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Electrophysiological signatures of hierarchical learning

(2022) CEREBRAL CORTEX. 32(3). p.626-639
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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.
Keywords
Cognitive Neuroscience, computational modeling, EEG, hierarchical learning, precision-weighted prediction error, PREDICTION ERRORS, ANTERIOR CINGULATE, FRONTAL THETA, NEURAL MECHANISMS, BEHAVIOR, NEGATIVITY, MIDBRAIN, FEEDBACK, CORTEX, MODEL

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MLA
Liu, Meng, et al. “Electrophysiological Signatures of Hierarchical Learning.” CEREBRAL CORTEX, vol. 32, no. 3, 2022, pp. 626–39, doi:10.1093/cercor/bhab245.
APA
Liu, M., Dong, W., Qin, S., Verguts, T., & Chen, Q. (2022). Electrophysiological signatures of hierarchical learning. CEREBRAL CORTEX, 32(3), 626–639. https://doi.org/10.1093/cercor/bhab245
Chicago author-date
Liu, Meng, Wenshan Dong, Shaozheng Qin, Tom Verguts, and Qi Chen. 2022. “Electrophysiological Signatures of Hierarchical Learning.” CEREBRAL CORTEX 32 (3): 626–39. https://doi.org/10.1093/cercor/bhab245.
Chicago author-date (all authors)
Liu, Meng, Wenshan Dong, Shaozheng Qin, Tom Verguts, and Qi Chen. 2022. “Electrophysiological Signatures of Hierarchical Learning.” CEREBRAL CORTEX 32 (3): 626–639. doi:10.1093/cercor/bhab245.
Vancouver
1.
Liu M, Dong W, Qin S, Verguts T, Chen Q. Electrophysiological signatures of hierarchical learning. CEREBRAL CORTEX. 2022;32(3):626–39.
IEEE
[1]
M. Liu, W. Dong, S. Qin, T. Verguts, and Q. Chen, “Electrophysiological signatures of hierarchical learning,” CEREBRAL CORTEX, vol. 32, no. 3, pp. 626–639, 2022.
@article{8728459,
  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.}},
  author       = {{Liu, Meng and Dong, Wenshan and Qin, Shaozheng and Verguts, Tom and Chen, Qi}},
  issn         = {{1047-3211}},
  journal      = {{CEREBRAL CORTEX}},
  keywords     = {{Cognitive Neuroscience,computational modeling,EEG,hierarchical learning,precision-weighted prediction error,PREDICTION ERRORS,ANTERIOR CINGULATE,FRONTAL THETA,NEURAL MECHANISMS,BEHAVIOR,NEGATIVITY,MIDBRAIN,FEEDBACK,CORTEX,MODEL}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{626--639}},
  title        = {{Electrophysiological signatures of hierarchical learning}},
  url          = {{http://dx.doi.org/10.1093/cercor/bhab245}},
  volume       = {{32}},
  year         = {{2022}},
}

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