Mathine — Evidence In, Trust Out

From Neural Fields to Fielded Cognition: Reframing Intelligence Beyond Representation

Mathine: Fielded Cognition Reframing Machine
Link: https://doi.org/10.5281/zenodo.19547149

Neural fields have become one of the most powerful representational ideas in contemporary machine learning, robotics, visual computing, and scientific modeling. Their achievement is clear: many important objects are better modeled as continuous functions over coordinates than as isolated discrete containers.

This paper argues, however, that the success of neural fields also reveals a deeper limitation. A framework that represents quantities continuously over coordinates is not, by itself, a sufficient theory of intelligence, cognition, or minded operation. Representation, no matter how expressive, remains only one layer of explanation.

The missing layer is broader and more architectural. Intelligence depends not only on what can be represented, but on the regime under which signals become receivable, admissible, salient, retainable, corrigible, and promotable into action. That is where the paper introduces the stronger concept of fielded cognition.

Under this view, neural fields are a major but local representational case inside a wider theory of structured fields. Cognition is not exhausted by coordinate-based representation because it depends on fields that govern what may count, what may matter, what may persist, and what may be corrected. In that sense, neural fields are preserved as technically valuable without being inflated into a complete theory of mind.

The paper also extends the field thesis across three structures: fields discovered in nature, fields formalized by humans, and fields emerging in black-box adaptive systems. This makes neural fields part of a larger architecture of intelligence rather than a final answer to it.

The broader implication is precise: the next step after neural fields is not a return to symbolic reductionism, nor a rejection of representational advances. It is a more complete architecture of receivability, admissibility, continuity, and governed promotion. The paper’s contribution is therefore a disciplined reframing, positioning neural fields inside a stronger explanatory structure for intelligence, AI systems, and governable action.