Article

Harnessing the flexibility of neural networks to predict dynamic theoretical parameters underlying human choice behavior

2023-04-21

Abstract excerpt

Reinforcement learning (RL) models are used extensively to study human behavior. These rely on normative models of behavior and stress interpretability over predictive capabilities. More recently, neural network models have emerged as a descriptive modeling paradigm that is capable of high predictive power yet with limited interpretability. Here, we seek to augment the expressiveness of theoretical RL models with...

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Literature Corpus work: 55257fdf-7a58-5c1f-859b-bd9e2abf80ad
DOI: 10.1101/2023.04.21.537666

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