Deep Structure Theory: An Introduction to a Unifying Theoretical Framework for the Analysis of Human Neuropsychology

Oliver Boxell

Warner Graduate School, University of Rochester

Abstract

Cognitive neuropsychology research has tended to proliferate in domain-specific and theory-specific siloes without a common understanding of human nature and developmental change processes. This paper will introduce Deep Structure Theory (DST) as a new analytic paradigm that synthetically integrates and unifies across all human neurocognitive modalities, providing a framework for theoretical formalisms and corresponding empirical research throughout the cognitive sciences. The aim is to provide a standard model of neuropsychology grounded in the common mechanisms shared by all its faculties, thereby enabling the mind sciences to develop detailed and testable analyses in an iterative and integrated form rather than in lateral siloed domains of inquiry. After reviewing the epistemological and historical contexts from which DST has arisen, the deep structure algorithms will be described as the core mechanism of neuropsychology that constitutes the basic technical apparatus of the theory. Specifically, the algorithms describe the manner in which abstract mental information emerges as the product of compiled electromagnetic oscillatory activity, which is in turn contingent on the nature of complex neural circuitry systems. Together, DST forms a basis for unifying mental states with the rest of nature, including biology, chemistry, and quantum electrodynamics.

Deep Structure Theory; dynamic complex systems; neurocognition; synthetic unified model