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In Situ Hybridization Techniques for Paraffin-Embedded Adult Coral Samples

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Closing the loop: how semantic closure enables open-ended evolution?

Amahury Jafet López-Díaz¹, Carlos Gershenson¹

¹School of Systems Science and Industrial Engineering, State University of New York at Binghamton, Binghamton, NY, USA.

Journal of the Royal Society, Interface

|January 15, 2026

Summary

This summary is machine-generated.

This study models semantic closure, the self-referential process in life, using computational enactivism. Self-reference is key for robust replication and evolution, enabling life

Keywords:

adaptation anticipation autopoiesis biocomputation biosemiotics open-ended evolution origins of life relational biology self-reference semantic closure

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Published on: November 2, 2012

Area of Science:

Theoretical Biology
Computational Enactivism
Biosemiotics

Background:

Life's defining characteristics include autopoiesis, anticipation, and adaptation.
Semantic closure, a self-referential mechanism, is crucial for understanding these properties.
Existing models lack a unified framework integrating relational biology, biosemiotics, and ecological psychology.

Purpose of the Study:

To develop a computational enactivism framework for modeling the evolutionary emergence of semantic closure.
To integrate concepts from relational biology, physical biosemiotics, and ecological psychology.
To provide a theoretical basis for the trialectic between autopoiesis, anticipation, and adaptation.

Main Methods:

Extending Hofmeyr's Fabrication and Assembly systems with temporal parametrization.
Developing a stepwise computational model from reaction networks to self-constructing chemical systems.
Applying a unified computational enactivism framework.

Main Results:

Identified self-reference as necessary for robust self-replication and open-ended evolution.
Demonstrated the evolution of semantic closure from simple recognition to anticipatory systems.
Established syntax-pragmatic transformations as essential for life's realization.

Conclusions:

The proposed model captures critical properties of life, including autopoiesis, anticipation, and adaptation.
Computational enactivism offers a cohesive theoretical basis for understanding biological information processing and agency.
This work opens avenues for novel computational models inspired by life's dynamics.