Predictive validity of explicit and implicit threat overestimation in contamination fear
Highlights
► Explicit and implicit threat overestimation variables have differential predictive validity. ► A latent explicit threat overestimation variable predicts contamination fear symptoms, distress, and state cognitions. ► A latent implicit threat overestimation variable predicts behavioral avoidance.
Introduction
Threat overestimation, marked by exaggerated beliefs about the probability and costs of negative events (Taylor et al., 2010), has been hypothesized to play a role in the origin and maintenance of contamination fear (Connolly, Lohr, Olatunji, Hahn, & Williams, 2009). In line with this hypothesis, individuals high (versus low) in contamination fear can generate more reasons why contamination-relevant situations are harmful and fewer reasons why they are safe (Olatunji, Connolly, Lohr, & Elwood, 2008). Moreover, in a study of covariation bias, individuals with high levels of contamination fear underestimated the pairing of contamination-related and neutral stimuli, perhaps indicative of a decreased likelihood of foreseeing a neutral or safe outcome following contact with “contaminated” stimuli (Connolly et al., 2009).
Though often studied as a unitary predictor of obsessive–compulsive symptoms, threat overestimation is a multifaceted construct (e.g., Moritz & Jelinek, 2009). In addition to probability and cost biases related to encountering potential threats, threat overestimation also includes a bias toward looming vulnerability, defined as the propensity to conceptualize threat as dynamic and the risk of harm as rapidly increasing (Riskind & Rector, 2007). Looming vulnerability cognitions predict symptom severity in OCD patients (Riskind & Rector, 2007) and contamination fear in a subclinical OCD sample (Riskind, Abreu, Strauss, & Holt, 1997a), and are thus included as part of the current study's assessment of threat overestimation in contamination fear.
Threat overestimation may also be multifaceted in the sense of being instantiated both explicitly (available to conscious introspection and control) and implicitly (residing outside conscious control). While multiple studies have examined explicit predictors of contamination fear-related outcomes, none to our knowledge has explored implicit predictors. The expected predictive role of implicit associations tied to threat overestimation and contamination fear follows from cognitive models of anxiety, which emphasize the automatic nature of anxious responding, especially the uncontrollable processing of threat information (McNally, 1995). Given that implicit associations are thought to reflect relatively uncontrollable associations stored in memory (see Teachman & Woody, 2004), they may predict contamination fear outcomes. There is also some preliminary evidence in healthy samples that maladaptive beliefs tied to OCD can be assessed using implicit associations (Teachman and Clerkin, 2007, Teachman et al., 2006).
Measuring explicit and implicit aspects of threat overestimation may allow us to examine variance in contamination fear outcomes that is not assessed through the use of either method alone. Though the two measures are often related, collapsing them into a unitary construct is not as informative as treating them as separate but complementary constructs (Nosek & Smyth, 2007). As Roefs et al. (2011) state, implicit measures “have the potential to reveal aspects of the dysfunctional beliefs that explicit measures cannot reveal and to predict behaviors that explicit measures do not predict” (p. 150). Thus, the current study examines the differential predictive validity of explicit and implicit associations relevant to threat overestimation.
A number of studies have found explicit threat overestimation measures to positively correlate with contamination-fear symptoms, state cognitions, subjective distress, and behavior, though none has examined all of these variables in the same study. For example, contamination-fearful (versus non-fearful) participants had higher scores on a measure of looming vulnerability (Riskindet al., 1997a), and on a measure of cognitions related to the likelihood and severity of becoming contaminated (Deacon & Olatunji, 2007; see also Tolin, Worhunsky, & Maltby, 2004, for related results in an OCD sample). Contamination-fearful persons also reported higher state anxiety and exhibited more behavioral avoidance during contamination-related behavioral avoidance tasks (BATs; Deacon & Olatunji, though this latter relationship was fully mediated by self-reported disgust sensitivity).
The predictive ability of implicit measures in a contamination fearful sample has not been previously tested to our knowledge. Though evidence suggests that implicit associations can also be correlated with subjective distress (Teachman et al., 2007, Teachman and Woody, 2003) and explicit cognitions (Steinman & Teachman, 2011; cf. de Jong et al., 2003, Swanson et al., 2001), the literature has produced mixed findings. This suggests that the strength of the relationships may be disorder- or association-dependent, and/or may vary based on different moderators, such as the social desirability of the evaluation (see Nosek, 2005). Nevertheless, there is growing empirical support that implicit measures can be unique predictors of clinical outcomes, including symptom severity and other indicators relevant to contamination fear. Recently, in a non-clinical sample, an implicit measure of disgust sensitivity was found to predict avoidance on BATs measuring disgust across a variety of domains, independently of self-reported anxiety and disgust propensity (Nicholson & Barnes-Holmes, 2012). Similarly, in a sample of individuals with pathological skin picking, an implicit measure of distraction tendencies in response to pictures of skin irregularities predicted unique variance in post-treatment severity of skin picking, beyond that accounted for by a self-report measure of skin picking severity given prior to treatment (Schuck, Keijsers, & Rinck, 2012).
Akin to threat overestimation, be it explicitly or implicitly measured, contamination fear can also be examined as a multifaceted construct. In their multisystem framework of emotion, Lang, Cuthbert, and Bradley (1998) highlight multiple response systems (e.g., behaviors, feelings) that can be involved in the expression of fear (see also Lang, 1968, Rachman, 1978), and they note that desynchrony among these systems is common. Therefore, to capture multiple components of contamination fear, we examine: (1) self-reported contamination fear symptoms, (2) subjective distress tied to contamination concerns, including fear (Rachman & Hodgson, 1980) and disgust (Olatunji, Lohr, Sawchuk, & Tolin, 2007), (3) cognitions tied to state overestimation of threat (Dorfan & Woody, 2006), and (4) avoidance as a behavioral indicator of contamination fear (Olatunji et al.).
We examine implicit and explicit measures tied to threat overestimation as simultaneous predictors of the multiple components of contamination fear among individuals high in contamination fear to learn whether different forms of pathological fear responses are uniquely predicted by explicit versus implicit biases. While it is widely established that cognitive threat biases are related to the expression of fear, there is little work determining if different forms of cognitive bias (e.g., explicit versus implicit) predict distinct components of the fear response, which is the focus of the current investigation. Understanding what factors differentially predict various facets of contamination fear problems may be important for determining distinct targets of therapy based on idiographic fear profiles. For example, if behavioral avoidance is better predicted by implicit (versus explicit) measures of threat overestimation, then cognitive bias interventions that specifically target maladaptive implicit associations (versus the focus on explicit cognitions with traditional cognitive restructuring) may be needed to decrease avoidant behavior.
Dual-process models of attitude–behavior relations (e.g., Strack & Deutsch, 2004), and the related double dissociation hypothesis (see Asendorpf et al., 2002, Roefs et al., 2011), theorize that implicit measures wield relatively greater predictive validity when the resources required for cognitive control are low or task responses are difficult to consciously control, whereas explicit measures are thought to have greater predictive validity when cognitive control resources are high or task responses are readily controllable. Though this study does not directly test dual process models, they are useful for generating hypotheses about potential differences in implicit versus explicit predictive validity. In particular, dual-process models suggest that explicit threat overestimation should predict markers of contamination fear that are available to conscious introspection and amenable to conscious control, which would include the self-reported symptoms, cognitive, and affective contamination fear responses in the current study.
Prediction of behavioral avoidance is less obvious. While performance on all of the outcome measures is controllable to some extent, we expected behavioral avoidance to be less controlled due to the presence of state anxiety during the task. Evidence suggests that under conditions of elevated state anxiety, certain kinds of behaviors are relatively less controllable, due to the depletion of cognitive control resources (see Eysenck, Derakshad, Santos, & Calvo, 2007). For example, individuals who show a restrained pattern of eating have been shown to eat more food when performing a high cognitive-load task than when performing a low cognitive-load task (Ward & Mann, 2000), and to increase their food intake in a laboratory setting when under conditions of increased state anxiety (Polivy, Herman, & McFarlane, 1994). Additionally, under conditions of heightened state anxiety due to an upcoming speech task, individuals with elevated trait social anxiety drink more alcohol than when they are not told about an upcoming speech (Kidorf & Lang, 1999). Together, these examples suggest that overriding certain motivated behaviors will be more difficult when resources are depleted, as occurs during state anxiety. Thus, to the extent that avoidance is a less controlled contamination fear response, dual process models suggest it should be predicted by implicit threat overestimation. However, given that participants have time to adjust their approach/avoidance behavior, potentially allowing for some strategic override, explicit threat overestimation may also be a predictor.
Section snippets
Participants
Participants (N=56) were undergraduates who were recruited from the university's psychology department participant pool who were invited to participate if they scored higher than the mean of a diagnosed OCD sample on the Padua Inventory–Washington State University Revision–Contamination Fear Subscale (PI–WSUR–C; Burns, Keortge, Formea, & Sternberger, 1996)3
Data scoring and reduction
The B-IAT data were scored according to the algorithm developed by Greenwald, Nosek, and Banaji (2003) for the IAT. A D score was calculated, representing the difference in mean response time across critical blocks divided by the standard deviations across blocks. Following the recommendations of Greenwald et al. (2003), we excluded data if the individual’s error rate was more than 30% overall, or if an individual took less than 300 ms to respond to more than 10% of trials. On the basis of these
Discussion
We examined multiple facets of threat overestimation, explicitly and implicitly, as predictors of four contamination fear-related outcomes: symptoms, subjective distress, state cognitions, and behavioral avoidance. As hypothesized, and in keeping with prior findings (Deacon & Olatunji, 2007), contamination-related symptoms were predicted by the latent explicit threat overestimation factor. Further, explicit threat overestimation predicted state looming vulnerability cognitions, in line with
Acknowledgements
The authors are thankful for the feedback provided by members of the Teachman Program for Anxiety, Cognition and Treatment lab.
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