Modulating hierarchical learning by high-definition transcranial alternating current stimulation at theta frequency. (10th September 2022)

• Record Type: Journal Article
• Authors: Liu, Meng; Dong, Wenshan; Wu, Yiling; Verbeke, Pieter; Verguts, Tom; Chen, Qi
• Is Part Of: Cerebral cortex. Volume 33:Number 8(2023)
• Page Start: 4421
• Page End: 4431
• Publication Date: 2022-09-10
• Subjects: high-definition tACS -- dorsolateral prefrontal cortex -- hierarchical learning -- computational modeling; Cerebral cortex -- Periodicals; Brain -- Periodicals; 612.825
• DOI: 10.1093/cercor/bhac352
• Languages: English
• ISSNs: 1047-3211

Abstract

Abstract: Considerable evidence highlights the dorsolateral prefrontal cortex (DLPFC) as a key region for hierarchical (i.e. multilevel) learning. In a previous electroencephalography (EEG) study, we found that the low-level prediction errors were encoded by frontal theta oscillations (4–7 Hz), centered on right DLPFC (rDLPFC). However, the causal relationship between frontal theta oscillations and hierarchical learning remains poorly understood. To investigate this question, in the current study, participants received theta (6 Hz) and sham high-definition transcranial alternating current stimulation (HD-tACS) over the rDLPFC while performing the probabilistic reversal learning task. Behaviorally, theta tACS induced a significant reduction in accuracy for the stable environment, but not for the volatile environment, relative to the sham condition. Computationally, we implemented a combination of a hierarchical Bayesian learning and a decision model. Theta tACS induced a significant increase in low-level (i.e. probability-level) learning rate and uncertainty of low-level estimation relative to sham condition. Instead, the temperature parameter of the decision model, which represents (inverse) decision noise, was not significantly altered due to theta stimulation. These results indicate that theta frequency may modulate the (low-level) learning rate. Furthermore, environmental features (e.g. its stability) may determine whether learning is optimized as a result.