Work

Editorial: Integrating multimodal approaches to unravel neural mechanisms of learning and cognition

Year: 2026

Type: editorial

Abstract: Understanding how learning and cognition unfold in real time has long been a central aim of cognitive neuroscience (Hennig et al., 2021). Considerable progress has been achieved in elucidating core cognitive functions such as attentional control (Hopfinger et al., 2000;Paneri & Gregoriou, 2017), working memory (D'Esposito & Postle, 2015), and executive functioning (Gilbert & Burgess, 2008;Toba et al., 2024).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 (Fu et al., 2023;Hayashi, 2024;Li et al., 2024).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 (Harezlak, 2017;Hunyadi et al., 2019); fNIRS and MRI can identify network-level activations (Chen et al., 2020;Smith et al., 2013); and large language models can process and interpret rich behavioral data (Liu et al., 2025;Naveed et al., 2025) The ten papers selected for this special issue collectively demonstrate how multimodal, neuroimaging, and computational approaches can advance our understanding of learning and cognitive states. They span methods from bibliometric 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.

Source: Frontiers in Neurology

DOI: 10.3389/fneur.2026.1753883

Authors: Tao Xu, Fang Luo, Ying Cui, Yun Zhou

Institutions: Northwestern Polytechnical University, Beijing Normal University, University of Alberta, Shaanxi Normal University

Language: English

Topic: Neural and Behavioral Psychology Studies

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $3,801