Using recurrent neural network to estimate irreducible stochasticity in human choice behavior.

ABSTRACT: Theoretical computational models are widely used to describe latent cognitive processes. However, these models do not equally explain data across participants, with some individuals showing a bigger predictive gap than others. In the current study, we examined the use of theory-independent models, specifically recurrent neural networks (RNNs), to classify the source of a predictive gap in the observed data of a single individual. This approach aims to identify whether the low predictability of behavioral data is mainly due to noisy decision-making or misspecification of the theoretical model. First, we used computer simulation in the context of reinforcement learning to demonstrate that RNNs can be used to identify model misspecification in simulated agents with varying degrees of behavior

SUBMITTER: Ger Y 

PROVIDER: S-EPMC11379453 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature