Finite Cognition and the Structural Conditions of Scientific Knowledge: A Formal and Conceptual Extension with Dynamical and Information-Theoretic Considerations

Scientific knowledge depends on cognitive systems that are finite in memory, processing capacity, and representational resources. This paper proposes that the stabilization of information is a necessary structural condition for cognition and, consequently, for the emergence of scientific knowledge. Rather than treating knowledge as a direct representation of reality “as it is,” the framework characterizes it as arising from the interaction between bounded cognitive systems and structured regularities in the environment. Mathematics is interpreted as an extension of cognitive representational capabilities, while scientific models correspond to stabilized structures that can be maintained, manipulated, and communicated under constraints. The framework further analyzes the role of replication in preserving representations across time and systems, and the emergence of thresholds of intelligibility when informational complexity exceeds cognitive capacity. It is argued that amplification—through conceptual tools, instrumentation, and artificial systems—extends the range of accessible structures without removing underlying constraints. These constraints give rise to regimes in which intelligibility degrades even when formal manipulation remains possible. The paper develops a conceptual framework supported by a minimal formal model inspired by dynamical systems and information-theoretic considerations, and situates the proposal in relation to existing approaches in philosophy of science, cognitive science, and learning theory. The result is a structural account of the conditions under which scientific knowledge becomes possible, persists, and breaks down.