Anxiety disorders are characterized by a range of aberrations in the processing and response to threat, but there is little clarity what core pathogenesis might underlie these symptoms. Here we propose a decision theoretic analysis of maladaptive avoidance and embody it in a reinforcement learning model, which shows how a localized bias in beliefs can formally explain a range of phenomena related to anxiety. The core observation, implicit in standard decision theoretic accounts of sequential evaluation, is that avoidance should be protective: if danger can be avoided later, it poses no threat now. We show how a violation of this assumption - a pessimistic, false belief that later avoidance will be unsuccessful - leads to a characteristic propagation of fear and avoidance to situations far antecedent of threat. This single deviation can explain a surprising range of features of anxious behavior, including exaggerated threat appraisals, fear generalization, and persistent avoidance.
Sam Zorowitz (zorowitz [at] princeton.edu)
├── figures <- Figures for presentations & manuscript. │ ├── manuscripts <- Manuscripts & RLDM 2019 abstract/poster. │ ├── notebooks <- Analysis notebooks for the projects. │ ├── 01_OpenField.ipynb <- Demo of pessimistic RL in toy MDP. │ ├── 02_AppAvo.ipynb <- Model of approach-avoidance bias. │ ├── 03_Helplessness.ipynb <- Model of anxiety-to-depression transition. │ ├── 04_DecisionTree.ipynb <- Model of aversive pruning. │ ├── 05_FreeChoice.ipynb <- Model of free choice premium. │ ├── 06_CliffWalking.ipynb <- Reproduction of Gaskett (2003). │ ├── sisyphus <- Source code used in notebooks (installation instructions below). │ ├── envs <- Task environments. │ ├── mdp <- RL algorithms (value iteration, temporal difference learning). │ ├── tests <- Continuous integration tests. │ ├── requirements.txt <- Python packages used in this project.
This repository hosts the sisyphus package, which is used in all of this project's simulations. The package contains functions for constructing arbitrary MDP environments and several reinforcement learning algorithms.
To install the code through Github, open a terminal and run:
pip install git+https://github.com/ndawlab/seqanx.gitAlternately, you can clone the repository and install locally:
git clone https://github.com/ndawlab/seqanx
cd seqanx
pip install -e .Once installed, the simulations (found in the notebooks folder) should be reproducible on any computer.
Jill de Ron very kindly translated the python code for the pessimistic model into R. With her permission, the code is available in the corresponding folder. Thanks Jill!