Attentional Bias and Mood Persistence as Prospective Predictors of Dysphoria

Transcription

1 Cognitive Therapy and Research, Vol. 27, No. 6, December 2003 ( C 2003), pp Attentional Bias and Mood Persistence as Prospective Predictors of Dysphoria Christopher G. Beevers 1,2,3 and Charles S. Carver 1 This study examined whether either a negative attentional bias or mood persistence would interact with intervening life stress to predict future increases in dysphoria among college students (N = 77). Dysphoria was assessed in the lab, and then attentional bias was measured with a dot-probe task before and after a negative mood induction. Mood recovery following the induction was also assessed. Seven weeks later, dysphoria and intervening life stress were measured. Prior shifts in attention toward negative information following a negative mood induction interacted with intervening life stress to predict increases in dysphoria 7 weeks later. Slower mood recovery following the mood induction also combined with intervening life stress to predict increased dysphoria at follow-up. These vulnerabilities each explained unique variance in follow-up dysphoria. Results suggest that both attentional bias and mood persistence may have significant roles in depression susceptibility. KEY WORDS: information processing; depression vulnerability; dot-probe; cognitive bias; mood regulation. Depressed people often have attentional biases for negative information (for reviews, see Ingram, Miranda, & Segal, 1998; Williams, Watts, MacLeod, & Mathews, 1988), particularly when stimuli are presented for longer durations (Bradley, Moog, & Lee, 1997). However, it is unclear if such biases persist beyond syndromal remission and thus contribute vulnerability to depression. Some evidence suggests that attentional biases wax and wane with the onset and remission of depression. That is, although depressed people consistently displayed attentional biases for negative information, these biases seemed to fade along with the depressive episode (e.g., Dobson & Shaw, 1987; Gotlib & Cane, 1987; McCabe & Gotlib, 1993). 1 Department of Psychology, University of Miami, Coral Gables, Florida. 2 Present address: Department of Psychiatry and Human Behavior, Brown University and Psychosocial Research Program, Butler Hospital, Providence, Rhode Island. 3 Correspondence should be directed to Christopher G. Beevers, Psychosocial Research Program, Butler Hospital, 345 Blackstone Boulevard, Providence, Rhode Island 02906; christopher beevers@brown.edu /03/ /0 C 2003 Plenum Publishing Corporation

2 620 Beevers and Carver Some have argued, however, that these earlier studies did not adequately test cognitive models (Ingram et al., 1998). The crux of the counterargument is that cognitive theory (e.g., Beck, Rush, Shaw, & Emery, 1979) is a diathesis stress model and that cognitive vulnerabilities remain dormant until activated, an idea that is commonly referred to as the priming hypothesis (Segal & Ingram, 1994). From this perspective, cognitive functioning of remitted depressed and never depressed people should be similar under most circumstances. Only under conditions reminiscent of prior depressive episodes (e.g., when in a sad mood) would cognitive differences emerge. This implies that priming procedures (e.g., a negative mood induction) are needed to elicit attentional biases in remitted depressed people. Several studies provide findings that are consistent with this argument. In one such study, Ingram, Bernet, and MacLaughlin (1994) used a dichotic listening task to examine attentional bias. The authors found that remitted depressed and never depressed people did not differ in their attentional bias in the absence of a negative mood. However, following a dysphoric mood induction, remitted depressed people displayed greater attentional bias for valenced stimuli (both positive and negative) than never depressed persons. In a more recent study that also used a dichotic listening task, after a dysphoric mood induction remitted depressed people showed greater attentional bias for negative stimuli than never depressed people (Ingram & Ritter, 2000). These groups did not differ in their response to positive or neutral stimuli. Importantly, remitted depressed people displayed an attentional bias only following a dysphoric mood induction. In a neutral mood condition, remitted depressed and never depressed people did not differ in their attentional bias. Additional work with a different task has found a similar pattern of results. Using an implicit attitudes task, Gemar, Segal, Sagratti, and Kennedy (2001) found that in the absence of a dysphoric mood induction, remitted depressed people and never depressed people did not differ in their implicit self-esteem. However, following a mood prime, these groups significantly differed, with remitted depressed people reporting levels of implicit self-esteem that resembled a comparison group of currently depressed people. Taken together, these results suggest that triggering events, such as dysphoric moods, are needed to activate negative cognitive biases associated with depression vulnerability. In addition to attentional biases, others have suggested that more sustained or elaborative cognitive processes may also confer vulnerability to depression (MacLeod & Mathews, 1991; Williams & Oaksford, 1992). For instance, Teasdale (1988) has suggested that dysphoric mood states can increase accessibility to negative memories, negatively bias how situations are perceived and interpreted, and negatively influence expected outcomes for future events. These cognitive biases, in turn, serve to reinforce dysphoric mood states. Mutual entrainment between cognitive bias and negative affective states then serves to create a persistent dysphoric mood that may put an individual at risk for a depressive episode. To date, relatively few studies have examined whether persistent dysphoric moods following a negative event reflects vulnerability. In a study by Gilboa and Gotlib (1997), remitted depressed and never depressed participants reported their

3 Predictors of Dysphoria 621 mood, completed an autobiographical recall task designed to induce a negative mood, and then completed a 5-min filler task. Peak negative mood was recorded immediately following the mood induction and delayed mood was reported after the task. Remitted depressed and never depressed people did not differ in their peak moods, but delayed mood was more negative among remitted depressed people than never depressed people, even after controlling for baseline mood ratings, peak mood ratings, BDI scores, and intensity of experiences recalled during the mood induction. Although additional replication is needed, this finding suggests that persistence of negative mood may be associated with depression vulnerability. To summarize, remitted depressed people tend to display negatively biased attentional processing following a dysphoric mood induction. There is also evidence to suggest that remitted depressed people experience a more persistent negative mood following a negative mood induction. However, these studies are limited in that they cannot address whether attentional bias and mood persistence render people vulnerable to future dysphoria. That is, most research to date has shown that attentional biases and mood persistence are associated with past depression (see Just, Abramson, & Alloy, 2001). Cognitive theories of depression vulnerability, however, posit that cognitive factors are causal agents in the disorder (Ingram et al., 1998). If so, cognitive functioning should prospectively predict future depression. Surprisingly few prospective studies of this sort have been done using a priming procedure prior to cognitive assessment. One study that used a dysphoric mood prime found that, among people recently treated for depression, change in dysfunctional thinking from before to after the mood induction predicted depressive relapse 1 4 years later (Segal, Gemar, & Williams, 1999). This is encouraging evidence for cognitive models of depression vulnerability. Additional tests are needed however, to examine whether other cognitive variables, such as attentional bias and mood persistence, prospectively predict changes in depression. The present research is one such test. This study was designed to test whether attentional bias and mood persistence could interact with life stress to predict increases in dysphoria 4 7 weeks later. We expected that greater attentional bias for negative information after a sad mood induction would interact with higher levels of intervening life stress to predict increased dysphoria at follow-up. Consistent with the priming hypothesis, we also expected that attentional bias prior to the mood induction would be less useful in predicting depression. We also expected slower mood recovery (greater mood persistence) following the mood induction to interact with intervening life stress to predict increases in subsequent dysphoria. Finally, we explored the interdependence of the vulnerabilities. Specifically, we examined whether negative attentional bias and mood persistence predicted unique or common variance in follow-up dysphoria. 4 Change in depressive symptoms across time was measured by a standard self-report depression inventory, but no effort was made to conduct interview-based clinical assessments of depression or to determine whether individuals met nosological criteria for a major depressive episode. As the term dysphoria is a more accurate reflection of the level of depression experienced by the present sample, this terminology is used throughout.

4 622 Beevers and Carver METHOD Overview of Design During a laboratory session, participants completed a depression questionnaire and a dot-probe task designed to measure attentional bias. Participants then completed a mood induction designed to elicit sadness, followed by a readministration of the attentional bias task. To assess mood persistence, sadness ratings were completed throughout the laboratory session. At a follow-up approximately 7 weeks later, participants completed a brief measure of intervening life stress and a reassessment of dysphoria. Participants Potential participants completed a depression history questionnaire during group sessions. To ensure we recruited people with a range of vulnerability, we recruited never depressed and remitted depressed people at approximately a 2:1 ratio. In addition, because we were interested in vulnerability to dysphoria, we did not want to recruit participants who were currently dysphoric. Therefore, all were required to have Beck Depression Inventory total score of less than 10 at the time of the first session (see Kendall, Hollon, Beck, Hammen, & Ingram, 1987). Seventy-seven people met these criteria. Of these, 54 (10 male and 44 female) were classified as never depressed and 23 (5 male and 17 female) were classified as remitted depressed; 70 of them completed the follow-up (90.9% completion rate). On average, there were days (7.05 weeks) between assessments. Completers and noncompleters did not differ in age, current dysphoria, depression history, and pre- or postinduction negative mood (all ts < 1). Of the noncompleters contacted (n = 4), the most common reason for not participating in the second time point was they were no longer enrolled in their introductory psychology class. Measures Depression History The Inventory to Diagnose Depression Lifetime (IDD-L; Zimmerman & Coryell, 1987) was used to determine the presence of a past episode of depression. The IDD-L assesses participants worst lifetime episode of depression and determines whether the severity of this episode met DSM-III-R symptom criteria for major depression. Each item on the IDD-L consists of a symptom severity rating from 0 to 4(0 = no disturbance; 1 = subclinical severity; 2 4 = varying levels of symptom presence). The IDD-L also assesses whether each symptom lasted for at least a 2-week period. To be classified as having experienced a previous episode of depression, respondents must meet two criteria. First, participants must have reported a score of 2 or more on the items measuring low mood, irritability, or hopelessness, or greater than 2 on items measuring decreased pleasure or interest. These symptoms must have been

5 Predictors of Dysphoria 623 present for more than 2 weeks. Second, participants were required to have scores of 2 or more on items from at least four of the eight symptom groups. These symptoms included appetite/weight change, psychomotor retardation/agitation, irregular sleep patterns, loss of interest in sex, fatigue, feelings of worthlessness/guilt, slowed thinking/indecisiveness, and thoughts of death/suicidal ideation. These symptoms also must have lasted for more than 2 weeks. The IDD-L has good sensitivity (74%) and specificity (93%) for major depression diagnoses (Zimmerman & Coryell, 1987) and it has been shown to have good test-retest reliability across cultures (Sato et al., 1996). Current Dysphoria To assess current dysphoria, the Beck Depression Inventory (BDI; Beck & Beamesderfer, 1974) was used at both time points. The BDI consists of 21 items and measures the presence and severity of cognitive, motivational, affective, and somatic symptoms of depression. Internal reliability for the BDI is good (α =.81), and the test-retest reliability is adequate (Beck, Steer, & Garbin, 1988). The BDI has been found to be valid for mildly depressed student samples (Beck et al., 1988). Negative Mood Adjective Checklist Negative mood was measured immediately before and after the mood induction with 12 descriptors taken from the Profile of Mood States (POMS; McNair, Lorr, & Droppleman, 1992). The items with the best factor loadings across three mood scales (anger, depression, and tension) reported in the POMS manual for an undergraduate sample were selected. Participants then rated how well each item described their current mood on a 5-point Likert scale ranging from not at all (0) to very much (4). At pre- and postinduction measurements, the scales were highly intercorrelated. Prior to the mood induction, depression was significantly associated with anger (r =.56) and tension (r =.41), and anger was significantly associated with tension (r =.45). After the mood induction, depression was significantly associated with anger (r =.73) and tension (r =.63), and anger was significantly associated with tension (r =.56). High intercorrelations among scales suggest it is acceptable to compute a total score across scales. This total score was used throughout. The overall alpha for the total score was.84 before the mood induction and.92 afterwards. Sadness Visual Analog Scale In addition to using the Negative Mood Adjective Checklist before and after the mood induction, level of sadness was assessed repeatedly throughout the laboratory session. Because of the repeated measurements, we used a visual analog scale that asked participants to make a mark along a line that represented their current level of sadness. The line was anchored with not at all (coded with an intensity of 0) and extremely (coded with an intensity of 60). These types of scales are commonly used when brief measures of mood are needed (e.g., Gilboa & Gotlib, 1997). Sadness ratings were made at the same point in time for each person with the first rating serving as the anchor (i.e., time 0). Following the first rating, additional assessments

6 624 Beevers and Carver were made at the following times (in minutes): 3, 6, 11, 17.5, 18.5, 21.5, 24.5, 27.5, and The timing of the sadness ratings coincided with pauses in between tasks. Adverse Events Questionnaire (AEQ) The AEQ measures adverse events in participants lives during the time between measurement periods. This brief measure was designed specifically for a college population (for items see, Carver, 1998). Two items measure difficulties in the domains of academics and relationships, one item measures the occurrence of negative events in any other domain, and the fourth item measures the accumulation of minor problems. For each question, using a Likert-type scale from 0 to 3, participants indicated the frequency with which they encountered difficulties in each domain (i.e., 0 = No, 1 = Yes, this happened to me once, 2=Yes, this happened to me twice, 3=Yes, this happened to me more than twice.). This measure has been found to interact with a cognitive vulnerability in the prospective prediction of dysphoria (Carver, 1998). Dot-Probe Task In this computerized task, pairs of words are presented simultaneously for 750 ms. The words are centered on the screen 3-cm apart. Both words then simultaneously disappear from the screen, there is a 200-ms pause, and a dot appears in the spatial location of one of the words. Participants press one of two response box buttons, as quickly as possible, to indicate the location of the dot. The rationale behind this task is that people will identify the location of the dot more quickly when it follows the location of a word to which they were attending. Depressed individuals, for example, identify the location of the dot more quickly when it follows a negative word (for a review, see Gotlib & Neubauer, 2000). There were 32 pairs of valenced stimuli. The pairings were randomly determined (with the constraint that each pair contains one negative and one positive word) and then presented in a random order. For 16 trials the probe (dot) followed a negative word. For 16 trials the probe followed a positive word. For 8 trials, baseline reaction time was determined by participants response to xxxxxxs instead of words. Baseline reaction time was used as a covariate in analyses to control for individual differences in reaction time. Prior to starting the task with the valenced stimuli, 16 trials of neutral word pairs served as practice trials. The stimuli were taken from a standardized list developed by John (1988). The same words were used across both administrations of the task. The positive and negative words were equivalent in their emotionality, frequency of the word per million, and word length (all Fs < 1, ns). Incorrect, extremely fast (less than 200 ms), and extremely long responses (more than 2000 ms) were deleted and not analyzed. These types of responses occurred on 4.6% of the trials, which is consistent with previous research (e.g., Gilboa-Schechtman, Revelle, & Gotlib, 2000). Filler Tasks Three filler tasks were also used. These tasks were computer-based information processing tasks and resembled the dot-probe task in that they measured responses to

7 Predictors of Dysphoria 625 stimuli presented on the monitor. All participants completed these tasks in the same order. These tasks were included in the study to prolong the time period following the mood induction so we could assess for mood persistence and to provide pilot data for future research. Because of their exploratory nature, they are not discussed further here. Procedure Time 1 Participants who completed the IDD-L during mass pretesting sessions were recruited. After briefly reviewing study procedures, participants gave their consent to participate. Participants then completed the BDI and Mood Checklist. From that point on, all instructions were presented via computer monitor. The experimenter was available to answer questions throughout the session. Next, participants completed the dot-probe task followed by the filler tasks. The first sadness rating was made immediately after the dot-probe task (minute 0) with additional sadness ratings made at minutes 3, 6, and 11. These ratings coincided with pauses between tasks. Next, the sadness mood induction was started. It occurred from minute 11 to minute For this procedure, participants were instructed to visualize their best friend suffering through various stages of cancer, from diagnosis to eventually culminating in death. Fourteen audio prompts guided the imagery. This procedure has been shown to induce a mild negative mood state (Cervone, Kopp, Schaumann, & Scott, 1994). A sadness rating was completed immediately afterward (minute 17.5) followed by the Negative Mood Adjective Checklist. Following the sadness induction, the information processing tasks were administered in the same order as before the dot-probe task followed by three filler tasks (minutes ). Participants rated their level of sadness on the visual analog scale immediately before the dot-probe task and then again in between each task. These sadness ratings occurred at minutes 18.5, 21.5, 24.5, 27.5, and Time 2 At the end of the first session, an appointment was made for the participant to return 6 to 8 weeks following the initial session. At follow-up, participants completed a measure of intervening life stress (AEQ) and dysphoria (BDI). RESULTS Manipulation Check We first examined whether the mood induction created an increase in negative mood, using a repeated measures ANOVA with total score on the Negative Mood Adjective Checklist (preinduction, postinduction) as the repeated measure. This test revealed a significant main effect for the repeated measure, F(1, 59) = 47.73, p <.001, η 2 =.45, indicating a significant increase in negative mood after the

8 626 Beevers and Carver induction (preinduction M = 15.11, SD = 3.27; postinduction M = 24.92, SD = 11.00). A similar pattern occurred for the visual analog sadness ratings: a significant increase in sadness, F(1, 69) = , p <.001, η 2 =.73, from preinduction (M = 3.70, SD = 5.74) to postinduction (M = 34.51, SD = 18.90) ratings. Analytic Strategy Attentional Bias Standardized change scores (Judd & Kenny, 1981) were used to assess change in information processing from preinduction to postinduction. Standardized change scores are preferred over raw score change because the former controls for differences in variance at the pre- and postinduction time points (see Campbell & Kenny, 1999 for a discussion). Campbell and Kenny (1999) also recommend using standardized change scores instead of residualized change scores when one is interested in correlates of change, because under such circumstances residualized change scores can become unstable and biased. To compute a standardized change score, the pretest score is first multiplied by the standard deviation of the posttest divided by the standard deviation of the pretest, and then that value is subtracted from the posttest score. To determine change in reaction time for dot-probe stimuli, the following equation was used: Dot-probe = T 2Dot-probe (T 1Dot-probe SD T2Dot probe /SD T1Dot probe ) (1) Standardized change scores were computed for negative and positive dot-probe stimuli as well as the baseline (control) stimuli. Recall that faster reaction times on the dot-probe task reflect attentional bias. After centering the standardized change score around its mean, negative values indicate a relative decrease in reaction time and thus an increase in attentional bias. Positive standardized change scores indicate a relative increase in reaction time and thus a decrease in attentional bias. Statistical Model Our main interest was in the interaction between each vulnerability factor and life stress as predictors of change in dysphoria. We conducted hierarchical multiple regressions to examine these interactions. In each case, predictors were mean centered to reduce collinearity between main effect and interaction terms (Aiken & West, 1991). In the first step, initial BDI score and covariates (e.g., change in baseline attentional bias) were entered. Because we controlled for initial levels of dysphoria in the first step of the model, the dependent variable is interpreted as change in dysphoria. In the second step, vulnerability factor and life stress main effects were entered, followed by their interaction in the third step. When a significant interaction was obtained, it was explored through conditional regressions examining the vulnerability factor and high and low levels of each variable (1 SD above and below the mean; Aiken & West, 1991).

9 Predictors of Dysphoria 627 Conditional regressions examine relationships between independent (X ) and dependent (Y ) variables at specific values of the other independent variable (Z ). For instance, we could examine the relationship between attentional bias and followup dysphoria at different levels of life stress. Alternatively, we could examine the relationship between life stress and dysphoria at different levels of attentional bias. Ideally, relationships between X and Y are explored at theoretically meaningful points of Z, such as empirically established cutoff points (e.g., presence vs. absence of pathology). If such cutoff points do not exist, as in the present study, then any value within the full range of Z can be used. One SD above and below the mean is an often-used convention to establish high and low levels of a variable (Aiken & West, 1991). Aiken and West (1991) describe how to compute and test conditional regressions. First, select a conditional value, Z cv (e.g., 1 SD above its mean). Then substitute Z cv for Z in the original regression equation where possible and compute the conditional slope. Next, determine the standard error of the conditional slope based on the variance covariance matrix of the regression coefficients from the original equation (for details, see Aiken & West, 1991). Finally, the conditional slope and its standard error can be used to test the statistical significance of the conditional slope (i.e., whether it significantly differs from 0) using a standard t test. Because conditional regressions are empirically derived from initial regression analyses, the degrees of freedom from the initial regression are retained. This approach does not involve comparing subgroups within the original sample. Prospective Analyses Descriptive statistics for the sample at the two time points are in Table I. Overall, participants were nondepressed at both time points. However, there was considerably more variance in depression at the second time compared to the first. The mean level of life stress reported was also relatively low. Such levels of depression and life stress are consistent with findings from other research (e.g., Carver, 1998) and are typical of a nonclinical, undergraduate sample. Table I. Descriptive Statistics % n Female Male Remitted depressed Never depressed M SD Age Initial Beck Depression Inventory total score Follow-up Beck Depression Inventory total score Adverse Events Questionnaire Note. Gender data is missing for two participants.

10 628 Beevers and Carver Attentional Bias For negative stimuli on the dot-probe task, we expected that increased attentional bias (i.e., a lower change score) would combine with higher life stress to predict an increase in dysphoria. In Step 1, initial levels of dysphoria and change in baseline dot-probe reaction time accounted for 21.4% of the variance; main effects of life stress and change in reaction time to negative dot-probe stimuli accounted for an additional 2.9%, ns; most importantly, the interaction between life stress and change in reaction time to negative dot-probe stimuli was significant, β =.37, t(64) = 3.54, p <.001, explaining another 12.4% of the variance. We explored this significant interaction with conditional regressions at high and low levels of each predictor. At high levels of life stress, attentional bias was significantly related to depression at follow-up, B = 0.04, β =.50, t(64) = 3.15, p <.002; among people reporting high levels of life stress, greater attentional bias for negative stimuli (lower standardized change score) was associated with higher levels of dysphoria. At low levels of life stress, change in reaction time was not significantly related to dysphoria at follow-up, B = 0.03, β =.31, t(64) = 1.84, p <.10. Among people with high attentional bias for negative stimuli, increases in life stress were associated with greater dysphoria, B = 0.98, β =.41, t(64) = 2.93, p <.01. Among people with low attentional bias, higher life stress was associated with a lower in dysphoria, B = 0.96, β =.40, t(64) = 2.02, p <.05 (see Fig. 1). We also examined whether an attentional bias for positive stimuli interacted with life stress to predict change in dysphoria. In contrast to negative stimuli, attentional bias for positive stimuli did not combine with life stress to predict change in dysphoria. The interaction explained less than 1% of the variance in dysphoria at follow-up, B = 0.00, β = 0.01, t(64) = 0.78, ns. Fig. 1. Life stress and attentional bias for negative stimuli as predictors of change in dysphoria. Dysphoria is centered around the mean. High and low levels of life stress and attentional bias defined as 1 SD above and below mean.

11 Predictors of Dysphoria 629 Depression History We next tested whether depression history could account for the findings of the previous analysis. This also gave us the opportunity to examine whether depression history moderated the effect of attentional bias on life stress. However, it should be noted that our sample size provides relatively low power to detect a three-way interaction. Therefore, we were interested primarily in whether the interaction between attentional bias and life stress remained significant even after statistically accounting for depression history. Control variables (initial depression, change in baseline attentional bias) explained 24.1% of the variance in follow-up dysphoria. The main effects of life stress, change in negative attentional bias, and depression history group status explained an additional 8.2%, F(3, 64) = 2.48, p <.07. The two-way interactions (Life Stress Attentional bias, Life stress Depression history, Attentional bias Depression history) explained an additional 14.0%, F(3, 61) = 7.64, p <.05. As before, the interaction between life stress and attentional bias was significant, β =.28, t(62) = 2.08, p <.05. However, the interactions between life stress and depression history, β =.16, t(61) = 1.28, ns, as well as negative attentional bias and depression history, β =.00, t(61) < 1, ns, did not reach significance. Finally, the threeway interaction explained less than 1% of the variance in follow-up dysphoria, β =.09, t(62) < 1, ns. Negative Attentional Bias Prior to Mood Induction We next addressed the importance of the mood induction. As just described, change in attentional bias for negative information from preinduction to postinduction prospectively predicted change in dysphoria. An important question is whether preinduction cognitive bias also predicts change in dysphoria. Such a finding would question the importance of the mood induction and the hypothesis that a priming procedure is needed to activate a cognitive vulnerability. To examine this question, we tested whether attentional bias for negative stimuli on the dot-probe prior to the mood induction would combine with life stress to predict change in dysphoria. Initial depression and baseline reaction time prior to the mood induction were entered in the first step of the hierarchical multiple regression. These variables explained 22.7% of the variance in dysphoria at follow-up. The main effects for life stress and preinduction attentional bias explained an additional 3.7% of the variance, ns. Most importantly, the interaction between life stress and preinduction attentional bias explained less than 1% of the variance in change in dysphoria, β =.09, t(64) < 1, ns. Thus, attentional bias for negative stimuli prior to the mood induction did not combine with life stress to predict change in dysphoria. Mood Reactivity We defined reactivity to the mood induction as the linear slope for the two sadness ratings made immediately before and after the mood induction. For each person, an Ordinary Least Squares linear slope estimate was computed using Hierarchical Linear Modeling software (HLM; Byrk & Raudenbush, 1992; Byrk, Raudenbush, &

12 630 Beevers and Carver Congdon, 1996). Larger slopes indicate stronger mood reactivity and smaller slopes reflect less mood reactivity. These slopes were then used to predict follow-up dysphoria. Initial dysphoria was entered in the first step of the hierarchical multiple regression, linear reactivity slope and life stress main effects were entered in the second step, and their interaction was entered in the third step. Initial depression explained 21.0% of the variance in dysphoria at follow-up. The main effects for life stress and reactivity to the mood induction explained an additional 2.3% of the variance, ns; and the interaction between life stress and reactivity explained 3.5% of the variance in depression at follow-up, β =.27, t(65) = 1.76, p =.08. Because this interaction approached significance, conditional regressions at high and low levels of each variable were examined. For people with strong increases in sadness from the mood induction, increases in life stress were significantly associated with increases in dysphoria, B =.83, β =.35, t(65) = 2.11, p <.05. For people with small increases in sadness, life stress was not significantly related to depression at follow-up, B = 0.12, β =.05, t(65) = 0.33, ns. At high levels of life stress, reactivity was not related to depression at follow-up, B = 0.16, β =.11, t(65) = 0.72, ns. At low levels of life stress, an inverse relationship between reactivity and dysphoria approached significance, B = 0.43, β =.29, t(65) = 1.83, p =.07. This pattern does not support the hypothesis that sadness reactivity represents a vulnerability to dysphoria. Mood Persistence We next examined whether mood persistence following the mood induction interacted with life stress to predict change in dysphoria. To operationalize affect persistence, we computed the linear slope for change in sadness for each person from immediately after the mood induction until the end of the laboratory session. After centering the recovery data around its mean, positive slope values represent a slower recovery and thus a more persistent negative mood whereas negative slope values reflect a faster recovery. We expected that slower mood recovery would combine with higher life stress to predict a greater increase in dysphoria. Initial depression was entered in the first step of the hierarchical multiple regression, linear recovery and life stress were entered in the second step, and its interaction was entered in the third. Initial depression explained 21.0% of the variance in depression at follow-up. The main effects for life stress and recovery from the mood induction explained an additional 1.5% of the variance, ns; and the interaction between life stress and recovery explained an additional 10.5% of the variance in depression at follow-up, β =.32, t(65) = 3.19, p <.01. This significant interaction was explored in the same manner as the reactivity analyses. For people with slower mood recovery, higher life stress was associated with increased dysphoria, B = 1.21, β =.51, t(65) = 3.22, p <.01. For people with faster recovery, there was a tendency toward an inverse association between life stress and dysphoria, B = 0.65, β =.27, t(65) = 1.69, p =.09. At high levels of life stress, linear mood recovery was significantly related to change in dysphoria, B = 8.84, β =.45, t(65) = 2.80, p <.05, with slower mood recovery predicting greater

13 Predictors of Dysphoria 631 Fig. 2. Life stress and affective reactivity to the mood induction as prospective predictors of change in dysphoria. Dysphoria is centered around its mean. High and low levels of life stress and reactivity defined as 1 SD above and below mean. dysphoria. At low levels of life stress, recovery was also related to depression at follow-up, B = 6.03, β =.32, t(65) = 2.16, p <.05, but in the opposite direction (see Fig. 2); for people who experienced low life stress, slower recovery was associated with less dysphoria. Independence of Vulnerabilities Our final analysis examined whether negative attentional bias and mood persistence predicted unique or overlapping variance in follow-up dysphoria. To do so, we completed a regression analysis where change in negative attentional bias, linear mood recovery, intervening life stress, and their two-way and three-way interactions predicted dysphoria at follow-up. Results of this analysis are presented in Table II. Findings are largely consistent with previous analyses. The Negative attentional bias Life stress interaction and the Mood persistence Life stress interaction each uniquely predicted variance in follow-up dysphoria. The three-way interaction between life stress, mood persistence, and negative attentional bias was not significant. To further test their independence, we computed a partial correlation between negative attentional bias and mood persistence, controlling for baseline attentional bias. The correlation was small and not significant (r =.10, ns), further suggesting the vulnerabilities operate somewhat independently. DISCUSSION This study examined whether an attentional bias for negative information and persistence of negative mood would interact with life stress to predict change in

14 632 Beevers and Carver Table II. Final Hierarchical Regression Analysis of Dysphoria at Follow-Up Variable B β t(df) p R 2 F(df ) p Step (2, 67).000 Time 1 BDI (67).001 Attentional bias baseline (67) ns Step (3, 64) ns Time 1 BDI (64).001 Attentional bias baseline (64) ns Life stress (64) ns Attentional bias negative (64) ns Mood persistence (64) ns Step (3, 61).001 Time 1 BDI (61).001 Attentional bias baseline (61) ns Life stress (61) ns Attentional bias negative (61) ns Mood persistence (61) ns Life stress bias negative (61).007 Life stress Mood persistence (61).025 Bias negative Mood (61) ns persistence Step (1, 60) ns Time 1 BDI (60).001 Attentional bias baseline (60) ns Life stress (60) ns Attentional bias negative (60) ns Mood persistence (60) ns Life stress bias negative (60).007 Life stress Mood persistence (60).025 Bias negative Mood (60) ns persistence Three-way interaction (60) ns dysphoria across a 7-week period. As expected, even after controlling for depression history, increased attentional bias for negative information from before to after a negative mood induction combined with higher levels of intervening life stress to prospectively predict increased dysphoria. Attentional bias for positive stimuli did not do so. Attentional bias for negative stimuli measured prior to the mood induction also did not do so. Slower mood recovery following the induction was also associated with increased dysphoria at follow-up among people who also reported higher levels of intervening life stress. Finally, these two vulnerabilities explained unique variance in dysphoria at follow-up and were not significantly correlated. The finding that attentional bias following a negative mood induction was associated with increases in dysphoria is consistent with some previous evidence (Gemar et al., 2001; Ingram et al., 1994; Ingram & Ritter, 2000). Previous research, however, focused primarily on whether remitted depressed and never depressed people differ on measures of attentional bias. An important limitation of that approach is that it cannot determine whether attentional biases render a person vulnerable to future increases in dysphoria, or whether such biases are inert psychological scars from past episodes (Lewinsohn, Steinmetz, Larson, & Franklin, 1981). We cannot rule out the possibility that attentional biases are remnants from previous dysphoric episodes.

15 Predictors of Dysphoria 633 However, the present study suggests that they are not inert; rather, they appear to moderate the impact of life stress on dysphoria. This finding is also consistent with recent work completed by MacLoed and colleagues (MacLeod, Rutherford, Campbell, Ebsworthy, & Holker, 2002). Across two studies, MacLoed et al. (2002) found that people who were trained to attend to aversive stimuli on a dot-probe task reported greater depressed and anxious mood following a stress test than people who were trained to avoid aversive stimuli. Interestingly, other work has also found an association between attentional biases and anxiety (see Williams et al., 1988). Because we assessed depression but not anxiety, we do not know whether the effects found here would generalize to anxiety. To examine the differential effects of attentional bias on depression and anxiety, future research should incorporate inventories that assess both. The findings of the present work are also supportive of the priming hypothesis (Segal & Ingram, 1994). Attentional bias following the mood induction did combine with life stress to predict increased dysphoria whereas attentional bias prior to the mood induction did not. These results suggest that although attentional biases are stable features of personality, they must be activated in order to be detected with validity. Future work should identify additional priming procedures, such as cognitive primes (e.g., Segal & Gemar, 1997), which can be used to activate cognitive vulnerabilities so that the parameters of the priming hypothesis can be further delineated. The speed at which a person s mood returned to baseline following a negative mood induction also interacted with intervening life stress to predict increases in dysphoria. Specifically, people who took longer to recover from the mood induction and also experienced higher life stress reported greater increases in dysphoria than people with high life stress but faster mood recovery. For people whose mood recovered quickly, life stress was unrelated to dysphoria at follow-up. It appears that mood persistence may be an important marker of vulnerability to future dysphoric episodes (Scott, Winters, & Beevers, 2000; Teasdale, 1988). An important endeavor for future research is to determine why emotional states decay more slowly for some people than others. Think-aloud protocols may provide insight into cognitive differences that contribute to slower mood dissipation. For instance, it may that ruminating about negative content activated by a dysphoric mood contributed to greater mood persistence (for a review, see Nolen-Hoeksema, 1998). Although differences in ruminative cognition may contribute to affective persistence, it should be noted that in the present work participants completed computer tasks throughout the laboratory session. Cognitive effort is required to complete such tasks, creating a condition that is not conducive to rumination. Other factors are likely to be involved. Interestingly, in the present study, attentional bias and mood persistence were only weakly associated with each other. In addition, these vulnerabilities explained unique variance in follow-up dysphoria. One explanation for this lack of association could be that the vulnerabilities are associated with different aspects of depression. According to tripartite models of depression and anxiety (Clark & Watson, 1991), negative affectivity is common to both depression and anxiety. Low positive affectivity is thought to be unique to depression. Attentional biases, which are often seen in depression and anxiety, may be more strongly associated with high negative

16 634 Beevers and Carver affectivity than low positive affectivity. Mood persistence, in contrast, may be more closely associated with low positive affectivity than high negative affectivity. Because the depression inventory used in the present study does not discriminate between negative and positive affectivity, we were unable to examine this hypothesis. Future work using measures that differentiate between negative and positive affectivity should examine this intriguing possibility. The present findings suggest an avenue for intervention, though such possibilities obviously go well beyond the data. Specifically, it may be of value to teach depression-vulnerable people to effectively regulate their attention and moods following negative events. Unfortunately, more is known about ineffective than effective affect regulation. Trying to suppress unwanted thoughts and feelings has been shown to ironically amplify the very thoughts and feelings targeted for suppression (for a review, see Beevers, Wenzlaff, Hayes, & Scott, 1999; Wenzlaff & Wegner, 2000). Ruminating about depressive thoughts and feelings can prolong negative mood states (Nolen-Hoeksema, 1998). Focusing on distracting thoughts that are unrelated to negative mood states appears to shorten mood persistence (Morrow & Nolen-Hoeksema, 1990), but several studies involving clinical samples have found that the dispositional use of distraction was not associated with reductions in depression (Kuehner & Weber, 1999; Schmaling, Dimidjian, Katon, & Sullivan, 2002). Other recent research has examined more accepting methods of affect regulation (Teasdale, Segal, & Williams, 1995). Preliminary work suggests that teaching acceptance-based cognitive and affect regulation strategies to depression vulnerable people reduces relapse among people with three or more previous episodes of depression compared to treatment as usual (Teasdale et al., 2000). Although there is some research to suggest that this type of training can reduce cognitive biases such as recall of overgeneralized autobiographical memories (Williams, Teasdale, Segal, & Soulsby, 2000), future research should also test whether this type of training also mitigates attentional biases and mood persistence. Several limitations of the present work should be noted. Despite using stimuli that were matched for emotionality, usage in the English language, and word length (John, 1988), concern relevance (i.e., the association between stimuli and the participant s current concern) was not taken into account. Recent research demonstrated that participant-provided stimuli produced more attentional interference on an emotional Stroop task than experimenter-provided stimuli even after controlling for lexical differences between the sets of words (Gilboa-Schechtman et al., 2000). The assessment of relevance in the present study may have produced an even more sensitive measure of attentional bias. Another limitation was the use of a self-report measure of life stress. Compared to checklist approaches, interview-based assessments have the strengths of affording more accurate dating of events (McQuaid, Monroe, Roberts, Johnson, & Garamoni, 1992), improving recall (Cannell, Miller, & Oksenberg, 1981), and minimizing the number of response errors due to individual differences among participants (Kessler & Wethington, 1991). It is possible, then, that increased precision afforded by an interview-based assessment of life stress may provide additional information about the types of events most likely to combine with cognitive and affective vulnerabilities to produce increased dysphoria.

17 Predictors of Dysphoria 635 The present study used a relatively short time period between assessments (approximately 7 weeks). The time between assessments combined with the sampling approach may have restricted the amount of change in dysphoria seen between assessment points. Certainly, the degree of change in dysphoria predicted by attentional bias and mood persistence was relatively small and not clinically significant. Indeed, the range of dysphoria was within normal limits for college populations. It is thus unclear whether attentional bias and mood persistence would predict change in more severe forms of depression. Additional research using interview-based assessments across longer time frames and with people that more comprehensively reflect clinical depression is clearly needed (e.g., Segal et al., 1999). A final limitation is the use of a convenience sample of college students. Although it is not uncommon to use this sampling approach early on in the investigation of a research question, this sampling strategy clearly limits the generalizabilty of the present findings. An additional limitation of our sample is that the majority of participants were female. Given that previous work has found gender differences for certain cognitive vulnerabilities (Nolen-Hoeksema, 1998), additional research is needed to test whether the vulnerabilities observed in the present study apply equally to men and women. Despite these limitations, the present study is a useful step towards understanding the contribution of attentional bias and mood persistence to depression vulnerability. Increased attention to negative information as well as longer mood recovery following a negative event may leave a person at risk for the intensification of dysphoric symptoms. Additional research is now needed that examines the potentially reciprocal relationship between mood persistence and cognitive bias. Cognitive biases that involve more elaborative and sustained information processing may be particularly good candidates (Teasdale, 1988). Such research may increase our understanding of depression vulnerability and inform treatment development. ACKNOWLEDGMENTS This study was supported by the Jennifer L. Strauss Dissertation Support Award from the University of Miami. REFERENCES Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Thousand Oaks, CA: Sage. Beck, A. T., & Beamesderfer, A. (1974). Assessment of depression: The depression inventory. In P. Pichot & R. Olivier-Martin (Eds.), Psychological measurements in psychopharmacology. (Vol. 7, pp ). Basel, Switzerland: Darger. Beck, A. T., Rush, A. J., Shaw, B. F., & Emery, G. (1979). Cognitive therapy of depression. New York: Guilford. Beck, A. T., Steer, R. A., & Garbin, M. G. (1988). Psychometric properties of the Beck Depression Inventory: Twenty-five years of evaluation. Clinical Psychology Review, 8, Beevers, C. G., Wenzlaff, R. M., Hayes, A. M., & Scott, W. D. (1999). Depression and the ironic effects of thought suppression: Therapeutic strategies for improving mental control. Clinical Psychology: Science and Practice, 6, Bradley, B. P., Moog, K., & Lee, S. C. (1997). Attentional biases for negative information in induced and naturally occurring dysphoria. Behaviour Research and Therapy, 35,