Joshua Taylor

Open peer commentary on the target article “How and Why the Brain Lays the Foundations for a Conscious Self” by Martin V. Butz. Excerpt: This paper is full of stimulating and creative ideas. It posits that an anticipatory drive is what guides the development in the brain of a set of internal motor models, specifically a set of inverse and forward models. Through these models becoming increasingly complex, a conscious self develops. This is a simple and important thesis, if true. But is it? As my title suggests, it may be so for sportsmen, with their emphasis on ever more refined motor responses. However, those of a more cerebral nature may find themselves burdened by all those coupled internal motor models and not able to think as clearly as they would like. This is not to say that prediction isn’t a useful property to possess, both for finance (especially now) and in one’s general living patterns. But the question I wish to consider is: What sort of predictive model can lead to thinking?

The functioning of the pre-reflective or inner self is considered in terms of its possible creation through the recently proposed CODAM 'attention copy' model of attention. In contradiction to the view of Western phenomenology that the inner self appears to serve no specific purpose except that of the ownership of experience, it is proposed here that the inner self acts rather as a call centre, enabling connections to be made between distant and functionally different components of brain processing. Even more importantly it functions by monitoring and speeding up ongoing calls or those about to be set up so that incorrect call routing is avoided and call switching occurs as fast as possible. The relations of such an approach to some recent and influential approaches to consciousness are described

We discuss experimental support for the existence of a corollary discharge signal of attention movement control and its formulation in terms of the corollary discharge of attention model of attention movement . The data is from fMRI, MEG and EEG activity observed about 200 ms after stimulus onset in various attention paradigms and in which the activity is mainly sited in parietal and extra-striate visual areas. Moreover the data arises from neural activity observed before report of a subject’s experience occurs. The overall experimental support for the existence of a copy of the attention movement control signal generates, it is suggested, a viable route to explore the relation between this signal and human consciousness, as concluded in the paper

Knowledge Representation and Reasoning (KR&R) is based on the idea that propositional content can be rigorously represented in formal languages long the province of logic, in such a way that these representations can be productively reasoned over by humans and machines; and that this reasoning can be used to produce knowledge-based systems (KBSs). As such, KR&R is a discipline conventionally regarded to range across parts of artificial intelligence (AI), computer science, and especially logic. This standard view of KR&R’s participating fields is correct — but dangerously incomplete. The view is incomplete because, as we explain herein, sophisticated KR&R must rely heavily upon philosophy. Encapsulated, the reason is actually quite straightforward: Sophisticated KR&R must include the representation of not only simple properties, but also concepts that are routine in the formal sciences (theoretical computer science, mathematics, logic, game theory, etc.), and everyday socio-cognitive concepts like mendacity, deception, betrayal, and evil. Because in KR&R the representation of such concepts must be rigorous in order to enable machine reasoning (e.g., machine-generated and machine-checked proofs that a is lying to b) over them, philosophy, devoted as it is in no small part to supplying analyses of such concepts, is a crucial partner in the overall enterprise. To put the point another way: When the knowledge to be represented is such as to require lengthy formulas in expressive formal languages for that representation, philosophy must be involved in the game. In addition, insofar as the advance of KR&R must allow formalisms and processes for representing and reasoning over visual propositional content, philosophy will be a key contributor into the future.

The vision of machines autonomously carrying out substantive conjecture generation, theorem discovery, proof discovery, and proof verification in mathematics and the natural sciences has a long history that reaches back before the development of automatic systems designed for such processes. While there has been considerable progress in proof verification in the formal sciences, for instance the Mizar project’ and the four-color theorem, now machine verified, there has been scant such work carried out in the realm of the natural sciences—until recently. The delay in the case of the natural sciences can be attributed to both a lack of formal analysis of the so-called “theories” in such sciences, and the lack of sufficient progress in automated theorem proving. While the lack of analysis is probably due to an inclination toward informality and empiricism on the part of nearly all of the relevant scientists, the lack of progress is to be expected, given the computational hardness of automated theorem proving; after all, theoremhood in even first-order logic is Turing-undecidable. We give in the present short paper a compressed report on our building upon these formal theories using logic-based AI in order to achieve, in relativity, both machine proof discovery and proof verification, for theorems previously established by humans. Our report is intended to serve as a springboard to machine-produced results in the future that have not been obtained by humans.