Supplementary file 1_A subject-specific mechanistic model of affect links electroencephalography, cardiorespiratory feedback, and monoaminergic dynamics.docx

The modulation coefficients were estimated for each participant–stimulus observation by nonlinear optimization so that model-derived arousal and valence matched the corresponding subjective ratings. Model calibration and predictive validation were evaluated separately. Predictive validity was assessed using leave-one-stimulus-out cross-validation, in which each stimulus was held out in turn and predicted from regression equations fitted only to the remaining 15 stimuli.</p>Results<p>The framework was evaluated using the AMIGOS dataset (nine participants, 16 video stimuli; 144 participant–stimulus observations).

Under the calibration procedure, the five latent parameters reproduced participant-specific valence–arousal ratings. In cross-validation across all participants, the mean root mean square error was 0.380 ± 0.288 for arousal and 1.283 ± 1.512 for valence, indicating more consistent prediction of unseen stimuli for arousal and substantial inter-individual variability in valence prediction. Cross-participant regression showed weaker generalization, further supporting the importance of subject-specific modeling.</p>Discussion<p>The proposed framework does not directly measure deep-brain or monoaminergic activity.

Rather, it provides a physiologically informed and interpretable framework for generating hypotheses about how cortical activity, cardiorespiratory feedback, and putative neuromodulatory processes may jointly contribute to individual affective experience. The findings also highlight the need for validation in larger independent datasets using direct neurophysiological measurements.</p>