Using the diffusion model to investigate the cognitive processes responsible for attentional and interpretation biases in anxiety and depression: The benefits outweigh the challenges

Author: Amy Manuel

  • Thesis download: available for open access on 28 May 2021.

Manuel, Amy, 2018 Using the diffusion model to investigate the cognitive processes responsible for attentional and interpretation biases in anxiety and depression: The benefits outweigh the challenges, Flinders University, School of Psychology

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Attentional and interpretation biases have long been considered a contributing factor to anxiety and depression. As such, research has turned to investigating ways to modify these biases to alleviate the distressing symptoms of anxiety and depression. However, the cognitive processes that are responsible for attentional and interpretation biases, and their successful modification, are not well understood. Furthermore, there are questions surrounding the reliability of the attentional bias score. This score is the primary measure of attentional bias; thus, the issues of reliability are a concern for the integrity of the findings in the attentional bias modification literature.

Through a series of studies, this thesis explores the value of applying a mathematical model, the diffusion decision model (Ratcliff, 1978), to data returned from the dot probe and yes/no tasks, two measures of biased attention toward, and interpretation of, emotional information, respectively. In doing so, the aim is to advance theoretical understanding of the mechanisms that underlie cognitive biases in anxiety and depression. The diffusion decision model belongs to a class of models called evidence accumulation models. Evidence accumulation models are a more sophisticated form of analysis that can isolate and identify different implicit decisional processes. By doing so, better understanding of these processes can potentially lead to the development of more targeted interventions, and ultimately improve treatment outcomes for individuals living with anxiety and depression.

Additionally, this thesis explores the potential of the diffusion decision model to be a more sensitive and reliable way to measure attentional bias. If the suitability of diffusion decision model analysis as an alternative, more reliable, way to analyse data from the dot probe task is established, the findings presented in this thesis may encourage other researchers without a mathematical psychology background to explore the benefits of these kinds of models in their own work.

This thesis presents the successful application of diffusion decision model analysis to data from the dot probe task for the first time. In doing so, the capacity for the diffusion model to identify implicit decisional processes that differ between anxiety and depression that are not captured by RTs alone, has been demonstrated. While the test-retest reliability of the diffusion model parameters was mixed, guidance has been provided for future research to gain clarity on the reliability of the diffusion model parameters, and their suitability as an alternative measure of attentional bias. Finally, the research in this thesis has demonstrated the value of adopting a mathematical psychology analytical approach to the field of applied clinical psychology.

Keywords: Diffusion Decision Model, Attentional Bias, Interpretation Bias, Anxiety, Depression

Subject: Psychology thesis

Thesis type: Doctor of Philosophy
Completed: 2018
School: School of Psychology
Supervisor: Prof Eva Kemps