Multimodal neurophysiological and behavioral modeling of psychofunctional human states across contexts

The articulation of how cognitive, affective and social processes emerge from the coordinated activity of neural and autonomic systems remains a central question in contemporary neuroscience and affective computing. This dissertation addresses this issue through a series of multimodal experimental studies that progressively move from controlled individual paradigms to ecologically valid, interactive and dyadic contexts. By integrating central measures with autonomic indices and behavioural and psychometric assessments, the work delineates a structure showing how psychophysiological responses evolve as task demands, emotional involvement and social context increase. The first part focuses on controlled perceptual and linguistic scenarios, demonstrating that stimulus authenticity, familiarity and semantic structure systematically modulate early neural dynamics and autonomic responses. Results show that the brain differentiates real versus artificial faces and words versus pseudowords at early processing stages, through distinct spectral and event-related patterns, while peripheral markers reflect differences in cognitive effort and arousal. The second part extends the investigation to naturalistic, goal-directed gaming scenarios, where participants engage in cognitively demanding tasks across different cultural populations. Multimodal analyses combining electroencephalography (EEG), pupillometry, cardiovascular dynamics and facial expressions reveal that task performance is shaped by the interplay between cognitive load, emotional regulation and autonomic adaptability. High performance is associated with efficient neural modulation and stable emotional dynamics, whereas lower performance corresponds to increased cognitive load and less regulated affective responses. The final part explicitly addresses the affective and interpersonal dimension of psychophysiological processing. Starting from individual neuroaesthetic experience, the work shows that art expertise and dispositional empathy differentially modulate large-scale brain connectivity over time. This individual-level perspective is then extended to dyadic settings, where shared affective stimulation elicits partial convergence of autonomic responses even in the absence of explicit interaction. Finally, EEG hyperscanning with biofeedback demonstrates that inter-brain synchronisation emerges as a dynamic and trait-dependent process, shaped by both individual characteristics and their relational configuration within the dyad. These studies offer conceptual and methodological advances by integrating controlled laboratory paradigms with interaction-rich experimental designs. They demonstrate that cognitive mechanisms, affective modulation and social resonance are not separable layers but mutually constraining processes expressed through coordinated central-autonomic patterns. Beyond its theoretical contribution, this work expands current knowledge in affective computing, providing a foundation for future developments in clinical assessment, cognitive training and socially adaptive technologies.