Computational Approach-avoidance Tendencies Related to Treatment Outcome in Patients Treated for Anxiety Disorders
Creators: name: Mingqian Guo
name: Breteler, Joppe Klein
name: Ikani, Nessa
Publisher: name: OSF Registries
Year: 2024
Subjects: subject: Social and Behavioral Sciences
subject: Anxiety
subject: Approach-avoidance
subject: Avoidance
subject: Computational modeling
subject: Depressive symptoms
subject: Drift Diffusion Modeling

description: Avoidance is seen as a major factor in development and maintenance of anxiety disorders. Anxiety has also been associated with tasks that measure approach-avoidance tendencies such as the Approach Avoidance Task (Loijen et al., 2020). However, to our knowledge, no studies thus far have investigated how approach-avoidance tendencies relate to treatment outcome. Furthermore, current analytical approaches have not incorporated recent developments in the analysis of reaction time and accuracy data. That is, traditional methods typically focus solely on reaction times, excluding incorrect trials, which fails to consider the speed-accuracy trade-off. For example, we might have two participants with one of them having faster RT whereas the other one having higher accuracy. In this case, deciding which participants have better performance on this task is a problem since RT and accuracy data give us contradictory result. An alternative that does take the speed-accuracy trade-off into account is the Sequential Sampling Model (SSM). It is regarded as the principal framework in decision-making tasks, conceptualizing decision-making as a process of noisy evidence accumulation (Forstmann et al., 2016; Ratcliff et al., 2016). 

The most commonly used SSM variant is the Diffusion Decision Model (DDM). In DDM, a response is generated when the accumulated evidence reaches a decision boundary, and the time for accumulating the decision evidence is the reaction time (RT). SSM decomposes the joint distribution of RT and choice data into several cognitively meaningful parameters, including among others: drift rate (v) for the speed of information processing, decision boundary (b) for decision caution, the starting point (z) for evidence accumulation bias, and the non-decision time for motor response (t0). These parameters offer valuable insights into decision-making mechanisms underlying approach-avoidance tendencies. For instance, von Krause et al. (2021) observed that while information processing speed remains unchanged when age increases, decision-making becomes more cautious, and motor action times are prolonged.

Although SSM has been broadly applied in computational psychiatry studies, it has thus far not been used to study approach-avoidance tendencies in patients with primary anxiety disorders. Our study aims to answer four research questions:

1) We will investigate differences between patients with primary anxiety disorders and an unselected online sample in the following DDM computational model parameters underlying approach-avoidance tendencies: Starting point bias, drift rate, rate of boundary collapse, non-decision time, and attention shift.

2) We will determine change during anxiety treatment from pre-treatment (i.e., start of treatment) to a 6-month follow-up in the following DDM parameters: Starting point bias, drift rate, rate of boundary collapse, non-decision time, and attention shift.

3) We will examine whether the pre-treatment DDM parameters can predict the changes in anxiety- and comorbid depressive symptoms. 

4) We will investigate whether changes in the DDM parameters before and after treatment are correlated with changes in anxiety and depressive symptoms.