A Dynamical Framework for Cognitive Processes Based on Transformations and Semantic Equivalence
Quick Take
This paper introduces a dynamical framework for cognitive processes, modeling cognitive states as evolving elements in a state space through iterative updates. The framework integrates transformations, observations, and stability, demonstrating its application in linguistic contexts to model context-dependent interpretations as stable semantic trajectories.
Key Points
- Cognitive states evolve via an iterative update rule involving transformations and interpretative mappings.
- The framework is analyzed through fixed-point arguments ensuring stability in cognitive processes.
- A computational illustration demonstrates the operational nature of the proposed framework.
- The model connects dynamical systems, category theory, and cognitive modeling.
- Context-dependent interpretations are modeled as trajectories toward stable semantic classes.
Article Content
From source RSS / original summaryarXiv:2605. 23942v1 Announce Type: new Abstract: This paper proposes a structural and dynamical framework for modeling cognitive processes within a cybernetic perspective. Cognitive states are represented as elements of a state space evolving through an iterative update rule of the form \[ X_{t+1} = \pi\big(F(f(X_t))\big), \] where $f$ describes internal transformations, $F$ represents interpretative mappings, and $\pi$ enforces semantic equivalence.
The model is interpreted as a feedback system integrating transformation, observation, and stabilization. A categorical formulation is introduced to capture compositional structure, while the associated dynamics are analyzed through fixed-point arguments and contraction conditions ensuring stability. To demonstrate the operational character of the framework, a computational illustration is provided, together with a qualitative analysis of the induced dynamics.
A concrete linguistic application shows how context-dependent interpretation can be modeled as a trajectory toward a stable semantic class. The proposed approach connects dynamical systems, category theory, and cognitive modeling, and provides a unified representation of cognition as a feedback-driven process evolving toward invariant interpretations.
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