The Parameters of Educability

TL;DR

This work presents educability as a scalable, robust computational framework for cognition, bridging human cognitive diversity and the potential for machine emulation. It anchors the framework in three pillars—learning from experience, teachability by instruction, and theory chaining—operating within a Mind’s Eye representation and supported by robust computation principles. The main contribution is a structured parameterization of educability, detailing Belief Choice, Belief Verification, Teaching to Reason, Mind’s Eye management, and New Concept Formation, plus other factors, to explain cognitive variability and educational design. The paper argues that there is no single intelligence metric; instead, a multiplicity of interacting parameters drives educated behavior, with implications for education science and the future of AI.

Abstract

The educability model is a computational model that has been recently proposed to describe the cognitive capability that makes humans unique among existing biological species on Earth in being able to create advanced civilizations. Educability is defined as a capability for acquiring and applying knowledge. It is intended both to describe human capabilities and, equally, as an aspirational description of what can be usefully realized by machines. While the intention is to have a mathematically well-defined computational model, in constructing an instance of the model there are a number of decisions to make. We call these decisions {\it parameters}. In a standard computer, two parameters are the memory capacity and clock rate. There is no universally optimal choice for either one, or even for their ratio. Similarly, in a standard machine learning system, two parameters are the learning algorithm and the dataset used for training. Again, there are no universally optimal choices known for either. An educable system has many more parameters than either of these two kinds of system. This short paper discusses some of the main parameters of educable systems, and the broader implications of their existence.