EEG-based classification of learning strategies : Model-based and model-free reinforcement learning

Human reinforcement learning (RL) has been known to utilize two distinctive learning strategies, model-based (MB) and model-free (MF) RL. The process of arbitration between MB and MF is thought to be located in the ventrolateral prefrontal cortex and frontopolar cortex. These loci are near the cortex, so we expect the related information can be represented in EEG signals. However, EEG signal patterns considering the arbitration of RL has not been investigated. In this paper, we tested a EEG-based classification model to separate these two different types of trials, each of which is meant to promote MB and MF RL. We found, for the first time, firm evidence to indicate that information pertaining to learning strategies is represented in prefrontal EEG signals.

I. Introduction

Recent studies on human RL found that humans use both MB and MF reinforcement learning strategies [1], [2]. However, classification of these different learning strategies is not investigated so far. The aim of this paper is to build and optimize a simple classifier which would attempt to distinguish between these two RLs. Specifically, we classified two types of trials, each of which is designed to promote MB and MF RL, respectively, from EEG data recorded from human subjects brains during performing a two-stage Markov decision task.