Electrophysiological Signatures of Hierarchical Learning. (31st July 2021)

• Record Type: Journal Article
• Authors: Liu, Meng; Dong, Wenshan; Qin, Shaozheng; Verguts, Tom; Chen, Qi
• Is Part Of: Cerebral cortex. Volume 32:Number 3(2022)
• Page Start: 626
• Page End: 639
• Publication Date: 2021-07-31
• Subjects: computational modeling -- EEG -- hierarchical learning -- precision-weighted prediction error; Cerebral cortex -- Periodicals; Brain -- Periodicals; 612.825
• DOI: 10.1093/cercor/bhab245
• Languages: English
• ISSNs: 1047-3211

Abstract

Abstract: Human perception and learning is thought to rely on a hierarchical generative model that is continuously updated via precision-weighted prediction errors (pwPEs). However, the neural basis of such cognitive process and how it unfolds during decision-making remain poorly understood. To investigate this question, we combined a hierarchical Bayesian model (i.e., Hierarchical Gaussian Filter [HGF]) with electroencephalography (EEG), while participants performed a probabilistic reversal learning task in alternatingly stable and volatile environments. Behaviorally, the HGF fitted significantly better than two control, nonhierarchical, models. Neurally, low-level and high-level pwPEs were independently encoded by the P300 component. Low-level pwPEs were reflected in the theta (4–8 Hz) frequency band, but high-level pwPEs were not. Furthermore, the expressions of high-level pwPEs were stronger for participants with better HGF fit. These results indicate that the brain employs hierarchical learning and encodes both low- and high-level learning signals separately and adaptively.