This study addresses the complex nature of human cognition within a dynamically complex interplay of distributed neural networks, oscillatory activity, and subjective experiential phenomena. Although computational models have made a rich addition to our understanding of memory, attention, and decision-making processes, it is often the case that these models neglect the phenomenological dimensions that make up the lived experience of thought. Accordingly, in the present paper, a neuro-phenomenological framework that synergistically couples EEG-based neural oscillation analysis with first-person cognitive narratives is introduced for mapping the architecture of consciousness and cognition in a more integrative way. Employing EEG recordings taken during conditions of both rest and task performance, the present study addresses the issue of correlations between oscillatory signatures (i.e., alpha, theta, and gamma bands) and self-reported body variable cognitive states of attention, working memory load, and perceptual switching. These empirical relationships are then interpreted in the context of predictive processing, and explain how the brain builds internal world models and how departures from these world models can bring about cognitive dissonance or attention drift. One or two secondary objectives are to explore the possibility of using artificial intelligence models of intelligence, especially architectures of deep learning style, trained on multimodal data, to simulate or approximate the aforementioned cognitive states. By combining human EEG signatures, and the internal state of representation of artificial neural networks, this work attempts to narrow down the gap between biological and computational understanding. The findings add to the emerging field of discussion in computational cognitive neuroscience, embodied cognition, and cognitive modeling that insists that future brain–behavior mapping efforts do not solely focus on the neural correlates of thought but also its structural and phenomenological "feel". This integrative way of thinking has very important implications for cognitive training, neuroadaptive interfaces, and diagnostics for mental health.