Information processing biases in posttraumatic stress disorder (PTSD): cross-sectional findings and exploratory investigation of bias changes following treatment.

Background

Information processing biases are ascribed an important role in the aetiology and maintenance of posttraumatic stress disorder (PTSD). While findings on attentional biases have been mixed, there are only a few studies that have used implicit measures to assess dysfunctional appraisals and their malleability over the course of treatment in PTSD.

Objective

The primary aim was to examine whether individuals with PTSD (1) show attentional biases towards trauma-related stimuli, (2) generate more implicitly assessed dysfunctional appraisals, and (3) more strongly associate the self as traumatized and vulnerable compared to traumatized individuals without PTSD and a healthy control group. The second and exploratory aim was to explore whether biases would be reduced after treatment.Method: Participants included individuals with PTSD (n = 50), traumatized individuals without PTSD (n = 54), and healthy controls (n = 57). Attentional biases were measured with a Visual Search Task, dysfunctional appraisals with a scenario task, and trauma-related self-appraisals with two Implicit Association Tests (self-traumatized, self-vulnerability). In exploratory analyses, changes in these measures were examined across treatment.

Results

No significant differences were found in attention allocation to trauma-related stimuli between groups. However, participants with PTSD generated more dysfunctional appraisals and demonstrated stronger trauma-related associations than both control groups. Importantly, treatment significantly reduced dysfunctional appraisals and fostered more functional self-appraisals in the IAT.

Conclusions

Implicitly assessed dysfunctional (self-) appraisals differentiate individuals with PTSD from controls and appear responsive to treatment. Future research should clarify whether these implicit biases function as mechanisms of change and predict long-term clinical outcomes.