2025-12-03
Iman Chatterjee, Maja Goršič, Robert A. Kaya, Joshua D. Clapp, Vesna D. Novak
IntroductionAutonomic nervous system responses provide valuable information about interactions between pairs or groups of people but have primarily been studied using group-level statistical analysis, with a few studies attempting single-trial classification. As an alternative to classification, our study uses regression algorithms to estimate the valence and arousal of specific conversation intervals from dyads' autonomic nervous system responses.MethodsForty-one dyads took part in 20-minute conversations following several different prompts. The conversations were divided into ten 2-minute intervals, with participants self-reporting perceived conversation valence and arousal after each 2-minute interval. Observers watched videos of the conversations and separately also rated valence and arousal. Four autonomic nervous system responses (electrocardiogram, electrodermal activity, respiration, skin temperature) were recorded, and both individual and synchrony features were extracted for each 2-minute interval. These extracted features were used with feature selection and a multilinear perceptron to estimate self-reported and observer-reported valence and arousal of each interval in both a dyad-specific (based on data from same dyad) and dyad-nonspecific (based on data from other dyads) manner.ResultsBoth dyad-specific and dyad-nonspecific regression using the multilinear perceptron resulted in lower root-mean-square errors than a simple median-based estimator and two other regression methods (linear regression and support vector machines).DiscussionThe results suggest that physiological measurements can be used to characterize dyadic conversations on the level of individual dyads and conversation intervals. In the long term, such regression algorithms could potentially be used in applications such as education and mental health counseling.
DOI: 10.3389/fnrgo.2025.16713112025-11-26
Mare Teichmann, Jaanus Kaugerand, Merik Meriste, Kalev Rannat
In the current paper our focus is on linking Public Mental Models with behavior, Situation Awareness and stress management, with predicting and intervening in public behavior in critical situations. Understanding and influencing behavior within complex Cyber-Physical-Social Systems (CPSS) requires an explicit link between mental models, behavior, situation awareness, and stress management. This paper introduces the Public Mental Models Framework (PMMF) as a systematic approach for analyzing and predicting public behavior in critical situations, thereby improving adaptive decision-making and person—AI collaboration. The PMMF explains how internal and external indicators such as cognitive, social, cultural, political, economic, and technological, that shape perception and behavioral responses across multiple levels: individual, team, organizational, community, and societal. By identifying these triggers and markers, the framework supports why behaviors deviate or stabilize under stress, providing an analytical basis for targeted interventions and resilience-oriented design. In contrast to traditional Situation Awareness models that emphasize what is perceived and how it is processed, PMMF focuses on the interpretive mechanisms through which actors construct meaning and make decisions. Integrating PMMF with the Motivation-Opportunity-Ability (MOA) theory enables systematic assessment of behavioral potential and performance within CPSS. This integration strengthens the neuroergonomic foundation for evaluating human and AI entities and enhances the capacity to design interventions that foster informed, adaptive, and ethically aligned behavior in complex sociotechnical environments.
DOI: 10.3389/fnrgo.2025.15204342025-11-12
Jiajun Yuan, Bo Jia, Chenyang Zhang, Lu Tian, Han Yi, Lin Wei
Pilot mental workload is a critical factor influencing flight safety, particularly during dynamic flight phases with high cognitive demands such as takeoff and landing. This study evaluates pilot workload across different flight phases (takeoff, climb, cruise, descent, and landing) using HRV (heart rate variability) features and machine learning methods. Heart rate data were collected through simulated A320 traffic pattern flight missions, combined with multidimensional task assessments, to obtain flight performance scores. Selected HRV features, Min_HR (minimum heart rate), SDNN (standard deviation of normal-to-normal intervals), SD2 (long-term variability index in Poincare Plot), Modified_csi (modified cardiac sympathetic index), were identified and used to train classifiers (RF, KNN, GBDT, XGBoost) for pilot mental workload level classification. The XGBoost model demonstrated optimal performance after feature selection, with accuracy increasing from 50.09% to 66.67% (a 16.58% improvement) and F1-score rising from 37.63% to 58.33% (a 20.70% improvement) compared with all HRV feature. The findings revealed selected HRV suppression during high-workload phases (landing) with the lowest performance scores, whereas HRV recovery and peak performance scores were observed in low-workload phases (cruise). This research establishes a reliable framework for real-time pilot mental workload monitoring and provides predictive insights into cognitive overload risks during critical flight operations.
DOI: 10.3389/fnrgo.2025.16724922025-10-15
Diana E. Gherman, Thorsten O. Zander
IntroductionLarge-language models (LLMs) are transforming most industries today and are set to become a cornerstone of the human digital experience. While integrating explicit human feedback into the training and development of LLM-based chatbots has been integral to the progress we see nowadays, more work is needed to understand how to best align them with human values. Implicit human feedback enabled by passive brain-computer interfaces (pBCIs) could potentially help unlock the hidden nuance of users' cognitive and affective states during interaction with chatbots. This study proposes an investigation on the feasibility of using pBCIs to decode mental states in reaction to text stimuli, to lay the groundwork for neuroadaptive chatbots.MethodsTwo paradigms were created to elicit moral judgment and error-processing with text stimuli. Electroencephalography (EEG) data was recorded with 64 gel electrodes while participants completed reading tasks. Mental state classifiers were obtained in an offline manner with a windowed-means approach and linear discriminant analysis (LDA) for full-component and brain-component data. The corresponding event-related potentials (ERPs) were visually inspected.ResultsMoral salience was successfully decoded at a single-trial level, with an average calibration accuracy of 78% on the basis of a data window of 600 ms. Subsequent classifiers were not able to distinguish moral judgment congruence (i.e., moral agreement) and incongruence (i.e., moral disagreement). Error processing in reaction to factual inaccuracy was decoded with an average calibration accuracy of 66%. The identified ERPs for the investigated mental states partly aligned with other findings.DiscussionWith this study, we demonstrate the feasibility of using pBCIs to distinguish mental states from readers' brain data at a single-trial level. More work is needed to transition from offline to online investigations and to understand if reliable pBCI classifiers can also be obtained in less controlled language tasks and more realistic chatbot interactions. Our work marks preliminary steps for understanding and making use of neural-based implicit human feedback for LLM alignment.
DOI: 10.3389/fnrgo.2025.15897342025-09-23
Gregory Bales, Allison P. A. Hayman, Torin K. Clark, Jason Dekarske, Sanjay Joshi, Zhaodan Kong
Efficient and effective teaming between humans and autonomous systems requires the establishment and maintenance of trust to maximize team task performance. Despite advances in autonomous systems, human expertise remains critical in tasks fraught with deviations from procedures or plans that cannot be pre-programmed. As autonomous systems become more sophisticated, they will possess the ability to positively influence interactions with their human partners, provided the autonomous systems have a real-time estimation of their human partner's cognitive state (including trust). In this paper, we report our results in ascertaining a human's trust in an autonomous system via electroencephalogram (EEG) measurements. We report that trust can be measured continuously and unobtrusively, and that using analysis techniques which account for interactions among brain regions shows benefits compared to more traditional methods which use only EEG signal-power. Inter-channel connectivity network-metrics, which measure dynamic changes in synchronous behavior between distant brain regions, appear to better capture cognitive activities that correlate with a human's trust in an autonomous system.
DOI: 10.3389/fnrgo.2025.16274832025-09-15
Miloš Pušica, Bogdan Mijović, Maria Chiara Leva, Ivan Gligorijević
The literature features a variety of tasks and methodologies to induce mental workload (MWL) and to assess the performance of MWL estimation models. Because no standardized benchmark task or set of tasks exists, the comparison of different machine learning (ML) solutions in this field is difficult, as their performance is significantly dependent on these factors. In this paper, we present the first comprehensive examination of ML models' performance in EEG-based MWL classification across task types. To achieve this, we categorized ML studies based on the task type used in their experiments and compared models' performances across these categories. Notably, a significant drop in MWL classification accuracy was observed among the best-performing models in multitasking studies where MWL was rated based on quantitative task load, compared to those in single-tasking studies and studies where MWL was subjectively rated. This points to the inherent challenges associated with estimating MWL in more complex tasks such as multitasking. This is particularly relevant for practical applications, as real-world tasks typically involve some degree of multitasking. By comparing ML models' performances across task types, this review provides valuable insights into the state-of-the-art of EEG-based MWL estimation, highlights existing gaps in the field, and points to open questions for further research.
DOI: 10.3389/fnrgo.2025.1621309