are given collapsed across all scenarios; negative = only negative content scenarios; positive = only positive content scenarios.

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Figure 1. Mean GOS for negative and positive intent scenarios in the paranoid and healthy control groups.

positive than negative content but this difference was not found in the paranoid group, who narrated both types of content equally coherently. GOS is assumed to measure a ‘deep’ property of the participant’s verbal responses and should represent more than the amount or gross complexity of information provided. For positive responses, those in the healthy control group scored higher GOS than those in the paranoid group. We therefore examined the group differences on the length of the scenario in words and the number of sentences produced. There were no betweengroup differences in the total number of words (t(26) = 1.6) or sentences (t(26) = .1) produced for these scenarios. Differences on simulation task variables collapsed across scenario content The theory underlying the simulation heuristic predicts that high GOS scores should be accompanied by high subjective probability and vice versa for low GOS responses. In the preceding analyses, there was a significant group difference on GOS for scenarios featuring positive intent but this was not accompanied by significant differences on subjective probability or ease of imagining for the same scenarios. However, these analyses included less than half the clinical sample because this group infrequently produced positive intent responses. We therefore tested group differences on subjective probability and ease of imagining in the full sample by comparing the groups on an

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Figure 2. Mean post-scenario worry ratings for negative and positive intent scenarios in the paranoid and healthy control groups.

average of the four main variables without taking the intent variables into account. The means for these variables are given in Table 3; there were significant group differences on all four simulation task indices with paranoid individuals producing less coherent responses to the scenarios, estimating them as less likely, less easy to imagine, and more worrying. Associations of simulation task measures with PSYRATS dimensions We next sought to determine if two main dimensions of the PSYRATS were associated with the frequency with which negative or positive intent was elicited by the scenarios. The cognitive factor was not associated with the overall number of responses that featured positive (␳ = −.22) or negative (␳ = .15) intent. However, the emotion factor was positively associated with the number of negative (␳ = .45, p = .047) but not positive (␳ = −.10) intent responses. We next examined the correlations between simulation task variables for negative intent responses and the PSYRATS dimensions. We did not examine correlations for positive intent because the number of responses in the paranoid group that featured positive intent was limited (N = 10). In the larger sample (N = 18) who generated negative intent responses, there were no significant correlations between simulation task variables and either PSYRATS dimension.

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Discussion The findings were consistent with our expectations in terms of the content produced. However, in other key areas, such as the GOS and subjective probability variables, the results were unexpected and require further explanation. The finding that paranoid group more often imagined negative intent in other people is consistent with data reported by Combs et al. (2009). This effect was accompanied by an attenuation of imagined positive intent in others, confirming a prior suggestion by Gumley et al. (2010). However, the main hypothesis that paranoid individuals would produce more coherent responses than healthy controls when narrating scenarios that featured the negative intentions of others was not supported. Instead, scenarios featuring negative content were narrated at the same level by the two groups. This indicates that preoccupation with paranoid fears is not necessarily associated with greater ability in reasoning about feared material, which is inconsistent with the prior work that provided the stimulus for the current study (Johnson-Laird et al., 2006; Keen et al., 2008). The theory underlying these prior studies assumed that repetitive patterns of thinking gradually shaped more plausible accounts of feared material. However, such cyclical patterns of thinking may not contribute to paranoia reasoning in the same way as they do OCD. Indeed, it has been suggested that reasoning in people with OCD versus paranoid fears may work in opposite directions, with detailed reasoning in the former case and more cursory thinking in the latter (Dudley & Over, 2003 but see also Jacobsen, Freeman & Salkovskis, 2012). We did not find the expected pattern of effects on the subjective probability variable: those in the paranoid group did not rate scenarios featuring negative content as any more likely than those in the healthy comparison group. There was also no group difference in subjective probability for scenarios that featured positive content. We did, however, find that people in the paranoid group rated the scenarios – across content – as less likely to take place and more difficult to imagine than those in the healthy control group. This finding contrasts with prior work on the availability heuristic (Bentall et al., 2008; Corcoran et al., 2006) where paranoid individuals produced higher estimates of the future threat. A key difference between the current and prior studies is the latter did not require the respondent to elaborate the hypothetical threat; instead, the threat was stated (e.g., ‘you are followed down the road’). This could have resulted in the activation of a complete narrative for a past experience that did not require any further effortful processing of ambiguity. We found that the paranoid group more frequently described a threat to them but they failed to resolve the ambiguity of the scenario with a coherent response. In this circumstance, the simulation model predicts that paranoid respondents would perceive the scenario as less likely. These data could indicate that the paranoid individuals were in a state of heightened uncertainty and confusion when responding to the scenarios. This state of mind was identified as a core theme in the prior qualitative study of paranoia by Boyd and Gumley (2007). Thus, while a defining aspect of paranoid delusions is the conviction with which they are held, these data indicate that paranoid thinking can be accompanied by uncertainty and confusion. A confused and uncertain state of mind could conceivably lead to the higher worry ratings produced by the clinical group via intolerance of uncertainty. Intolerance of uncertainty is the tendency to find uncertain situations upsetting and stressful (Dugas et al., 2005). A related tendency is need for closure, which is the tendency towards a desire for an answer on a topic in preference to ambiguity. Heightened need for closure has been found to be associated with delusional and paranoid ideation in several studies

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(Bentall & Swarbrick, 2003; Colbert & Peters, 2002; Colbert, Peters, & Garety, 2006). Intolerance of uncertainty has also been linked to paranoia in a study by White and Gumley (2010). They reported a positive association between intolerance of uncertainty and negative beliefs about paranoia in people with schizophrenia. In another study, healthy people who scored high on an intolerance of uncertainty scale went on to express more concern about ambiguous events than people who scored lower on the scale (Dugas et al., 2005). Thus, it is possible that because paranoid individuals produce responses to scenarios that are incoherent, this leads to a state of uncertainty and confusion, and this state of mind leads to distress. Similar steps have been described by Lysaker et al. (2011) and highlighted as key stages in effective psychotherapy for schizophrenia. In their formulation, clients develop greater awareness of the nature of their thoughts, their reactions to their thoughts, and then to their emotions as they progress through therapy. This methodology may help untangle some of these relationships. As was anticipated, people in the healthy control group produced more coherent responses that featured the positive intentions of others than those in the paranoia group. Healthy controls additionally rated these responses as less worrying than people in the paranoid group – this difference was not found for negative intent responses. These findings are partially consistent with predictions of the simulation model that better simulated positive responses should appear less worrying when they are more coherent. However, the simulation model also predicts that this relationship is mediated by subjective probability, which was not borne out as we did not find group differences in subjective probability or ease of imaging differences following positive content scenarios. Still, across content, people in the paranoid group found the scenarios more difficult to imagine and less likely to happen to them. Thus, overall, the results are partially consistent with the simulation model. Another way of framing the results is that people in the healthy control group produced less coherent responses when their responses featured negative compared to positive content, while those in the paranoia group were incoherent regardless of content. The difference in the healthy control group may indicate that there is something inherently more difficult about narrating scenarios where others hold negative or malign, compared to positive or benign, intent. Work by Burbridge, Larsen, and Barch (2005) demonstrated more incoherent speech when people from the general population narrate negative, but not positive or neutral topics. Burbridge et al. (2005) suggest that when speech is focused on negative material this triggers negative emotion that then impairs cognitive function, and this leads to speech disturbance. It is possible that similar mechanism is at work in the current study, at least for the healthy control group. The presence of malign intent in the response could have triggered negative emotion in healthy controls leading to greater difficulty narrating such responses. However, when participant’s responses envisaged positive intent in others, this dampened down negative emotion and those in the healthy control group were able to respond more coherently. The worry ratings produced by those in the healthy control group were consistent with this suggestion. In contrast, those in the paranoid group may not have successfully activated developed positive representations of others, as suggested by Gumley et al. (2010). This could have had the consequence that negative emotion was not inhibited, and their responses were less incoherent as a result. This suggestion remains speculative because the causal relationship between the worry and coherence variables would act in the opposite direction to that proposed by the simulation heuristic – where changes in coherence lead to affective changes – but this explanation remains a possibility.

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In contrast to the group differences on simulation task measures, there was little evidence for associations between the PSYRATS dimensions and the simulation task variables – either the frequency of each type of intent, the coherence of the response, or the degree of worry elicited. The only association we found was that greater frequency of negative intent responses was positively correlated with the PSYRATS emotion factor. Therefore, individuals who generated more negative content in the scenarios were more distressed about their paranoid fears. Thus, perceiving the negative intentions of others in ambiguous scenarios may be a factor in the distress associated with paranoia. A related finding that increased perception of hostility in ambiguous scenarios was associated with higher scores on the fear of negative evaluation scale has been reported previously (Combs et al., 2009). Although group differences emerged on positive intent variables, individual differences in the paranoid group were not associated with PSYTRATS dimensions. However, the number of responses that featured positive intent was very low in the paranoid group, with only half the sample producing any positive content at all, which meant there was little opportunity for any associations to be demonstrated. The current study had a number of limitations. The clinical sample consisted of people with paranoid symptoms, but the study did not employ a further psychiatric control group without paranoia. This omission means that any inference of a causal relationship between paranoia and group differences could easily be confounded by other characteristics that differ between the clinical and control samples, such as time spent in institutional care or long-term effects of psychotropic medication. There were only four paranoia-evoking scenarios, giving participants a limited range of material that could trigger their potentially idiosyncratic core fears. It is also possible that participants did simulate scenarios that featured paranoid themes but did not verbalize because of distressing content. However, control participants found a means of describing the scenarios that was not distressing, through describing benign explanations. The sample consisted of people with persecutory beliefs, the majority of which had been diagnosed with schizophrenia. Language abnormalities are associated with schizophrenia (e.g., Cohen & Docherty, 2004; Docherty, Hall, & Gordinier, 1998) and may therefore have interfered with elements coded by GOS, confounding the interpretation of these scores. There are two reasons why the results are unlikely to be explained by general language disturbances alone. First, group differences on the GOS coherence variable were specific to content, with healthy controls narrating positive content more coherently but no differences emerging on negative content. In addition, aside from the differences in the interpersonal context of the responses, paranoid individuals rated the scenarios more difficult to imagine and less likely to take place than the healthy controls did. These group differences across scenario content are consistent with the operation of the simulation heuristic because the higher GOS scores produced by healthy controls were accompanied by higher likelihood and ease of imagining ratings. These data suggest the scenarios did not come to mind as easily for paranoid individuals compared to healthy controls, and as a whole, the results do not simply reflect impairment in language expression in the clinical group. Summary and conclusion This study demonstrated that paranoid individuals have difficulty constructing coherent narratives in response to ambiguous scenarios. The results were consistent with the possibility that they do not produce positive narratives that serve to dismiss the threat

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implied by the scenario and consequently they experience more worry. These data suggest that psychological therapy for paranoia should seek to bolster such alternative – and less distressing – narratives of ambiguous scenarios that trigger paranoid thoughts. The simulation task methodology – with modifications – could be used for this purpose.

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Received 3 November 2011; revised version received 1 March 2012

Appendix

Simulation task scenarios (1) At the beginning of the scenario, you are at home and someone nearby has been making a lot of noise. A friend arrives and the noise stops. At the end of the scenario, your friend leaves and the noise immediately resumes.

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(2) At the beginning of the scenario, you are a guest for dinner and your host makes some food for you with some herbs you do not recognize. At the end of the scenario, it is later in the evening, there is a strange taste in your mouth and you feel a little odd. (3) At the beginning of the scenario, you are sitting in a public place and an older man sits down next to you and starts speaking to you. He is very keen to talk and asks you about yourself. At the end of the scenario, you are making your way home when you see the man speaking on his mobile phone. (4) At the beginning of the scenario, you are checking your telephone messages and there are a number of hang ups – someone has phoned and not left a message. At the end of the scenario, your doorbell rings but when you go to the door no one is there.