Neural Computations Underlying Phenomenal Consciousness: A Higher Order Syntactic Thought Theory
- 1Oxford Centre for Computational Neuroscience, Oxford University, United Kingdom
- 2University of Warwick, United Kingdom
Problems are raised with the global workspace hypothesis of consciousness, for example about exactly how global the workspace needs to be for consciousness to suddenly be present. Problems are also raised with Carruthers (2019) version that excludes conceptual (categorical or discrete) representations, and in which phenomenal consciousness can be reduced to physical processes, with instead a different levels of explanation approach to the relation between the brain and the mind advocated. A different theory of phenomenal consciousness is described, in which there is a particular computational system involved in which Higher Order Syntactic Thoughts are used to perform credit assignment on first order thoughts of multiple step plans to correct them by manipulating symbols in a syntactic type of working memory. This provides a good evolutionary reason for the evolution of this kind of computational module, with which, it is proposed, phenomenal consciousness is associated. Some advantages of this HOST approach to phenomenal consciousness are then described with reference not only to the global workspace approach, but also to Higher Order Thought (HOT) theories. It is hypothesized that the HOST system which requires the ability to manipulate first order symbols in working memory might utilize parts of the prefrontal cortex implicated in working memory, and especially the left inferior frontal gyrus, which is involved in language and probably syntactical processing. Overall, the approach advocated is to identify the computations that are linked to consciousness, and to analyze the neural bases of those computations.
Keywords: Consciousness, higher order thought, Levels of explanation, syntax, Global Workspace, backward masking, inattentional blindness, Attention
Received: 12 Jan 2020;
Accepted: 18 Mar 2020.
Copyright: © 2020 Rolls. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Prof. Edmund Rolls, Oxford Centre for Computational Neuroscience, Oxford University, Oxford, England, United Kingdom, Edmund.Rolls@oxcns.org