A new approach called computational phenomenology uses generative modeling techniques from computational neuroscience to study conscious experience. The paper reviews efforts to naturalize phenomenology, addresses philosophical objections, and explains how generative models can simulate the inferential processes underlying specific types of lived experience. This differs from prior uses of generative modeling for consciousness by focusing on modeling the interpretive process that best accounts for particular phenomenal experiences.
This theoretical paper proposes a computational model of mental action—the deliberate control of one's own thoughts and attention—by combining insights from phenomenology and active inference. The authors develop a deep-parametric active inference framework to simulate meta-awareness and attentional control, showing how agents can learn to monitor and regulate their own cognitive processes. The model suggests that meta-awareness emerges from hierarchical inference about attentional states, enabling flexible control of attention. This work bridges phenomenological philosophy and computational neuroscience, offering a formal account of how conscious agents can intentionally shape their own mental activity.