Jumping to delusions in early psychosis.

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Jumping to delusions in early psychosis a

b

a

Robyn Langdon , Megan Still , Michael H. Connors , Philip B. c

d

Ward & Stanley V. Catts a

ARC Centre of Excellence in Cognition and its Disorders and Department of Cognitive Science, Macquarie University, NSW 2109, Australia b

Rehabilitation Services, Sydney South West Area Health Service, Sydney, Australia c

School of Psychiatry, University of New South Wales, NSW 2052, Australia d

Department of Psychiatry, University of Queensland, St Lucia, QLD 4072, Australia Published online: 11 Nov 2013.

To cite this article: Robyn Langdon, Megan Still, Michael H. Connors, Philip B. Ward & Stanley V. Catts (2014) Jumping to delusions in early psychosis, Cognitive Neuropsychiatry, 19:3, 241-256, DOI: 10.1080/13546805.2013.854198 To link to this article: http://dx.doi.org/10.1080/13546805.2013.854198

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Cognitive Neuropsychiatry, 2014 Vol. 19, No. 3, 241–256, http://dx.doi.org/10.1080/13546805.2013.854198

Jumping to delusions in early psychosis Robyn Langdona, Megan Stillb, Michael H. Connorsa*, Philip B. Wardc and Stanley V. Cattsd


a

ARC Centre of Excellence in Cognition and its Disorders and Department of Cognitive Science, Macquarie University, NSW 2109, Australia; bRehabilitation Services, Sydney South West Area Health Service, Sydney, Australia; cSchool of Psychiatry, University of New South Wales, NSW 2052, Australia; dDepartment of Psychiatry, University of Queensland, St Lucia, QLD 4072, Australia (Received 24 December 2012; accepted 1 October 2013) Introduction. Patients with delusions typically seek less information when making decisions than controls (“jumping-to-conclusions”, JTC) and paradoxically over-adjust to counter-evidence on probabilistic reasoning tasks. Previous studies have examined JTC bias across the delusion-prone continuum, but have not considered the cooccurrence of both biases at early stages of psychosis. This was our aim. Method. Twenty-three early psychosis patients and 19 healthy controls completed two versions of the probabilistic reasoning task: a “draws-to-decision” version (to assess JTC) and a “graded-estimates” version (to assess over-adjustment). Both versions have been used previously with clinically delusional people with schizophrenia. IQ, memory and executive function were also examined. Results. Patients took fewer trials to reach a decision in the draws-to-decision version and showed greater over-adjustment to counter-evidence in the graded-estimates version than controls. Across groups, those who jumped to conclusions showed greater over-adjustment. Poor executive function predicted more extreme biases in controls but not in patients. Task performances were unrelated to memory. Similar results were evident in patient and control subgroups matched on IQ, and years of formal education. Conclusions. A jumping-to-conclusions bias and an over-adjustment bias co-occurred in the early psychosis patients. Implications are discussed concerning the role of such biases in delusion-proneness. Keywords: early psychosis; first episode psychosis; jumping-to-conclusions bias; probabilistic reasoning; schizophrenia

A large body of research has found that many patients with delusions perform differently on probabilistic reasoning tasks compared to non-deluded controls (for reviews, see Fine, Gardner, Craigie, & Gold, 2007; Freeman, 2007; Garety & Freeman, 1999). In a frequently used paradigm known as the “beads task”, the researcher shows participants two jars containing complementary ratios of differently coloured beads. Each jar contains more of one colour than the other. The researcher then reveals a sequence of beads drawn from one of the jars (this can be done verbally or on a computer screen). Participants are asked to select the jar that they believe the beads were most likely drawn from. Patients with delusions typically reach a decision more quickly than controls and many patients

*Corresponding author. Email: [email protected] © 2013 Taylor & Francis


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decide only after one or two beads (Fine et al., 2007; Freeman, 2007; Garety & Freeman, 1999). This is known as a “jumping-to-conclusions” (JTC) bias, a tendency to reach a decision on the basis of very little information. This bias towards hasty decisions may contribute to the formation of delusions in some patients. It is possible, for example, that the JTC bias could lead to the premature acceptance of delusional beliefs (Garety & Freeman, 1999). The JTC bias, however, may also reflect a more general susceptibility to generate delusional beliefs. In support of this view, studies have found the JTC bias on “draws-to-decisions” variants of the beads task in various non-clinical samples. These non-clinical samples include helpseeking young people who are clinically assessed as being at high risk of psychosis (Broome et al., 2007), non-clinical people who score highly on psychometric self-report measures of delusion proneness (Colbert & Peters, 2002; McKay, Langdon, & Coltheart, 2006) and people who are first-degree relatives of schizophrenia patients (Van Dael et al., 2006). As a result, many cognitive interventions for delusions and psychosis focus on reducing the JTC bias (Garety et al., 2011; Moritz & Woodward, 2007). In addition to making hasty decisions, patients with delusions may also show a related tendency to overly revise their confidence in response to contrary evidence – that is, to “over-adjust” and discard the current decision to jump to a new conclusion. This latter tendency can be measured in a “graded-estimates” variation of the beads task in which participants are told (or read) a sequence of 20 or so beads and, after each bead, rate their confidence in which jar the beads are drawn from. In some studies, patients with delusions show a much larger readjustment of their confidence ratings in response to contradictory evidence than healthy controls (Garety, Hemsley, & Wessely, 1991; Langdon, Ward, & Coltheart, 2010; Moritz & Woodward, 2005; Peters & Garety, 2006). However, not all paradigms that are used to investigate over-adjustment have replicated this effect (see Fine et al., 2007, for a review). The co-occurrence of the JTC bias and a tendency to over-adjust to new evidence in delusional people appear perplexing because the latter seems inconsistent with the incorrigibility and resistance to counter-evidence that is typically associated with delusions. Nevertheless, this cooccurrence, if replicated across the delusion-proneness spectrum, has important theoretical implications concerning the underlying mechanism(s) that contribute to these biases in delusion-prone people. Different theories of the JTC bias offer different predictions about whether people who are delusion-prone should over-adjust to new evidence. According to an influential account, patients with delusions experience current stimuli with abnormally high salience (Kapur, 2003), leading them to place inappropriate weight on the current bead colour in decision-making tasks (Menon, Pomarol-Clotet, McKenna, & McCarthy, 2006). On this view, patients should show both the JTC bias and a tendency to over-adjust to new evidence. According to other accounts, however, patients with delusions have problems understanding sequential information (Young & Bentall, 1995) or have a high need for closure (Colbert & Peters, 2002), leading them to make hasty decisions. These latter theories seem to predict that delusional patients should show the JTC bias but not overadjust to new evidence (Fine et al., 2007). Despite the theoretical accounts that predict the co-occurrence of JTC and overadjustment biases, some authors question the evidence for over-adjustment. These authors instead argue that the apparent over-adjustment merely reflects patients’ miscomprehension of the graded-estimates task instructions (Moritz & Woodward, 2005; Moritz et al.,


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2010). These authors note findings from other paradigms (i.e., not probabilistic reasoning tasks) which show evidence of a bias against readjusting hypotheses in response to sequential information that contradicts the original belief – a bias against disconfirming evidence (BADE) – in delusional and delusion-prone people. One such paradigm involves presenting participants with story vignettes that suggest a particular interpretation and then presenting sequential evidence that ought to lead to belief revision (e.g., Moritz & Woodward, 2005). Delusional and delusion-prone people have been found to be less inclined to revise their initial beliefs in this BADE task (Moritz et al., 2010; Woodward, Moritz, Cuttler, & Whitman, 2006). Additional support for the miscomprehension account includes the findings that additional instructions designed to improve task comprehension in a graded-estimates variation of the beads task reduces the overadjustment in patients (Balzan, Delfabbro, Galletly, & Woodward, 2012b). Non-clinical participants with a greater comprehension of the task have also shown less overadjustment than participants with a lesser comprehension of the task (Balzan, Delfabbro, & Galletly, 2012a). An important issue, however, is that changing task instructions fundamentally alters the nature of a task. Providing explicit directions about how to perform a task inevitably reduces any evidence of participants’ spontaneous responses to the original task instructions. With this concern in mind, we adopted the alternate approach of using the original format of graded-estimates task instructions to examine whether findings of overadjustment in response to these instructions co-occur with a JTC bias in delusion-prone people. Our reasoning was that the miscomprehension of task instructions has not been suggested to explain the JTC bias, only the over-adjustment bias. Hence, if the JTC bias and the over-adjustment bias co-occur and interrelate, this would suggest that while miscomprehension may exacerbate over-adjustment, some other common underlying process or processes contribute to both the JTC bias and the over-adjustment bias in delusion-prone people. We focused on patients in the early stages of psychosis because of a gap in the literature concerning the co-occurrence of a JTC bias and an over-adjustment bias in this population. Whereas previous studies have found evidence of a JTC bias in patients in the early stages of psychosis (Broome et al., 2003; Dudley et al., 2011; Menon, Mizrahi, & Kapur, 2008; Ormrod et al., 2012; So, Freeman, & Garety, 2008) – though one study failed to do so (Colbert, Peters, & Garety, 2010) – no studies have investigated the tendency for patients in early psychosis to concurrently over-adjust to contrary evidence. In addition, focusing on this population allowed us to investigate the probabilistic reasoning biases before the cognitive and neuropsychological decline that is typically associated with chronic illness becomes more marked. It has been suggested, for example, that the poorer neuropsychological functioning that characterises patients with confirmed schizophrenia contributes to their JTC bias independent of any specific relation to delusion-proneness (see, e.g., Bentall et al., 2009; Lincoln, Ziegler, Mehl, & Rief, 2010). Consistent with this view, non-delusional individuals with prefrontal lesions also show a JTC bias relative to control groups (Lunt et al., 2012). In sum, our aims were to: (1) investigate the co-occurrence of a JTC bias and an overadjustment bias in patients at the early stages of psychosis compared to controls; (2) examine relations between biases and levels of delusion-proneness, as well as current ratings of symptoms in patients; and (3) investigate any contributing effects of executive functioning levels in patients and controls.


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Method Participants Twenty-three patients, who were in the first two years of their first treatment by mental health services at two early psychosis intervention programmes in New South Wales agreed to participate in research. Both sites had a relatively large proportion of patients from a migrant background who were excluded because of poor English-speaking abilities. Clinical participants were interviewed using the Diagnostic Interview for Psychosis (DIP; Castle et al., 2006) to confirm diagnosis. According to the ICD-10 criteria, 17 had a diagnosis of “Paranoid Schizophrenia”, four had a diagnosis of “Undifferentiated Schizophrenia”, one had a diagnosis of “Schizoaffective Bipolar Disorder” and one had a diagnosis of “Other Non-Organic Psychotic Disorder”. Consistent with the gender imbalance in young cohorts with a schizophrenia-like psychosis (Falkenburg & Tracy, 2012; Ochoa, Usall, Cobo, Labad, & Kulkarni, 2012; Salem & Kring, 1998), 22 of the patients were male, with only one female patient agreeing to take part in the research. Nineteen healthy controls (17 males, 2 females) were recruited from the general community to match the patient group on mean age and gender ratio, and were screened using the affective, psychotic and substance abuse screening modules from the Structured Clinical Interview for DSM-IV Axis 1 Disorders (SCID-I; First, Spitzer, Gibbon, & Williams, 1996). Exclusion criteria for both groups included epilepsy, organic brain disorders and substance dependence. All participants spoke good English and gave written informed consent. Demographic features of both groups are summarised in Table 1, and the clinical features of patients are summarised in Table 2. Materials and procedure Probabilistic reasoning Two versions of the beads task, identical to those used in a previous study of JTC and over-adjustment in clinical delusion-prone people with schizophrenia (Langdon et al., 2010), were used: “draws-to-decision” and “graded-estimates”. As previous research has found that the draws-to-decision task most reliably differentiates between delusional and non-delusional samples (Fine et al., 2007; Garety & Freeman, 1999), this task always came first. For the draws-to-decision task, the experimenter showed participants two jars of red and green beads in complementary ratios (85:15). Participants were told that before they arrived one jar had been selected and a sequence of beads drawn from that single jar. Table 1. Basic demographics and delusion-proneness ratings of patients and controls.

Males: females Age (years) Formal education (years) Delusion-proneness (PDI)a

Patients

Healthy controls

Significance Test

22:1 20.91 ± 1.83 (18–25) 11.43 ± 2.02 (8–18) 9.14 ± 5.04

17:2 20.79 ± 1.81 (17–24) 12.82 ± 1.94 (9–16) 7.21 ± 3.03

χ2(1) = .599 t(40) = .219

Note: Continuous data expressed as means ± SD (range in parentheses). a One patient refused to complete the PDI. *p < .05.

t(40) = 2.25* t(39) = 1.45


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Table 2. Clinical features of patients.


Mean ± SD (range) Age of illness onset (years) Duration of illness (weeks) SAPS positive symptoms Delusions Hallucinations Bizarre behaviour Formal thought disorder SANS negative symptoms Affective flattening Alogia Anhedonia Apathy Attention

19.91 50.74 1.25 2.13 1.52 .70 .65 2.18 2.43 1.87 2.87 2.65 1.09

± ± ± ± ± ± ± ± ± ± ± ± ±

1.95 (16–24) 29.50 (12–104) .94 (.00–3.75) 1.42 (0–5) 1.56 (0–4) 1.11 (0–3) .98 (0–3) .72 (.60–3.80) 1.24 (0–5) 1.14 (0–4) .87 (1–4) .89 (0–4) 1.28 (0–4)

Notes: Data expressed as means ± SD (range in parentheses); Positive and negative symptoms assessed using the Scales for the Assessment of Positive and Negative Symptoms of Schizophrenia (SAPS and SANS: Andreasen, 1983, 1984); The overall Positive and Negative ratings are the average of global ratings on the SAPS and SANS respectively (‘0’ = absent; ‘1’ = questionable; ‘2’ = mild; ‘3’ = moderate; ‘4’ = marked; ‘5’ = severe).

They were also told that after drawing each bead, the colour of the bead was written down, returned to the jar, and another bead drawn from the same jar. This process was then illustrated. The participants’ task was to identify which jar the beads had been drawn from. The experimenter then read out the sequence of beads, one at a time, until the participants reached a decision (for more details, see McKay et al., 2006, who also showed a JTC bias in delusion-prone individuals using this same presentation format). The number of beads taken before reaching a decision was recorded. Participants also rated their confidence in their decision on a 6-point Likert scale that ranged from 50% unsure to 100% certain. For the graded-estimates task, the experimenter showed participants another two jars, this time of blue and yellow beads. As mentioned earlier, participants were told that one jar had been selected earlier and a sequence of beads drawn from that one jar. This time, however, the participants rated how certain they were the beads came from one jar or the other after each bead of a sequence of 20 was read out. They did so using a series of 20 10-cm rating scales presented one under the other on a two-page response sheet. Each rating scale ranged from “100% sure jar A (mainly yellow)”, “75% sure jar A”, “50–50 chance A or B”, “75% sure jar B” to “100% sure jar B (mainly blue).” Participants were encouraged to write down the colour of the bead for each draw beside the relevant rating scale. The first 10 draws were consistent with the beads being drawn from jar B, whereas the next 10 draws were consistent with the beads being drawn from jar A. In accord with Langdon et al. (2010), who had used this same presentation format to show abnormalities on the graded-estimates version of beads task in delusion-prone people with schizophrenia, all ratings were later converted to scores ranging from 0 to 100 (indicating certainty in jar B) and analysed for ratings at draws 1, 10 (after the first 10 trials were consistent with jar B), and 20 (after the next 10 trials were consistent with jar A). Also in accord with Langdon et al. (2010), we calculated the “shift in certainty”: the average change in

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certainty ratings whenever the bead changed colour. This indexed the tendency of patients to over-adjust to contrary evidence. Three sequences of draws of beads were taken from Huq, Garety, and Hemsley (1988) and allocated in a counterbalanced manner to the draws-to-decision task, the first 10 trials of the graded-estimates task, and the second 10 trials of the graded-estimates task to produce three different versions of the tasks. Baseline cognition


IQ was estimated using the National Adult Reading Test (NART; Nelson & Willison, 1991) and memory span was assessed using the Digits Forward and Backward subtests from the Wechsler Adult Intelligence Scale-Revised (Wechsler, 1997). Executive function These same participants had also taken part in a study examining theory of mind and executive predictors of socio-occupational function in early psychosis. For the purposes of the present study, and given the small sample size, we used a composite executive score derived from summing the standardised z-scores of number of categories achieved on the Wisconsin Card Sort Test (WCST: Heaton, 1981), time taken on the colour-word interference condition of a bespoke Stroop Task (based on Golden, 1978), verbal fluency (Controlled Oral Word Association Test: Spreen & Strauss, 1998) and number of planning moves on a computerised Tower of London task (see Langdon, Coltheart, Ward, & Catts, 2002). All scores were rescaled, as appropriate, such that higher scores indicated better performance. Interviews and questionnaires After completing the tasks, all participants completed the Peters et al. Delusions Inventory (PDI; Peters, Joseph, Day, & Garety, 2004). Participants responded “yes/ no” to 21 questions and the number of “yes” responses was used to index delusion-proneness (range 0–21). Patients were interviewed using the Scales for Assessment of Positive and Negative Symptoms of Schizophrenia (Andreasen, 1983, 1984) to rate symptom severity, and controls were interviewed using the screening modules. Results Non-parametric statistics are reported for the probabilistic reasoning measures (with the exception of the confidence ratings) since the distributions were skewed and transformations did not sufficiently reduce the skewness. Background variables and delusion proneness Patients and controls were well matched on age and gender ratio (see Table 1). Whereas the range of formal education was similar across groups, patients had fewer years of formal education on average than controls, t(40) = 2.25, p = .03. Patients showed current delusions of mild severity on average, with less severe ratings of the other positive symptoms. The levels of negative symptoms were also mild on average (see Table 2). Compared to controls, patients showed higher levels of delusion-proneness, as assessed using the PDI, though this difference did not reach statistical significance, t(39)


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= 1.45, p = .15 (also see Table 1). Patients also had significantly lower mean IQs, t(40) = 2.47, p = .02, poorer memory, t(40) = 2.67, p = .01, and worse executive functioning, t (40) = 24.78, p < .01, despite their illness being at a relatively early stage (see Table 3). Between-group differences on probabilistic reasoning tasks On the draws-to-decision task, patients significantly took fewer beads to reach a decision than controls, Mann–Whitney Z = 2.49, p = .01 (see Table 3). Whereas 16 (70%) patients reached a decision within two draws, only seven (37%) controls did so, χ2(1) = 4.50, p = .03. Although patients decided earlier than controls, a t-test also showed no difference in the confidence ratings of the patients’ and controls’ decisions, t(40) = .77, p = .44. On the graded-estimates task, two patients steadfastly refused to complete the procedure and so were excluded from the analysis (both patients exhibited high levels of paranoia). The remaining 21 patients showed a significantly greater shift in certainty in response to changes in evidence than controls, Mann–Whitney Z = 2.66, p = .01. The median shift in certainty for patients was 14.28, compared to 4.50 for controls. Figure 1 illustrates the responses of two patients and one control on the graded-estimates task. Patients also showed a significant difference in their ratings of confidence across draws 1, 10 and 20 than controls in an ANOVA, F(3, 36) = 3.98, p = .02. However, planned comparisons revealed that patients only differed from controls in their ratings on draw 1, t(38) = 3.49, p < .01, and not on draws 10 or 20 (ps > .45). Relationships between probabilistic reasoning tasks Across groups, participants who decided in two or less trials on the draws-to-decision task showed a significantly greater shift in certainty on the graded-estimates task (Median = 14.28) than participants who decided in three or more trials (Median = 4.50), Mann– Whitney Z = 2.90, p < .01. Within groups, there was a similar pattern of results in patients (Median = 14.28 for patients deciding in two or less trials versus Median = 9.42 for patients deciding in three or more trials). This difference did not reach statistical Table 3. Task results comparing patients to controls. Patients

Healthy controls

Draws-to-decision task Median number of draws Confidence

2 79.91 ± 15.63

4 83.16 ± 10.38

Graded-estimates task Draw 1 certainty Draw 10 certainty Draw 20 certainty Shift

73.10 85.71 52.00 21.84

57.21 90.42 52.89 7.62

Basic cognition IQ Memory (Digits total) Executive function Composite score

± ± ± ±

16.45 23.15 36.30 24.17

± ± ± ±

11.65 14.45 25.45 11.46

Mann–Whitney Z = 2.49* t(40) = .77 t(38) = 3.49* t(38) = .76 t(38) = .09 Mann–Whitney Z = 2.66*

96.65 ± 8.41 14.13 ± 2.75

103.42 ± 9.32 17.47 ± 5.19

t(40) = 2.47* t(40) = 2.67*

−1.48 ± 2.25

1.79 ± 2.17

t(40) = 4.78*

Note: Data (with exception of median number of draws) expressed as means ± SD. *p < .05.

R. Langdon et al.

Percentage confidence mainly blue jar


248 100

80

Patient SD

60

Patient AN Control

40

20

0 B B Y B B Y B B B B Y Y Y Y B Y Y Y Y Y

Figure 1. The conﬁdence ratings of two patients (one of whom, SD, was the most extreme responder and who had the highest rating of ‘5’ for delusions at the time of testing, as well as a PDI score of 15/21) and one control in the graded-estimates task. ‘B’ on the x-axis indicates that the draw was a blue bead, ‘Y’ that the draw was a yellow bead.

significance, Mann–Whitney Z = 1.42, p = .16, most likely because there were too few patients deciding in three or more draws (n = 6). Controls who decided in two or less trials on the draw-to-decision task, however, showed a significantly greater shift on the graded-estimates task (Median = 7.14) than the controls who took longer to decide (Median = 3.29), Mann–Whitney Z = 2.20, p = .03. In a similar way, across groups, there was a significant negative correlation between the number of draws taken on the draws-to-decision task and shift in certainty on the graded-estimates task, ρ(40) = −.41, p < .01. Within group, these correlations, although in the right direction, failed to reach significance in patients, ρ(21) = −.25, p = .28, and in controls, ρ(19) = −.32, p = .18. Overall, across participants, the between-group and the correlation results were consistent; participants who took fewer draws to reach a decision on the draws-to-decision task adjusted more to counter-evidence on the gradedestimates task. Influence of background variables on probabilistic reasoning To examine possible neuropsychological contributors to the JTC bias, we examined the influences of memory, education, IQ and executive function on the results of the probabilistic reasoning tasks. Given that these variables might confound the results and that we were using non-parametric techniques (i.e., analyses of covariance were not appropriate for this data), the data was reanalysed comparing subgroups matched on the respective variable where possible. Memory Memory (indexed by the Digits Total score) did not correlate with either number of beads taken on the draws-to-decision task or amount of shift on the graded-estimates task.


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Neither the results across participants (for draws-to-decision, ρ(42) = .12, p = .47; for shift-in-certainty, ρ(42) = −.29, p = .07), nor within groups, (see Table 4) were significant. Adopting a conservative approach, we also compared a subgroup of 21 patients and 15 controls matched on memory (patients’ M = 14.57 ± 2.44; controls’ M = 15.60 ± 4.07), t(34) = .95, p = .35). Results were unchanged: Patients in this subgroup took significantly fewer beads (Median = 2.00) to reach a decision in the draws-to-decision task than controls in this subgroup (Median = 3.00), Mann–Whitney Z = 2.12, p = .03. Patients in this subgroup also showed significantly greater shift in the graded-estimates task (Median = 14.28) than controls in this subgroup (Median = 4.50), Mann–Whitney Z = 2.52, p = .01. Education Results for years of education were more suggestive of a potential confound. Across participants, there was a significant moderate correlation between years of education and number of draws taken on the draws-to-decision task, ρ(42) = .48, p < .01. This correlation was also present within patients, albeit not within controls (see Table 4). There was no correlation between years of education and amount of shift on the gradedestimates task either across participants, ρ(40) = −.22, p = .18, or within groups (see Table 4). The data were reanalysed comparing a subgroup of 21 patients matched to 17 controls on years of formal education (patients’ M = 11.43 ± 2.02; controls’ M = 12.50 ± 1.79, t(38) = 1.73, p = .09). Results for the draws-to-decision task were again similar, with patients taking significantly fewer beads (Median = 2.00) to reach a decision than controls (Median = 3.00), Mann–Whitney Z = 2.11, p = .04. Patients in this subgroup also showed significantly greater shift in the graded-estimates task (Median = 14.28) than controls in this subgroup (Median = 4.50), Mann–Whitney Z = 2.46, p = .01. IQ IQ was moderately correlated with both number of draws taken to make a decision, ρ(42) = .37, p = .02, and the amount of shift, ρ(40) = −.31, p = .05, across participants. Within patients and within controls, these latter correlations were not significant, however (see Table 4). This suggests that the apparent relations with IQ found across groups were Table 4. Intercorrelations between draws to decision and shift in certainty with IQ, education, memory and executive function within patients and within controls. Patients IQ Draws-to-decision Shift-in-certainty

.10 −.21

Education

Memory

Executive Function

−.24 −.26

.23 .09

Education

Memory

Executive function

.29 −.36

−.04 −.21

−.55* −.43

.44* .06

Controls IQ Draws-to-decision Shift-in-certainty *p < .05.

.24 −.25

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a reflection of between-group differences, rather than significant relations between these variables within the groups. We nevertheless adopted a conservative approach and similarly compared a subgroup of 21 patients and 17 controls matched on IQ (patients’ M = 97.86 ± 7.76; controls’ M = 101.59 ± 7.96), t(36) = 1.46, p = .15). Results again remained largely the same. Patients in this subgroup took significantly fewer beads (Median = 2.00) to reach a decision in the draws-to-decision task than controls in this subgroup (Median = 4.00), Mann–Whitney Z = 2.40, p = .02. Patients in this subgroup also showed significantly greater shift in the graded-estimates task (Median = 14.28) than controls in this subgroup (Median = 4.50), Mann–Whitney Z = 2.67, p < .01.


Executive dysfunction Across groups, the composite measure of executive function correlated significantly with the number of draws to reach a decision, ρ(42) = .57, p < .01, and shift in certainty, ρ(42) = −.34, p = .03. The across-group correlations, however, were driven by the overall differences between groups in executive function (see Table 3). The correlations were not present within the patients (see Table 4). Large variability was evident within the patients. To illustrate, one patient who gave extreme responses – patient SD – took two draws to reach a decision, and yet had an above-average composite executive function (CEF) score (CEF = .40). In contrast, another patient – patient RM – took four draws to reach a decision (the median score for controls), despite having a below average CEF score (CEF = −.93). Given the extent of executive dysfunction in the patients, it was not possible to reanalyse subgroups matched on executive function. Influence of delusion-proneness on probabilistic reasoning Across groups, there was also a significant correlation between levels of delusionproneness and number of beads taken on the draws-to-decision task, ρ(41) = −.440, p < .01; those with higher levels of delusion-proneness “jumped to conclusions” quicker. This correlation was also present in patients, ρ(22) = −.48, p = .03, with a consistent, albeit non-significant, trend in controls, ρ(19) = −.41, p = .08. There was no correlation, however, between delusion-proneness and shift on the graded-estimates task either across all participants or within groups (all ps > .46). Current ratings of symptom severity, including ratings of current delusions, did not correlate with either the number of beads taken on the draws-to-decision task or the measure of shift on the graded-estimates task (all ps > .08). Thus, delusion-proneness appeared to be a stronger predictor of JTC in this sample than current severity of delusions.

Discussion Overview Patients in the early stages of psychosis showed both a JTC bias and a tendency to overadjust to new evidence. These features have been previously seen in studies of delusional patients at chronic stages of psychotic illness and in non-clinical delusion-prone individuals. The findings also suggest that these two types of responding bias relate to each other: Across groups, participants who showed the JTC bias also showed much greater readjustment to new evidence than participants who did not. In addition, the JTC bias, but not the over-adjustment bias, correlated significantly with levels of delusion-


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proneness but not with ratings of current symptom severity. This is consistent with the view that these biases may contribute to a vulnerability to develop delusions.


Performance on probabilistic reasoning tasks In the draws-to-decision task, the early psychosis patients showed a tendency to make hasty decisions. This finding is consistent with previous research which has shown that the JTC bias is present in patients in the early stages of psychosis (Broome et al., 2003; Dudley et al., 2011; Menon et al., 2008; Ormrod et al., 2012; So et al., 2008), as well as patients with chronic psychosis (Garety & Freeman, 1999) and non-clinical people at high risk of psychosis (Broome et al., 2007). The fact that the JTC bias is present across all stages of psychosis-proneness and clinical psychotic illness suggests that the JTC bias may be related to a general predisposition towards delusions and that it may even play a causal role in the formation of delusions in some patients. In the graded-estimates task, early psychosis patients also showed a tendency to overly adjust to contrary evidence. Indeed, patients in this study showed similar, though slightly lower, levels of over-adjustment to patients with chronic psychosis (Langdon et al., 2010). This finding is consistent with other research which has shown that patients with delusions over-adjust to evidence (Garety et al., 1991; Moritz & Woodward, 2005; Peters & Garety, 2006), although other studies have not always found this effect (Fine et al., 2007). Interestingly, in the graded-estimates task, there was no difference between patients and controls at draws 10 and 20. This is significant because the first 10 draws provided evidence for jar B, whereas the second 10 draws provided evidence for jar A. The fact that the 21 patients who completed the task performed similarly to controls at the end of each of these sequences of 10 draws suggests that they understood the task (with the possible exception of the two patients who refused to complete the task) and did not think that each bead in the sequence was coming from a different jar, as some authors have proposed might be the case in some patients (Balzan et al., 2012a, 2012b; Moritz & Woodward, 2005). It is worth discussing the extreme responses of patient SD (shown in Figure 1) with regard to the issue of task comprehension. We are of the view that there is no independent evidence to suggest that his responses reflected misunderstanding the task instructions. SD had no difficulty understanding any of the other task instructions and indeed displayed above average executive function. Although his responses to the gradedestimates task instructions might appear incomprehensible – at least to non-clinical populations – the phenomenon under investigation – delusions – can appear similarly incomprehensible. Of note, SD had the highest delusion-proneness score and was very delusional at the time of testing. As such, we take the view that extreme responding to the graded-estimates task instructions is not an a priori indication of a patient’s misunderstanding, but, instead, may be theoretically informative of the underlying mechanisms that contribute to delusion-proneness. The finding that patients performed similarly on draws 10 and 20 when compared to controls also suggests that patients did not have a BADE on this particular task. There is evidence that this bias exists in delusional and delusion-prone individuals on other tasks that do not specifically examine probabilistic reasoning (Moritz et al., 2010; Woodward et al., 2006). In the current study, however, patients and controls reached similar conclusions after draw 10 and then again after the pattern of beads reversed in the second set of 10 draws. This latter finding is consistent with some previous research which has


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failed to show a difference between patients and controls after the pattern of beads is reversed during this version of beads task (e.g., Dudley, John, Young, & Over, 1997). Nevertheless, despite providing similar ratings at the end of each set of 10 draws on the graded-estimates task, patients over-adjusted their rating on each trial whenever the current colour of bead was counter to the preceding trial. This pattern instead suggests that patients who are delusion-prone have a tendency to be overly swayed by the content of their immediate experience. Our findings also suggest that this tendency is unlikely to be due to poor neuropsychological function alone. Although patients displayed lower scores on tests of memory, education, IQ and executive function than controls, these variables were not related to the JTC bias or tendency towards over-adjustment in patients. This is consistent with other research which indicates that the JTC is not merely an epiphenomenon of cognitive deficits that are usually present in psychosis (for a review, see Fine et al., 2007). Of note, the version of the graded-estimates task we used allowed participants to see a record of their previous responses and there was no correlation between memory and probabilistic reasoning. These findings are consistent with previous evidence that the use of memory aids does not eliminate the JTC bias in deluded patients (see, e.g., Dudley et al., 1997). In addition, the findings are consistent with other research which has demonstrated that the JTC bias in early psychosis is unrelated to working memory (Ormrod et al., 2012). Although there was no evidence in the current study that neuropsychological deficits caused abnormal probabilistic reasoning in patients, we acknowledge that these deficits could contribute to the JTC bias in some cases. Relationships between task scores Importantly, our findings suggest some relationship between hasty decisions in the drawsto-decision task and a tendency to over-adjust to immediate conflicting evidence in the graded-estimates task. This relationship is consistent with a hypersalience account of the JTC bias in which immediate evidence is said to be weighted with greater significance than it should (Menon et al., 2006). According to this account, as a result of this hypersalience experienced by people with a vulnerability to delusions, immediate experience holds more sway than regularities of prior experience, consistent with the earlier views of Hemsley (1993) and Gray (1998). Our findings are not consistent with other theories concerning, for example, the role of need for closure, which do not predict the co-occurrence of over-adjustment. Nevertheless, there was considerable variability in the patient group, and many patients who made hasty decisions in the draws-to-decision task did not show high levels of shift in the graded-estimates task. Indeed, the associations between JTC and over-adjustment were not statistically significant within patients alone, suggesting the possibility of different contributors to hasty decisionmaking in those who develop a frank psychotic illness. The lack of correlation between JTC and over-adjustment in this study’s clinical group and the previous mixed findings concerning the co-occurrence of JTC and overadjustment requires explanation. One possibility is that a common mechanism may indeed underpin both effects, but the measures used to detect these different effects may tap different underlying processes and have different levels of sensitivity. Tasks measuring how participants respond to new evidence that contradicts their original confidence estimates inevitably require participants to process and integrate more information than tasks measuring how quickly participants first arrive at a decision. As



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a result, different task demands may influence individual participants’ decision-making style on each version of the beads task. For example, although delusional patients may jump to conclusions after one or two beads in the draws-to-decision version, when presented with a longer sequence of beads in the graded-estimates version, some of these patients may be more overwhelmed by the task demands of the latter version (e.g., because they are less confident in their memory of the previous run of beads or are less able to integrate information). They may thus adopt a strategy of relying more on their previous response on the rating scale in front of them, thus making the over-adjustment effect more difficult to detect in these individuals. It should also be noted that previous research has used a number of different measures of over-adjustment. These measures include, for example, participants’ change in confidence in response to the first bead that goes against the sequence (Garety et al., 1991; Peters & Garety, 2006), participants’ change in confidence to each bead in the second set of 10 beads that suggest the beads are now coming from a different jar (Moritz & Woodward, 2005), and the number of beads in the second set of 10 beads until participants change their decision (Dudley et al., 1997). We focused on the average shift across all 20 trials for which there was disconfirming evidence (relative to the preceding trial) since this was the measure used by Langdon et al. (2010) who had also used the same variant of graded-estimates procedure that was used in the present study. We also suggest that this measure may better represent the nature of immediate over-adjustment that one sees with the graded-estimates procedure of the beads task (refer again to Figure 1). Conclusion Overall, findings indicate that both a tendency to make premature decisions and to overadjust in accord with immediate experience is present in early psychosis. Such findings are consistent with the hypersalience account of delusion formation (Kapur, 2003), Hemsley’s (1993) early ideas concerning the failure of past experience to influence immediate perception in acute psychosis, and the related suggestion that delusion-prone people find it difficult to sustain a “maybe it’s true/maybe it’s not” mental working space (e.g., in order to critically evaluate immediate evidence relative to past beliefs on the beads task; Langdon, 2011). The findings also support early psychosis interventions that focus on treating data-gathering biases (Garety et al., 2011; Moritz & Woodward, 2007). Such interventions could target the need for balance, so as to not be overly swayed by the content of immediate experience whilst also avoiding the tendency to make hasty decisions, the latter of which has been the primary focus of current interventions. Future longitudinal research could also examine whether such early interventions reduce reasoning biases and protect against relapse into psychosis and severity of delusions at later stages of illness. References Andreasen, N. C. (1983). Scale for the assessment of negative symptoms (SANS). Iowa City: University of Iowa. Andreasen, N. C. (1984). Scale for the assessment of positive symptoms (SAPS). Iowa City: University of Iowa. Balzan, R., Delfabbro, P., & Galletly, C. (2012a). Delusion-proneness or miscomprehension? A reexamination of the jumping-to-conclusions bias. Australian Journal of Psychology, 64, 100–107. doi:10.1111/j.1742-9536.2011.00032.x


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Jumping to perceptions and to conclusions: specificity to hallucinations and delusions.

Jumping to the wrong conclusions? An investigation of the mechanisms of reasoning errors in delusions.

Jumping to conclusions and the persistence of delusional beliefs in first episode psychosis.

Neuropsychological functioning and jumping to conclusions in recent onset psychosis patients.

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

Cognitive factors maintaining persecutory delusions in psychosis: the contribution of depression.

Early intervention in psychosis: much done, much more to do.

Impaired metabolic reactivity to oxidative stress in early psychosis patients.

Transient Delusions Due to Syphilis.

Jumping to conclusions in schizophrenia.

Dimensions of Delusions and Attribution Biases along the Continuum of Psychosis.

Delusions in first-episode psychosis: Principal component analysis of twelve types of delusions and demographic and clinical correlates of resulting domains.

Metacognition-augmented cognitive remediation training reduces jumping to conclusions and overconfidence but not neurocognitive deficits in psychosis.

Early psychological interventions for psychosis.

Early intervention in psychosis: Insights from Korea.

Early psychosis in Asia: Insights from Japan.

Early intervention in psychosis: obvious, effective, overdue.

Facial emotion identification in early-onset psychosis.

Jumping to conclusions style along the continuum of delusions: delusion-prone individuals are not hastier in decision making than healthy individuals.

Review of international early psychosis programmes and a model to overcome unique challenges to the treatment of early psychosis in the United States.

Grey matter morphological anomalies in the caudate head in first-episode psychosis patients with delusions of reference.

Gender differences in patients with first-episode psychosis in the Singapore Early Psychosis Intervention Programme.

Insane Delusions.

The early natural history of childhood psychosis.