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Neurophysiological Correlates of Probabilistic Reward-Based Learning: Using Decoding Approach on MEG Data
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Prediction error and volatility estimate are important concepts in the predictive coding theory. In the present study, we derive the values of prediction error and volatility estimate from a hierarchical Bayesian model - Hierarchical Gaussian Filter. Using support vector machine (SVM) method, we predict the values of prediction error and volatility estimate from brain activity measured by magnetoencephalography (MEG). Our findings suggest that these computational values are indeed represented in the neural data, supporting the neural basis of predictive coding mechanisms.
Liaukovich K., Martynova O., Brain Sciences 2026 Vol. 5 No. 16 P. 439
Highlights
What are the main findings?
Temporal predictability may enhance auditory frequency discrimination, but only under active listening and when discrimination is difficult; the local irregularity (five-tone bundles) yields higher hit rates and perceptual sensitivity (d’) compared to oddball or two-tone paradigms, accompanied by enhanced P300 amplitude.
Participants may show a dissociation between objective performance and subjective awareness: ...
Added: September 4, 2026
Mariya Protopova, Bolgina T., Arutiunian V. et al., European Journal of Neuroscience 2025 Vol. 62 No. 8 Article e70282
For noninvasive language mapping, the choice of imaging method, task, and baseline remains an area of active research. While the sentence completion task is a recommended option for fMRI studies, the indirect nature of the signal is a limitation of the imaging method. This study presents a sentence completion paradigm for group- and individual-level language ...
Added: November 10, 2025
А.Е. Осадчий, А.Е. Кубяк, Нейротехнологии и нейроэлектроника (N&N) 2025 № 2 С. 4–31
Added: September 10, 2025
Dogonasheva O., Doelling K., Zakharov D. et al., Nature Computational Science 2025 Vol. 5 P. 915–926
Unraveling how humans understand speech despite distortions has long intrigued researchers. A prominent hypothesis highlights the role of multiple endogenous brain rhythms in forming the computational context to predict speech structure and content. Yet, how neural processes may implement rhythm-based context formation remains unclear. Here, we propose the Brain-Rhythm-Based Inference model (BRyBI) as a possible neural implementation of speech processing ...
Added: September 2, 2025
Tretyakova V., Pavlova A., Arapov V. et al., Plos One 2025 Vol. 20 No. 7 Article e0325977
Action word learning is believed to rely on mechanisms of Hebbian learning. However, this biological mechanism requires activation of the neural assemblies representing a word form and a corresponding movement to repeatedly overlap in time. In reality, though, these associated events could be separated by seconds. In the current MEG study, we examined trial-and-error learning ...
Added: July 4, 2025
Ali S., Khizhik A., Ryzhikov A. et al., , in: 2025 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), 12-13 May 2025.: IEEE, 2025. P. 357–360.
Three-phase induction motors play a crucial role in industrial applications due to their efficiency, durability, and reliability. However, effective fault detection remains challenging, primarily due to the scarcity of labeled failure data, which limits the performance of traditional machine learning (ML)-based diagnostic models and increases the risk of overfitting and poor generalization. Conventional methods, such ...
Added: July 3, 2025
Saranskaia I., Boris Gutkin, Zakharov D., European Physical Journal: Special Topics 2025 Vol. 234 P. 4159–4177
This review explores the interplay between data representation and machine learning (ML) methods in classifying functional, cognitive, and pathological brain states using magnetoencephalography (MEG). Two primary data representations are considered: sensor-level signals and reconstructed source signals. Sensor signals, when combined with classical ML methods such as linear discriminant analysis (LDA) and support vector machines (SVM), offer computational efficiency and simplified preprocessing, ...
Added: March 15, 2025
Vlasenko D., Zaikin A., Zakharov D., , in: 2024 8th Scientific School Dynamics of Complex Networks and their Applications (DCNA).: IEEE, 2024. P. 258–261.
The goal of this work is to propose a method for representing functional magnetic resonance imaging data and electroencephalography/magnetoencephalography data in the form of graphs reflecting the interactions of brain regions for brain state classification tasks. This method is based on ensemble learning to improve generalizability, accuracy, robustness and parallelism of the classification model, which ...
Added: December 7, 2024
Ivanova M., Germanova K., Petelin D. et al., / Series 005140 "Biorxiv". 2024.
Bipolar disorder (BD) involves altered reward processing and decision-making, with inconsistencies across studies. Here, we integrated hierarchical Bayesian modelling with magnetoencephalography (MEG) to characterise maladaptive belief updating in this condition. First, we determined if previously reported increased learning rates in BD stem from a heightened expectation of environmental changes. Additionally, we examined if this increased ...
Added: November 30, 2024
Samoylov I., Arcara G., Buyanova I. et al., International Journal of Psychophysiology 2024 Vol. 203 Article 112405
Objective: Some studies have hypothesized that atypical neural synchronization at the delta frequency band in the auditory cortex is associated with phonological and language skills in children with Autism Spectrum Disorder (ASD), but it is still poorly understood. This study investigated this neural activity and addressed the relationships between auditory response and behavioral measures of ...
Added: August 9, 2024
Kopytin G., Ivanova M., Herrojo-Ruiz M. D. et al., Behavioral Sciences 2024 Vol. 14 No. 2 Article 124
A central question in behavioural neuroscience is how different rewards modulate learning. While the role of monetary rewards is well-studied in decision-making research, the influence of abstract rewards like music remains poorly understood. This study investigated the dissociable effects of these two reward types on decision making. Forty participants completed two decision-making tasks, each characterised ...
Added: April 15, 2024
Arutiunian V., Arcara G., Irina B. et al., Brain Sciences 2023 Vol. 13 No. 9 Article 1313
Alpha-band (8–12 Hz) event-related desynchronization (ERD) or a decrease in alpha power in electro- and magnetoencephalography (EEG and MEG) reflects the involvement of a neural tissue in information processing. It is known that most children with autism spectrum disorder (ASD) have difficulties in information processing, and, thus, investigation of alpha oscillations is of particular interest ...
Added: November 7, 2023
Hein T., Gong Z., Ivanova M. et al., Communications Biology 2023 Vol. 6
Anxiety has been linked to altered belief formation and uncertainty estimation, impacting learning. Identifying the neural processes underlying these changes is important for understanding brain pathology. Here, we show that oscillatory activity in the medial prefrontal, anterior cingulate and orbitofrontal cortex (mPFC, ACC, OFC) explains anxiety-related learning alterations. In a magnetoencephalography experiment, two groups of ...
Added: June 7, 2023
Marina Ivanova, Herrojo-Ruiz M. D., , in: 2022 Fourth International Conference Neurotechnologies and Neurointerfaces (CNN) Kaliningrad, 14-16 Sept. 2022.: IEEE, 2022. Ch. 19 P. 55–58.
Predictive processing currently is one of the major lines of research in computational psychiatry. There is substantial evidence for the impaired Bayesian learning in affective disorders, and in particular impaired learning about uncertainty estimations. Based on our previous results in state and trait anxiety, we developed a study design, completed a pilot study and are ...
Added: February 10, 2023
Manyukhina Viktoriya, Prokofyev A., Galuta I. et al., Molecular Autism 2022 Vol. 13 No. 1 Article 20
Background: Altered neuronal excitation–inhibition (E–I) balance is strongly implicated in ASD. However, it is not
known whether the direction and degree of changes in the E–I ratio in individuals with ASD correlates with intellectual disability often associated with this developmental disorder. The spectral slope of the aperiodic 1/f activity reflects
the E–I balance at the scale of ...
Added: May 31, 2022
Viktoriya O. Manyukhina, Rostovtseva E., Prokofyev A. et al., Scientific Reports 2021 Vol. 11 No. 1 P. 1–10
Gamma oscillations are driven by local cortical excitatory (E)–inhibitory (I) loops and may help to
characterize neural processing involving excitatory-inhibitory interactions. In the visual cortex
reliable gamma oscillations can be recorded with magnetoencephalography (MEG) in the majority
of individuals, which makes visual gamma an attractive candidate for biomarkers of brain disorders
associated with E/I imbalance. Little is known, however, ...
Added: May 31, 2022
Retrospective confidence judgements in general-knowledge questions: Magnetoencephalograhy correlates
Martín-Luengo B., Altukhov D., Alexeeva M. et al., / Series 10.31234 "osf.io". 2021.
Added: May 30, 2022
Golosheykin S., Blagoveschenskiy Evgueni D., Agranovich O. et al., Frontiers in Pediatrics 2021 Vol. 9 Article 626734
Arthrogryposis multiplex congenita (AMC) has recently drawn substantial attention from researchers and clinicians. New effective surgical and physiotherapeutic methods have been developed to improve the quality of life of patients with AMC. While it is clear that all these interventions should strongly rely on the plastic reorganization of the central nervous system, almost no studies ...
Added: October 31, 2021
Remnev N., , in: 2019 International Conference on Data Mining Workshops (ICDMW).: IEEE, 2019. P. 1–7.
The task of recognizing the author’s native language based on a text (Native Language Identification - NLI) is the task of automatically recognizing native language (L1) based on texts written in a language that is not native to the author. The NLI task was studied in detail for the English language, and two shared tasks ...
Added: October 18, 2021
Remnev N., , in: Компьютерная лингвистика и интеллектуальные технологии: по материалам ежегодной международной конференции «Диалог» (Москва, 17–20 июня 2020 г.)Issue 19(26): дополнительный том.: -, 2020. P. 1123–1133.
The task of recognizing the author’s native (Native Language Identification—NLI) language based on a texts, written in a language that is non-native to the author—is the task of automatically recognizing native language (L1). The NLI task was studied in detail for the English language, and two shared tasks were conducted in 2013 and 2017, where ...
Added: October 18, 2021