An Experimental Investigation of the Effect of Worry on Responses to a Discrimination Learning Task

Available online at www.sciencedirect.com

Behavior Therapy 39 (2008) 251 – 261

www.elsevier.com/locate/bt

An Experimental Investigation of the Effect of Worry on Responses to a Discrimination Learning Task Kristalyn Salters-Pedneault,a University of Massachusetts, Boston Michael Suvak,b VA Boston Healthcare System and Boston University Lizabeth Roemer,c University of Massachusetts, Boston

The current study examined the impact of both the tendency to worry (trait worry) and the process of worry (state worry) on subsequent behavioral responding in a schedule discrimination learning task. High and low trait worriers were randomly assigned to a state worry or relaxation incubation condition and completed a test of executive functioning and a dual contingency learning task that utilized neutral discriminative cues over the course of 2 contingency phases. Although state and trait worry did not impact executive functioning, the state worry condition was associated with diminished sensitivity to learning task contingencies over the course of the first contingency learning trials in comparison to the relaxation condition. This relationship was unique to the state worry condition above and beyond shared variance with subjective anxiety level. Results suggest that state worry may lead to a decrement in selective behavioral responding to neutral discriminative cues in the environment. The findings suggest that the process of worry may lead to less adaptive responding to neutral cues and interfere with adaptive behaviors, which may thereby contribute to and maintain anxiety.

Data collection and preparation of this article was supported in part by National Institute of Mental Health Grant MH068962 to the first author. Portions of this dataset were presented at the 2003 annual meeting of the Association for the Advancement of Behavior Therapy (now the Association for Behavioral and Cognitive Therapies). The authors thank Douglas Mennin, Alice Carter, Kelly Wilson, and Eric Blaser for their advice on the design of this study, and Shireen Rizvi for her helpful comments on this manuscript. Address correspondence to Kristalyn Salters-Pedneault, National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System (116B-5), 150 South Huntington Avenue, Boston, MA, 02130. Tel.: 857 364 5638; e-mail: [email protected]. 0005-7894/08/251–261$1.00/0 © 2008 Association for Behavioral and Cognitive Therapies. Published by Elsevier Ltd. All rights reserved.

GENERALIZED ANXIETY DISORDER (GAD), which is characterized by excessive and uncontrollable worry about a variety of topics (along with associated features such as trouble sleeping and impaired concentration; American Psychiatric Association, 1994), is a common (Wittchen, Zhao, Kessler, & Eaton, 1994), chronic (Noyes et al., 1992) condition associated with significant costs to the individual and to society (Wittchen, 2002). Unfortunately, GAD is also treatment-resistant relative to other anxiety disorders (Brown, Barlow, & Liebowitz, 1994). Theorists have recently suggested that a better understanding of the process and function of worry (the central feature of GAD), rather than a continued focus on worry content, is warranted to improve treatment efficacy (e.g., Borkovec, Alcaine, & Behar, 2004; Roemer & Orsillo, 2002). However, to date, the likely wideranging cognitive, emotional, and behavioral costs of worry are not fully understood, and there are a variety of proposed sequelae of worry that have not yet been explored. Roemer and Orsillo (2002) have hypothesized that one consequence of the worry process may be an interruption of active engagement with internal and external stimuli, including thoughts, sensations, emotions, and environmental contexts and cues. This reasoning is in line with findings that worry is associated with narrowed attention toward threatening or negatively valenced stimuli (e.g., Mogg, Bradley, Millar & White, 1995; Mogg, Mathews, & Weinman, 1987) and that worry tends to be future, rather than present, focused (Molina, Borkovec, Peasley, & Person, 1998). As attention narrows toward threat and cognition is drawn toward the consideration of future events, worry may deplete the cognitive resources available to incorporate and adaptively respond to stimuli in the current moment context. This process may be

252

salters-pedneault et al.

particularly true for neutral or appetitive stimuli. If worry promotes hypervigilance for negatively valenced stimuli, important stimuli without particular threat relevance may be missed, whereas threatrelevant stimuli may be overvalued. This disengagement from internal and external stimuli may have a host of negative consequences for worriers, including diminished clarity about internal experiences (a possibility supported by recent findings from the literature on the role of emotionregulation deficits in GAD; Mennin, Heimberg, Turk, & Fresco, 2005; Salters-Pedneault, Roemer, Tull, Rucker, & Mennin, 2006), and potentially disrupted responding to key environmental cues. If, in addition to being a subjectively distressing experience, worry also impedes adaptive engagement with nonthreatening external cues that predict the most appropriate or adaptive behavior, individuals who experience pathological worry may respond less adaptively than nonworriers and may come to feel increasingly confused or frustrated by their interactions with the world. This potential cost of worry may be particularly troublesome because the effects of worry may last beyond the time that the individual is actively engaged in worrying. Studies have suggested an incubation effect of worry; worrying for a short period of time appears to affect responding for minutes (e.g., Borkovec, Robinson, Pruzinsky, & DePree, 1983) to days (Wells & Papageorgiou, 1995) afterwards. Borkovec and colleagues (1983) proposed that the worry incubation effect may be due to an exacerbation of distracting, negative thoughts that occurs during subsequent nonworry periods. Thus, worrying for even a short duration may interfere with adaptive functioning over longer periods of time and may thereby contribute to chronic anxious feelings. In the current study we were interested in examining the impact of both the tendency to worry (trait worry) and the process of worry (state worry) on subsequent adaptive responding to a learning task involving discrimination of neutral behavioral cues. To date, some studies have suggested that worry may affect scores on a variety of performance tasks (e.g., reading tests; Calvo & Eysenck, 1996; ambiguous categorization tasks; Metzger, Miller, Cohen, Sofka, & Borkovec, 1990). Although the precise mechanism of this effect is unknown, it may be the result of increased working memory load (e.g., Calvo & Eysenck, 1996) and/or distraction from the target task (Alting & Markham, 1993) during worry. Unfortunately, little is known about the specific types of performance tasks that are affected, or whether state or trait worry, or both, may have an impact on task performance. In order to examine our specific question, high and low

worriers assigned to a worry or relaxation incubation condition completed a test of executive functioning (the Wisconsin Card Sorting Task; WCST; Heaton, 1981) and a two-phase discrimination learning task. The WCST was included as a control task to examine the impact of state and trait worry on various aspects of general executive functioning (i.e., is it simply that worry, through subsequent effects on cognitive load/distraction in general, affects general functioning on performance feedback tasks?), whereas the learning task was included to specifically examine the effects of worry on interaction with, and responding to, explicit neutral discriminative cues for behavior. The learning task included two cued contingency phases, which allowed us to examine the impact of state and trait worry on learning of the contingencies cued by the neutral discriminative stimuli, as well as on adaptation to a shift in cued contingencies. We hypothesized that both state and trait worry would interfere with adaptive responding on the discrimination learning task. Specifically, we predicted that the effects of state worry (versus relaxation) on adaptive responding to the learning task would be evident both in high and low worriers (i.e., all individuals in the state worry condition would exhibit less sensitive responding to initial learning trials and to trials in a second contingency phase, after a contingency shift, and would demonstrate more difficulties adapting to the contingency shift, compared to individuals in the state relaxation condition). Also, given the often pervasive nature of trait worry (and the possibility of a worry incubation effect that may occur for some time after worry), we predicted that high trait worriers would demonstrate less sensitive responding on both contingency phases of the learning task and diminished adaptation to the contingency shift (regardless of state context), compared to low worriers. We did not make a priori hypotheses about the interaction of state and trait worry because we expected all individuals to be able to engage in state manipulations to a sufficient degree to produce effects on outcomes (as has been the case in previous studies utilizing a worry incubation manipulation; e.g., Borkovec et al., 1983) and because we expected that the effect of worry on outcomes would be limited by a ceiling effect (i.e., high worriers would not necessarily be more affected by the worry incubation manipulation than low worriers). Also, given evidence that normal worry is not different than pathological worry (except in terms of frequency and perceived controllability; cf. Roemer, Orsillo, & Barlow, 2002), we did not expect a differential effect on the moment-to-moment functioning of low and high worriers (although, we might expect

worry and discrimination learning a cumulative effect of pervasive worry on the functioning of high worriers in their daily lives due to the frequency of their worry). We hypothesized that the impact of state and trait worry on learning task outcomes would be evident above and beyond any impact of worry on general executive functioning and task performance (as measured by the WCST). Finally, we hypothesized that the effects of state and trait worry would remain significant when variance contributed by subjective state anxiety was controlled.

Method participants Sixty participants (40 female) were recruited through prescreening questionnaires distributed at the University of Massachusetts, Boston, from September 2002 through May 2004. Potential participants completed the Penn State Worry Questionnaire (PSWQ; Meyer, Miller, Metzger, & Borkovec, 1990) and the Shipley Institute of Living–Vocabulary Scale (SIL-V; Shipley, 1940). Individuals who scored below 16 total correct answers on the SIL-V were excluded from the study due to the possibility of cognitive impairment. Individuals who scored 60 or above (high trait worry) or 44 or below (low trait worry) on the PSWQ were contacted for participation in the experimental study. These cutoff scores were chosen based on published normative data for the PSWQ; each cutoff was within one standard deviation of the more conservative of two published norms for individuals with GAD and selected nonanxious individuals (Brown, Antony, & Barlow, 1992; Molina & Borkovec, 1994).1 The final sample included 30 low worriers and 30 high worriers, ranging in age from 18 to 66, with a mean age of 22.92 (SD = 7.54).

1

The normative data on which we based the cut off scores are as follows: Brown et al. (1992), GAD M = 68.11, SD= 9.59, non-anxious controls M = 34.90, SD = 10.98; Molina and Borkovec (1994), GAD, M = 67.66, SD = 8.86, non-anxious controls M = 44.27, SD = 11.44. A receiver operating characteristic analysis of the PSWQ in an unselected college sample was published after data collection began for this study (Behar, Alcaine, Zuellig, & Borkovec, 2003), and suggests only a slightly more stringent criterion (PSWQ score = 62) for selecting individuals with clinical levels of worry in this population. Of note, our criterion for high worry was more conservative than that chosen in previously published experimental studies of worry in analogue samples (e.g., Startup & Davey, 2001; McKay, 2005), and the cutoff scores we chose produced groups with means (see Table 1) comparable both to those reported both by Brown et al. (1992) and Molina and Borkovec (1994), and those reported for the selected high and low worry groups in other analogue studies (e.g., Startup & Davey, 2001).

253

measures Trait worry. The Penn State Worry Questionnaire (PSWQ; Meyer et al., 1990) is a 16-item self-report questionnaire that measures individual differences in intensity and excessiveness of worry; scores range from 16 to 80. This measure has excellent psychometric properties in both nonclinical and clinical populations (Molina & Borkovec, 1994), and discriminates worry within anxiety disordered samples (Brown et al., 1992). The PSWQ is also sensitive to changes in worry resulting from psychological treatment (e.g., Borkovec & Costello, 1993). In the current sample, the PSWQ demonstrated excellent internal consistency (Cronbach's α = .96). General intellectual functioning. The vocabulary subtest of the Shipley Institute of Living Scales of Intelligence (SIL-V; Shipley, 1940), a 40-item vocabulary test that requires the respondent to choose which of four listed words means the same or nearly the same as a specified target word, was used as a prescreening measure of general intellectual capabilities. The SIL-V has been used extensively as an estimate of intellectual ability (Dalton, Pederson, & McEntyre, 1987), and psychometric data suggest that this scale provides as accurate an estimate as the full test, with significantly less administration time (Dalton et al., 1987). In this study, individuals with vocabulary scores lower than two standard deviations below the mean raw score (scores of 16 or below) of a representative sample at the research setting (M = 29.10, SD = 6.47; this corresponds with means and standard deviations in normative samples; Paulson & Lin, 1970) were excluded due to possible intellectual impairment. A total of 3 potential participants were excluded for this reason. In the current sample, the SIL-V demonstrated good internal consistency (Cronbach's α = .81). General executive functioning. The 64-card computerized version of the WCST (Heaton, 1981) was administered to assess the effects of state and trait worry on general executive functioning. The WCST includes many properties similar to the discrimination learning task (described below); both require nonverbal reasoning, absorb cognitive resources (i.e., working memory), and involve performance feedback. The WCST, however, does not include discrete discriminative cues that precede and predict the most adaptive response, and thus inclusion of this task allowed us to examine the effects of state and trait worry on effective engagement with the neutral discriminative cues of the learning task beyond any potential impact of worry on general executive performance and/or depletion of cognitive resources. The computerized

254

salters-pedneault et al.

WCST has demonstrated fair to good test-retest reliability and convergent validity with other neuropsychological tests (Kongs, Thompson, Iverson & Heaton, 2000). Raw scores for total errors on the WCST were used as the dependent measure of executive functioning, and mean scores obtained in this sample were commensurate with norms reported for normal adults (Kongs et al.). Subjective distress. The Subjective Units of Distress Scale (SUDS; Wolpe, 1990) is a self-report measure of general subjective anxiety. Participants rated, on a scale from 0 to 100, their current level of anxiety. The SUDS was administered at baseline, after the two experimental manipulations, and after the learning task, to provide a manipulation check and track the potency of the manipulation throughout the protocol. Discrimination learning. A computerized dual contingency discrimination learning task, adapted from a paradigm used in numerous previous studies of schedule discrimination learning (e.g., Hayes, Brownstein, Haas, & Greenway, 1986; Rosenfarb, Burker, Morris, & Cush, 1993; Rosenfarb, Newland, Brannon, & Howey, 1992), was used to measure sensitivity to current contingency cues and adaptation to a contingency/cue shift. Participants were seated in front of a computer screen on which a 5-inch square grid, with a circle in the upper left corner, appeared. A light bulb appeared on the bottom right corner of the screen and at the top of the screen was a score counter. A keypad, with three red buttons, was placed within the participants' reach. During the first contingency phase, the circle on the grid moved according to a differentialreinforcement-of-low-rate 1-second (DRL 1-s) or fixed ratio 1 (FR 1) schedule. The DRL and FR schedules alternated in 2-minute segments. During the 2 minutes of the DRL schedule, the light bulb was in the bottom right corner of the screen, whereas during the 2 minutes of FR, the light bulb was in the bottom left corner of the screen. Thus, successful learning depended on using these cues. During both schedules, the left button moved the circle across, the right button moved the circle down, and the middle button had no effect on the circle's movement. During the DRL schedule, the first button push after 1 second moved the circle. Participants were able to push the button as many times as they wished during the DRL with the caveat that the circle would not move any faster than once every second. During the FR schedule, the circle moved with each button press. In order to earn a point during both schedules, participants moved the circle to the lower right section of the grid and then pressed the middle button. At this time, one point was added to the score counter, the circle was returned to the upper

section of the grid, and the participant began again. After 30 minutes, there was a 2-minute break in the task. During the break participants were asked to engage in another 1-minute worry or relax period. Once the task resumed, the DRL and FR schedules continued as programmed for another 10 minutes, for a total of 40 minutes of trials (or 10 DRL/FR trial blocks). Then, without notice, the contingencies and cues shifted. After the shift, the DRL phase was associated with the light bulb in the lower left-hand corner and the FR phase was associated with that in the lower right-hand corner. And, rather than operating on a DRL 1-s/FR 1 schedule, the task now followed DRL 3-s/FR 3 schedules. These new cues operated for a total of 20 minutes (or 5 DRL 3s/FR 3 trial blocks). Thus, the first 10 trial blocks assessed sensitivity to the initial contingencies of the task, whereas the next 5 blocks assessed adaptation to a new set of contingencies and sensitivity to these new contingencies.

procedure This study employed a 2 (Group: high worry, low worry) × 2 (Condition: worry, relax) between-participants design. Participants were randomly assigned to a worry or relaxation incubation condition (30 per condition, balanced for group membership). A between-(rather than within-) participants design was chosen to reduce overall participant burden/ fatigue and to avoid carryover effects resulting from experimental instructions. Participants first rated their subjective level of anxiety using the SUDS, and next received experimental instructions to either worry or relax. Individuals in the worry incubation condition heard a brief description of worried thought and were asked to worry “about any topic or topics that come to mind” for 5 minutes. Those in the relaxation condition heard instructions on deep, diaphragmatic breathing, and were asked to “practice this relaxation technique” for 5 minutes.2 Participants completed another SUDS rating (to check the effect of the manipulation). Following the manipulation, participants completed the WCST. Participants then engaged in a second worry/relax

2

Although worry incubation manipulations used in previous experimental studies have typically included a 12-to 15-minute worry period in order to ensure the incubation effect (e.g., Borkovec et al., 1983; Hazlett-Stevens & Borkovec, 2001), we chose to include two 5-minute worry/relax periods and one additional brief (1 minute) worry/relax period (11 minutes total), due to concerns about participant burden and fatigue, given the length of the protocol, and to ensure that the incubation effect was potent for the full duration of the study. Although this is a departure from the previous literature, results of the manipulation checks suggest that the worry incubation effect was achieved.

255

worry and discrimination learning Table 1 Means, Standard Deviations, and Analysis of Variance of Individual Difference Variables, Baseline SUDS, and WCST (N = 60)

SIL-V PSWQ SUDS WCST a

Relax (n = 30)

Worry (n = 30)

ANOVA F(d)

Group

M

SD

M

SD

Group (G)

High Low High Low High Low High Low

30.10 30.39 68.00 35.14 20.00 10.00 15.19 13.36

3.93 6.04 6.78 7.74 14.83 11.44 7.83 6.89

29.31 32.11 65.54 32.44 23.00 13.13 13.86 14.07

3.91 5.67 4.07 5.29 22.19 9.11 9.50 5.84

Condition (C)

G×C

1.43(0.31)

0.13(0.09)

0.96(0.26)

432.08⁎⁎(5.41)

2.65(0.42)

0.01(0.26)

5.43⁎(0.61)

1.24(0.29)

0.77(0.23)

0.17(0.11)

0.02(0.04)

0.26(0.13)

Note. SIL-V = Shipley Institute of Living Vocabulary Scale; PSWQ = Penn State Worry Questionnaire; SUDS = Subjective Units of Distress Scale baseline; WCST = Wisconsin Card Sorting Test total errors. a n = 59. ⁎ p b .05, ⁎⁎ p b .01.

induction to ensure that the incubation effect was potent throughout the session and completed another SUDS rating. Next, participants were told that they would be engaging in a learning test and that over the course of the test they could receive up to an additional 10 dollars by earning 1 cent per point; one point was awarded each time they moved the circle from the upper left-hand corner to the lower right-hand corner of the grid using the three red buttons. (Actually, all participants earned an extra $10 for a total of $30; this deception was used to heighten continued motivation for the task.) They were also told that light bulbs on the screen would give them additional information, but were given no further instructions on the discriminative cues or contingency schedules. After the participant finished the learning task, they completed a brief follow-up questionnaire that assessed current SUDS and adherence to experimental instructions. Finally, participants were debriefed by a graduatelevel clinician and compensated.

Results data scoring and transformation The data from the learning task were scored in the same manner as previous studies (e.g., Rosenfarb et al., 1993). To create an index of sensitivity to the dual contingencies of the task, percentage of FR responding was computed by dividing the number of responses given during each 2 minutes of the FR schedule by the number of responses given during the 2 minutes of the FR schedule plus the number of responses during the 2-minute DRL schedule (FR/ [FR + DRL]). More sensitive responding was indicated by a higher percentage of responding on the FR trials (because the most efficient way to earn points was to respond quickly during the FR and

more slowly during the DRL). These calculations resulted in FR percentage values for 10 DRL/FR trial blocks before the cue shift and 5 DRL/FR blocks following the cue shift. Four participants (2 high worry/relax condition, 1 low worry/worry condition, and 1 low worry/relax condition participant) withdrew due to the long duration of the task. Additionally, for 3 participants, data from one or more trials (totaling less than 2% of data) were missing due to technical error. There were no differences between individuals who dropped out of the learning task and completers on any of the key study variables. SUDS ratings were significantly positively skewed across all administrations; square root transformations resulted in normal distributions (for clarity, untransformed values are reported in Table 1).

repeated measures analysis plan We used the Hierarchical Linear and Nonlinear Modeling software program (HLM 6; Raudenbush, Bryk, & Congdon, 2005) to conduct multilevel regression analyses on the repeated measures data; these analyses allowed us to examine the impact of the predictors on the trajectory of change over time (e.g., the learning curve) and to include participants with missing data for some trials. The analyses were conducted in a hierarchical manner. First, an unconditional model with no predictors entered was evaluated to establish a baseline to compare later models. Next, models including the main effects, two-way interactions (e.g., Group× Trial, Condition× Trial, and Group× Condition), and three-way interactions (e.g., Group × Condition × Trial) were evaluated. The overall significance of each step was assessed by examining improvement in model fit over the previous model (as assessed by the change in the deviance [Δdev] statistic; Raudenbush & Bryk,

256

salters-pedneault et al.

FIGURE 1 Mean Subjective Units of Distress Scale (SUDS) scores at baseline and across the experimental protocol as a function of group by condition.

2002). Regression coefficients were then examined only for significant steps. Because multilevel regression does not produce a standardized regression coefficient, we report Cohen's d as an estimate of effect size and partial regression coefficients (pr) for comparison on a common metric.

preliminary analyses Randomization and group differences check. Means and standard deviations of individual difference measures and baseline SUDS scores for groups by condition are reported in Table 1. To test the success of the random assignment to conditions, and to ensure no unexpected differences between groups, 2 (Group: high worry v. low worry) × 2 (Condition: relax v. worry) ANOVAs were conducted for SIL-V, PSWQ, and baseline SUDS ratings. No main effects of condition emerged on any measure, suggesting successful randomization. As expected, main effects of group emerged for the PSWQ and the baseline SUDS, with high worriers reporting greater tendency to worry and greater distress at baseline. No main effect of group emerged for the SIL-V, and no significant interactions emerged for any measure. Manipulation check. In order to determine the impact of the experimental manipulation, a multilevel regression model predicting SUDS scores across the four time points (see Figure 1) was evaluated. Results indicated a significant two-way interaction model (Δdev = 6.38, df = 3, p b .01). The three-way interaction model was not significant. Examining the regression coefficients (see Table 2) revealed a significant condition-by-time interaction; the group-by-time and group-by-condition interactions were not significant. As can be seen in Figure 1, individuals in the worry condition reported increased distress following the initial manipulation and continued to manifest more distress throughout

the protocol, whereas individuals in the relaxation condition reported diminished distress following the manipulation (although this result is less robust and may have been somewhat limited by a floor effect). In addition, ANOVAs were conducted on postexperimental manipulation check self-report items. For the item “I worried about a number of topics,” significant main effects of group (F = 24.35, p b .01, d = 1.29) and condition (F = 9.61, p b .01, d = 0.81) emerged, with individuals in the high worry group and worry condition endorsing more worry during the manipulation periods. For the item “I practiced relaxation,” a significant main effect of condition (F = 10.87, p b .01, d = .86), but not group, emerged, with individuals in the relax condition endorsing more practice of relaxation during the manipulation period. There were no significant interaction effects for either item.

general executive functioning An ANOVA was conducted on WCST total errors (see Table 1). No main effect of group or condition and no interaction effect emerged. Thus, WCST total errors scores were not included as a covariate in the learning task analyses. sensitivity to contingencies and adaptation to the contingency shift The hypothesis that state and trait worry is associated with diminished sensitivity to the contingencies of the discrimination learning task was tested by evaluating two multilevel regression models predicting FR percentage responding scores prior to (trial blocks 1–10) and after (trial blocks 11–15) the cue shift (depicted in Figure 2). Results

Table 2 Results of Multilevel Regression Analyses of SUDS and Learning Task Percentage of FR Responding Scores (N = 60) Outcome SUDS Condition × Time Group × Time Group × Condition Learning Trials 1-10 Condition × Trial Group × Trial Group × Condition Learning Trials 11-15 Condition Group Trial

B

t

Pr

D

-.61 .26 .25

- 2.24⁎ 0.93 0.29

.28 .12 .04

0.59 0.25 0.08

.01 b.01 .06

2.43⁎ - 0.51 1.10

.31 .07 .15

0.64 0.14 0.29

.01 .01 b.01

0.28 0.16 0.60

.04 .02 .08

0.07 0.04 0.16

Note. SUDS = Subjective Units of Distress Scale; FR = fixed ratio. ⁎ p b .05.

worry and discrimination learning

FIGURE 2 Mean percentage of fixed ratio (FR) responding on the discriminative learning task as a function of group by condition.

for trial blocks 1–10 indicated a significant two-way interaction model (Δdev = 7.36, df = 3, p b .01). The three-way interaction model was not significant. Examining the regression coefficients (Table 2) revealed a significant condition-by-trial interaction, whereas the group-by-trial and group-by-condition interactions were not significant. As shown in Figure 2, individuals in the worry and relaxation conditions began the learning task with similar levels of sensitivity to the dual contingencies, but the sensitivity of responding of those in the worry condition declined over the course of the first 10 learning trials. Results for trial blocks 11–15 (trials after the cue shift) indicated that neither the two-nor threeway interaction models significantly improved model fit above and beyond the main effects model. Examining the regression coefficients for the main effects model revealed that percentage of FR responding did not vary as a function of condition, group, or trial. To examine the hypothesis that state and trait worry would lead to diminished adaptation to the contingency shift, an index of sensitivity of contingent responding at the end of the first 10 learning trial blocks was calculated by computing the mean FR percentage responding for trials 9 and 10, and this index score was added as a covariate to the model evaluated for trial blocks 11–15. The index score was not related to either initial sensitivity of responding or change in sensitivity of responding over time during trial blocks 11–15, and sensitivity of responding on the initial trial block of the second contingency phase (trial block 11) and trajectory of change of sensitivity over learning trial blocks 11–15 did not vary as a function of group or condition with the covariate in the model (Bs b .05, ts b 0.08, psN.10, prs b .15, ds b 0.18). Next, to examine whether or not the relationship between sensitivity to contingencies

257

at the end of the first contingency phase and sensitivity during blocks 11–15 varied as a function of group or condition, terms representing the interaction of the index score with group and condition were created, and a model including these terms was evaluated. Results indicated that sensitivity to contingencies at the end of the first phase learning trials did not interact with group or condition to predict sensitivity on the initial trial block of the second contingency phase (trial block 11) or trajectory of change over trial blocks 11–15 (Bs b .33, ts b 1.15, psN .10, prs b .31, ds b 0.33), suggesting that adaptation to the contingency shift did not vary as a function of group or condition. To test whether the effects of the experimental manipulation on sensitivity to task contingencies in the first contingency phase were unique above and beyond variance accounted for by subjective anxiety, a two-way interaction model including post-manipulation SUDS score as a covariate was evaluated. Results revealed that the condition-by-trial interaction remained significant (B = .02, t = 3.16, p b .01, pr= .39, d = 0.85) with the SUDS covariate added to the model.

post hoc analyses Because the finding that condition affected responding over the course of the first phase (1–10) of contingency trial blocks could be explained by general fatigue or disengagement from the task and subsequent lower overall rates of responding, we examined the effects of group and condition on total (FR + DRL) number of responses for trials blocks 1– 10. As can be seen in Figure 3, low worriers in the worry condition had the highest number of overall responses, whereas low worriers in the relax condition had the lowest number of responses, and

FIGURE 3 Mean total responses on the discriminative learning task as a function of group by condition.

258

salters-pedneault et al.

high worriers in both conditions fell between. We conducted an ANOVA on mean overall responses to the first 10 trial blocks. This analysis revealed a marginally significant main effect of condition (but not group), which was qualified by a significant interaction of group by condition (F = 5.40, p b .05, d = 0.61). Follow-up tests revealed a significant effect of condition for low worriers (F = 11.64, p b .01, d = 1.27), but no effect of condition for high worriers. Thus, individuals in the low worry group assigned to the worry condition showed the highest level of overall responding; however, because they exhibited the lowest FR/total response ratio, this responding was inefficient in terms of the reinforcement schedule.

Discussion The prediction that state worry and trait worry would be associated with less sensitive responding to the dual contingencies of the discrimination learning task (in comparison to state relaxation and trait low worry) was partially supported. The state worry condition was associated with less sensitive responding to contingencies than the relaxation condition over the course of the initial 10 learning trial blocks, as evidenced by a significant conditionby-trial interaction for these trial blocks. Further examination of the data suggests that although worry and relaxation condition participants began the learning task with responding that reflected fair sensitivity to the contingencies, the sensitivity of responding of those in the worry condition diminished over the course of the task. Thus, the process of worry may interfere with subsequent adaptive responding to discriminative cues in the environment. When the effects of the worry and relaxation manipulations were examined for these learning trials above and beyond their association with anxiety, the findings remained significant, suggesting that the effect of condition may be due to phenomena unique to the learning and engagement processes interrupted in worry versus relaxation (rather than to the effect of anxiety more generally). The manipulation did not affect scores on the WCST, suggesting that the effects demonstrated in the learning task may not be explained by general effects of condition on executive functioning or task performance generally. In addition, the effects of the manipulation on learning task responding did not appear to be attributable to fatigue or general disengagement from the task, as trait and state worry did not lead to less responding overall over the course of the first 10 trial blocks. Instead, low worriers in the worry condition exhibited the highest rate of general responding during these

trial blocks, whereas low worriers in the relaxation condition exhibited the lowest rate of responding (with high worriers in both conditions falling between these two cells). There were no significant effects of trait worry group on responding to the task contingencies, and the hypothesis that state and trait worry are associated with diminished adaptation to contingency changes on the discrimination learning task was not supported, as there were no effects of group or condition for the learning task trial blocks after the contingency change (blocks 11–15) when sensitivity at the end of the initial contingency phase was controlled for, nor any interaction of sensitivity to the first contingency phase and group or condition. Several limitations should be considered in interpreting these findings. This study investigated the phenomenon of interest in a sample of high worriers rather than a clinical sample, similar to other research in this area that has identified clinically relevant phenomena in analogue samples prior to validating their importance in clinical samples (e.g., Mennin et al., 2005; Roemer, Molina, & Borkovec, 1997). A recent ROC analysis of the PSWQ (Behar et al., 2003) suggests that a more stringent criterion (scores 62 or above) reflects clinical levels of worry in unselected college samples. Thus, it is possible that the lack of demonstrated group effects was due to the high worry group being less impaired than a clinical group (although, in light of this limitation, data were reanalyzed with high worry participants scoring below 62 on the PSWQ dropped [N = 52], and group effects remained nonsignificant). Another limitation is the absence of a no-instruction control group. Instructions to worry versus relax were chosen in order to heighten betweencondition effects of the manipulation on the dependent variables. Thus, it may be that the effects are due to the consequences of relaxation on learning processes, rather than the effects of the worry process. A comparison of the current data to those obtained by Rosenfarb et al.'s (1993) participants, who completed a nearly identical learning task, suggests that the effects seen here are due to the consequences of worry. Rosenfarb and colleagues reported that individuals who, like our participants, received no instructions about task contingencies demonstrated approximately 70% to 80% fixed ratio responding in trial blocks 3 to 10 (similar to our relaxation condition participants). By the end of the learning task, worry condition participants in our study were responding with much less sensitivity than the no-instruction participants in Ro, senfarb et al. s study. Further, examination of the SUDS data indicates that the worry manipulation appeared to be more effective in creating a sustained

worry and discrimination learning state (possibly due to a floor effect on relaxation), suggesting that the effects seen may be better explained by state worry. An additional limitation of the study is that participants completed the WCST prior to their completion of the learning task (these tasks were not counterbalanced), which may have affected their performance on the latter task. However, no effects of group or condition (or interaction effects) emerged for the WCST, suggesting that participants were not differentially affected by this test. Finally, given the nature of the learning task, it was not possible to include a direct baseline assessment of selective responding to task contingencies prior to the manipulation. Although scores on the SIL-V provided data on the equivalence of groups and conditions on factors that may predict ability to engage in learning, these do not constitute a direct baseline measurement of learning, and it is possible that groups/conditions were not equivalent on factors related to the learning outcomes prior to the manipulation. Despite these limitations, these findings may have implications for our understanding of the process of worry. As predicted, the worry incubation manip, ulation interfered with individuals ability to respond adaptively to the contingencies of the learning task compared to relaxation. However, the finding that the manipulation led to diminished sensitivity to discriminative stimuli over the course of the first phase of learning trials was unexpected, and implies that the effect of the manipulation was not on learning, per se, but rather on ability to remain selective in responding to the environment over the course of the first phase of the learning task. Together with the finding that low worriers in the worry condition had higher overall rates of responding over the course of the task, whereas low worriers in the relax condition had the lowest rates of responding, this suggests that the state of worry may prompt nonselective behavioral overresponding (which perhaps is not unlike the phenomenon of worry itself: excessive verbal-linguistic responding to cues in the environment, even when threat is not present). Why overall responding was not affected in high worriers is not clear, and must be explored further. It may be that high worriers were less able to engage in the manipulation (i.e., less able to disengage from worry during the relaxation phase or to engage in relaxation, although the manipulation check data suggest that this was not the case) or are more familiar with worry and thus less functionally affected by the worry process than naïve low worriers. That group and condition did not impact adaptation to the cue shift was unexpected. It appears that all participants exhibited significant difficulty

259

adapting to the contingency/cue shift; immediately after the cue shift the average percentage of FR responding dropped from 68% to 57%, with little variability between groups and conditions. Further, over the course of the five trial blocks during this second phase of the learning task, there is little evidence that participants were becoming more sensitive to the task contingencies. Thus, it appears that the effects seen during the first phase of the learning task were essentially lost during the second phase due to a general lack of adaptation to the new cues in all participants, and a failure of adaptation across the course of the second learning task phase. It may be that more learning trials were needed in order for participants to begin to adapt to the shift, and future research may address this question. Given that worry is hypothesized to deplete cognitive resources generally, and that the WCST and the learning task share a number of properties, it was surprising that group and condition had no impact on WCST scores. One explanation for the differential effects of the manipulation on the WCST versus the discriminative learning task is that the unique properties of the learning task (i.e., that participants were required to use discriminative cues to predict and engage in the most adaptive behavior) are specifically affected by state worry, whereas general task performance/functioning are not. This is consistent with some studies that have failed to demonstrate an impact of worry on some aspects of general performance functioning (e.g., Dugas, Letarte, Rhéaume, Freeston, & Ladouceur, 1995). Another explanation for the lack of effect on the , WCST is related to Eysenck and Calvo s processing efficiency theory (1992; see also Eysenck, Derakshan, Santos, & Calvo, 2007), which suggests that state and trait worry have two competing effects on performance: (a) worry depletes working memory resources; and (b) worry increases motivation to minimize aversive anxiety states through enhanced effort. Because worry directly interferes with the cognitive resources available for task performance, it may lead to poorer processing effectiveness (i.e., the quality of task performances, e.g., accuracy). But, worry may also have a compensatory effect on processing effectiveness due to increased motivation to summon auxiliary effort and resources. In cases where performance can be augmented through increased effort, worry is unlikely to have an impact on effectiveness. However, in cases where auxiliary resources are not helpful, worry may impede effectiveness. The learning task, unlike the WCST, included a fixed-rate component; participants would respond most adaptively to the task if behavior was inhibited in response to a differential-reinforcement-of-low-rate (DRL) of

260

salters-pedneault et al.

behavior cue. Thus, whereas the proposed compensatory effects of worry (e.g., summoning of auxiliary resources) might offset the cognitive consequences of worry on the WCST, these effects may lead to less adaptive performance on the learning task (i.e., greater effort during the DRL periods of the learning task would paradoxically lead to diminished sensitivity to task contingencies). This possibility is in line with the apparent finding of nonselective overresponding in the cell that demonstrated the least sensitivity to the task contingencies. Taken together, these findings may have implications for our models of the development and treatment of chronic worry. It may be that worry, perhaps particularly in the early developmental stages of chronic worry, interferes with individuals' ability to engage adaptively and selectively with nonthreatening discriminative cues in their environment, thereby leading to less adaptive behavior and in turn to more distress, frustration, and possibly more severe worry. This finding is consistent with evidence of attentional deficits in GAD; individuals with GAD appear to exhibit biases toward threat-relevant and negatively valenced information (cf. Mogg & Bradley, 2005), and may thereby be disengaged from neutrally valenced but relevant information in their environment. Whereas numerous previous studies have documented the attentional aspects of this phenomenon, the current study highlights a potential behavioral consequence; worry may interfere with how adaptively individuals actively respond to neutral discriminative cues of behavior. Although traditional cognitive behavioral treatments for GAD have targeted worry itself, this particular consequence of worry may be an additional target for treatment. If worry interferes with engaged selective responding to the current environment, treatments that increase present-moment awareness of and appropriate responding to nonthreatening environmental cues may prove efficacious. For example, Roemer and Orsillo (2007) have recently developed an acceptance-based behavior therapy for GAD, which incorporates traditional cognitive behavioral strategies with mindfulness and acceptance-based strategies. In particular, elements of mindfulness training (e.g., expanding attention and awareness to all aspects of the internal and external environment) may directly target worriers' adaptive engagement with their environment. Applied relaxation may also be indicated to target the aspects of worry that interfere with engagement when the need for adaptive interaction with external cues is particularly salient. This was a preliminary study of the effects of worry on discriminative learning, and the phenomena

observed in this study must be explored further. Future studies should address the mechanism of the observed effect (e.g., intentional disengagement from the neutral discriminative stimuli, progressively more attention to the aversive [e.g., frustrating] aspects of the task, behavioral overresponding in an attempt to avoid failure). Use of a no-instruction and/or a nonworry verbal linguistic activity control condition would help to elucidate the mechanism. This effect should also be tested in a clinical GAD population, and individual difference factors and variables that may moderate the processes observed should be examined. Finally, in future studies these phenomena could be observed over the course of treatment; it may be that one mechanism of change that has been overlooked in GAD treatments is increased adaptive engagement with nonthreatening environmental cues. References Alting, T., & Markham, R. (1993). Test anxiety and distractibility. Journal of Research in Personality, 27, 134–147. American Psychiatric Association, (1994). Diagnostic and statistical manual of mental disorders, 4th ed. Washington, DC: Author. Behar, E., Alcaine, O., Zuellig, A. R., & Borkovec, T. D. (2003). Screening for generalized anxiety disorder using the Penn State Worry Questionnaire: A receiver operating characteristic analysis. Journal of Behavior Therapy and Experimental Psychiatry, 34, 25–43. Borkovec, T. D., Alcaine, O., & Behar, E. (2004). Avoidance theory of worry and generalized anxiety disorder. In R. G. Heimberg, C. L. Turk, & D. S. Mennin (Eds.), Generalized anxiety disorder: Advances in research and practice (pp. 77–108). New York: The Guilford Press. Borkovec, T. D., & Costello, E. (1993). Efficacy of applied relaxation and cognitive-behavioral therapy in the treatment of generalized anxiety disorder. Journal of Consulting and Clinical Psychology, 61, 611–619. Borkovec, T. D., Robinson, E., Pruzinsky, T., & DePree, J. A. (1983). Preliminary exploration of worry: Some characteristics and processes. Behaviour Research and Therapy, 21, 9–16. Brown, T. A., Antony, M. M., & Barlow, D. H. (1992). Psychometric properties of the Penn State Worry Questionnaire in a clinical anxiety disorders sample. Behaviour Research and Therapy, 30, 33–38. Brown, T. A., Barlow, D. H., & Liebowitz, M. R. (1994). The empirical basis of generalized anxiety disorder. American Journal of Psychiatry, 151, 1272–1280. Calvo, M. G., & Eysenck, M. W. (1996). Phonological working memory and reading in test anxiety. Memory, 4, 289–305. Dalton, J. E., Pederson, S. L., & McEntyre, W. L. (1987). A comparison of the Shipley vs. WAIS-R subtests in predicting WAIS-R Full Scale IQ. Journal of Clinical Psychology, 43, 278–280. Dugas, M. J., Letarte, H., Rhéaume, J., Freeston, M. H., & Ladouceur, R. (1995). Worry and problem solving: Evidence of a specific relationship. Cognitive Therapy and Research, 19, 109–120. Eysenck, M. W., & Calvo, M. G. (1992). Anxiety and performance: The processing efficiency theory. Cognition and Emotion, 6, 409–434.

worry and discrimination learning Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: Attentional control theory. Emotion, 7, 336–353. Hayes, S. C., Brownstein, A. J., Haas, J. R., & Greenway, D. E. (1986). Instructions, multiple schedules, and extinction: Distinguishing rule-governed from schedule-controlled behavior. Journal of the Experimental Analysis of Behavior, 46, 137–147. Hazlett-Stevens, H., & Borkovec, T. D. (2001). Effects of worry and progressive relaxation on the reduction of fear in speech phobia: An investigation of situational exposure. Behavior Therapy, 32, 503–517. Heaton, R. K. (1981). A manual for the Wisconsin Card Sorting Test. Odessa, FL: Psychological Assessment Resources. Kongs, S. K., Thompson, L. L., Iverson, G. L., & Heaton, R. K. (2000). Professional manual for the administration of the Wisconsin Card Sort Test-64 Card Version. Lutz, FL: Psychological Assessment Resources. McKay, D. (2005). Studies of cognitive processing during worry. Cognitive Therapy and Research, 29, 359–376. Mennin, D. S., Heimberg, R. G., Turk, C. L., & Fresco, D. M. (2005). Preliminary evidence for an emotion dysregulation model of generalized anxiety disorder. Behaviour Research and Therapy, 43, 1281–1310. Metzger, R. L., Miller, M. L., Cohen, M., Sofka, M., & Borkovec, T. D. (1990). Worry changes decision making: The effect of negative thoughts on cognitive processing. Journal of Clinical Psychology, 46, 78–88. Meyer, T. J., Miller, M. L., Metzger, R. L., & Borkovec, T. D. (1990). Development and validation of the Penn State Worry Questionnaire. Behaviour Research and Therapy, 28, 487–495. Mogg, K., & Bradley, B. P. (2005). Attentional bias in generalized anxiety disorder versus depressive disorder. Cognitive Therapy and Research, 29, 29–45. Mogg, K., Bradley, B. P., Millar, N., & White, J. (1995). A follow-up study of cognitive bias in generalized anxiety disorder. Behaviour Research and Therapy, 33, 927–935. Mogg, K., Mathews, A., & Weinman, J. (1987). Selective processing of threat cues in anxiety states: A replication. Behaviour Research and Therapy, 27, 317–323. Molina, S., & Borkovec, T. D. (1994). The Penn State Worry Questionnaire: Psychometric properties and associated characteristics. In G. C. L. Davey, & F. Tallis (Eds.), Worrying: Perspectives on theory, assessment, and treatment (pp. 265–283). New York: Wiley. Molina, S., Borkovec, T. D., Peasley, C., & Person, D. (1998). Content analysis of worrisome streams of consciousness in anxious and dysphoric participants. Cognitive Therapy and Research, 22, 109–123. Noyes, R., Woodman, C., Garvey, M. J., Cook, B. L., Suelzer, M., Clancy, J., & Anderson, D. J. (1992). Generalized anxiety disorder versus panic disorder: Distinguishing characteristics and patterns of comorbidity. Journal of Nervous and Mental Disease, 180, 369–379. Paulson, M. J., & Lin, T. T. (1970). Predicting WAIS IQ from Shipley-Hartford scores. Journal of Clinical Psychology, 26, 453–461.

261

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods, 2nd ed. Thousand Oaks CA: Sage. Raudenbush, S., Bryk, A., & Congdon, R. (2005). HLM 6: Hierarchical linear and nonlinear modeling [Computer software and manual]. Lincolnwood, IL: Scientific Software International. Roemer, L., Molina, S., & Borkovec, T. D. (1997). An investigation of worry content among generally anxious individuals. Journal of Nervous and Mental Disease, 185, 314–319. Roemer, L., & Orsillo, S. M. (2002). Expanding our conceptualization of and treatment for generalized anxiety disorder: Integrating mindfulness/acceptance-based approaches with existing cognitive-behavioral models. Clinical Psychology Science and Practice, 9, 54–68. Roemer, L., & Orsillo, S. M. (2007). An open trial of an acceptance-based behavior therapy for generalized anxiety disorder. Behavior Therapy, 38, 72–85. Roemer, L., Orsillo, S. M., & Barlow, D. H. (2002). Generalized anxiety disorder. In D. H. Barlow (Ed.), Anxiety and its disorders (pp. 477–515). New York: The Guilford Press. Rosenfarb, I. S., Burker, E. J., Morris, S. A., & Cush, D. T. (1993). Effects of changing contingencies on the behavior of depressed and nondepressed individuals. Journal of Abnormal Psychology, 102, 642–646. Rosenfarb, I. S., Newland, M. C., Brannon, S. E., & Howey, D. (1992). Effects of self-generated rules on the development of schedule-controlled behavior. Journal of the Experimental Analysis of Behavior, 58, 107–121. Salters-Pedneault, K., Roemer, L., Tull, M. T., Rucker, L., & Mennin, D. S. (2006). Evidence of broad deficits in emotion regulation associated with chronic worry and generalized anxiety disorder. Cognitive Therapy and Research, 30, 469–480. Shipley, W. C. (1940). A self-administering scale for measuring intellectual impairment and deterioration. Journal of Psychology, 9, 371–377. Startup, H. M., & Davey, G. C. L. (2001). Mood as input and catastrophic worrying. Journal of Abnormal Psychology, 110, 83–96. Wells, A., & Papageorgiou, C. (1995). Worry and the incubation of intrusive images following stress. Behaviour Research and Therapy, 33, 583. Wittchen, H. -U. (2002). Generalized anxiety disorder: Prevalence, burden, and cost to society. Depression and Anxiety, 16, 162–171. Wittchen, H. -U., Zhao, S., Kessler, R. C., & Eaton, W. W. (1994). DSM-III-R generalized anxiety disorder in the National Comorbidity Survey. Archives of General Psychiatry, 51, 355–364. Wolpe, J. (1990). The practice of behavior therapy, (4th ed.). New York: Pergamon Press.

R E C E I V E D : December 28, 2006 A C C E P T E D : August 7, 2007 Available online 1 February 2008