🧠 Cmputational Modeling and Comparison of Single and Differential Learning

Rates,Inverse Temperatures and Salience of probability of Aversaive Stimuli in a Probabilistic Reversed Reinforced Learning Task among High and Low Anxious Population💻

Welcome to my computational psychiatry project! This project is focused on the study of anxiety using computational methods and models. One of the key approaches used in this field is reinforcement learning, which is a type of machine learning that involves decision-making based on rewards and punishments.

📋 Table of Contents

• Introduction
• Literature Review
• Reinforcement Learning
• Application to Mental Disorders
• Results
• Discussion and Recommendations

🎉 Introduction

Computational psychiatry is a new interdisciplinary field that aims to study mental disorders using computational methods and models. Reinforcement learning is one of the key approaches used in this field to model decision-making behavior in individuals with mental disorders.

📚 Literature Review

As part of this project, I conducted a literature review and found several studies that have used reinforcement learning models to study decision-making behavior in individuals with mental disorders. For example, a study by Hsu et al. (2015) used a reinforcement learning model to understand the decision-making behavior of individuals with social anxiety disorder and found that these individuals exhibit an increased sensitivity to punishment and a decreased sensitivity to reward compared to healthy controls. Another study by Whelan et al. (2017) used reinforcement learning models to study decision-making in individuals with depression and found similar results.

💡 Reinforcement Learning

Reinforcement learning is a type of machine learning that involves decision-making based on rewards and punishments. In this approach, an agent (e.g., a person) interacts with an environment and learns to perform actions that maximize a reward signal. This approach has been applied to various mental disorders, including depression, anxiety, and addiction.

🧪 Application to Mental Disorders

In the context of computational psychiatry, reinforcement learning models have been used to study decision-making in individuals with mental disorders. For example, studies have used reinforcement learning models to understand the decision-making behavior of individuals with social anxiety disorder and depression. The results of these studies have found that individuals with these disorders exhibit an increased sensitivity to punishment and a decreased sensitivity to reward, compared to healthy controls.

📊 Results

The results of the study in this project showed that Model 3 had the highest precision score of 0.7635, followed by Model 3 with 0.7561 and Model 2 with 0.7030. In terms of accuracy, Model 3 had the highest score of 0.5879, followed by Model 1 with 0.5666 and Model 2 with 0.5622. Finally, Model 1 also had the highest F1 score of 0.6847, followed by Model 3 with 0.6832 and Model 2 with 0.6640.

💬 Discussion and Recommendations

The results of this study provide valuable insights into the predictability of different models for studying decision-making behavior in individuals with mental disorders. Based on these findings, it is recommended to use Model 3 for future studies in this area. Additionally, the results of the study have potential implications for the development of new treatments for anxiety disorders, particularly for those populations that exhibit an increased sensitivity to punishment and a decreased sensitivity to reward. Further research is needed to fully understand the underlying mechanisms of mental disorders and to develop effective treatments.