Behaviour Research and Therapy

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1 Behaviour Research and Therapy 47 (2009) Contents lists available at ScienceDirect Behaviour Research and Therapy journal homepage: Cognitive biases in three prediction tasks: A test of the cognitive model of depression Daniel R. Strunk *, Abby D. Adler Department of Psychology, Ohio State University, 1835 Neil Avenue, Columbus, OH 43210, USA article info abstract Article history: Received 4 January 2008 Received in revised form 4 September 2008 Accepted 8 October 2008 Keywords: Depression Cognitive therapy Optimism and pessimism Cognition Cognitive therapy for depression is based on an assumption that depressed individuals have inaccurate, negative biases. Whether this assumption is accurate remains unresolved. Thus, this study sought to examine the relationship between depressive symptoms and bias in three sets of predictions (i.e., predictions of future life events, how one would be rated by a significant other, and performance on a vocabulary test). Following study announcements made to a subset of people pre-screened for depressive symptoms, 85 participants with widely varying depressive symptoms (17 of whom met diagnostic criteria for depression) made predictions on three judgment tasks and the outcomes for these tasks were assessed. Optimistic/pessimistic biases were related to depressive symptoms for each of the three tasks. Participants with high levels of depressive symptoms and depressed participants exhibited substantial pessimistic bias. Those high in depressive symptoms exhibited significant pessimistic bias on all three tasks. Participants meeting diagnostic criteria for depression exhibited pessimistic bias on two of three tasks. There was no evidence that depressive symptoms were associated with greater accuracy in judgments. Results are largely consistent with cognitive models of depression which postulate that depression is associated with pessimistic biases. Published by Elsevier Ltd. Introduction Cognitive therapy (CT) for depression is arguably the most studied of all psychotherapies for depression (Butler, Chapman, Forman, & Beck, 2006). A large body of research supports both the efficacy of CT and some important elements of the model underlying this treatment (Clark, Beck, & Alford, 1999). However, questions persist about the validity of a fundamental component of the model underlying CT the claim that the negative views of people with depression tend to be inaccurate. The first empirical challenge to this claim was Alloy and Abramson s (1979) work. In their prototypical test, participants chose whether to press a button and then observed whether a light illuminated on each of a series of trials. They were then asked to estimate the degree of control button pressing had over the light. Participants with higher levels of depressive symptoms showed less of a tendency to overestimate their control than participants with low levels of depressive symptoms. Such evidence was taken to support the depressive realism hypothesis, which posits that although depression is associated with more negative views, these views are more accurate as they are less reflective of the optimistic bias seen among people who are not depressed. * Corresponding author. Tel.: þ ; fax: þ address: strunk.20@osu.edu (D.R. Strunk). Since Alloy and Abramson s (1979) early work, the depressive realism hypothesis has been interpreted very broadly to suggest that depression may not be generally associated with negative biases in judgment (Alloy, Albright, Abramson, & Dykman, 1990). If true, this claim would require substantial changes to the model underlying CT for depression, which assumes negative biases are characteristic of depression (Haaga & Beck, 1995). Despite substantial research attention to this issue, limitations of findings from contingency judgment paradigms and other approaches to testing the depressive realism hypothesis have meant that the claim of whether unrealistic negative bias in depression is valid has remained largely unresolved. Common limitations of the depressive realism literature There are at least four important and common limitations to the depressive realism literature. First, evidence supporting the depressive realism hypothesis from contingency judgment studies has been shown to be limited to particular contexts, namely when actual contingency is low (Carson, 2001) and when the interval between trials is quite long (Msetfi, Murphy, Simpson, & Kornbrot, 2005). Second, most of the research on depressive realism has employed unselected or convenience samples which are unlikely to include many participants with depressive symptoms of the severity typical of people with clinical depression (Ackerman & /$ see front matter Published by Elsevier Ltd. doi: /j.brat

2 D.R. Strunk, A.D. Adler / Behaviour Research and Therapy 47 (2009) DeRubeis, 1991; Dobson & Franche, 1989). Failure to include people with a wider range of depressive symptoms in studies of depressive realism has been shown to lead to discrepant results (Strunk, Lopez, & DeRubeis, 2006). Third, several reviewers have expressed concerns about the ecological validity of much of the depressive realism literature, particularly the contingency judgment paradigm (Ackerman & DeRubeis, 1991; Dobson & Franche, 1989). These reviewers noted that the judgments studied appear to be less consequential and less emotionally engaging for participants than the types of judgments for which Beck has hypothesized strong negative biases in depression (e.g., predictions of one s future success; Beck, 1976). Indeed, the relationship between depressive symptoms and the accuracy of probability judgments has been found to vary as a function of how consequential the judgments being made are, with people with more depressive symptoms failing to maintain their optimistic views for more consequential judgments (Pacini, Muir, & Epstein, 1998). Many of those who have studied more consequential, emotionally engaging judgments have employed designs with another limitation. This limitation is the evaluation of the accuracy of judgments using standards that are likely to be contaminated with systematic bias (Ackerman & DeRubeis, 1991). For example, Lewinsohn, Mischel, Chaplin, and Barton (1980) had depressed and non-depressed participants interact in a group and provide ratings of their social competence. The depressed participants more negative ratings of their own social competence more closely corresponded to the ratings provided by observers than was true for the non-depressed group, who saw themselves as more competent than the observers did. However, the participants were simply asked to rate themselves. They were not asked to predict the ratings of observers. Ackerman and DeRubeis (1991) asserted that participants not being asked to predict observer ratings combined with evidence that observers tend to be critical suggests that this may not have been a fair standard of comparison. Recently, Strunk et al. (2006) utilized a sample with a wide range of depressive symptoms to test the depressive realism hypothesis as applied to consequential predictions of future events in one s own life. To assess optimistic/pessimistic bias, predictions were compared to outcomes experienced and a score reflecting a one-dimensional construct ranging from highly pessimistic to highly optimistic bias was created. This score reflecting optimistic/ pessimistic bias in predictions was associated with depressive symptoms such that those with the highest levels of depressive symptoms, typical of people with clinical depression, were pessimistically biased. Interestingly, the relationship between optimistic/pessimistic bias and depressive symptoms was particularly strong among women. Overview of considerations for this study Building on the work of Strunk et al. (2006),wesoughttotestthe depressive realism hypothesis as applied to some of the kinds of judgments most strongly implicated in the cognitive model of depression. Following Strunk et al., expectations for future events were selected for study partly because these expectations are thought to play an important role in the cognitive model of depression and correction of such biases is thought to be central to CT (Hollon & Garber,1980). However, in an effort to provide an advance over Strunk et al. we included multiple methods of assessing whether the events about which predictions were made occurred (to avoid some systematic biases that might be inherent in any one specific method) and we formally assessed Major Depressive Disorder. We also sought to examine the generalizability of our findings across additional judgment domains. Beck has hypothesized that depressive symptoms are associated with a biased expectation of future negative outcomes (Beck, 1976). The life events prediction task used by Strunk et al. (2006) appears to assess this type of bias. However, Beck (1976) also hypothesized that depressive symptoms are characterized by both a view of the self as being deficient in the attributes required to achieve important goals and a negative view of one s immediate interpersonal world. To assess the former view, we assessed individual differences in cognitive abilities because these abilities are likely to be seen as relevant to the pursuit of many possible future goals. To assess the latter view, we examined participants predictions of how a significant other would rate them on a series of personal characteristics (an adaptation of the task utilized by Lewinsohn et al., 1980). For each task, we were careful to avoid systematic bias in our method of assessing whether the predicted outcomes occurred. Using the same one-dimensional scale of optimistic/pessimistic bias as Strunk et al. (2006), we predicted that greater pessimistic (or less optimistic) bias would be displayed across all three measures of bias by those with more severe depressive symptoms. Moreover, we expected that participants with Major Depression would exhibit bias across each of the domains examined. Methods Participants A total of 85 participants enrolled in introductory psychology courses participated in this study for research credit. The sample was predominantly female (64%) and Caucasian (86%). The average age was 18.7 years (SD ¼ 1.0). A sample of 87 participants was drawn from a total of 2593 students who completed the Beck Depression Inventory (BDI; Beck, Ward, Mendelson, Mock, & Erbaugh, 1961) during an initial pre-screening period. A subset of the total group pre-screened received announcements of the study. Announcements were made in an effort to obtain equal representation of students with low (0 8), middle (9 17), and high (18 63) scores on the BDI. Two participants who had initially volunteered to participate in the study were excluded because they met criteria for Bipolar Disorder based on a structured clinical interview. There were no other exclusion criteria. Thus, our sample was carefully selected so as to represent participants with a wide range of depressive symptoms, including a subset of participants who, based on our formal evaluation (described below), were identified as clinically depressed. All participants were asked to recruit a significant other (i.e., friend, family member, or romantic partner) to complete questionnaires on their behalf in return for entry into a lottery with a prize of $100. A total of 48 significant others returned the requested material, of whom 54% were female and 84% were Caucasian. The average age of significant others was 24.9 years (SD ¼ 12.0). The Institutional Review Board at Ohio State University approved this study. All participants provided informed consent. Measures Diagnostic status Portions of the Structured Clinical Interview for DSM-IV (SCID; First, Spitzer, Miriam, & Williams, 2002) were administered (viz., Modules A (mood disorders), and D (mood differential) and history of past major depressive episodes were assessed using Module J). Depressive symptoms Depressive symptom severity was indexed with two continuous measures: the Beck Depression Inventory-II (BDI-II; Beck, Steer, & Brown, 1996) and the Hamilton Rating Scale for Depression (HRSD; Hamilton, 1960; Williams, 1988). The BDI-II is a 21-item self-report measure of depressive symptoms. The 17-item version of the HRSD is an interviewer administered measure of depressive symptom severity. The reliability and validity of the BDI-II (Beck et al., 1996)

3 36 D.R. Strunk, A.D. Adler / Behaviour Research and Therapy 47 (2009) and the HRSD (Hamilton, 1960; Knesevich, Biggs, Clayton, & Ziegler, 1977; O Hara & Rehm, 1983) have been well established. Prediction tasks Three distinct prediction tasks were employed. Based on previous work (Strunk et al., 2006), the first of these tasks was an assessment of optimistic/pessimistic bias in the prediction of future life events (LE). The LE task involves 20 desirable (e.g., will be invited to a party ) and 20 undesirable (e.g., will have a serious family disagreement ) events for which participants are asked to predict the probability (0 100) of each event occurring to them over the next month. Strunk et al. (2006) selected these events on the basis of the events being relevant to the general population, reflecting a range of base rates, and reflecting a range of controllability. Previous research has assessed the occurrence of these events at the end of the month via self-report (Strunk et al.). In addition to this assessment of event occurrence, repeated selfreports of event occurrence throughout the one-month period were collected online. These reports were collected at 7, 14, 21, and 30 days after the predictions were made. Participants were asked to report on the occurrence of each event since their previous report. These repeated self-reports were used as they may be less susceptible to memory biases. Finally, interviewers also assessed the occurrence of events at the end of the one-month period using a semi-structured interview developed for this purpose. Interviewers asked participants to describe each event that occurred or any events which might be relevant (i.e., an event that did not quite fit what was being assessed). Using a manual, interviewers then judged whether the specific criteria for each event having occurred had been satisfied. The interview assessment of event occurrence was used to ensure that standardized criteria for judging whether each event occurred were uniformly applied across participants. For the second prediction task, participants were asked to invite a significant other (i.e., friend, family member, or partner) to participate in the study. Each significant other was contacted and asked to assign a percentile score for the participant for each of 21 positive personal characteristics (e.g., friendly, popular, assertive). l These scores reflect how the significant other viewed the participant relative to the general adult American population on each characteristic. These 21 personal characteristics (PC) were adapted from Lewinsohn et al. (1980). Each participant then predicted the ratings that his or her significant other had provided. In addition to the PC ratings, significant others provided demographic data and completed a BDI-II. The third prediction task involved participants predicting their percentile score on a measure correlated with overall IQ, specifically the Wechsler Adult Intelligence Scale 3rd Edition Vocabulary Subscale (WAIS-III-VOC). Of the WAIS-III subscales, the vocabulary score has been found to correlate most highly with general intelligence (Kaufman & Lichtenberger, 1999). Participants were given clear instructions about the meaning of the percentile scores they were to predict. After making their predictions, the WAIS-III-VOC was administered. In administering the WAIS-III-VOC, the evaluator asked the participant to define up to 35 words. Participants responses were scored for correctness based on criteria included in the WAIS-III Administration and Scoring Manual (Wechsler, 1997). Scores from the WAIS-III-VOC have been shown to have good reliability (Matarazzo, Wiens, Matarazzo, & Manaugh, 1973) and evidence of validity (Feingold, 1982). Thus, the three prediction tasks were the LE task, the PC task, and the IQ task. 1 A full list of the characteristics used for this study is available from the first author. Scoring of bias across three tasks The primary variables of interest from the three judgment tasks are measures of bias. The bias scores reflect the extent of optimistic/ pessimistic bias in participants predictions for each judgment task. All predictions in each task were recorded as percentages, such that predictions ranged from 0 to 1. With regard to the LE task, the three sets of event occurrence reports (i.e., one-month self-report, repeated measures self-report, and evaluator-rated report) were used separately to generate three corresponding measures of LE bias. For each of these indices of LE bias, a difference score was calculated for each event. For desirable events, this score was calculated as the probability judgment for the event minus a score indicating whether the event occurred (0 indicated no occurrence, 1 indicated occurrence). For undesirable events, the difference score was calculated as a score indicating whether the event occurred (0 indicated no occurrence, 1 indicated occurrence) minus the probability judgment for the event. The average of the difference scores for each event was then taken, resulting in a LE bias score for each participant. Using this approach, three LE bias scores were calculated based on the three indices of event occurrence (for additional explanation of scoring procedures, see Strunk et al., 2006). For the PC task, bias was calculated in two steps. First, a difference score was calculated for each characteristic as the predicted percentile score minus the significant other s rating of the participant s percentile score. Then, the average of these difference scores was calculated, producing a PC bias score. For the IQ task, bias was calculated as participants predicted percentile score minus their actual percentile score. To obtain actual percentile scores, raw scores were first converted to standard scores (M ¼ 10; SD ¼ 3) using age specific normative data. Then, percentile scores were derived from these standard scores assuming a normal distribution. The possible range of bias scores on each task is 1 to 1. Using such a scale, higher positive scores reflect more optimism and lower negative scores reflect more pessimism. Scores of 0 indicate the absence of any optimistic or pessimistic bias. Procedures At their initial assessment, participants made predictions for the LE and IQ tasks, completed additional self-report measures (BDI-II) and study interviews (WAIS-III-VOC, SCID, HRSD). Predictions for the IQ task were made prior to completing the WAIS-III-VOC. Participants were also informed about the request to invite a significant other to participate. Participants were given study materials to provide to their significant other. Significant others were provided with addressed stamped envelopes to return their completed measures directly to study personnel. Participants completed online assessments of LE occurrence at 7, 14, and 21 days following the initial assessment. One month after their initial assessment, participants came for a second in-person assessment. At this time, participants reported which, if any, of the life events had occurred since their last assessment (to complete the repeated measures assessment of LE). As additional measures of event occurrence, they completed a self-report measure at the onemonth follow-up and an interview evaluation at the one-month follow-up. Participants also made predictions for the PC task with knowledge of the specific individuals who had served as their raters. Finally, they completed measures of depressive symptoms (BDI-II, HRSD). All assessments were conducted individually. Instructions regarding the prediction tasks were provided for each task. The instructions were designed to ensure that participants were fully aware of all basic information about the task that could be useful to them in making unbiased predictions. The instructions included a brief explanation of probability judgments and percentile rankings. For the PC task, the instructions were clear that the participant

4 D.R. Strunk, A.D. Adler / Behaviour Research and Therapy 47 (2009) and the significant other were not to discuss the ratings. The instructions also clearly stated that the participants task was to predict the percentile ratings provided by the significant other, who would be asked to provide their honest, best estimates of the participants true percentile on each of the personal characteristics. For tasks involving predictions or estimations of percentiles, the reference groups were clearly specified both for the participants and the significant others. For the IQ task, participants were asked to estimate their percentile score on a standardized measure involving vocabulary knowledge which has been found to be highly correlated with overall IQ. Study personnel checked to ensure that participants fully understood all study instructions. Interview-based assessments Evaluators were graduate students and advanced undergraduate students trained by the first author in administering the HRSD and selected modules of the SCID. Training included guided readings, reviewing instructional videotapes, and a series of roleplays and interactive training exercises. Videos of the interviews were recorded, and a subset of the interviews (67%) was rated by another interviewer blind to any ratings made previously. Inter-rater reliability for a single rater on the HRSD was acceptable (ICC ¼ 0.76). Inter-rater reliability was also calculated for diagnoses of current Major Depressive Disorder (MDD) on the SCID. Inter-rater agreement of two raters on SCID diagnoses of current MDD was adequate (k ¼ 0.60). Discrepancies were reviewed by a third rater (a doctoral level clinician who has been trained in the use of the SCID), who made a final judgment about each case involving a disagreement. Missing data Eight participants did not complete the predictions portion of the LE task due to computer error. Five participants did not return for the one-month follow-up session, and only 48 participants completed both the participant and significant other versions of the PC task. For these reasons, sample size varies in the analyses reported. Results Based on the SCID, 17 participants met criteria for current Major Depressive Disorder. BDI-II scores ranged from 0 to 52 (M ¼ 12.9, SD ¼ 11.8, n ¼ 83) and HRSD scores ranged from 0 to 27 (M ¼ 7.7, SD ¼ 5.8, n ¼ 85). Thus, the sample represented a wide range of depressive severity and included a number of participants with depression. Consistent with previous studies (Basco, Krebaum, & Rush, 1997), scores on the HRSD and BDI-II were highly correlated (r(81) ¼ 0.77, p < ). Therefore, we standardized and averaged these scores to yield a single composite score for depressive symptom severity. When we examined the correlations among the three bias variables, we were surprised to find that none of the variables were significantly correlated with each other (rs ranged from 0.06 to 0.16, ps > 0.15). Therefore, we conducted our analyses keeping the variables from the different tasks distinct in our analyses. As previously mentioned, life event occurrence was assessed using three methods to consider potential systematic bias which might have been present using one method, but not another. The means of assessing events were repeated self-report measures, a comprehensive self-report measure at the one-month follow-up, and an interview evaluation at the one-month follow-up. The three measures of event occurrence were highly correlated with each other (rs range from 0.79 to 0.97). Results for all of the analyses we report were consistent across each of these three assessments. Therefore, for simplicity of presentation, we report only the results based on the repeated self-report measure of event occurrence. Optimistic/pessimistic bias and depressive symptoms To test our primary hypothesis, we examined the relationship of the three bias variables with the composite depressive symptoms measure. In a regression model in which the three bias measures were entered simultaneously as predictors of depressive symptoms, the bias measures collectively accounted for 26% of the variance in depressive symptoms (F(37) ¼ 4.34, p ¼ 0.01). We also examined the relationship between bias for each of the three tasks and depressive symptoms separately. As shown in Table 1, each bias measure was significantly negatively correlated with depressive symptoms (rs range from 0.25 to 0.37). To examine whether bias was related to depressive symptoms in a curvilinear manner, a separate regression model was examined for each task. In each model, bias and a quadratic term for bias were examined as predictors of depressive symptoms. The quadratic term was significant in the model for LE bias (b ¼ 0.23, t(73) ¼ 2.04, p ¼ 0.04). The results of this model reflect a tendency for people with greater depressive symptoms to make more pessimistic predictions of future life events (i.e., linearly) and for people with the highest levels of depressive symptoms to make especially pessimistic predictions of their future life events. The quadratic terms in the models for PC bias and IQ bias were not significant (b ¼ 0.10, t(45) ¼ 0.45, p ¼ 0.6; b ¼ 0.02, t(81) ¼ 0.11, p ¼ 0.9, respectively). To further illustrate the relationship between bias and depressive symptoms, Fig. 1 plots the average bias scores among those with low-, middle-, and high scores on depressive symptoms. To define low-, middle-, and high scoring groups, we first examined the number of participants who scored low (0 12), middle (13 19), and high (20 63) on the BDI-II. We then selected group-defining cut points on the depressive symptom composite score so that the same number of participants were in the three groups as were present for groups defined on the basis of their BDI-II scores. The negative relationships between depressive symptoms and the three measures of bias are illustrated in Fig. 1. We also examined whether each depressive symptom group showed either systematic optimistic or pessimistic bias by testing whether the bias scores for each group differed from zero. As the figure shows, for both the LE and PC tasks, the low and middle scoring groups did not exhibit significant bias (all ps > 0.15), whereas the high depressive symptom group showed significant pessimistic bias on both of these tasks (LE: t(18) ¼ 2.36, p ¼ 0.03; PC: t(15) ¼ 4.06, p ¼ 0.001). On the IQ task, the high scoring group showed the strongest pessimistic bias (high: t(22) ¼ 6.58, p < ), but the low and middle groups also exhibited significant pessimistic bias on this task (low: t(39) ¼ 4.40, p < ; middle: t(20) ¼ 3.10, p ¼ 0.006). This pattern of results is depicted along with the negative relationships between bias and depressive symptoms shown in Fig. 1. We also examined bias among those who had been diagnosed with depression. To form a group against which the depressed group might be compared, we identified the 1/3 of the sample with the lowest scores on the composite measure of depressive Table 1 Correlations of bias on three tasks and depressive symptoms Depressive symptoms r p df Life event task Personal characteristic task IQ task

5 D.R. Strunk, A.D. Adler / Behaviour Research and Therapy 47 (2009) When examining whether systematic bias was shown by the depressed and low symptom groups (i.e., bias differing from zero), we found that the depressed participants exhibited pessimistic bias on two of three tasks (PC: t(9) ¼ 4.44, p ¼ and IQ: t(16) ¼ 5.63, p < ). In contrast, we only found evidence of systematic bias on the IQ task for the low symptom group, with this group also showing pessimistic bias (t(25) ¼ 2.65, p ¼ 0.01). These results are depicted in Fig. 2. To put the pessimistic bias observed in the low symptom group in context, it is worth noting that 85.7% of the sample exhibited a negative (i.e., pessimistic) IQ bias score Low Middle high BDI-II = 12.1 ± 5.4 HRSD = 8.4 ± 2.3 BDI-II = 4.4 ± 3.8 HRSD = 3.3 ± 2.2 Depression Group LE PC IQ BDI-II = 29.2 ± 8.6 HRSD = 14.9 ± 4.7 Fig. 1. on three tasks by composite depression severity. Mean bias scores on each task are reported for portions of the sample that were low-, middle-, and high scoring on the depression composite score. Nine t-tests were conducted to examine whether mean bias differed from zero in each depression severity group for each task. Blackened shapes represent tests that were significant (p < 0.05). Listed in order for the LE, PC, and IQ tasks, sample sizes were 38, 24, and 40 for the low depression group, 19, 8, and 21 for the middle depression group, and 19, 16, and 23 for the high depression group, respectively. LE ¼ life events task, PC ¼ Personal characteristics task, IQ ¼ IQ task. symptoms. As shown in Fig. 2, the participants with diagnosed depression showed significantly more pessimistic bias than the low symptom group on LE bias (depressed: M ¼ 0.04, SD ¼ 0.10, n ¼ 14; low symptom: M ¼ 0.03, SD ¼ 0.08, n ¼ 25; t ¼ 2.43, p ¼ 0.02, d ¼ 0.7), PC bias (depressed: M ¼ 0.16, SD ¼ 0.11, n ¼ 10; low symptom: M ¼ 0.05, SD ¼ 0.09, n ¼ 14; t ¼ 2.69, p ¼ 0.01, d ¼ 1.1), and IQ bias (depressed: M ¼ 0.23, SD ¼ 0.17, n ¼ 17; low symptom: M ¼ 0.10, SD ¼ 0.19, n ¼ 26; t ¼ 2.24, p ¼ 0.03, d ¼ 0.7). Thus, the depressed group showed significantly more pessimistic bias than the low symptom group on all three tasks Non-Depressed Depressed BDI-II = 3.0 ± 2.8 BDI-II = 26.5± 11.2 HRSD = 2.4 ± 1.8 HRSD = 13.9 ± 6.6 Depression Group LE PC IQ Fig. 2. among depressed participants and participants with low depressive symptoms. Mean bias scores on each task are reported for the third of the sample that was low scoring on the depression composite score and those diagnosed with Major Depression. Six t-tests were conducted to examine whether mean bias differed from zero in each depression severity group for each task. Blackened shapes represent tests that were significant (p < 0.05). Listed in order for the LE, PC, and IQ tasks, sample sizes were 25, 14, and 26 for the non-depressed group, and 14, 10, and 17 for the currently depressed group, respectively. LE ¼ life events task, PC ¼ personal characteristics task, IQ ¼ IQ task. Gender as a moderator Following Strunk et al. (2006), we examined gender as a potential moderator of the relationship between bias and depressive symptoms. Replicating the effect they reported, we found that gender moderated the relationship between LE bias and depressive symptoms (b ¼ 0.31, t(72) ¼ 2.37, p ¼ 0.02). This effect was due to a large significant negative correlation between bias and depressive symptoms among women (r(46) ¼ 0.52, p ¼ ), which was not evident among men (r(26) ¼ 0.05, p ¼ 0.79). Neither women nor men showed an overall (i.e., different than zero) LE bias (ps > 0.1). We failed to find any evidence of an interaction of gender and bias in predicting depressive symptoms for either the PC or IQ task (ps > 0.2). Accuracy We also examined accuracy in predictions for the LE task. Recall that a difference score was calculated for each event and that the average of these difference scores was taken to form the bias measure. Taking the average of the absolute value of those difference scores yielded a measure of general inaccuracy in participants predictions. We subtracted this inaccuracy score from 1 to obtain a measure of accuracy in predictions. After controlling for LE bias, LE accuracy was not significantly correlated with the composite measure of depressive symptoms (b ¼ 0.12, t(73) ¼ 1.14, p ¼ 0.3). The tendency to make fairly negative predictions on the PC and IQ tasks precluded a meaningful test of this kind for those tasks. 2 Additional analyses of PC bias Given the large number of participants who did not complete the PC task (n ¼ 38), we examined potential differences between those who did and did not complete the task. There were no significant differences between these two groups on depressive symptom severity, age, or gender (all ps > 0.25). The relationship between PC bias and depressive symptoms is potentially confounded with the depressive symptoms of the significant others. Perhaps participants with greater depressive symptoms tended to invite significant others with more depressive symptoms and the depressive symptoms of the significant others led them to be biased in the percentile ratings they provided. There was a non-significant trend for participants BDI-II scores to be 2 In examining the difference scores of participants predictions on PC items and the ratings of the significant others, participants had an average of less than 1% of the PC item difference scores with a positive score (indicating optimism). For the IQ task, only 12 participants (14.3%) had a difference score greater than or equal to 0. Because of this, accuracy for these measures was highly correlated with bias scores from the same tasks. Whereas bias and accuracy were not significantly correlated for the LE task (r(74) ¼ 0.07, p ¼ 0.5), accuracy and bias were highly correlated for the PC and IQ tasks (r(46) ¼ 0.59, p < and r(82) ¼ 0.72, p < , respectively). Therefore, not surprisingly, accuracy was not correlated with depressive symptoms after controlling for bias on either the PC or IQ task (b ¼ 0.23, t(45) ¼ 1.33, p > 0.15; b ¼ 0.00, t(81) ¼ 0.02, p > 0.95).

6 D.R. Strunk, A.D. Adler / Behaviour Research and Therapy 47 (2009) related to the BDI-II scores reported by the significant others (r(41) ¼ 0.29, p ¼ 0.06). However, the BDI-II scores of significant others and the PC ratings these significant others provided were not significantly correlated (r(42) ¼ 0.11, p ¼ 0.5). Therefore, it does not appear that significant others depressive symptoms impacted PC bias. Discussion Consistent with the cognitive model of depression, optimistic/ pessimistic bias was negatively associated with depressive symptoms such that participants with more depressive symptoms reported more pessimistic bias. This was true across all three tasks: predicting future life events, predicting ratings of personal characteristics made by a significant other, and predicting performance on a test reflecting IQ. Participants with the highest levels of depressive symptoms (based on cut-off scores rather than diagnostic criteria) exhibited significant pessimism on all three judgment tasks. For the LE task only, there was evidence of a nonlinear relationship between bias and depressive symptoms such that people with high levels of depressive symptoms exhibited even greater pessimism than would have been predicted based upon the linear relationship between bias and depressive symptoms. Furthermore, the relationship between LE bias and depressive symptoms was particularly strong among women. Thus, the relationship between bias on each judgment task and depressive symptoms was consistent with the cognitive model of depression and contrary to the depressive realism hypothesis. We failed to find any evidence that depressive symptoms were associated with more accurate judgments. Given the fact that we had a substantial number of participants diagnosed with Major Depressive Disorder, we were able to compare these participants to a subset of participants who reported relatively few depressive symptoms. When compared to a subset of the sample with low depressive symptoms, the depressed group exhibited significantly more pessimism on all three judgment tasks. In examining just the depressed group, bias was significantly pessimistic (i.e., less than 0) on the PC and IQ tasks, but failed to reach significance for the LE task. Given that only 14 participants were included in that LE analysis, it was an underpowered test. Even after considering the high levels of depressive symptoms in the current sample, pessimistic biases were surprisingly common in the judgment tasks (particularly the IQ task), which appears at odds with the body of research showing that optimism is quite common in the general population (Taylor & Brown, 1988). However, the methods underlying studies showing that optimistic biases are common and those used in the current study do differ. For example, the judgments made in this study appear to be the kinds of judgments for which participants would be more likely to have obtained considerable feedback throughout their lives (see Colvin & Block, 1994 for a more in depth discussion). The tasks included in this study were chosen to be relevant to the cognitive model of depression. To what extent they are representative of a broader class of judgments examined in other research is a potentially important issue for future research. Considerations regarding individual tasks Some comment on issues related to the PC and IQ tasks is warranted. One source of potential bias in the PC task was the possibility that participants depressive symptoms might have led them to select people likely to introduce biases in the ratings they provided. Although there was a non-significant trend for participants BDI-II scores to be correlated with the BDI-II scores of their significant others, there was no significant relationship between BDI-II scores of significant others and the PC ratings these significant others provided. Thus, the influence of such a problem is not likely to have been substantial. Significant pessimism was evident on the IQ task in the low-, middle-, and high scoring depressive symptom groups. The lack of an optimistic bias on any task, especially among the low depressive symptom severity group may be somewhat surprising. Pessimistic bias in this group is certainly surprising. Some participants did obtain scores reflecting optimistic bias on these tasks. For the PC task, 27.1% of the sample scored above zero (indicating optimism). For the IQ task, where pessimism was most common in our sample, 14.3% of the sample scored at or above 0. While these percentages are low compared to the 50.0% of the sample which scored greater than 0 on LE bias, they do show that participants with scores reflecting optimism were not exceedingly rare on these tasks. Limitations A few of the most important limitations of this study are worthy of note. While we would not expect our results to vary as a function of participants ages, the restricted age of our sample is a limitation. Participants in this study tended to be young and similarly aged as they were selected from students in university courses. Strunk et al. (2006) had examined participants with more variability on age. Analyses of that data set failed to find significant differences in the relationship between bias and depressive symptoms as a function of age. Nonetheless, future examination of age and other potential moderators of the relationship between bias and depressive symptoms would be of interest. In designing this study, we sought to maximize ecological validity. Our instructions in the use of probability judgments were brief (taking less than one minute per task) and appeared to be easily understood. Assessing the accuracy of participants judgments may inherently necessitate some compromise of ecological validity, as participants may not naturally generate probability estimates. However, individual differences in probability estimates are frequently assessed with success in a number of literatures (e.g., clinical, social and judgment decision making; Baron, 2008). Minimally, we believe that our measures likely generalize to contexts such as CT, in which therapists commonly ask clients to generate probability estimates in order to assess their negative predictions (Beck et al., 1979). Additionally, the reliability of the diagnostic judgments made was somewhat lower than is typical. This suggests that the differences we observed between the depressed and non-depressed groups may be an underestimate of the true effects of interest. However, as significant group differences emerged on all three measures of bias, our lower reliability does not appear to have prevented us from finding these effects. Finally, the lack of correlations among our three measures of bias leads to a larger question about the factor structure of optimistic/pessimistic biases. Although very few studies have examined multiple methods of assessing bias in the same sample, previous researchers appear to have largely assumed that optimistic/pessimistic biases would be highly correlated across different kinds of judgments. Given that our study failed to find such correlations, this is an important issue for future research. Beck has proposed that biases likely vary among depressed people as a function of their schemas (Beck, 1976). Thus, it is possible that our three measures of bias are each tapping into distinct schemas, which although unrelated to one another, are individually related to depression. Conclusions As predicted by the cognitive model of depression, optimistic/ pessimistic bias in three distinct tasks was negatively correlated

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