2022 Fiscal Year Annual Research Report
Hierarchical interactions of predictions and prediction errors in normal and schizophrenic brains
| Project/Area Number |
19K06906
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Chao Zenas 東京大学, ニューロインテリジェンス国際研究機構, 准教授 (30532113)
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| Project Period (FY) |
2019-04-01 – 2023-03-31
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| Keywords | Predictive coding / Prediction signal / Hierarchy / Theoretical model / Auditory sequence / Cortical oscillation / EEG / Schizophrenia |
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| Outline of Annual Research Achievements |
The human brain is proposed to harbor a hierarchical predictive coding neuronal network underlying perception, cognition, and action. In support of this theory, feedforward signals for prediction error have been reported. However, the identification of feedback prediction signals has been elusive due to their causal entanglement with prediction-error signals. Here, we use a quantitative model to decompose these signals in electroencephalography during an auditory task, and identify their spatio-spectral-temporal signatures across two functional hierarchies. Two prediction signals are identified in the period prior to the sensory input: a low-level signal representing the tone-to-tone transition in the high beta frequency band, and a high-level signal for the multi-tone sequence structure in the low beta band. Subsequently, prediction-error signals dependent on the prior predictions are found in the gamma band. Our findings reveal a frequency ordering of prediction signals and their hierarchical interactions with prediction-error signals supporting predictive coding theory.
The above results are published: Chao, Z. C., Huang, Y. T., & Wu, C. T. (2022). A quantitative model reveals a frequency ordering of prediction and prediction-error signals in the human brain. Communications Biology, 5(1), 1076. I was also invited to present the results in Neuro2022 in Okinawa.
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