An Empirical Framework for Objective Testing for P-Consciousness in an Artificial Agent
Identifiers and Pagination:Year: 2009
First Page: 1
Last Page: 15
Publisher Id: TOAIJ-3-1
Article History:Electronic publication date: 27/1/2009
Collection year: 2009
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Two related and relatively obscure issues in science have eluded empirical tractability. Both can be directly traced to progress in artificial intelligence. The first is scientific proof of consciousness or otherwise in anything. The second is the role of consciousness in intelligent behaviour. This document approaches both issues by exploring the idea of using scientific behaviour self-referentially as a benchmark in an objective test for P-consciousness, which is the relevant critical aspect of consciousness. Scientific behaviour is unique in being both highly formalised and provably critically dependent on the P-consciousness of the primary senses. In the context of the primary senses P-consciousness is literally a formal identity with scientific observation. As such it is intrinsically afforded a status of critical dependency demonstrably no different to any other critical dependency in science, making scientific behaviour ideally suited to a self-referential scientific circumstance. The ‘provability’ derives from the delivery by science of objectively verifiable ‘laws of nature’. By exploiting the critical dependency, an empirical framework is constructed as a refined and specialised version of existing propositions for a ‘test for consciousness’. The specific role of P-consciousness is clarified: it is a human intracranial central nervous system construct that symbolically grounds the scientist in the distal external world, resulting in our ability to recognise, characterise and adapt to distal natural world novelty. It is hoped that in opening a discussion of a novel approach, the artificial intelligence community may eventually find a viable contender for its long overdue scientific basis.