1 Introduction
Understanding how learning and cognition unfold in real time has long been a central aim of cognitive neuroscience (1). Considerable progress has been achieved in elucidating core cognitive functions such as attentional control (2, 3), working memory (4), and executive functioning (5, 6). However, the neural mechanisms that integrate these processes as learners perceive, consolidate, and apply information in authentic contexts remain only partially understood. Recent work has begun to trace these dynamics by employing multimodal approaches that combine diverse signals, including neural activity, eye movements, facial expressions, and interactive behaviors, to capture the complexity of learning in action (7–9).
Advancing this agenda requires methodological frameworks that can capture learning and cognitive states as multimodal and dynamically evolving phenomena. This necessitates the integration of high-temporal-resolution techniques (e.g., EEG, eye-tracking) with hemodynamic imaging methods (e.g., fNIRS, MRI) and computational analyses of behavior, enabling the derivation of quantitative indicators that are reliable across tasks, contexts, and populations. The convergence of these state-of-the-art technologies now makes such integration increasingly feasible. EEG and eye-tracking can capture rapid fluctuations (10, 11); fNIRS and MRI can identify network-level activations (12, 13); and large language models can process and interpret rich behavioral data (14, 15), from spoken explanations to written reflections, at scale. When synchronized along common temporal axes and analytic pipelines, these multimodal data streams can uncover coherent neural-behavioral signatures spanning information acquisition, consolidation, and application, thereby enabling real-time measurement, adaptive feedback, and the development of neuroscience-informed educational interventions.
This editorial introduces the Research Topic “Investigating Learning and Cognitive States Using Multimodal Approaches,” which seeks empirical studies, methodological innovations, and reviews that: (a) apply multimodal approaches to recognize and assess learning and cognitive states; (b) examine the use and implications of EEG, fNIRS, MRI, eye-tracking, and LLMs in learning contexts; (c) develop quantitative metrics and validation strategies for cognitive state measurement; (d) present novel technologies for studying learning and cognition; and (e) leverage neuroimaging for assessing learning-relevant states and outcomes. Contributions that provide open resources, reproducible workflows, and translational pathways to educational practice are especially encouraged.
2 Contributions of the papers to this Research Topic
The 10 papers selected for this Research Topic collectively demonstrate how multimodal, neuroimaging, and computational approaches can advance our understanding of learning and cognitive states. They span methods from bibliometric analysis and neurophysiological measurement to deep learning and predictive modeling, showing how interdisciplinary perspectives can enhance assessment, diagnosis, and intervention in diverse learning and cognitive contexts.
The first article, Research hotspots and trends of non-invasive vagus nerve stimulation: a bibliometric analysis from 2004 to 2023, maps two decades of progress in non-invasive vagus nerve stimulation. By identifying global research trends, leading contributors, and emerging application areas, it provides a comprehensive overview that helps clarify how neuromodulation research informs clinical and cognitive domains (Chen et al.).
The second article, Understanding emotional influences on sustained attention: a study using virtual reality and neurophysiological monitoring, integrates VR-based emotion induction with EEG and PPG monitoring to explore how emotional valence and arousal affect sustained attention. Its findings highlight gender-specific patterns and demonstrate the value of immersive technologies and physiological data in studying emotion-attention interactions (Shen et al.).
The third article, Automatic screening for posttraumatic stress disorder in early adolescents following the Ya'an earthquake using text mining techniques, applies language models and machine-learning classifiers to self-narratives for early PTSD detection. This study exemplifies how text mining can transform qualitative self-reports into quantitative indicators of psychological states, improving early screening and intervention (Yuan et al.).
The fourth article, Modulation of vigilance/alertness using beta (30 Hz) transcranial alternating current stimulation, investigates how different stimulation parameters influence vigilance through behavioral performance measures. The results support the oscillatory nature of attentional vigilance and lay a foundation for closed-loop brain-stimulation interventions (Chu et al.).
The fifth article, Development and validation of a postoperative delirium risk prediction model for non-cardiac surgery in elderly patients, constructs and validates a predictive model for postoperative delirium by integrating cognitive assessments, sleep quality, and physiological indicators. It demonstrates how data-driven models can aid early clinical decision-making in cognitive risk management (Lin et al.).
The sixth article, The application of radiomics in the diagnosis and evaluation of cognitive impairment related to neurological diseases, reviews radiomic approaches for Alzheimer's disease, Parkinson's disease, and other neurological disorders. By summarizing imaging markers and analytical workflows, it underscores radiomics as an emerging multimodal method for early cognitive-impairment assessment (Xiao et al.).
The seventh article, Advances in two-photon imaging for monitoring neural activity in behaving mice, synthesizes recent progress in two-photon imaging and its applications in behaviorally engaged animal models. It highlights how fine-grained optical imaging links neural dynamics with behavioral outputs, expanding methodological toolkits for cognitive neuroscience (Li et al.).
The eighth article, Cognitive training gain transfer in cognitively healthy aging: per protocol results of the German AgeGain study, examines cognitive-training transfer effects and their neurobiological modulators using diffusion and functional MRI. It advances understanding of structural-functional connectivity mechanisms that support learning plasticity in healthy aging (Fischer et al.).
The ninth article, Convolutional neural networks decode finger movements in motor sequence learning from MEG data, validates a compact deep-learning model for decoding finger movements from non-invasive MEG signals. This work bridges machine learning and motor-learning neuroscience, illustrating efficient and interpretable decoding of fine-grained neural activity patterns (Zabolotniy et al.).
Finally, the tenth article, Graph neural networks in Alzheimer's disease diagnosis: a review of unimodal and multimodal advances, reviews the use of GNNs in processing multimodal neuroimaging data for Alzheimer's disease diagnosis. It systematically compares architectures, datasets, and performance metrics, offering future directions for integrating AI and neuroimaging in clinical cognitive assessment (Ali et al.).
Taken together, these studies illustrate the diversity and potential of multimodal approaches in cognitive neuroscience, ranging from non-invasive stimulation and imaging to AI-driven analytics, and offering methodological insights and translational implications for advancing learning and cognitive-state assessment.
Statements
Author contributions
TX: Writing – original draft, Writing – review & editing. FL: Writing – review & editing. YC: Writing – review & editing. YZ: Writing – original draft, Writing – review & editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the National Natural Science Foundation of China [Grant 62377039] and the Higher Education Research Fund of Northwestern Polytechnical University, 2025 [Grant GJJJM202504].
Acknowledgments
The guest editors sincerely thank the Frontiers in Neuroscience Editorial Team for their guidance and support throughout the preparation of this Research Topic. We are also grateful to all contributing authors and reviewers for their dedication, which ensured the successful completion of this Research Topic.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1.
Hennig JA Oby ER Losey DM Batista AP Yu BM Chase SM . How learning unfolds in the brain: toward an optimization view. Neuron. (2021) 109:3720. doi: 10.1016/j.neuron.2021.09.005
2.
Hopfinger JB Buonocore MH Mangun GR . The neural mechanisms of top-down attentional control. Nat Neurosci. (2000) 3:284–91. doi: 10.1038/72999
3.
Paneri S Gregoriou GG . Top-down control of visual attention by the prefrontal cortex. Functional specialization and long-range interactions. Front. Neurosci. (2017) 11:545. doi: 10.3389/fnins.2017.00545
4.
D'Esposito M Postle BR . The cognitive neuroscience of working memory. Ann Rev Psychol. (2015) 66:115–42. doi: 10.1146/annurev-psych-010814-015031
5.
Gilbert SJ Burgess PW . Executive function. Curr Biol. (2008) 18:R110–4. doi: 10.1016/j.cub.2007.12.014
6.
Toba MN Malkinson TS Howells H Mackie M-A Spagna A . Same, same but different? A multi-method review of the processes underlying executive control. Neuropsychol Rev. (2024) 34:418–54. doi: 10.1007/s11065-023-09577-4
7.
Fu B Gu C Fu M Xia Y Liu Y . A novel feature fusion network for multimodal emotion recognition from EEG and eye movement signals. Front Neurosci. (2023) 17. doi: 10.3389/fnins.2023.1234162
8.
Hayashi Y . Modeling synchronization for detecting collaborative learning process using a pedagogical conversational agent: investigation using recurrent indicators of gaze, language, and facial expression. Int J Artif Intell Educ. (2024) 34:1206–47. doi: 10.1007/s40593-023-00381-y
9.
Li Q Luximon Y Zhang J Song Y . Measuring and classifying students' cognitive load in pen-based mobile learning using handwriting, touch gestural and eye-tracking data. Br J Educ Technol. (2024) 55:625–53. doi: 10.1111/bjet.13394
10.
Harezlak K . Eye movement dynamics during imposed fixations. Inf Sci. (2017) 384:249–62. doi: 10.1016/j.ins.2016.07.074
11.
Hunyadi B Woolrich MW Quinn AJ Vidaurre D De Vos M . A dynamic system of brain networks revealed by fast transient EEG fluctuations and their fMRI correlates. Neuroimage. (2019) 185:72–82. doi: 10.1016/j.neuroimage.2018.09.082
12.
Chen W-L Wagner J Heugel N Sugar J Lee Y-W Conant L et al . Functional near-infrared spectroscopy and its clinical application in the field of neuroscience: advances and future directions. Front Neurosci. (2020) 14:724. doi: 10.3389/fnins.2020.00724
13.
Smith SM Vidaurre D Beckmann CF Glasser MF Jenkinson M Miller KL et al . Functional connectomics from resting-state fMRI. Trends Cogn Sci. (2013) 17:666–82. doi: 10.1016/j.tics.2013.09.016
14.
Liu Y Cao J Liu C Ding K Jin L . Datasets for large language models: a comprehensive survey. Artif Intell Rev. (2025) 58:403. doi: 10.1007/s10462-025-11403-7
15.
Naveed H Khan AU Qiu S Saqib M Anwar S Usman M Akhtar N Barnes N Mian A . A comprehensive overview of large language models. ACM Trans Intell Syst Technol. (2025) 16:106:1–72. doi: 10.1145/3744746
Summary
Keywords
assessment of learning and cognitive states, multi-modal approaches, brain–computer interfaces, eye-tracking, neuroimaging, large language model
Citation
Xu T, Luo F, Cui Y and Zhou Y (2026) Editorial: Integrating multimodal approaches to unravel neural mechanisms of learning and cognition. Front. Neurol. 17:1753883. doi: 10.3389/fneur.2026.1753883
Received
25 November 2025
Accepted
06 January 2026
Published
26 January 2026
Volume
17 - 2026
Edited and reviewed by
Paolo Frigio Nichelli, University of Modena and Reggio Emilia, Italy
Updates
Copyright
© 2026 Xu, Luo, Cui and Zhou.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yun Zhou, zhouyun@snnu.edu.cn
ORCID: Yun Zhou orcid.org/0000-0002-2306-8986
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.