In the field of human-centered systems, personalized interactions are becoming increasingly relevant, driven by advancements in technology that utilize diverse data sources. Psychophysiological modeling is evolving as a pivotal element in understanding user states, capitalizing on a range of signals including EEG, heart rate, GSR, and pupil dilation, as well as emerging modalities like fNIRS, thermal imaging, and multimodal micromovements. These signals offer in-depth insights into user-related aspects such as attention, stress, cognitive load, engagement, and emotional states. Integrating these insights with computational frameworks and machine learning methodologies fosters the creation of response-adaptive procedures, enabling systems to react promptly to psychophysiological cues. This interdisciplinary field marries computer science, cognitive neuroscience, behavioral psychology, biomedical engineering, and human-computer interaction, with applications spanning healthcare, education, immersive technologies, assistive systems, and workplace tools.

This Research Topic aims to advance the understanding and application of psychophysiological user modeling to facilitate the dynamic adaptation of intelligent systems across varied domains. It seeks to explore cutting-edge methodologies, computational frameworks, and response-adaptive procedures and methods that employ psychophysiological data for real-time personalization of systems. The focus encompasses both proactive strategies, allowing systems to anticipate and adjust to user needs, and reactive methods, addressing immediate changes in user states. By tackling challenges such as scalability, multimodal data integration, and interpretability, the initiative seeks to build robust, user-centric systems optimizing engagement, performance, and accessibility, while also exploring ethical challenges such as privacy, transparency, and bias for equitable and trustworthy technology applications.

Our Research Topic seeks contributions addressing, but not limited to, the following themes:

- Psychophysiological User Modeling: Innovative methods for identifying and interpreting user states from physiological signals with advanced computational and machine learning approaches for AI-assisted decision-making.
- Response-Adaptive Procedures and Methods: Developing algorithms and frameworks for proactive and reactive system adaptation based on real-time psychophysiological models.
- Dynamic Adaptation in Multimodal Environments: Merging psychophysiological signals with sensory and behavioral data to form cohesive, adaptive systems.
- Applications Across Domains: Presenting use cases highlighting the effect of response-adaptive procedures in healthcare, education, workplace productivity, immersive technologies, and assistive systems.
- Ethical and Practical Considerations: Empirical research confronting challenges in privacy, bias, interpretability, and equitable access within psychophysiological modeling systems.

We encourage submissions highlighting novel methodologies or insights advancing our understanding of psychophysiological modeling within the context of dynamic adaptation and response-adaptive methods. Submissions should bridge theoretical frameworks with practical implementations, potentially setting new standards for embedding psychophysiological data in adaptive computing systems. Given the interdisciplinary nature of the field, we particularly welcome collaborative submissions from diverse disciplines such as computer science, psychology, neuroscience, biomedical engineering, and human-computer interaction.

The following types of contributions will be accepted:

- Original Research
- Systematic Review
- Review
- Methods
- Data Report
- Brief Research Report

Are you interested in contributing a manuscript to this Research Topic?

Advancements in Psychophysiological User Modeling for Real-Time Adaptable Systems: Response-Adaptive Procedures and Methods