Varieties Of Evolved Forms Of Consciousness, Including Mathematical Consciousness.

I shall introduce a complex, apparently unique, cross-disciplinary approach to understanding consciousness, especially ancient forms of mathematical consciousness, based on joint work with Jackie Chappell (Birmingham Biosciences) on the Meta-Configured Genome (MCG) theory. All known forms of consciousness (apart from recent very simple AI forms) are products of biological evolution, in some cases augmented by products of social, or technological evolution. Forms of consciousness differ between organisms with different sensory mechanisms, needs and abilities; and in complex animals can vary across different stages of development before and after birth or hatching or pupation, and before or after sexual and other kinds of maturity (or senility). Those forms can differ across individuals with different natural talents and environments, some with and some without fully functional sense organs or motor control functions (in humans: hearing, sight, touch, taste, smell, proprioception and other senses), along with mechanisms supporting meta-cognitive functions such as recollection, expectation, foreboding, error correction, and so forth, and varying forms of conscious control differing partly because of physical differences, such as conjoined twins sharing body parts. Forms of consciousness can also differ across individuals in different cultures with different shared theories, and social practices (e.g., art-forms, musical traditions, religions, etc.). There are many unanswered questions about such varieties of consciousness in products of biological evolution. Most of the details are completely ignored by most philosophers and scientists who focus only on a small subset of types of human consciousness-resulting in shallow theories. Immanuel Kant was deeper than most, though his insights, especially insights into mathematical consciousness tend to be ignored by recent philosophers and scientists, for bad reasons. This paper, partly inspired by Turing's 1952 paper on chemistry-based morphogenesis, supporting William James' observation that all known forms of consciousness must have been products of biological evolution in combination with other influences, attempts to provide (still tentative and incomplete) foundations for a proper study of the variety of biological and non-biological forms of consciousness, including the types of mathematical consciousness identified by Kant in 1781.

What else can brains do?

The approach Clark labels "action-oriented predictive processing" treats all cognition as part of a system of on-line control. This ignores other important aspects of animal, human, and robot intelligence. He contrasts it with an alleged "mainstream" approach that also ignores the depth and variety of AI/Robotic research. I don't think the theory presented is worth taking seriously as a complete model, even if there is much that it explains.

How to build an information gathering and processing system: lessons from naturally and artificially intelligent systems.

Imagine a situation in which you had to design a physical agent that could collect information from its environment, then store and process that information to help it respond appropriately to novel situations. What kinds of information should it attend to? How should the information be represented so as to allow efficient use and re-use? What kinds of constraints and trade-offs would there be? There are no unique answers. In this paper, we discuss some of the ways in which the need to be able to address problems of varying kinds and complexity can be met by different information processing systems. We also discuss different ways in which relevant information can be obtained, and how different kinds of information can be processed and used, by both biological organisms and artificial agents. We analyse several constraints and design features, and show how they relate both to biological organisms, and to lessons that can be learned from building artificial systems. Our standpoint overlaps with Karmiloff-Smith (1992) in that we assume that a collection of mechanisms geared to learning and developing in biological environments are available in forms that constrain, but do not determine, what can or will be learnt by individuals.