This study aimed to clarify information processing conditions in which negatively-biased interpretations of faces manifest among individuals varying in self-reported, sub-clinical social anxiety. Existing findings are mixed, with conflicting research variously suggesting the presence (e.g., Bell et al., 2011; Yoon & Zinbarg, 2008) or the absence (e.g., Philippot & Douilliez, 2005; Schofield, Coles, and Gibb, 2011) of a negative interpretation bias for faces. Likely contributing to these equivocal findings is considerable methodological variability across studies that appear to tap two different levels of information processing (automatic and controlled). In this study, experimental conditions designed to elicit automatic versus controlled processing were compared in a single adapted learning paradigm (Yoon & Zinbarg, 2008). Hierarchical regression results did not support hypotheses that social anxiety would predict a negative interpretation bias in either condition. Further analysis of the learning paradigm revealed unexpected patterns of learning that varied according to face emotion.

Purpose: The tendency to prioritize negative or threatening social information, a cognitive process known as cognitive bias, has been linked to the development of social anxiety. Given the increased risk for social anxiety among adolescents who stutter (aWS), this project extended the research on cognitive bias to aWS to inform our understanding of the psychosocial factors associated with stuttering in adolescence – the period of development when social anxiety typically emerges. The purpose of this two-part study was to examine group and individual differences in two forms of cognitive bias among aWS and typically fluent controls (TFC) – attentional and interpretation biases. Methods: A sample of 102 adolescents (49 aWS and 53 TFC; 13- to 19-years-old) completed a self-report measure of social anxiety, a computerized attentional bias task, and a computerized interpretation bias task. To assess attentional bias, neutral-negative face pairs were presented in a modified dot-probe paradigm in which response times to engaging and disengaging from neutral, fearful, and angry expressions were measured. To assess interpretation bias, ambiguous verbal and nonverbal social scenarios were presented in a vignette-based recognition task, after which participants endorsed possible negative and positive interpretations of those scenarios. Results: The aWS and TFC reported comparable degrees of social anxiety, although female aWS reported higher levels than male aWS. For the attentional bias task, aWS were faster to engage with fearful faces than to maintain attention on neutral faces, and they were also faster to disengage from fearful and angry faces than to maintain attention on those negative faces. TFC did not demonstrate an attentional preference for any particular face type. For the interpretation bias task, while aWS and TFC rated negative and positive interpretations of verbal and nonverbal scenarios similarly, social anxiety moderated the effect of interpretation characteristics on endorsement of those interpretations; participants with greater social anxiety endorsed negative interpretations of verbal scenarios to a greater degree than those with lower social anxiety, and participants with lower social anxiety endorsed positive interpretations of verbal and nonverbal scenarios to a greater degree than those with higher social anxiety. Conclusions: This study contributes to the existing literature in several meaningful ways. First, this sample of aWS and TFC demonstrated comparable rates of social anxiety, which counters many other reports of group differences in social anxiety in this population. Second, it supports previous preliminary accounts of attentional bias among individuals who stutter. The present findings are novel in that aWS’ rapid engagement with and rapid disengagement from negative faces were observed in the absence of group differences in social anxiety. Third, the results challenge the speculation that stuttering is associated with negative interpretation bias – a relationship that has been proposed in the literature but never empirically investigated. Taken together, these findings provide the groundwork for continued investigation into the role of social information processing on psychosocial outcomes for aWS.

There is abundant evidence of anxiety-linked threat-biased attention and anxiety-linked threat-biased interpretation (cf. Mathews & MacLeod, 1994, 2005). The present research aimed to determine whether these cognitive biases reflect a single common underlying mechanism (the Common Mechanism Account) or multiple independent underlying mechanisms (the Independent Mechanisms Account). To address this question, a battery of eight experimental tasks was developed; four tasks measured attention bias and four measured interpretation bias. Participants with different levels of trait anxiety, completed pairs of these tasks. The pattern of associations amongst all eight tasks was compared with the pattern of associations between the four tasks that measured attention bias and the pattern of associations between the four tasks that measured interpretation bias. Both Accounts predicted strong associations between the four tasks that measured attention bias, and between the four tasks that measured interpretation bias. However, the Common Mechanism Account predicted generally strong associations between all of the eight tasks, that were equivalent in strength to the associations between tasks measuring attention bias and to the associations between tasks measuring interpretation bias. In contrast, the Independent Mechanisms Account predicted weaker associations between all of the eight tasks than the associations either between the tasks measuring attention bias or between the tasks measuring interpretation bias. The obtained pattern of associations between internally reliable measures of anxiety-linked attention bias and anxiety-linked interpretation bias failed to support the Common Mechanism Account, but rather was consistent with the predictions of the Independent Mechanisms Account. Theoretical and applied implications of the results are discussed.

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