Consecutive interpreting involves a demanding language task where mental workload (MWL) is crucial for assessing interpreters' performance. An elevated cognitive load in interpreters may lead to the interpretation failures. The widely used NASA-TLX questionnaire effectively measures MWL. However, a global score was employed in previous interpretation studies, overlooking the distinct contributions of MWL components to the interpreters' performance. Accordingly, we recruited twenty novice interpreters who were postgraduate students specializing in interpreting to complete the consecutive interpreting task. Throughout the process, we used functional near-infrared spectroscopy (fNIRS) to monitor the hemodynamic response in participants' brains. The NASA-TLX was used to measure the MWL during interpreting with six components, including mental demand, physical demand, temporal demand, performance, effort, and frustration. Five interpretation experts were invited to assess the interpretation quality. The Bayes factor approach was employed to explore the components that contributes the most to the interpretation quality. It indicated that mental demand strongly contributed to the interpretation quality. Moreover, the mediation analysis revealed a positive correlation between mental demand and brain activation in three brain areas, which, in turn, was negatively correlated with interpretation quality, indicating the predictive role of mental demand in interpretation quality through the mediating of brain activation. The functions of the mediating brain areas, including the inferior frontal gyrus, middle temporal gyrus, and inferior temporal gyrus, aligned with the three efforts proposed by Gile's effort model, which emphasizes the significance of three fundamental efforts in achieving successful interpreting. These findings have implications for interpreter learning and training.

Objective. Feedback training is a practical approach to brain–computer interface (BCI) end-users learning to modulate their sensorimotor rhythms (SMRs). BCI self-regulation learning has been shown to be influenced by subjective psychological factors, such as motivation. However, few studies have taken into account the users’ self-motivation as additional guidance for the cognitive process involved in BCI learning. In this study we tested a transfer learning (TL) feedback method designed to increase self-motivation by providing information about past performance. Approach. Electroencephalography (EEG) signals from the previous runs were affine transformed and displayed as points on the screen, along with the newly recorded EEG signals in the current run, giving the subjects a context for self-motivation. Subjects were asked to separate the feedback points for the current run under the display of the separability of prior training. We conducted a between-subject feedback training experiment, in which 24 healthy SMR-BCI naive subjects were trained to imagine left- and right-hand movements. The participants were provided with either TL feedback or typical cursor-bar (CB) feedback (control condition), for three sessions on separate days. Main results. The behavioral results showed an increased challenge and stable mastery confidence, suggesting that subjects’ motivation grew as the feedback training went on. The EEG results showed favorable overall training effects with TL feedback in terms of the class distinctiveness and EEG discriminancy. Performance was 28.5% higher in the third session than in the first. About 41.7% of the subjects were ‘learners’ including not only low-performance subjects, but also good-performance subjects who might be affected by the ceiling effect. Subjects were able to control BCI with TL feedback with a higher performance of 60.5% during the last session compared to CB feedback. Significance. The present study demonstrated that the proposed TL feedback method boosted psychological engagement through the self-motivated context, and further allowed subjects to modulate SMR effectively. The proposed TL feedback method also provided an alternative to typical CB feedback.

In everyday face-to-face communication, speakers use speech to transfer information and rely on co-occurring nonverbal cues, such as hand and facial gestures. The integration of speech and gestures facilitates both language comprehension and the skill of the theory of mind. Consecutive dialogue interpreting (DI) allows dyads of different linguistic backgrounds to communicate with each other. The interpreter interprets after the interlocutor has finished a turn, so the interlocutor watches the gesture first and hears the target language a few seconds later, resulting in speech–gesture asynchrony. In this study, we used the functional near-infrared spectroscopy hyperscanning technique to investigate the influence of speech–gesture asynchrony on different levels of communication. Twenty groups were recruited for the DI experiments. The results showed that when the interpreter performed consecutive interpreting, the time-lagged neural coupling at the temporoparietal junction decreased compared to simultaneous interpreting. It suggests that speech–gesture asynchrony significantly weakened the ability of interlocutors to understand each other’s mental state, and the decreased neural coupling was significantly correlated with the interpreter’s interpretation skill. In addition, the time-aligned neural coupling at the left inferior frontal gyrus increased, which suggests that, as compensation, the interlocutor’s verbal working memory increases in line with the communication process.

Background: Motor imagery-based brain-computer interfaces (MI-BCIs) enable individuals who are unable to perform physical movements to interact with the external world by imagining movements. Users are typically classified as good performers or BCI-illiterate based on the classification accuracy of distinct EEG patterns (e.g., 60% or 70%). Yet, studies show that approximately 70% of users fall within intermediate accuracies between 60% and 80%, and although exceed the chance level, they often fail to achieve reliable MI-BCI control. Intermediate users often exhibit asymmetric motor imagery abilities between left and right hands, highlighting the need for refined early assessment and stratified training approaches. Methods: We employed ICA to decompose each participant’s EEG data and extract independent ERD/ERS components as indicators using a rule-based automated framework. This framework integrated dipole localization, ERD/ERS characteristics, and frequency-band power features of ICs. Importantly, we applied a power spectral parameterization approach to remove the 1/f-like background activity in power estimation and used statistical methods to precisely estimate the latency and duration of ERD. The extracted indicators were subsequently subjected to clustering analysis to categorize participants into four groups. Results: In addition to good performers (24.8%) and poor performers (35.8%), two groups were identified: LgoodRpoor (27.5%), who performed well in left-hand MI but poorly in right-hand MI, and LpoorRgood (11.9%), who showed the opposite pattern. Notably, these unilateral performers did not show significant differences in contralateral ERD but exhibited substantial differences in ipsilateral ERS. Conclusions: The proposed independent event-related brain dynamics model enables more refined stratification of MI-BCI users. Findings from this characterization study may inform the design of graded training protocols, especially for users demonstrating unilateral motor imagery proficiency.