Towards a computational phenomenology of mental action: modelling meta-awareness and attentional control with deep parametric active inference.
Lars Sandved-Smith, Casper Hesp, Jérémie Mattout, Karl Friston, Antoine Lutz, Maxwell J D Ramstead
Neuroscience of consciousness January 1, 2021 DOI: 10.1093/nc/niab018 via PubMed
Summary
AI-generated from the abstractMeta-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.
Study at a glance
| Characteristics | Theoretical or philosophical paper Qualitative Case report Peer reviewed |
|---|---|
| Topics | Meditation |
| Keywords | Active inference Focused attention Free energy principle Metacognition Mind-wandering |
| Citations | 116 |
| Key finding | Meta-awareness can be formally modeled as a hierarchical active inference process that modulates precision in mapping observations to cognitive states, enabling control over attention and mind-wandering. |
Abstract
Meta-awareness refers to the capacity to explicitly notice the current content of consciousness and has been identified as a key component for the successful control of cognitive states, such as the deliberate direction of attention. This paper proposes a formal model of meta-awareness and attentional control using hierarchical active inference. To do so, we cast mental action as policy selection over higher-level cognitive states and add a further hierarchical level to model meta-awareness states that modulate the expected confidence (precision) in the mapping between observations and hidden cognitive states. We simulate the example of mind-wandering and its regulation during a task involving sustained selective attention on a perceptual object. This provides a computational case study for an inferential architecture that is apt to enable the emergence of these central components of human phenomenology, namely, the ability to access and control cognitive states. We propose that this approach can be generalized to other cognitive states, and hence, this paper provides the first steps towards the development of a computational phenomenology of mental action and more broadly of our ability to monitor and control our own cognitive states. Future steps of this work will focus on fitting the model with qualitative, behavioural, and neural data.