Model-based BCI: A novel brain-computer interface framework for reading out learning strategies underlying choices

Abstract

Recent studies in decision neuroscience revealed that two different strategies guide trial-by-trial choice behavior during reinforcement learning (RL): a goal-directed strategy and a habitual learning strategy. An increasing number of studies have provided evidence for neural substrates underlying respective strategies, suggesting that a single RL algorithm cannot make accurate predictions about individual choices. Despite rapid progress in predicting humans intentions such as movements or choices, there has been no attempt to read out human learning strategies. We proposed a novel brain-computer interface (BCI) framework for decoding human learning strategies from electroencephalography (EEG) data. To circumvent the issue that there is no gold standard for labeling learning strategies, we trained the proposed framework classifier to best match predictions of the computational model of the neural process underlying human learning strategies. The simulation used a 1D, 2D, and 3D convolutional neural network (CNN) on 18 subjects; the EEG data demonstrated that the proposed framework successfully read out human learning strategies with very high accuracy (98.4%). Subsequently, we examined whether those learning strategies labeled from the computational model exhibited distinctive EEG patterns. We used class activation mapping (CAM) for visualization, and we identified distinctive strategy-dependent patterns in the EEG feature space. We argue that the proposed framework has great potential for decoding high-level cognitive states.