Examining reasoning biases in schizophrenia using a modified “Jumping to Conclusions” probabilistic reasoning task.

Accepted Manuscript

Examining reasoning biases in schizophrenia using a modified “Jumping to Conclusions” probabilistic reasoning task. Hans S. Klein , Amy E. Pinkham PII: DOI: Reference:

S0165-1781(18)30339-1 https://doi.org/10.1016/j.psychres.2018.09.020 PSY 11734

To appear in:

Psychiatry Research

Received date: Revised date: Accepted date:

22 February 2018 15 June 2018 12 September 2018

Please cite this article as: Hans S. Klein , Amy E. Pinkham , Examining reasoning biases in schizophrenia using a modified “Jumping to Conclusions” probabilistic reasoning task., Psychiatry Research (2018), doi: https://doi.org/10.1016/j.psychres.2018.09.020

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Highlights:  Replicated previous findings of a “Jumping to conclusion” (JTC) bias in schizophrenia  Faulty assessment of probabilities (or task misunderstanding) accounts for the JTC bias  Results indicate JTC response may not represent inherent bias, but cognitive limitation  Emphasizes consideration of cognitive ability when interpreting JTC bias findings

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Running Title: Examining reasoning biases using a modified JTC task

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Abstract word count: 194 Main text word count: 4860 Number of tables: 5 Number of figures: 0

Examining reasoning biases in schizophrenia using a modified “Jumping to Conclusions” probabilistic reasoning task.

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Authors: Hans S. Klein, MS. a*, Amy E. Pinkham, Ph.D. a,b

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Department of Psychiatry, University of Texas Southwestern Medical School, Dallas, TX, USA

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School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, USA

*Correspondence should be addressed to Hans Klein, School of Behavioral and Brain Sciences, The University of Texas at Dallas, 800 West Campbell Road, GR 41, Richardson, TX 75080. Phone: (972) 883-4704, E-mail: [email protected]

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Examining reasoning biases in schizophrenia using a modified “Jumping to Conclusions” probabilistic reasoning task.

Abstract

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Although the Jumping To Conclusion (JTC) bias has been extensively studied in relation to schizophrenia and persecutory delusions, the relationship between JTC and other reasoning biases implicated in delusional ideation is not fully understood. We modified the traditional JTC task to assess co-occurrence of reasoning biases in decision making. Forty-six patients with

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schizophrenia and 46 healthy controls completed two versions [neutral colored beads and salient comments] of the modified task. We replicated previous findings indicating that patients showed a greater JTC bias, and in both groups, the JTC bias was more pronounced for the salient task.

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However, we observed a significant effect for non-Bayesian judgments, indicating that patients showed greater difficulty in probabilistic reasoning. When controlling for probabilistic reasoning

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ability, the observed JTC bias effects were diminished. Our findings that faulty probability assessment accounts for the JTC bias indicates that the traditional JTC bias task may not

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represent an inherent hasty decision-making bias, but rather an inability to fully understand and

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execute the stated goals of the task. These results call into question the current understanding of

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the JTC bias and the independence of this bias apart from the cognitive demands of the task.

Keywords: delusion formation, delusional maintenance, persecutory delusions, evidence integration

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1. Introduction Recent efforts to reduce persecutory delusions in individuals with schizophrenia have indicated a central role for targeting cognitive biases and aberrant reasoning (Moritz, Andreou et al., 2014; Sanford et al., 2013) beyond just neurochemical irregularities (Kapur, 2003). However,

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the process by which these biases contribute to delusional ideation remains unclear. These

reasoning biases can be broadly theorized as playing a role in either delusion formation, delusion maintenance, or possibly, both.

In regard to delusion formation, the most robustly examined cognitive bias is the Jumping

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to Conclusions (JTC) bias. The JTC bias is a deficient search strategy that results in an

individual‟s early termination of evidence gathering and hasty decision making (Dudley et al., 1997). It is thought that this bias contributes to delusion formation by early acceptance of

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unlikely or wholly illogical explanations for innocuous events. Meta-analyses indicate that between 50 – 70% of clinical individuals with persecutory delusions (McLean et al., 2016; Fine

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et al., 2007; Freeman et al., 2008; Startup et al, 2008) and 10 – 20% of the nonclinical population demonstrate this bias (Freeman, 2007; Freeman et al., 2008; Garety et al., 2007; Startup et al.,

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2008; Ross et al., 2015).

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Though recent tasks have been developed as alternate forms for measuring the JTC bias (e.g. the BADE paradigm, Veckenstedt et al., 2011; the box paradigm, Moritz et al., 2017) the

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JTC bias is often operationalized for research purposes as early termination of evidence gathering on the standard probabilistic reasoning, or JTC task. The task requires a subject to determine the source of a string of stimuli (e.g., colored beads in a jar in the “beads task”, or positive or negative words in a list in the “salience task1”) from one of two alternately proportioned sources (e.g., ratios of 60 red beads or positive words: 40 blue beads or negative

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words). Performance is assessed by counting the number of items viewed prior to a decision, referred to as draws to decision (DTD), and a subject is considered to have demonstrated an extreme JTC bias when a decision is made after two or fewer items are presented (Garety et al., 2005).

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Proposed mechanisms that account for the JTC bias range from faulty probability

assessments (Garety & Freeman, 1999) to insufficient cognitive resources and/or neurocognitive deficits (Kinderman, 2011). However, the ability for some individuals to mitigate the bias through effortful self-assessment (So & Kwok, 2015) suggests either an over-reliance on

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intuitive judgments or a metacognitive deficit may contribute to the bias (Ward & Garety, 2017). Two additional models that attempt to explain hasty decision making on the JTC task, and therefore the JTC bias, are the liberal acceptance of information based on a lowered

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decision-making threshold (Moritz et al., 2017; Moritz et al., 2008; Juarez-Ramos et al., 2014; Rubio et al., 2011) and the hypersalience of stimuli bias (Jolley et al. 2014; Speechley et al.,

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2010). Though not mutually exclusive, both biases attempt to explain why specific information may be appealing to individuals with delusions and therefore accepted more readily. The lowered

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threshold model argues that individuals with schizophrenia are “bad statisticians” (Moritz et al.,

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2012), and therefore more readily accept any potential outcome or solution as valid. This would manifest as hastier decision making on a forced decision task such as the JTC task. The

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“hypersalience of evidence-hypotheses matches” (Speechley et al., 2010) model highlights a tendency to over-value currently available evidence, neglecting previously accumulated evidence (Hemsley, 2005). In other words, the individual prioritizes the current piece of evidence over any prior evidence when making decisions, reducing deliberation and resulting in a hasty decision.

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While these biases may theoretically explain why a belief is adopted, they do not fully account for how beliefs are maintained over time. Three cognitive biases have been proposed to explain delusion maintenance: faulty evidence integration, belief inflexibility, and poor introspective assessment of knowledge.

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First, faulty evidence integration may be critical to delusion maintenance as it works to protect delusions from being disproven. Specifically, a bias against disconfirmatory evidence (BADE) (Woodward et al., 2006) could explain how beliefs may persist despite direct

contradictory evidence, particularly if individuals refuse to acknowledge the disconfirmatory

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evidence or to integrate it into their knowledge base. This bias may be a part of a more unified evidence integration bias rather than distinctly separate biases of evidence type (i.e., disconfirmatory vs. confirmatory; Eifler et al., 2014; McLean et al., 2016).

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Second, belief inflexibility, or the inability to consider that beliefs could be inaccurate, has been implicated in maintenance of delusions, potentially mediating the relationship of JTC

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and delusional conviction (Colbert et al., 2010; Garety et al., 2005). Through an inability to reflect upon alternate causes for an event (Ward et al., 2017), a delusional theory may be

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solidified and made impervious to change shortly after adoption.

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Third, the subject‟s ability to self-monitor during the decision-making process may also be important in assessing delusion maintenance. Introspective accuracy (IA) refers to the ability

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to assess self-performance in an objective way (Harvey & Pinkham, 2015) and overlaps with one‟s metacognitive ability to self-monitor knowledge and capability. IA is often assessed by examining how closely an individual‟s confidence in his/her decision making aligns with his/her true ability. Individuals should be less confident on hasty or incorrect decisions and more confident about decisions that are ultimately correct; however, individuals with schizophrenia

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often demonstrate overconfidence in incorrect responses on the JTC task (Moritz et al., 2016), as well as perceptual judgments (Moritz, Ramdani, et al., 2014) and memory tasks (Moritz et al., 2005; Moritz et al., 2006). This discrepancy between ability and confidence may have detrimental effects when assessing accuracy or probability of delusional beliefs.

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As reviewed above, a large number of cognitive and reasoning biases are implicated in delusional ideation. However, we do not yet know whether these biases co-occur or how they may interact with each other. The majority of previous research has focused on independently identifying and characterizing each of the biases, but as Freeman and colleagues (2008) note, it is

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unlikely that these biases work in isolation, and more likely that these biases co-occur to produce the complex delusional experiences observed across psychiatric disorders (Freeman & Garety, 2014; McLean et al., 2016). For example, a complex relationship between faulty evidence

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integration, belief inflexibility, and poor IA may be understood in light of the hypersalience of evidence model, as new information overweighs past accumulated evidence, and top down

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processes adjust to make sense of the belief system in light of new evidence. Difficulties in overriding hyper salient information may diminish the influence of accumulated evidence (Langdon

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et al., 2010), leading to inaccurate IA and stronger conviction for false beliefs.

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Similarly, we do not yet know whether some biases may be more strongly related to delusions than others. For example, the inability to consider that a belief is ill-conceived may

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strengthen delusional beliefs, acting independently of the other biases to reduce possibilities to self-assess and rectify false beliefs. The current study attempts to address how these biases may interact and if some are more

strongly related to delusions by examining the JTC bias in conjunction with other reasoning biases using a modified version of the JTC task. Our modifications provided variables addressing

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each of the biases believed to be involved in delusion formation and maintenance, namely the JTC bias, lowered decision threshold, hypersalience of evidence, belief inflexibility, evidence integration, introspective accuracy. We also assessed the number of non-Bayesian judgments made during the task, which may indicate faulty probabilistic reasoning and/or poor task

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understanding, as these types of errors in the traditional JTC task impact interpretation of observed results (Balzan et al., 2012; Moritz & Woodward, 2005)

Examining these new variables with both neutral and emotionally salient versions of this task, we hypothesized that patients with schizophrenia would simultaneously show several biases

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in conjunction with one another rather than just a single bias alone. Specifically, we predicted that patients would require less evidence when making a decision (JTC), would report higher initial confidence levels, and show inflexibility in confidence ratings across items. Additionally,

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we predicted that individuals with schizophrenia would match their decisions more often with the presented item (e.g., deciding red jar when red bead is displayed) due to hypersalience of

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evidence, display more non-Bayesian judgments, and have a higher rate of poor IA and evidence integration as compared to healthy individuals. These patterns were expected to be exaggerated

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in the salient task versus the beads task. We also hypothesized that these responses would be

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exaggerated in those individuals displaying “extreme” response bias (DTD ≤ 2). Finally, to demonstrate specificity of these biases to delusional ideation, we predicted that modified

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variables would be moderately to strongly related to clinician ratings of delusions. 2. Method 2.1.

Participants. The sample consisted of 47 stable, outpatient participants diagnosed

with schizophrenia or schizoaffective disorder and 46 healthy controls between the ages of 18 and 60, inclusive. One participant with schizophrenia was excluded from the analyses due to

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being an extreme outlier (+7 SD for confidence variability), resulting in 46 participants per group. A priori power analyses using G*Power 3.1 ( = .05 and power = .80, Faul et al., 2009) determined this an effective sample to replicate prior JTC comparisons between groups. Discussion on determining effect size for power analyses can be found in the supplemental

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materials. All participants were assessed as part of two ongoing research projects within the Schizophrenia and Social Cognition Lab at the University of Texas at Dallas, and only data from the initial study visit were utilized for participants who enrolled in both studies.

Psychiatric diagnoses for clinical participants were obtained via medical records when

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available and confirmed via the Mini International Neuropsychiatric Interview (MINI) (Sheehan et al., 1998) and Structured Clinical Interview for DSM Disorders - Psychosis Module (SCID-P) (First et al., 2002). Healthy controls were matched on age and race, although we did observe

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overall lower levels of educational attainment for our patient sample as compared to our healthy controls. All healthy controls were screened for current or history of psychiatric symptoms with

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SCID screener. Group comparisons can be viewed in Table 1, and additional inclusion and

[Table 1]

Clinical and Neuropsychological Assessments. Symptom severity was assessed

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2.2.

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exclusion criteria can be found in the supplemental materials.

using the Positive and Negative Syndrome Scale (PANSS, Kay et al., 1987) for clinical

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participants, and all participants completed the Wide Range Achievement Test – reading recognition subtest (Wilkinson, 1993) to estimate pre-morbid IQ. Additionally, all participants completed the MATRICS Consensus Cognitive Battery – Brief (MCCB-B) (Nuechterlein et al., 2008) to assess neurocognitive functioning and the Davos Assessment of the Cognitive Biases Scale (DACOBS), a 42 item self-report scale assessing a range of cognitive biases, specifically

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the jumping to conclusions bias and belief flexibility as alternate measures of our modified variables (Van der Gaag et al., 2013). 2.3.

Modified JTC Task. Participants completed two versions of the modified JTC

task, both of which allowed simultaneous evaluation of each previously discussed cognitive bias.

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The first version utilized neutral stimuli (blue and red colored beads), and the second used

emotionally salient stimuli (positive and negative words displayed on a screen). Both versions of the tasks used a 60 (i.e., blue and negative):40 (i.e. red and positive) ratio for stimuli, and order of task versions was counterbalanced between subjects. Stimuli were presented one at a time, and

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participants were instructed to determine the source from which stimuli were drawn (Jar A or Jar B; Survey 1 or Survey 2), similar to instructions from Huq, Garety, and Hemsley (1988). Subjects were instructed to only decide when certain, and modifications began after the number

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of items presented prior to decision, draw to decision (DTD), was recorded. After recording the participant‟s initial decision and confidence in correctness of the decision on a scale from 0 to

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100, the researcher continued to present each additional bead or word in the series, similar to Moritz et al. (2016), until all 20 items were displayed. Participants were allowed to adjust their

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decision and/or confidence-level based on the additional stimuli. A more detailed outline of the

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procedure for task administration is available in the supplemental materials. The modified variables, and their respective biases, that were calculated from each of the

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JTC tasks are listed in Table 2 and described below: a) Draw to decision (DTD). The number of stimuli shown prior to the first decision was used to index the JTC bias. This item was also dichotomized to create a subgroup of those individuals showing extreme responding (DTD ≤2, ≥3).

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b) Confidence at DTD. Initial confidence at DTD was used to index the lowered threshold, with low confidence ratings at DTD indicating a lowered subjective decision-making threshold. c) Confidence Variability. The variance for reported confidence levels during the task was

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used to assess belief inflexibility, with smaller variances indicating more belief inflexibility. d) Matched decisions. The proportion of total decisions made that matched the item presented was used to index the Hypersalience of evidence-match hypotheses bias.

e) Non-Bayesian judgments. The number of errored judgments made during the course of the

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task was calculated based on the formulas derived from Bayes‟ theorem (Speechley et al., 2010) to determine the probability of the presented items belonging to one jar, or word list, versus the other. Decisions were counted as non-Bayesian judgments when the probability of

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that choice being correct was less than or equal to 50%. Probability calculations are listed in the supplemental materials. Non-Bayesian judgments were used to assess faulty probabilistic

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reasoning.

f) Decision change. Whether the individual changed their response after initial DTD was

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used to index inappropriate evidence integration, with fewer changes indicating less evidence

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integration. Decision change was initially assessed on a continuum as the percentage of changed responses made over the course of the task, but due to a large number of individuals

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who did not change their decision throughout the task, this variable was dichotomized to represent those that either made a decision change and those that did not. g) Introspective accuracy. Poor introspective accuracy was operationalized as high confidence (> 50%) for an inaccurate decision at the end of the task. IA was dichotomized to

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represent those that made an inaccurate decision with high confidence at the end of the task, and those that did not. [Table 2] 3. Results Group comparisons. In order to assess our hypothesis that individuals with

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3.1

schizophrenia would show more exaggerated biases than healthy controls on each version of the task (neutral and salient), separate two-factor mixed ANOVAs were computed for DTD,

confidence at DTD, confidence variability, matched decisions, and non-Bayesian judgments.

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Due to violations of normality for confidence variability and non-Bayesian judgments, a log transformation was performed on each of these variables prior to running the two-factor mixed ANOVAs, and transformed variables were utilized for subsequent analyses. Statistics for

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normality of these variables before and after transformation are available in the supplemental materials.

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Main effects for task type (neutral and salient), F(1, 90) = 4.724, MSE = 12.927, p = .032, η2p = .050, and group (schizophrenia and healthy controls), F(1, 90) = 4.149, MSE = 45.321, p =

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.045, η2p = .044, were observed for DTD. DTD was lower for individuals with schizophrenia

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across both tasks, and DTD was lower for the salient task across groups. No significant interactions were observed for DTD or any of the modified variables.

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Confidence at DTD did not differ by group, F(1, 90) = .480, MSE = 819.580, p = .490, η2p = .005, or task type, F(1, 90) = .405, MSE = 177.278, p = .526, η2p = .004. Similarly, confidence variability did not differ by group, F(1,90) = 0.033, MSE = 6.495, p = .856, η2p < .001, although a significant main effect was observed for task type, F(1, 90) = .6.170, MSE = 2.849, p = .015, η2p = .064, with larger variance in reported confidence on the salient task.

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A significant main effect of group was observed for non-Bayesian judgments, F(1, 90) = 11.596, MSE = 1.569, p = .001, η2p = .114, with patients making more non-Bayesian judgments than healthy controls. No effect for task type was observed, F(1, 90) = .986, MSE = .287, p = .346, η2p = .010. Groups significantly differed on the proportion of matched decisions, F(1,90)

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=6.190, MSE = 0.043, p = 0.015, η2p = 0.064, indicating patient decisions matched the item

presented more often than healthy controls. No effect was seen for task type, F(1,90) = 0.084, MSE = .025, p = .773, η2p = .001. Means and standard deviations for each of these analyses can be found in Table 3.

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[Table 3]

To assess differential presentation of the evidence integration bias and poor introspective accuracy between groups (schizophrenia or healthy control) and task type (neutral or salient),

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individual multi-level logistic regression models were estimated for decision change and

repeated measure.

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introspective accuracy, with group acting as a between-subjects variable and task type as a

Results of the multi-level logistic regression indicated that individuals with schizophrenia

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were twice as likely as healthy controls to change response during the course of the task, OR =

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2.200, 95% CI [1.523, 3.179], p < .001. There was no significant effect of task on changed responses, OR = 1.181, 95% CI [0.898, 1.553], p = .233, and individuals were not significantly

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more likely to commit an IA error on the salience task compared to the beads task, OR = 1.181, 95% CI [0.663, 1.796], p = .728, nor was the clinical population more likely to commit an IA error, OR = 0.527, 95% CI [0.237, 1.174], p = 116. In order to assess if task understanding contributed to the noted task and group effects, all analyses were repeated with non-Bayesian judgments included as a covariate. Previously noted

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group effects were no longer significant for DTD, F(1,89) =0.169, MSE = 23.045, p = 0.682, η2p = 0.002. Group effect for matched decisions remained significant, F(1,89) =4.721, MSE = 0.044, p = 0.032, η2p = 0.050, as well as the odds ratio for changed responses, OR = 1.736, 95% CI [1.121, 2.688], p = .014. A significant task by group interaction was observed for IA after using

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non-Bayesian judgments as a covariate, OR =0.404, 95% CI [0.189, 0.865], p= 0.020, though due to the limited number of IA observations across the groups and tasks, this result is harder to interpret. 3.2

Clinical subgroups. To determine if the modified variables were more

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exaggerated in participants who expressed the JTC bias, patients were dichotomized into extreme responders (DTD ≤ 2, n=27), and non-extreme responders (DTD ≥ 3, n=19). We utilized data from the salience task to perform these analyses as the effect was stronger, with approximately

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60% of the clinical participants demonstrating an extreme response. Individual t-tests were performed for confidence at DTD, confidence variability, matched decisions, and non-Bayesian

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judgments. Fisher‟s exact test was used to assess the differential presentation of decision change and introspective accuracy between extreme and non-extreme responders.

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Results for group comparisons between extreme and non-extreme responders are listed in

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Table 4. When comparing extreme responders to non-extreme responders in the clinical group, extreme responders did not differ from non-extreme responders on confidence at DTD,

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confidence variability, nor were they more likely to commit an IA error. Furthermore, extreme responders did not make significantly more matched decisions. However, extreme responders made a significantly larger number of non-Bayesian judgments, t(44) = 7.661, p < .001, dUNB = 2.258, 95% CI [1.532, 4.046], and were more likely to change their responses after DTD, X2(1) = 21.595, Fisher’s Exact p < .001, Cramer’s V = .685, compared to non-extreme responders.

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[Table 4] 3.3

Reasoning biases symptom specificity. Finally, to assess the degree of the

relationship between these variables/biases, clinical symptoms, and neuropsychological deficits,

as the DACOBS as a validity check for our modified variables.

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Pearson‟s r was calculated for the modified variables, PANSS item scores, and MCCB, as well

When looking at associations between the modified variables in our clinical population, a strong correlation is noted between an earlier decision (DTD) and more non-Bayesian decisions throughout the task, r = -.708. A moderate negative correlation was also observed between

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earlier decisions and higher confidence variability, r = -.387, during the task. No significant correlations were observed between biases and symptom severity or medication within our sample, nor self-reports of cognitive biases. Moderate correlations were noted between higher

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pre-morbid IQ with later decisions, r = .335 and fewer non-Bayesian judgments, r = -.455, as well as higher working memory as measured by the letter number span and fewer non-Bayesian

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judgments, r = -.317. Correlations between biases, clinical symptoms, and neuropsychological deficits are listed in Table 5, and correlations between biases and medication can be found in the

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[Table 5]

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supplementary material.

Discussion

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This study examined the co-occurrence of reasoning biases in schizophrenia using a modified decision-making task. Although biases were not as pronounced as expected, the patient group did show a tendency to display the JTC bias as compared to healthy controls. The effect size for this difference was on par with previous studies (So et al., 2016; Dudley et al., 2016). However, we noted no substantial difference between groups on the initial level of confidence

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reported at time of decision, indicating that, although patients requested fewer items, there did not seem to be a lowered subjective threshold of certainty for making a decision. In line with our hypotheses, patients with schizophrenia, and extreme responders specifically, made significantly more non-Bayesian judgments and were more likely to change

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their responses after their initial decision, indicating faulty assessment of probabilities and poor evidence integration when making decisions. Clinical participants also tended to match decisions to the item presented more than healthy controls, indicating a potential reliance on the

hypersalience of evidence bias when performing this task. Finally, subjects did not differ on IA,

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though this may be a limitation of the task and is discussed in further detail below.

As the task assumes subjects will utilize probabilities to make a logical decision, this pattern of results indicates that clinical participants either have difficulty performing these

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probability assessments throughout the task, or do not fully understand the instructions when making decisions. These results are in line with previous research indicating miscomprehension

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of task instruction can lead to a similar “over-adjustment” in responding, confounding potential interpretation of JTC results (Balzan et al., 2012; Moritz & Woodward, 2005). Additionally,

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although previous research has noted a relationship between the JTC bias and intelligence

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(Garety et al., 1991; Lincoln et al., 2010; Ochoa et al., 2014) as well as faulty reasoning (Jolley et al., 2014), we utilized these reasoning errors on the task itself to account for potentially limited

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cognitive abilities, instead of an estimation of ability from IQ subtests or cognitive batteries that assess domains peripheral to this task. Significant correlations between non-Bayesian judgments and both pre-morbid IQ and working memory provides validation in using this item as an assessment of cognitive ability. When controlling for non-Bayesian judgments as a marker of poor probability assessment and/or task understanding, the observed JTC bias for patients

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relative to controls disappeared, indicating that cognitive capabilities may account for this phenomenon as demonstrated on this task. Furthermore, the JTC bias assumes that hasty decision making will lead to strongly held delusional beliefs; however, our clinical sample was nearly twice as likely to change their

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response compared to healthy controls even after controlling for non-Bayesian judgments. Paired with a tendency to match decisions, this indicates a unique response pattern inconsistent with task instruction. While this may be evidence for an inability to over-ride hyper-salient

information in line with the hypersalience of evidence-hypotheses matches (Speechley et al.,

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2010), this response pattern could also arise out of a misunderstanding of the task goal, i.e. to determine the single source for all beads or comments presented. Thus, the measurement of the JTC bias in the traditional JTC task may inaccurately characterize the conviction of a hasty

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decision.

Our data also seem to suggest a disconnect between reported levels of confidence and

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performance on the task, as clinical participants maintain similar levels of confidence across the task despite changing responses and making more non-Bayesian judgments. This disconnect may

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be due to poor introspective accuracy, although our measure of IA was unable to differentiate

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between the clinical and non-clinical groups as well as between extreme and non-extreme responders. This may be a potential limitation of our measure of introspective accuracy, as it is a

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single measure based upon the participant‟s final decision. Both the neutral and salient tasks concluded with a piece of evidence that is in line with the ultimately “correct” decision, which may have inadvertently primed individuals to still make the correct decision at the end of the task.

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In relating these biases to clinical presentation, our modified variables failed to correlate significantly with ratings of symptom severity or a self-report of cognitive biases. The relatively stable nature of our clinical population may have contributed to the lack of associations with symptoms; however, symptom ratings did show adequate variability, ranging from 1-6. Using a

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self-report measure of cognitive biases may also be somewhat problematic in that it requires accuracy in self-assessment, which, as indicated by the disconnect between confidence ratings and non-Bayesian judgments, individuals may be lacking, increasing the chance of underrating biases impacting cognitive processes.

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There are several limitations to consider in regard to this study. First, our modified

variables are attempts to measure previously identified biases within a single task. However, these variables may be inadvertently tapping into alternate constructs or inadequately measuring

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the intended bias, as there may be potential disconnects between patient reported confidence, a metacognitive measure, and the underlying cognitive biases. Although it is important to

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acknowledge this potential issue while interpreting these results for our measures of evidence integration, lowered threshold, belief inflexibility, and hypersalience of evidence, the impact of

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these biases on delusional ideation should be observable via multiple theory-based methods.

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Future research should continue to attempt replications of these biases in alternate tasks to insure a thorough understanding of how these biases may relate to various aspects of delusional

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ideation. Furthermore, the significant impact of cognitive abilities on the JTC bias in our clinical population is considerable and consistent with previous literature linking cognitive capabilities to performance on the traditional JTC task. Secondly, some of our variables may be interdependent due to the task design, as those who made an earlier decision would have more opportunity to make non-Bayesian judgments or

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to change their decision. As noted above, when controlling for non-Bayesian judgments, JTC loses its effect. This indicates a central role of cognitive capabilities to task performance, rather than a simple tendency to make decisions quickly. Additionally, consistent with our discussion of our IA variables above, the task design

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may illicit specific responses unintentionally. Notably, unlike previous administrations of the task, our neutral beads task started with a disconfirmatory item, i.e. the first bead was red when the correct jar is the mainly blue jar. This may have increased ambiguity, which has been shown to affect participants‟ response in the JTC task (Moritz, Woodward, & Lambert, 2007). We also

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noted significant effects for confidence variability between tasks, with individuals reporting a wider range of confidences for the salient task. This could be related to the fact that some items on the salient task do not clearly belong to the positive or negative groups. A few participants

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noted that words such as “alert” and “realistic” do not readily have a positive or negative connotation without a larger context. Therefore, these items may need further review to insure

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accurate performance on this version of the task. Finally, individual factors such as personal meaning assigned to presented items may have a mechanistic role in task performance (Moritz et

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al., 2017). Future research should consider not only the decision made, but how much the

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individual weighs each item and the richness of each item toward making their decision. This study utilized a modified probabilistic reasoning task in order to observe multiple

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reasoning biases in a single decision-making paradigm and to determine which ones may correlate most strongly with delusions. Although our study was unable to correlate any modified variables with delusional ideation, we were initially able to replicate previous findings of the JTC bias in a clinical sample. Despite the theory that the JTC bias leads to incorrigible delusional beliefs (Freeman, 2007), we noted a stronger likelihood to change response after making an

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initial decision. We also noted a unique disconnect between fluctuating decisions and reported confidence, indicating a dissociation between performance and self-assessment on this task. Furthermore, noting that the JTC bias was no longer observed after controlling for faulty probability assessment, we propose that an early decision as demonstrated on the traditional JTC

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bias task may not represent an inherent hasty decision-making bias, but rather an inability to fully understand and execute the stated goals of the task. These results therefore call into

question the nature of the JTC bias and underscore the need to determine whether this bias exists independently from cognitive difficulties. Future research using the traditional JTC tasks should

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also carefully consider the role of probability assessment and task understanding.

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Footnote 1.

Although this task is typically referred to as the salience task in as an attempt to make the task

more personally meaningful to the participant by using emotionally charged positive and negative words, the true salience of this task is yet to be confirmed. We thank an anonymous

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reviewer for noting this potential false association in the naming convention of this version of the

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task.

Funding: The research did not receive any specific grant from funding agencies in the public,

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commercial, or not-for-profit sectors.

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Colbert, S., Peters, E., & Garety, P. (2010). Delusions and belief flexibility in psychosis. Psychology and Psychotherapy: Theory, Research and Practice, 83, 45-57. Dudley, R., John, C., Young, A. & Over, D. (1997). Normal and abnormal reasoning in people with delusions. British Journal of Clinical Psychology, 36, 243-258. Dudley, R., Taylor, P., Wickman, S., & Hutton, P. (2016). Psychosis, delusions and the “jumping to conclusions” reasoning bias: A systematic review and meta-analysis. Schizophrenia Bulletin, 42(3), 652 – 665.

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Freeman, D. (2007). Suspicious minds: The psychology of persecutory delusions. Clinical Psychology Review, 27, 425-257. Freeman, D., & Garety, P. (2014) Advances in understanding and treating persecutory delusions: a review. Social Psychiatry and Psychiatric Epidemiology, 49(8), 1179 – 1189. Freeman, D., Pugh, K., & Garety, P. (2008). Jumping to conclusions and paranoid ideation in the general population. Schizophrenia Research, 102, 254 – 260.

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Garety, P.A., Bebbington, P., Fowler, D., Freeman, D., & Kuipers, E. (2007). Implications for neurobiological research of cognitive models of psychosis. Psychological Medicine, 37, 1377–1391. Garety, P. & Freeman, D. (1999). Cognitive approaches to delusions: A critical review of theories and evidence. British Journal of Clinical Psychology, 28 ,113 – 154.

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Juárez-Ramos, V., Rubio, J. L., Delpero, C., Mioni, G., Stablum, F., & Gómez-Milán, E. (2014). Jumping to conclusions bias, BADE and feedback sensitivity in schizophrenia and schizotypy. Consciousness and Cognition, 26, 133-144.

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Kapur, S. (2003). Psychosis as a state of aberrant salience: A framework linking biology, phenomenology, and pharmacology in schizophrenia. American Journal of Psychiatry, 160(1), 13 – 23. Kay, S., Fiszbein, A., & Opler, L. (1987). The Positive and Negative Syndrome Scale (PANSS) for Schizophrenia. Schizophrenia Bulletin, 13(2), 261-276. Kinderman, P. (2001). Changing causal attributions. In P. W. Corrigan, & D. L. Penn (Eds.), Social Cognition and Schizophrenia (pp. 195–211). Washington: American Psychological Association.

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Langdon, R., Ward, P., & Coltheart, M. (2010). Reasoning anomalies associated with delusions in schizophrenia. Schizophrenia Bulletin, 36(2), 321-330. Lincoln, T., Ziegler, M., Mehl, S., & Rief, W. (2010). The jumping to conclusions bias in delusions: Specificity and changeability. Journal of Abnormal Psychology, 119(1), 40 – 49.

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Moritz, S., Scheu, F., Andreou, C., Pfeuller, U., Weisbrod, M., & Roesch-Ely, D. (2016). Reasoning in psychosis: risky but not necessarily hasty. Cognitive Neuropsychiatry, 21(2), 91 – 106.

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Moritz, S., Ramdani, N., Klass, H., Andreou, C., Jungclaussen, D., Eifler, S.,…Zink, M. (2014). Overconfidence in incorrect perceptual judgments in patients with schizophrenia. Schizophrenia Research: Cognition, 1, 165–170.

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Moritz, S., & Woodward, T. (2005). Jumping to conclusions in delusional and non-delusional schizophrenic patients. British Journal of Clinical Psychology, 44, 193 – 207.

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Moritz, S., Woodward, T., & Lambert, M. (2007). Under what circumstances do patients with schizophrenia jump to conclusions? A liberal acceptance account. British Journal of Clinical Psychology, 46, 127 – 137. Moritz, S., Woodward, T., Jelinek, L, & Klinge, R. (2008). Memory and metamemory in schizophrenia: a liberal acceptance account of psychosis. Psychological Medicine, 38, 825 – 832.

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Moritz, S., Woodward, T., & Rodriguez-Raecke, R. (2006). Patients with schizophrenia do not produce more false memories than controls but are more confident in them. Psychological Medicine, 36, 659–667. Moritz, S., Woodward, T., Whitman, J., & Cuttler, C. (2005). Confidence in errors as a possible basis for delusions in schizophrenia. The Journal of Nervous and Mental Disease, 193 (1), 9 – 16.

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Nuechterlein, K., Green, M., Kern, R., Baade, L., Barch, D., Cohen, J… Marder, S. (2008). The MATRICS Consensus Cognitive Battery, Part 1: Test selection, reliability, and validity. The American Journal of Psychiatry, 165 (2):203–213.

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Ochoa, S., Haro, J. M., Huerta-Ramos, E., Cuevas-Esteban, J., Stephan-Otto, C., Usall, J.,… Brebion, G. (2014). Relation between jumping to conclusions and cognitive functioning in people with schizophrenia in contrast with healthy participants. Schizophrenia Research, 159, 211-217. Ross, R.M., McKay, R., Coltheart, M., & Langdon, R. (2015). Jumping to conclusions about the beads task? A meta-analysis of delusional ideation and data-gathering. Schizophrenia Bulletin, 41(5), 1183 – 1191.

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Rubio, J. L., Ruiz-Veguilla, M., Hernández, L., Barrigón, M. L., Salcedo, M. D., Moreno, J. M.,… Ferrín, M. (2011). Jumping to conclusions in psychosis: A faulty appraisal. Schizophrenia Research, 133, 199-204.

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So, S., & Kwok, N. (2015). Jumping to conclusions style along the continuum of delusions: delusion-prone individuals are not hastier in decision making than healthy individuals. PloS One, 10(3): e0121347. Doi: 10.1371/journal.pone.0121347. So, S., Siu, N., Qong, H., Chan, W., & Garety, P. (2016). „Jumping to conclusions‟ datagathering bias in psychosis and other psychiatric disorders – Two meta-analyses of comparisons between patients and healthy individuals. Clinical Psychology Review, 46, 151 – 167. Speechley, W. J., Whitman, J. C., Woodward, T. S. (2010). The contribution of hypersalience to the “jumping to conclusions” bias associated with delusions in schizophrenia. Journal of Psychiatry Neuroscience, 35(1), 7-17.

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Startup, H., Freeman, D., & Garety, P. (2008). Jumping to conclusions and persecutory delusions. European Psychiatry, 23, 457 – 459

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Ward, T., Peters, E., Jackson, M., Day, F., & Garety, P. (2017) Data-gathering, belief flexibility, and reasoning across the psychosis continuum. Schizophrenia Bulletin, doi:10.1092/schbul/sbx029.

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Wilkinson, G. S. (1993). WRAT3: Wide Range Achievement Test Administration Manual. Wide Range, Inc. 15 Ashley Place, Suite 1A. Wilmington, DE

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Table 1 Demographic information for clinical participants (SCZ) and non-clinical controls (HC). p

23 (50.00) 21 (45.65) 2 (4.35) 0 6 (13.04) 40 (86.96) 36.30 (8.13) 13.42 (1.37) 99.22(10.49) 26.10 (6.98) 54.98 (8.98) 16.11 (3.67) 23.20 (5.84) -

-0.013 2.296 2.510 -3.223 4.154 4.875 1.995

0.990 0.024 0.014 0.002 < .001 < .001 0.049

dUNB

95% CI

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Statistics t(df = 90)

-0.475 -0.519 0.667 -0.859 -1.007 -0.413

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Gender, Female(%) Race Caucasian (%) 21 (45.65) African American (%) 23 (50.00) Asian (%) 1 (2.17) American Indian 1 (2.17) Ethnicity Hispanic (%) 10 (21.74) Non-Hispanic (%) 36 (78.26) Age 36.33 (8.55) Years of Education 12.54 (2.21) WRAT 3 93.24(12.28) Trial Making Test 34.04 (15.17) BACS Symbol 46.37 (10.81) Letter Number 12.46 (3.52) Category Fluency: 20.87 (5.33) PANSS 5-Factor Pos 18.74 (6.72) PANSS 5-Factor Neg 13.11 (5.29) PANSS 5-Factor Dis 16.07 (4.63) PANSS 5-Factor Exc 14.22 (3.35) PANSS 5-Factor Emo 21.61 (7.35) PANSS delusion 9.83 (3.80)

HC Mean (SD) 21(45.65)

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[-0.892, -0.063] [-0.938, -0.107] [0.250, 1.091] [-1.292, -0.436] [-1.447, -0.578] [-0.829, -0.002]

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Note. D adjusted for bias (Hedges, 1981). PANSS five-factor calculated per van der Gaag et al. (2006). PANSS delusion is composite score of PANSS delusion ratings (P1, P5, & P6).

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Table 2 Modified task variables and the corresponding biases they are intended to measure. Associated Bias

Number of draws to decision (DTD) Initial confidence at DTD Proportion of matched decisions

   

Number of changed responses Confidence variability Number of “non-Bayesian” judgments Self-assessment of accuracy via confidence at final decision

Jumping to Conclusions (JTC) Bias Lowered threshold Hypersalience of Evidence – Hypothesis Matches Evidence integration (BADE and BACE) Belief flexibility Probabilistic reasoning Introspective accuracy

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  

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Modified Task Variables

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Table 3 Raw means and standard deviations for task variables. SCZ Mean (SD)

HC Mean (SD)

Neutral Salient

7.05 (5.67)* 5.90 (5.26)*

5.47 (5.18)* 6.28 (6.15) 4.65 (5.27)

7.49 (4.30)* 7.83 (5.10) 7.15 (5.00)

Neutral Salient

70.87 (22.04) 72.12 (22.51)

70.03 (22.58) 70.50 (24.58) 69.57 (25.32)

72.96 (17.60) 71.24 (19.45) 74.67 (19.25)

Neutral Salient

63.17 (20.52) 63.84 (16.78)

67.33 (18.19)* 67.29 (22.78) 67.36 (20.27)

59.68 (10.15)* 59.05 (17.25) 60.32 (11.56)

Neutral Salient

90.38 (138.43)* 121.06 (166.76)*

Draw to decision (DTD)

Confidence at DTD

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Matched Decisions

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Combined Samples

127.89 (158.58) 103.17 (150.47) 152.62 (210.58)

83.55 (88.13) 77.58 (125.60) 89.51 (99.27)

Number of non-Bayesian judgments 3.93 (4.04)*** Neutral 2.86 (4.66) 4.00 (4.85) Salient 2.82 (4.15) 3.87 (4.19) *p < 0.05, **p < 0.01, ***p ≤ 0.001 for group or task comparisons (ANOVA)

1.74 (3.72)*** 1.72 (4.20) 1.76 (3.88)

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Confidence variability

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Note. Log transformations performed for “confidence variability” and “number of non-Bayesian judgments,” prior to statistical analyses.

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Raw means and standard deviations for extreme responders (DTD ≤ 2) compared with non-extreme responders (DTD ≥ 3) within the clinical group. Extreme n=27 (58.7%) Mean (SD)

Non-Extreme n=19 (41.3%) Mean (SD)

Test statistic

WRAT

90.04(11.09)

97.79(12.74)

t(44) = -2.196

0.033

dUNB = 0.646

Confidence at DTD

75(26.35)

61.84(22.19)

t(44) = 1.777

0.083

dUNB = 0.523

[-1.256, 0.051] [-0.068, 1.126]

Effect Size

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p

95% CI

Matched Decisions

72.04 (21.61)

60.71 (16.51)

t(43.645) = 2.013

0.050

dUNB = 0.526

[-0.065, 1.129]

Confidence variability

216.76(250.20)

61.46(74.15)

t(44) = 1.583

0.121

dUNB = 0.466

[-0.124, 1.067]

Non-Bayesian judgments

5.96(3.632)

.89(2.979)

t(44) = 7.672

< .001

dUNB = 2.258

[1.532, 3.046]

2

0.115

2

X (1) = 21.595

< .001

[-0.214, 0.971]

Changed responses

25(92.6)

5(26.3)

Trail Making Test Part A

36.41 (16.83)

30.67 (12.09)

t(44) = 1.272

0.210

Cramer’s V = .268 Cramer’s V = .685 dUNB = 0.374

BACS Symbol Coding

45.52 (11.30)

47.58 (10.25)

t(44) = -0.632

0.531

dUNB = 0.186

[-0.776, 0.400]

Letter Number Span

11.96 (3.48)

13.16 (3.55)

t(44) = -1.138

0.261

dUNB = 0.336

[-0.931, 0.251]

Category Fluency: Animal Naming

20.52 (4.98)

21.37 (5.90)

t(44) = -0.528

0.600

dUNB = 0.155

[-0.745, 0.431]

1(5.3)

X (1) = 3.314,

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Introspective Accuracy (IA)

-

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Table 5 Pearson‟s correlations for variables within clinical population. 1.

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1. -0.195 2. -0.387** 0.004 3. -0.708** 0.131 0.15 4. -0.114 0.135 -0.255 0.256 5. 0.054 0.073 -0.053 -0.082 -0.029 6. -0.044 0.139 -0.135 -0.101 0.141 .359* 7. 0.083 0.068 -0.080 0.028 0.039 -0.327* -.282 8. -0.041 0.226 0.087 0.137 0.1 -0.141 0.112 -0.218 9. -0.085 0.107 -0.032 0.285 -0.117 0.032 0.266 0.249 10. 0.335* -0.165 -0.184 -.455** -0.04 -0.153 -0.166 -0.032 11. -0.225 0.042 -0.211 0.275 0.236 -0.207 0.026 0.218 12. 0.15 -0.227 0.103 -0.182 -0.107 0.11 -0.228 -0.021 13. 0.184 -0.215 0.164 -.317* 0.005 0.081 0.029 -0.237 14. 0.212 -0.092 -0.09 -0.155 -0.057 0.11 0.141 -0.243 15. 1. DTD, 2. Confidence at DTD, 3. Confidence variability, 4. Number of non-Bayesian judgments, 5. Matched decisions 6. Composite score of PANSS delusion ratings (P1, P5, & P6), 7. PANSS hallucination rating (P3), 8. PANSS five-factor composite of negative symptoms, 9. DACOBS Jumping to conclusions subscale, 10. DACOBS belief flexibility subscale, 11. WRAT, 12. Trail Making Task, 13. Symbol Coding, 14. Letter Number Span, 15. Animal Fluency. * p < .05, ** p < .01 Note. Data from the salience task used for correlations. Log transformations performed for “confidence variability” and “number of non-Bayesian judgments,” prior to statistical analyses due to violations of normality.