Learning to Be Conscious. Issue 2 (February 2020)

• Record Type: Journal Article
• Authors: Cleeremans, Axel; Achoui, Dalila; Beauny, Arnaud; Keuninckx, Lars; Martin, Jean-Remy; Muñoz-Moldes, Santiago; Vuillaume, Laurène; de Heering, Adélaïde
• Is Part Of: Trends in cognitive sciences. Volume 24:Issue 2(2020)
• Page Start: 112
• Page End: 123
• Publication Date: 2020-02
• Subjects: consciousness -- learning -- global workspace theory -- higher-order theories -- predictive processing -- metacognition; Cognitive science -- Periodicals; Cognitive neuroscience -- Periodicals; 153.05
• DOI: 10.1016/j.tics.2019.11.011
• Languages: English
• ISSNs: 1364-6613

Abstract

Abstract : Consciousness remains a formidable challenge. Different theories of consciousness have proposed vastly different mechanisms to account for phenomenal experience. Here, appealing to aspects of global workspace theory, higher-order theories, social theories, and predictive processing, we introduce a novel framework: the self-organizing metarerpresentational account (SOMA), in which consciousness is viewed as something that the brain learns to do. By this account, the brain continuously and unconsciously learns to redescribe its own activity to itself, so developing systems of metarepresentations that qualify target first-order representations. Thus, experiences only occur in experiencers that have learned to know they possess certain first-order states and that have learned to care more about certain states than about others. In this sense, consciousness is the brain's (unconscious, embodied, enactive, nonconceptual) theory about itself. Highlights: We introduce a novel framework dubbed the self-organizing metarepresentational account (SOMA), according to which consciousness is something that the brains learns to do. We propose representational redescription as the core mechanism through which higher-order monitoring systems learn about and characterize target first-order states. We suggest that theory of mind, self-awareness, and perceptual awareness share common prediction-driven learning mechanisms that operate over three loops: an inner loop, a perception–action loop, and a self–other loop.