This seminar presents a line of work attention, multimodal integration, and neural signal modeling to understand how context shapes behavior and brain activity. I will first describe information-theoretic and EEG-based methods for therapy evaluation in auditory schizophrenia, using a parity decision task to build databases that track symptom evolution and support continuity-of-care decisions. I then turn to hierarchical linear modeling of EEG and fMRI to compare visual and auditory tasks, and to show how prior context in auditory oddball paradigms modulates prefrontal and motor networks. Complementary computational models of prefrontal minicolumns in maze learning, and videogame-based 2D/3D/AR reaction time paradigms, illustrate how task structure and environment influence selective attention. Finally, I introduce new spectral decomposition methods for trial-to-trial EEG variability and I am now working at SCCN with x-ICA, arguing that variability reflects structured cognitive dynamics rather than noise, with implications for clinical (e.g. anemia) and brain–computer interface applications.