Meta-awareness, the ability to notice the current content of consciousness, is crucial for controlling cognitive states like directing attention. This paper models meta-awareness and attentional control using hierarchical active inference, treating mental actions as policy choices over higher-level cognitive states. A further hierarchical level represents meta-awareness states that modulate the expected confidence in the mapping between observations and hidden cognitive states. Simulations of mind-wandering during a sustained selective attention task illustrate how this inferential architecture enables accessing and controlling cognitive states, offering a computational foundation for a phenomenology of mental action and self-monitoring.
A version of neurophenomenology is presented that uses generative modelling techniques from computational neuroscience and biology to formally model descriptions of lived experience from the phenomenological tradition (e.g., Husserl, Merleau-Ponty). The approach, called computational phenomenology, is situated within the broader project of naturalizing phenomenology. Philosophical objections to that project are evaluated, and the generative modelling framework is reviewed. The approach differs from previous uses of generative modelling for consciousness by constructing computational models of inferential or interpretive processes that best explain particular kinds of lived experience.