Towards a computational phenomenology of mental action: modelling meta-awareness and attentional control with deep parametric active inference. Issue 2 (27th August 2021)

• Record Type: Journal Article
• Authors: Sandved-Smith, Lars; Hesp, Casper; Mattout, Jérémie; Friston, Karl; Lutz, Antoine; Ramstead, Maxwell J D
• Is Part Of: Neuroscience of consciousness. Issue 2(2021)
• Publication Date: 2021-08-27
• Subjects: active inference -- metacognition -- opacity -- transparency -- free energy principle -- focused attention -- neurophenomenology -- mind-wandering -- mindfulness; Consciousness -- Periodicals; Consciousness -- Physiological aspects -- Periodicals; 612.823
• DOI: 10.1093/nc/niab018
• Languages: English
• ISSNs: 2057-2107

Abstract

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.