Towards a computational (neuro)phenomenology of mental action: modelling meta-awareness and attentional control with deep-parametric active inference
Lars Sandved-Smith, Casper Hesp, Antoine Lutz, Jérémie Mattout, Karl Friston, Maxwell James Ramstead
PsyArXiv June 10, 2020 preprint DOI: 10.31234/osf.io/5jh3c
Summary
AI-generated from the abstractThis 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.
Study at a glance
| Characteristics | Theoretical or philosophical paper |
|---|---|
| Citations | 11 |
| Key finding | Deep-parametric active inference can model meta-awareness and attentional control as hierarchical inference processes, providing a computational framework for understanding mental action. |
Abstract
Towards a computational (neuro)phenomenology of mental action: modelling meta-awareness and attentional control with deep-parametric active inference