Disentangling perceptual mechanisms maintaining social anxiety disorder using VR and eye tracking

Social anxiety disorder (SAD) is highly prevalent and confers significant life impairment. Attention processes associated with social evaluative threat have been broadly implicated in the conceptualization of SAD. This dissertation investigates specific attentional mechanisms maintaining SAD. Both theoretical and empirical research have emphasized two important attentional processes in SAD: hypervigilance to social threat (e.g. quickly scanning faces) and avoidance of social information (e.g. avoiding looking at people). This dissertation consists of three studies investigating attentional avoidance in social anxiety. Study 1 investigated the relationship between social anxiety and eye movements during a real social-evaluative situation – giving a speech. We used 360º-video because it was both very realistic and allowed for a high degree of experimental control. The primary findings from Study 1 revealed that fear of public speaking was associated with greater avoidance of the uninterested (socially threatening) audience members compared with interested audience members.
Drawing from the findings from Study 1, Study 2 addressed whether direct modification of attentional (through attention guidance) during virtual reality exposure therapy could enhance intervention outcomes compared with standard virtual reality exposure. Our pilot randomized controlled trial (n =21) indicated a strong effect of both intervention groups on fear of public speaking as well as evidence that the guidance component engaged the target attentional mechanism (decreased avoidance of audience members). However, our Bayesian analyses provided no conclusive support for either the null or alternative hypotheses. Further research with larger sample sizes is needed to elucidate the link between attentional avoidance and social anxiety disorder. Study 3 used data from study 2 to test whether a machine learning approach well suited to high-eye dimensional eye movement data (hidden Markov models) could identify heterogenous attentional styles among those with social anxiety disorder and predict differential treatment outcomes. We identified two distinct groups reflecting “avoidant” and “vigilant” styles pre-treatment. Moreover, we found meaningful differences between the groups post-treatment – with only the hypervigilant group showing treatment response to virtual reality exposure therapy. These findings suggest that evaluating attentional processes in flexible, data-driven ways may provide unique insights into social anxiety disorder and has implications for treatment.