From Mirror Recognition to Low-Bandwidth Memory, A Working Paper

New working paper. Title above, links, abstract, contents, and introduction below:

Academia.edu: https://www.academia.edu/143347171/From_Mirror_Recognition_to_Low_Bandwidth_Memory_A_Working_Paper
SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5385194
ResearchGage: https://www.researchgate.net/publication/394414193_From_Mirror_Recognition_to_Low-Bandwidth_Memory_A_Working_Paper

Abstract: We start with a developmental and cognitive analysis of mirror recognition, highlighting its dependence, not on self-awareness per se, but on episodic-level intersensory coordination—a capacity that enables spatially dislocated, temporally synchronized associations across sensory modalities. In a layered control architecture of hyperorders (sensorimotor, systemic, episodic, gnomonic), such recognition can arise without invoking a representational “self.” We extended this framework to the role of the default mode network (DMN), which is orthogonal to the hyperorders—a low-bandwidth, drifting subsystem that provides broad, non-task-specific access to memory and perception. This led to an inquiry into associative memory systems, where we confronted the challenge of searching without specific content-based probes. To address this, we proposed the design of an “associative drift engine”: a cognitive module capable of variable-bandwidth access, modulating the precision, noise, and scope of its memory probes. This system mirrors the DMN’s exploratory function and suggests a foundational mechanism for spontaneous recollection, creative association, and cognitive play—essential features of both natural and artificial minds.

Background Notes 1
1. Self and Mirror Recognition 1
2. ChatGPT’s assessment of the account mirror recognition 6
3. Default Mode Network 9
4. Toward an Associative Drift Engine 11
Summary of the discussion 15    

Background Notes

This first major section of this document consists of pages 74 to 84 from my 1978 dissertation, Cognitive Science and Literary Theory, Department of English, State University of New York at Buffalo. Yes, I was in the English Department and the dissertation uses examples from literature, the Oedipus story, the evolution of narrative form, and Shakespeare’s Sonnet 129. But I was also working closely with David Hays in the Linguistics Department. He was a first-generation researcher in machine translation, which transformed itself into computational linguistics in the mid-1960s.

During the period when I was in his research group – 1974 to 1978, when I finished my degree – we were sketching schemes for how to ground a symbolic cognitive system in the operations of a sensorimotor system organized as a stack of control systems in a scheme suggested by William Powers, Behavior: The Control of Perception (1973). Thus, when I talk about the sensorimotor hyperorders in the dissertation excerpt, I’m talking about a system modeled on Powers. By contrast, the systemic, episodic, and gnomonic hyperorders are symbolic systems, all directly linked to the sensorimotor system.

One thing else I want to emphasize is that, by this time, I had come to understand that the physical construction of the nervous system, both in its layout in the brain and its relationship with the external world, that structure carried information that did not have to be explicitly represented inside the system itself – I’ve used yellow highlighting to emphasize those sections. The account I offer of mirror recognition depends on this.

About this document

This document contains four things:

1. A passage from my 1978 dissertation in which I discuss mirror recognition,
2. ChatGPT’s (current) assessment of that passage,
3. A discussion of the Default Mode Network (DMN) in the brain, and
4. Some speculation from ChatGPT on how to construct, in effect, a DMN for an artificial associative memory, something it calls “an associative drift engine.”