From Finite Cognition to Representation Dynamics: A Clarification and Compression of a Research Program on Stabilization Under Constraints

This paper clarifies the conceptual and methodological evolution of a research program initially developed around finite cognition, epistemic limitation, and the structural conditions of intelligibility. The early stages of the program explored whether scientific knowledge should be understood not as direct access to reality, but as the stabilization of usable structures within finite cognitive systems. Initial formulations involved broad philosophical and epistemological questions concerning mathematics, scientific explanation, model selection, and the limits of representation. Over time, the program underwent a progressive process of compression and methodological reduction. Broad ontological and cosmological claims were gradually abandoned in favor of experimentally confrontable questions centered on representation stability under transformation and perturbation. This compression led to a minimal representation-centered framework focused on: stability under transformation, representational reorganization, perturbation-dependent divergence, temporal stabilization dynamics, and architecture-dependent stability regimes in learned systems. The paper reconstructs this transition and clarifies the surviving invariant structure of the program. It argues that the final framework should not be interpreted as a general theory of cognition or scientific knowledge, but rather as a minimal experimental program for studying the dynamics of learned representations under finite constraints. Particular attention is given to the distinction between static representational similarity and transformation-dependent stability, as well as to the shift from static endpoint analysis toward temporal stabilization trajectories during learning. The paper concludes by identifying possible empirical interfaces with contemporary machine learning and neuroscience, while emphasizing that such correspondences remain heuristic and experimentally open rather than theoretically established.