Evolution of Representation–Interpretation Systems: From Molecular Replicators to Artificial Intelligence

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Abstract

evolution has repeatedly generated systems in which representations acquire causal significance through increasingly sophisticated processes of interpretation (2)

progressively more capable representation–interpretation systems enable representations to retain, transmit, and extend their causal influence (2)

1. Introduction: Information, Representation, and Evolution

physical systems have increasingly acquired the capacity to act upon the world through representations rather than through direct physical interaction (2)

representations influence physical processes by standing for, encoding, or specifying states of affairs beyond themselves (2)

representation is understood as a physical structure that carries information about some other state, process, or object (2)

Interpretation refers to the processes through which such structures are read, decoded, or transformed (2)

content carried by representations is typically functional information (3)

feature of many complex systems is the separation between representation and implementation (3)

DNA sequences specify proteins but are not themselves proteins (3)

Words refer to objects, events, and abstract concepts without being identical to them (3)

Software directs machine behavior while remaining distinct from the hardware (3)

emergence of such architectures constitutes a recurrent pattern across evolutionary history (3)

increasingly capable representation–interpretation systems allow representations to become progressively less dependent on the particular physical substrates through which they are realized (3)

Representational Autonomy.

representational thresholds: points at which a new representation–interpretation architecture emerges (3)

biosemiotic approaches that emphasize the role of sign processes and interpretation (3)

biological codes which emphasize the organizational significance of coding relationships (3)

common dynamic links major transitions ranging from molecular replication and genetic coding to language and artificial intelligence. (3)

2. RNA Replication and the Emergence of Representational Autonomy

first major threshold in the evolution of representation emerged with the appearance of self-replicating RNA molecules (3)

1. Self-replicating RNA

RNA-world hypothesis, early life relied on RNA both as a carrier of hereditary information and as a catalyst of chemical reaction (3)

catalytic RNAs (3)

RNAdirected replication and catalysis (3)

RNA in modern translation (3)

prebiotic pathways for RNA synthesis (3)

RNA occupied a unique position at the transition from prebiotic chemistry to biological evolution (4)

Prior to the emergence of RNA-based replication, a variety of molecular systems may have explored (4)

autocatalytic networks, peptide–nucleotide interactions, and alternative informational polymers such as PNA, TNA, and GNA (4)

PNA: Peptide Nucleic Acid TNA: Threose Nucleic Acid GNA: Glycol Nucleic Acid

Natural selection therefore began to operate not only on molecular structures themselves but also on the informational patterns those structures embodied (4)

Representation existed, but the mechanisms that generated its functional consequences remained embedded within the same molecular substrate that carried it. (4)

emergence of peptide synthesis provided such an opportunity. Proteins, assembled from amino acids with diverse physicochemical properties (4)

mechanism capable of systematically relating one representational domain—the nucleotide sequence of RNA—to another—the amino acid sequence of proteins (4)

genetic code, the next major threshold in the evolution of representation (5)

2. Genetic Code

3. The Genetic Code and the Differentiation of Representation and Interpretation

mechanism capable of reliably connecting two distinct representational domains: the nucleotide sequences of nucleic acids and the amino acid sequences of proteins (5)

systematic relationship between codons and amino acids (5)

Codon: triplet code

coordinated action of transfer RNAs, aminoacyl-tRNA synthetases, and the translational machinery, nucleotide sequences came to specify amino acid sequences (5)

Representation and interpretation thus became partially independent components (5)

emergence of translation created a new level of representational autonomy (5)

epistemic cut: the distinction between symbolic description and physical dynamics (5)

Proteins produced through translation became essential for maintaining and reproducing the very machinery required for their own synthesis (5)

Aminoacyl-tRNA synthetases, ribosomal proteins, metabolic enzymes, and nucleotide-synthesizing pathways collectively formed a network in which representations, interpretations, and functional activities became mutually dependent (5)

reciprocal dependency created one of the first autonomous representational architectures in evolution (6)

4. Language and the Social Distribution of Representation and Interpretation

As nervous systems evolved, organisms acquired the capacity to construct increasingly sophisticated internal representations of their environments through patterns of neural activity (6)

neural representations remained closely tied to the individual organism and its sensorimotor experience (6)

emergence of language transformed this condition by enabling representations to be externalized, shared, and preserved beyond the boundaries of individual minds (6)

3. Language

spoken language and, later, writing, representational structures could persist independently of the immediate cognitive states (6)

linguistic representations remain open to continual reinterpretation as communities, historical circumstances, and cultural practices change (6)

Interpretation thus became increasingly distributed across communities of language users rather than being embodied in fixed molecular mechanisms (6)

Human cognition acquired the capacity to create models of possibilities (7)

emergence of metarepresentation: the capacity to represent representations themselves (7)

next evolutionary threshold: artificial systems capable of performing interpretive processes that had previously been restricted to biological organisms (7)

4. Artificial Intelligence

5. Artificial Intelligence and the Externalization of Interpretation

AI employs specialized interpretive systems that map one representational domain onto another (7)

relocation of interpretive functions beyond biological organisms (8)

representational transformations operate within technological substrates (8)

interpretive processes to occur within artificial systems that can operate across scales of time, complexity, and connectivity beyond those available to individual organisms (8)

Such systems therefore participate in ongoing cycles of representation, interpretation, and modification, even though their goals, constraints, and operational environments remain shaped by human design and cultural context (8)

6. Discussion and Outlook

four major transitions in the history of life, cognition, culture, and technology through the lens of representation and interpretation (8)

representational thresholds developed here may be understood as an evolutionary extension of Patteeʹs epistemic cut—the distinction between symbolic description and physical dynamics (8)

emergence of increasingly sophisticated mechanisms capable of interpreting information and acting upon it (9)

This increasing autonomy allows representations to persist over longer timescales, circulate among larger populations, and be recombined in novel contexts (9)

representation–interpretation systems become capable of coordinating increasingly complex forms of organization across multiple spatial, temporal, and social scales (9)

emergence of higher-order representations, allowing systems not only to represent aspects of the world but also to represent, evaluate, and transform their own representational structures (9)

comparative study of interpretive architectures, including the identification of additional representational thresholds in domains such as collective intelligence, distributed cognition, and human–AI systems (10)