Synthetic consciousness: the distributed adaptive control perspective

Understanding the nature of consciousness is one of the grand outstanding scientific challenges. The fundamental methodological problem is how phenomenal first person experience can be accounted for in a third person verifiable form, while the conceptual challenge is to both define its function and physical realization. The distributed adaptive control theory of consciousness (DACtoc) proposes answers to these three challenges. The methodological challenge is answered relative to the hard problem and DACtoc proposes that it can be addressed using a convergent synthetic methodology using the analysis of synthetic biologically grounded agents, or quale parsing. DACtoc hypothesizes that consciousness in both its primary and secondary forms serves the ability to deal with the hidden states of the world and emerged during the Cambrian period, affording stable multi-agent environments to emerge. The process of consciousness is an autonomous virtualization memory, which serializes and unifies the parallel and subconscious simulations of the hidden states of the world that are largely due to other agents and the self with the objective to extract norms. These norms are in turn projected as value onto the parallel simulation and control systems that are driving action. This functional hypothesis is mapped onto the brainstem, midbrain and the thalamo-cortical and cortico-cortical systems and analysed with respect to our understanding of deficits of consciousness. Subsequently, some of the implications and predictions of DACtoc are outlined, in particular, the prediction that normative bootstrapping of conscious agents is predicated on an intentionality prior. In the view advanced here, human consciousness constitutes the ultimate evolutionary transition by allowing agents to become autonomous with respect to their evolutionary priors leading to a post-biological Anthropocene. This article is part of the themed issue ‘The major synthetic evolutionary transitions’.