Learning to Be Conscious

Author: Axel Cleeremans1, Dalila Achoui2, Arnaud Beauny2, Lars Keuninckx2, Jean-Remy Martin2, Santiago Muñoz-Moldes2, Laurène Vuillaume2, Adélaïde de Heering2
Author Information:
1 Consciousness, Cognition & Computation Group (CO3), Center for Research in Cognition & Neuroscience (CRCN), ULB Neuroscience Institute (UNI), Université libre de Bruxelles, 50 ave. F-D. Roosevelt CP191, B1050 Bruxelles, Belgium. Electronic address: axcleer@ulb.ac.be.
2 Consciousness, Cognition & Computation Group (CO3), Center for Research in Cognition & Neuroscience (CRCN), ULB Neuroscience Institute (UNI), Université libre de Bruxelles, 50 ave. F-D. Roosevelt CP191, B1050 Bruxelles, Belgium.
Conference/Journal: Trends Cogn Sci
Date published: 2020 Feb 1
Other: Volume ID: 24 , Issue ID: 2 , Pages: 112-123 , Special Notes: doi: 10.1016/j.tics.2019.11.011. , Word Count: 141


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.

Keywords: consciousness; global workspace theory; higher-order theories; learning; metacognition; predictive processing.

PMID: 31892458 DOI: 10.1016/j.tics.2019.11.011

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