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

ABSTRACT: 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 the high flexibility and predictive power of neural networks. We introduce a novel framework, which we term theoretical-RNN (t-RNN), whereby a recurrent neural network is trained to predict trial-by-trial behavior and to infer theoretical RL parameters using artificial data of RL agents performing a two-armed bandit task. In three studies, we then examined the use of our approach

SUBMITTER: Ger Y 

PROVIDER: S-EPMC10793919 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature