Open Autonomous Intelligence Initiative

A new framework for integrating experience, interpretation, and structure in cognitive modeling

Efforts to model Mind have long faced a persistent challenge: how to integrate experiential content, relational organization, and interpretive structure within a single coherent framework.

Across scientific domains, measurable processes can be described with high precision. However, relational and interpretive aspects—those that give structure and meaning—are often modeled using separate and incompatible approaches. In cognitive science, this fragmentation becomes especially pronounced, as perception, cognition, and behavior must be understood across multiple interacting levels.

This paper introduces the Polarity Modeling Framework (PMF) as a structurally grounded approach to this problem.

At its core, PMF proposes polarity (σ) as a fundamental modeling primitive. A polarity consists of two mutually defining and complementary poles—for example, experiential content and interpretive structure. Rather than treating these aspects separately, PMF models them as interdependent components of a single structured relation.

Building on this idea, the framework introduces a minimal set of constructs:

• polarity systems (interacting axes of differentiation)
• fields (structured spaces defined by these systems)
• positions (configurations within fields)
• transformations (structured changes over time)

Together, these constructs allow mental phenomena to be modeled as positions and trajectories within structured systems, rather than as isolated variables.

The framework aligns with several contemporary approaches in cognitive science, including predictive processing, dynamical systems theory, and enactive cognition. It also draws on structured self-observation to demonstrate how everyday experiences—such as anticipation, habit, attention, and social interaction—can be understood in terms of covariance, coupling, and transformation.

The goal of PMF is not to replace existing approaches, but to provide a unifying structural perspective that can relate them within a common framework.

Download the full white paper as a PDF