# Bandit Models of Human Behavior: Reward Processing in Mental Disorders

arXiv: https://arxiv.org/abs/1706.02897 · HTML: https://mlsanity.com/paper/1706.02897/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2017.

## Built on by (most cited first, 8 of 8)

- [Differentially Private Stochastic Linear Bandits: (Almost) for Free](https://mlsanity.com/paper/2207.03445/) (2022)
- [Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook](https://mlsanity.com/paper/2210.13623/) (2022)
- [Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits](https://mlsanity.com/paper/2106.14866/) (2021)
- [Assessing AI Utility: The Random Guesser Test for Sequential Decision-Making Systems](https://mlsanity.com/paper/2407.20276/) (2024)
- [Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling](https://mlsanity.com/paper/2502.14379/) (2025)
- [Etat de l'art sur l'application des bandits multi-bras](https://mlsanity.com/paper/2101.00001/) (2021)
- [Learning in Distributed Contextual Linear Bandits Without Sharing the Context](https://mlsanity.com/paper/2206.04180/) (2022)
- [On the convergence of the MLE as an estimator of the learning rate in the Exp3 algorithm](https://mlsanity.com/paper/2305.06660/) (2023)

## Abstract (arXiv)

Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for multi-armed bandit problem, which extends the standard Thompson Sampling approach to incorporate reward processing biases associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain. We demonstrate empirically that the proposed parametric approach can often outperform the baseline Thompson Sampling on a variety of datasets. Moreover, from the behavioral modeling perspective, our parametric framework can be viewed as a first step towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions.