Investigating the effect of anxiety on model-based reinforcement learning in the punishment condition

Autor: Hur, Jihyun
Rok vydání: 2022
Předmět:
DOI: 10.17605/osf.io/4m2ae
Popis: One of the well-known symptoms of anxiety is avoidance. In the face of potential threats or aversive outcomes, anxiety patients often show aberrant learning (Aylward et al., 2019; Browning et al., 2015; Wise & Dolan, 2020). For example, they tend to quickly adjust their choices based on the recent punishment history. This tendency has been observed during the model-free or habitual learning in a punishment domain. The model-free learning governs habit directed actions that are reinforced by successful outcomes without considering the task structure (Daw et al., 2011). Despite this learning bias in anxiety towards aversive outcomes, it has not been clearly investigated whether anxiety is associated with similar deficits in the model-based learning with punishment. The model-based learning controls goal-directed actions that prospectively evaluate future possibilities by using an internal model of the task (Daw et al., 2011). Most of the previous literature examined that highly anxious participants have shown no deficits in the model-based decision-making in the reward condition (Gillan et al., 2016; Gillan et al., 2020; Patzelt et al., 2019). Given that anxiety has affected the model-free decision-making during aversive tasks, we hypothesize anxiety would negatively influence the model-based decision-making in the punishment condition. In this study, we will ask healthy adult participants with a range of anxiety scores to conduct a multi-step decision-making task in both reward and punishment conditions. Then we will compare the model-based learning weights from both conditions and see whether the difference is correlated with anxiety level. Lastly, we will probe how acute stress during the task impact the model-based decision-making in an aversive environment.
Databáze: OpenAIRE