Guang Ouyang

Working memory (WM) is typically understood to comprise two core components: maintenance (short-term retention) and manipulation (active processing of information). However, many existing WM tasks are composite in design, involving both components and even other cognitive processes or constructs. Explicit characterization of the two components in terms of cross-individual variability in both behavioral performance and neural correlates remains limited. To address this gap, we developed two structurally matched task paradigms to compare the behavioral variability in WM maintenance and manipulation while minimizing extraneous influences such as strategy use and domain-specific skills. We also used phase-amplitude cross-frequency coupling (CFC) as a neural measure to investigate the neural dynamics of each WM component. Our psychometric analyses converged in supporting the differentiation between the two components. In addition, neural evidence showed that they engaged different brain networks. Specifically, WM manipulation recruited more extensive networks, reflected in stronger and structurally distinct phase-amplitude CFC across brain regions. Moreover, individual differences in manipulation task performance were reliably predicted by the corresponding CFC patterns, whereas maintenance task performance was not, further highlighting the two components’ differential association with neural dynamics.

Handwriting imagery has emerged as a promising paradigm for brain-computer interfaces (BCIs) aimed at translating brain activity into text output. Compared with invasively recorded electroencephalography (EEG), non-invasive recording offers a more practical and feasible approach to capturing brain signals for BCI. This study explores the limit of decoding non-invasive EEG associated with handwriting imagery into English letters using deep neural networks. To this end, five participants were instructed to imagine writing the 26 English letters with their EEG being recorded from the scalp. A measurement of EEG similarity across letters was conducted to investigate letter-specific patterns in the dataset. Subsequently, four convolutional neural network (CNN) models were trained for EEG classification. Descriptively, the EEG data clearly exhibited letter-specific patterns serving as a proof-of-concept for EEG-to-text translation. Under the chance level of accuracy at 3.85%, the CNN classifiers trained on each participant reached the highest limit of around 20%. This study marks the first attempt to decode non-invasive EEG associated with handwriting imagery. Although the achieved accuracy is not sufficient for a usable brain-to-text BCI, the model's performance is noteworthy in revealing the potential for translating non-invasively recorded brain signals into text outputs and establishing a baseline for future research.

Inspired by the high engagement and sustained behavioral excitement observed in video game players, we hypothesized that distinct brain activity patterns occur during gaming compared to a generic nongame setting. Using electroencephalography (EEG), we characterized the brain’s response to critical in-game events, focusing on the response magnitude and adaptation. Data from a large participant cohort (n = 140 for game tasks, n = 200 for nongame tasks) revealed a clear, definite, and consistent temporospatial structure of brain response patterns triggered by critical in-game events. Most notably, this response displayed unusually large magnitudes and a lack of adaptation over repeated exposures—markedly different from the observed neural responses to typical events in lab-based tasks (e.g., regarding novelty, unexpected events or errors). The identification of this distinctive response component in gaming contexts may inspire further research into fundamental cognitive systems, such as motivation, reward, emotional engagement, and their dynamic interplay in gaming environments.

The brain operates as a complex dynamic system, continuously generating both structured spontaneous activity and stimulus‐evoked responses. Because these activities originate from the same neural architecture, they are hypothesized to be interconnected. However, research has yet to establish a definitive relationship between spontaneous and response patterns, as findings have been mixed and inconclusive. We argue that this ambiguity stems from significant theoretical and methodological challenges in characterizing the relevant variables amidst the brain's complexity. In this study, we investigated the cross‐individual correlation between spontaneous Alpha amplitude and the magnitude of brain responses to simple stimuli. Our analysis revealed a robust correlation, but only after accounting for two key confounding factors inherent to the brain's complex dynamics: (1) strong adaptation effects across repeated stimulus exposures and (2) the mixture of aperiodic and band‐specific dynamic activity signals. These results demonstrate a close association between the strength of Alpha oscillations—a primary brain rhythm implicated in various functions—and the magnitude of stimuli‐evoked responses. Specifically, individuals with higher resting‐state Alpha amplitudes exhibit stronger brain responses. This discovery not only highlights methodological challenges in relating spontaneous and evoked brain activity, but also demonstrates that they can be addressed. Our findings have significant implications for research aimed at understanding the mechanistic models and functional roles of the brain's dynamic system, shedding light on future investigations into the interplay between intrinsic and evoked neural dynamics.

Cognitive load is a critical internal state associated with learners' learning process and significantly influences learning outcomes. With the worldwide popularity of video‐based learning (VBL), tracking real‐time cognitive load variations becomes more and more important for the timely provision of adaptive learning support during the learning process. This study proposed and validated an electroencephalogram (EEG)‐supported approach to tracking real‐time cognitive load variations during continuous VBL. We recruited 108 healthy adult participants to watch a specially designed video lecture with a sequence of interconnected slides of equal length. EEG signals were continuously recorded throughout the session. The video lecture was designed with varying levels of content difficulty (ie, rated from 1 to 5) across slides and was narrated at three different speeds (ie, slow, normal and fast) to induce cognitive load variations. For each slide, the cognitive load was quantified using both subjective ratings (ie, self‐reported difficulty) and an EEG‐derived measure (ie, alpha power). Through linear mixed model analysis, we demonstrated the feasibility of using alpha power to track real‐time cognitive load variations during the continuous VBL process after controlling the effect of mental fatigue. This study provides a foundation for developing learning enhancement technologies that enable the timely provision of adaptive learning support in VBL. Practitioner notes: What is already known about this topic Video‐based learning has become a prevailing learning method for the current generation. Tracking the internal learning state of learners is essential for the timely provision of adaptive learning support during the video‐based learning process. Cognitive load is a critical aspect of internal learning state. While EEG has been proven to be valuable in assessing average cognitive load of a task, few studies have investigated the feasibility of utilizing EEG to track real‐time cognitive load variations in a task. What this paper adds An EEG‐supported approach was proposed to track real‐time cognitive load variations in video‐based learning. A high consistency was found between subjective ratings and EEG‐derived measure of cognitive load. The presence of mental fatigue exerted a significant impact on EEG‐derived measure of cognitive load. Implications for practice and/or policy Generative AI can be leveraged to facilitate mass production of lectures required in the approach. Real‐time tracking of cognitive load variations in video‐based learning enables the timely provision of adaptive learning supports. Additional research is warranted to mitigate the effect of mental fatigue on real‐time tracking of cognitive load variations.

Handwriting is an outstanding case of a highly complex and efficient fine motor skill. However, little is known about its neural underpinnings during continuous handwriting production. In the present study, we examined the effects of orthographic character complexity (i.e. the stroke number of a Chinese character) on both neural and behavioural activities during an EEG‐based naturalistic fluent sentence‐handwriting task from 102 adult Chinese native speakers. For each written character, the interval between finishing the preceding character and its onset (inter‐character interval) as well as the amplitudes of the onset‐synchronized event‐related potential (ERP) in pre‐ and post‐onset time windows was defined as dependent variables. The effects of character complexity and other confounding factors were analysed with linear mixed models. Character complexity increased the inter‐character interval and significantly affected ERP amplitudes in both pre‐ and post‐onset time windows. The ERP pattern in the pre‐event time window exhibited a dipole‐like activation in the left motor cortex, and its amplitude increased with character complexity in line with the documented relationship between the lateralized readiness potential and motor complexity. This study demonstrates the feasibility of studying neurocognitive processes in complex naturalistic motor tasks and extends our knowledge about the dynamic pattern of handwriting‐related neural activities.

Age of first exposure (AoFE) is an important factor that influences the quality of L2 acquisition. This study aims to investigate the AoFE effect on the contextual learning of L2 novel words at the neural level, as measured by the N400 component from event-related potentials (ERPs). Eighty-eight participants were recruited for the experiment of L2 pseudoword learning, which includes a learning session and a testing session. The participants’ EEG data were recorded from the testing session, and the N400 effect was derived from target words that were either congruous or incongruous with the context. The linear mixed model and multiple regression model revealed a positive AoFE effect on the N400 effect in discourses that were designed for testing retrieval of episodic and semantic memory even after accounting for the variance contributed by several confounding factors. In addition to AoFE, the effects of total L2 exposure, L2 proficiency and personality on the L2 novel word learning performance indicated by the N400 effect were also confirmed in the statistical results.

This study aims to examine the process of L2 novel word learning through the combination of episodic and semantic memory, and how the process differs between the formation of thematic and taxonomic relations. The major approach adopted was observing the neural effects of word learning, which is manifested in the N400 from event-related potentials (ERPs). Eighty-eight participants were recruited for the experiment. In the learning session, L2 contextual discourses related to novel words were learned by participants. In the testing session, discourses embedded with incongruous and congruous novel words in the final position were used for participants to judge the congruency which affected the N400 neural activity. The results showed that both recurrent and new-theme discourses elicited significant N400 effects, while taxonomic sentences did not. These results confirmed the formation of episodic and semantic memory during L2 new word learning, in which semantic memory was mainly supported by thematic relations.