Information processing biases and panic disorder: Relationships among cognitive and symptom measures

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Abstract

To test cognitive models of panic disorder, a range of information processing biases were examined among persons with panic disorder (N=43) and healthy control participants (N=38). Evidence for automatic associations in memory was assessed using the Implicit Association Test, interference effects related to attention biases were assessed using a modified supraliminal Stroop task, and interpretation biases were assessed using the Brief Body Sensations Interpretation Questionnaire. In addition, the relationship between information processing biases and clinical markers of panic (including affective, behavioral, and cognitive symptom measures) was investigated, along with the relationships among biases. Results indicated more threat biases among the panic (relative to control) group on each of the information processing measures, providing some of the first evidence for an implicit measure of panic associations. Further, structural equation modeling indicated that the information processing bias measures were each unique predictors of panic symptoms, but that the bias indicators did not relate to one another. These findings suggest that cognitive factors may independently predict panic symptoms, but not covary. Results are discussed in terms of their support for cognitive models of panic and the potential for automatic versus strategic processing differences across the tasks to explain the low relationships across the biases.

Introduction

The choice to observe one's pounding heart without jumping to the conclusion that it is a sign of a heart attack or other impending disaster is thought by cognitive theorists to be the key to unraveling panic attacks. The cognitive model of panic disorder was developed in part from observations of pharmacological and neurochemical studies of agents that promoted panic attacks, but only in select individuals—those who tended to interpret the bodily sensations induced by the agents in a disastrous way (Clark, 1986). This led Clark (1986) to suggest that panic attacks occur because certain bodily sensations are misinterpreted as indicating a catastrophe, such as a heart attack or loss of control (see also Goldstein & Chambless, 1978).

The specific cognitive model of panic derives from more general cognitive theories to explain anxiety and fear. The general model proposes that maladaptive schemata (cognitive frameworks) influence information processing so that vulnerable individuals are more attentive to potentially threatening cues, more likely to interpret ambiguous cues as threatening, and more likely to remember cues relevant to fear (e.g., Beck, 1976; Beck, Emery, & Greenberg, 1985). These cognitive biases are thought to maintain anxiety and panic by keeping threat cues salient (see Clark, 1999; Young, 1999). Despite the strong theoretical emphasis on anxious schemata, there has been little empirical support for the schema construct, in part because of the difficulty in operationalizing the concept of interconnected associations in memory (the definition for schema advocated by Segal, 1988, which we follow here). In the current study, we examined a range of information processing biases expected to be present among persons with panic disorder in order to evaluate: (a) evidence for panic associations in memory; (b) the relationship among information processing biases; and (c) the relationship of information processing biases to clinical markers of panic.

There have been numerous prior studies investigating the independent role of one panic-related information processing bias or another, but few studies that have examined the relations among different information processing biases or that have tried to operationalize panic associations at an implicit level to capture elements of the schemata construct (specifically, associations in memory that are activated automatically). These missing pieces are critical to fully characterize the role of cognitive processing in emotional disorders (Wells & Matthews, 1996). Further, while there is considerable evidence for different types of information processing biases in panic disorder (attention: e.g., Beck, Stanley, Averill, Baldwin, & Deagle, 1992; Ehlers, Margraf, Davies, & Roth, 1988; Hope, Rapee, Heimberg, & Dombeck, 1990; interpretation: e.g., Clark et al., 1997; McNally & Foa, 1987; memory: e.g., Cloitre, Shear, Cancienne, & Zeitlin, 1994; Nunn, Stevenson, & Whalan, 1984), there are also many null findings that are challenging to explain based on current cognitive models (see Austin & Richards, 2001; Casey, Oei, & Newcombe, 2004; Coles & Heimberg, 2002).

Investigating single information processing biases in isolation makes it difficult to compare discrepant results because of the possibility that sample differences may explain inconsistent findings. Further, examining information processing biases individually leaves critical theoretical proposals unexamined. For instance, in their seminal work on the cognitive model for anxiety disorders, Beck and colleagues suggested, “When specific schemas or a constellation of schemas is activated, their content directly influences the content of a person's perceptions, interpretations, associations, and memories at a given time” (Beck et al., 1985, p. 55). This implies that we should expect to see significant relationships between different cognitive processes. Variations of this expectation have been described in more recent models that offer refinements to the generic schema theory, such as Beck and Clark's (1997) discussion of automatic and strategic biases in anxiety, Wells’ (1997) review of cognitive theories of anxiety, and Wells and Matthews’ (1996) model of Self-Regulatory Executive Function (S-REF). Moreover, the theoretical expectation of inter-relations among information processing biases is implicit in Clark's (1986) writings on panic.

At the same time, other cognitive theories have challenged this assumption, at least tacitly, based on proposals that only certain information processing biases will be related to a given disorder (thus, presumably the biases may not be strongly related). For instance, Williams, Watts, MacLeod, and Mathews (1997) differentiated between early processing of stimuli, where information is primed in the system, versus later elaboration of information. They suggest that anxiety disorders are characterized by biased processing of threatening information that occurs mainly at the priming stage, affecting automatic aspects of encoding and retrieval. Hence, researchers see a robust attentional bias, but not a consistent explicit memory bias. In contrast, depression is characterized by biased elaborative processing, leading to more consistent memory effects (though see important modifications of this position described in Mathews & MacLeod, 2005). Currently, the field is at a challenging juncture with little consensus about when to expect biases to be related and when to predict independence.

It is surprising that the relationship among information processing biases has not received more empirical attention, given the remarkable impact of cognitive models of anxiety (see Hirsch, Clark, & Mathews, 2006). Yet, we found few published studies that investigated multiple panic-related information processing measures within the same sample. Further, many of the studies that did include multiple bias measures did not report the relationship among the measures (e.g., Beck, Stanley, & Averill, 1992; Lim & Kim, 2005). Among the few studies that have examined multiple biases in the same sample and reported on their relationship, the results have generally suggested no significant correlations. Lundh, Czyzykow, and Öst (1997) found no relationship between measures of explicit and implicit memory biases among patients with panic disorder and agoraphobia (see similar findings from Baños, Medina, & Pascual, 2001; Cloitre, Shear, & Cancienne, 1994). Further, Lundh, Wikstrom, and Westerlund (1999) found no correlation between memory and attention biases. While small in number, these results are problematic for those cognitive models that hypothesize significant relationships among biased processes, suggesting either that the models need to be revised or that there are concerns about the validity and/or reliability of the paradigms used to reflect the information processing biases.

To date, we are unaware of any studies that have looked at measures related to interference/attention, interpretation and automatic associations in the same study. Evaluating this broad range of biases is important not only to reflect different types and stages of cognitive processing and their inter-relations, but also to capture both automatic (outside of conscious awareness or control) and strategic (deliberate and available to self-report) biases in information processing (see Beck & Clark, 1997). Of course, we recognize that most paradigms capture a range of processes and implicit/explicit components (see Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005; Jacoby, 1991), rather than truly looking at these issues in isolation. Thus, when we refer to attention versus interpretation or implicit versus explicit (or the related constructs, automatic versus strategic), we are referring to the dominant—rather than exclusive—mode of processing measured by a given task. Keeping in mind this caveat, we evaluated measures tied to: (a) panic associations that are automatic in the sense of being involuntary; (b) interference effects activated by panic cues (associated with selective attention to threat information); and (c) interpretation of ambiguous situations tied to bodily sensations (reflecting somewhat more elaborative thinking).

Our goal in assessing the three different information processing biases was to determine not only how they show known-group differences (i.e., differentiate between persons with and without panic disorder), but also how they inter-relate and predict clinical markers of panic. Thus, we assessed self-reported panic symptom severity, agoraphobic avoidance, and subjective distress during a panic-related provocation (task designed to elicit mild suffocation sensations). In addition, we assessed anxiety sensitivity, which is a measure of explicit threat appraisals related to the meaning of anxiety symptoms. This follows McNally's (2001) recommendation to include both appraisal and information processing approaches when trying to understand cognitive functioning in anxiety. This multi-modal assessment of panic symptoms has two primary advantages: (1) it takes into account the frequently observed desynchrony across indicators of anxiety (Lang, 1985) by measuring a variety of responses, so that it is possible to look at convergence across measures; and (2) it provides continuous (rather than dichotomous) symptom measures that can show variance among persons with and without panic disorder, which can help address, though admittedly not resolve, the problem of examining correlations among measures in an extreme groups design.

Based on general information processing models of anxiety (Beck et al., 1985; Beck & Clark, 1997) and the cognitive model of panic (Clark, 1986), it was hypothesized that each of the information processing biases would differentiate persons with and without panic disorder, and also predict the continuous measures of panic-related symptoms. These hypotheses are not particularly novel for the attention and interpretation bias measures, but this study is unique in trying to establish evidence for implicit measures of panic disorder associations.

In an earlier study investigating information processing biases among individuals high and low in anxiety sensitivity (a known vulnerability marker for panic; Schmidt, Lerew, & Jackson, 1997), Teachman (2005) found that a measure of implicit associations in memory differentiated the anxiety sensitive groups and was positively related to anxiety and panic symptoms. This measure examined associations with the self (versus others) as panicked versus calm (using the Implicit Association Test (IAT); Greenwald, McGhee, & Schwartz, 1998). Given its established validity in an anxiety sensitive sample, we will use this same measure in the current study to investigate panic associations in a diagnosed sample. An additional implicit measure of panic associations tied specifically to beliefs about the dangerousness of bodily sensations (reflecting Clark's, 1986, model) will also be included.

As noted, each of the information processing bias measures is expected to distinguish individuals diagnosed with panic disorder from healthy control participants, and each bias measure is anticipated to predict a range of panic symptoms. However, the relationships among the information processing measures are considerably harder to predict. On the one hand, many theoretical models of anxiety and panic (e.g., Beck et al., 1985; Clark, 1986) suggest that the different cognitive processes should be inter-related. On the other hand, some models are more cautious in this regard, and the limited available data have not supported this hypothesis, finding no significant correlations among bias measures in samples diagnosed with panic disorder. Further, mixed results were observed in Teachman's (2005) study with a high anxiety sensitive sample, which used similar bias measures to those used in the current study.

Thus, McNally, Hornig, Hoffman, and Han's (1999) suggestion that cognitive factors may independently present risk for panic but not covary is quite compelling. While this idea was based on their evaluation of anxiety sensitivity and its low relation to interpretive, attentional, and memory tasks (they did not report correlations among the information processing measures), their suggestion may be informative for panic disorder as well. Perhaps, as McNally and colleagues suggest, “it is entirely possible that within the cognitive domain, risk factors may function independently of one another and not figure as different aspects of the same construct” (p. 52). If independence among the measures were evident in the current study, it would imply unrelated correlates or maintaining factors for panic. We tentatively hypothesized little relationship among the information processing measures in the present study, following from the lack of significant relationships observed in prior research.

Section snippets

Participants

Participants with panic disorder were recruited as part of a larger treatment study through newspaper, television, e-mail, radio, print ads and flyers posted around the Charlottesville-Albemarle community and University of Virginia campus that invited individuals who had experienced panic attacks to contact our confidential phone line. Interested individuals were then screened over the phone to evaluate whether they would likely meet criteria for panic disorder, and to confirm they had

Sample characteristics

As anticipated, independent samples t-tests indicated that the panic and healthy control groups differed on each of the mood and panic symptom measures in the expected direction (see Table 1). This was true for both the questionnaires (BDI: t79=6.44, p<0.001, d=1.45; FQ-Agoraphobia: t79=5.13, p<0.001, d=1.15; ASI: t79=11.93, p<0.001, d=2.68; PDSS: t79=19.08, p<0.001, d=4.29), where the panic group reported more depressive symptoms, agoraphobic avoidance, anxiety sensitivity and panic severity,

Discussion

To evaluate cognitive models of panic disorder, a range of information processing biases were examined among individuals with and without panic disorder, including measures designed to reflect biases in automatic panic associations, interference/attention and interpretation of threatening information. The goal in assessing this broad array of biases was to: (1) establish evidence for implicitly measured panic associations; (2) examine the relationship between information processing biases and

Acknowledgment

The authors are thankful to the clinical interviewers, the research assistance provided by members of the PACT lab at the University of Virginia, and to Adam Radomsky for comments on an earlier version of the manuscript. This research was supported by an NIMH R03 PA-03-039 Grant to Bethany Teachman.

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