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

1. Introduction: Information, Representation, and Evolution

Across the history of life, cognition, culture, and technology, physical systems have increasingly acquired the capacity to act upon the world through representations rather than through direct physical interaction alone. Genomic sequences guide the synthesis of proteins, linguistic expressions convey meanings across individuals and generations, and computer programs direct the behavior of machines. Although these systems differ profoundly in their material composition and historical origins, they share a common feature: representations influence physical processes by standing for, encoding, or specifying states of affairs beyond themselves (Pattee, 1972; Deacon, 2012).

In this paper, a representation is understood as a physical structure that carries information about some other state, process, or object and can therefore serve as its functional surrogate within a system capable of responding to that information. Interpretation refers to the processes through which such structures are read, decoded, or transformed. Representations influence physical processes not merely because they exist as material objects, but because they stand for something beyond themselves within an organized system of relations (Pattee, 2001; Hoffmeyer, 2008; Barbieri, 2015). In biological, cognitive, and technological systems, the content carried by representations is typically functional information—that is, information whose interpretation contributes to the maintenance, adaptation, or operation of the system in which it occurs.

A defining feature of many complex systems is the separation between representation and implementation (Pattee, 1969, 1982). DNA sequences specify proteins but are not themselves proteins (Maynard Smith, 2000). Words refer to objects, events, and abstract concepts without being identical to them. Software directs machine behavior while remaining distinct from the hardware that executes it. In each case, representational structures exert causal influence through mechanisms that interpret and implement them. The effectiveness of a representation therefore depends not only on its material existence but also on the interpretive architecture within which it operates.

This paper argues that the emergence of such architectures constitutes a recurrent pattern across evolutionary history (Maynard Smith & Szathmáry, 1997). Through a series of major transitions, representations became progressively less dependent on their immediate material realization while retaining—and often expanding—their capacity to guide and constrain physical processes through increasingly sophisticated systems of interpretation. This process may be described as the progressive expansion of representational autonomy. Representational autonomy refers to the increasing capacity of representations to persist, circulate, and exert causal influence independently of the material entities, organisms, or contexts in which they originate, while remaining effective through systems of interpretation. Representational autonomy does not imply independence from interpretation. Rather, it arises because increasingly capable representation–interpretation systems allow representations to become progressively less dependent on the particular physical substrates through which they are realized.

To analyze the historical expansion of representational autonomy, the paper introduces the concept of representational thresholds: points at which a new representation–interpretation architecture emerges that significantly increases the autonomy, persistence, transferability, or productive capacity of representations. The framework proposed here shares certain affinities with biosemiotic approaches that emphasize the role of sign processes and interpretation in living systems (Hoffmeyer, 1996), as well as with theories of biological codes which emphasize the organizational significance of coding relationships in living systems (Barbieri, 2003).

While these traditions have provided important insights into representation, semiosis, and coding relationships within living systems, the present framework adopts a different emphasis. Rather than focusing primarily on the structure of representational processes within particular domains, it examines the historical succession of representational thresholds across biological, cultural, and technological evolution. The central claim is that a common dynamic links major transitions ranging from molecular replication and genetic coding to language and artificial intelligence. These transitions are not identical, nor do they imply a predetermined direction of evolution. Rather, they represent successive expansions in the autonomy of representations made possible by progressively more accomplished representation–interpretation systems. Evolution has repeatedly generated increasingly capable representation–interpretation systems through which representations become progressively more autonomous from their original physical realizations while preserving and expanding their capacity to organize physical processes.

2. RNA Replication and the Emergence of Representational Autonomy

Arguably, the first major threshold in the evolution of representation emerged with the appearance of self-replicating RNA molecules. According to the RNA-world hypothesis, early life relied on RNA both as a carrier of hereditary information and as a catalyst of chemical reactions (Gilbert, 1986; Joyce, 2002). Evidence supporting this hypothesis includes the discovery of catalytic RNAs (Kruger et al., 1982; Guerrier-Takada et al., 1983), experimental demonstrations of RNA-directed replication and catalysis (Johnston et al., 2001; Lincoln & Joyce, 2009), the central role of RNA in modern translation (Cech, 2012), and plausible prebiotic pathways for RNA synthesis (Powner et al., 2009). Together, these findings suggest that RNA occupied a unique position at the transition from prebiotic chemistry to biological evolution (Joyce, 2002; Koonin, 2012).

Prior to the emergence of RNA-based replication, a variety of molecular systems may have explored alternative forms of information storage and transmission. Models involving autocatalytic networks, peptide–nucleotide interactions, and alternative informational polymers such as PNA, TNA, and GNA indicate that early informational chemistry was likely both diverse and experimentally dynamic (Kauffman, 1993; Nielsen, 1993; Eschenmoser, 1999; Koonin & Novozhilov, 2017; Szostak, 2012).

These systems could exhibit template-directed assembly and selective amplification of particular molecular configurations, creating increasingly stable relationships between molecular structure and functional consequence (Szostak, 2012). Although these systems cannot yet be regarded as biological representations in the sense developed here, they foreshadowed mechanisms through which molecular patterns could persist and influence subsequent chemical events.

The appearance of self-replicating RNA introduced a fundamentally new organizational principle. Through template-based replication, the nucleotide sequence of an RNA molecule could be reproduced across successive generations despite the continual replacement of its material constituents (Joyce, 2002). As a result, evolutionary continuity became increasingly associated with the preservation of sequence organization rather than with the persistence of particular molecules. Information was no longer confined to transient molecular structures; it became instantiated in reproducible patterns capable of surviving material turnover (Maynard Smith & Szathmáry, 1997).

This development marked the emergence of a primitive form of representational autonomy. Representational autonomy refers here to the capacity of informational organization to persist and retain causal significance despite the continual replacement of the material substrates in which it is instantiated. RNA sequences functioned as reproducible specifications of molecular organization that could guide their own reproduction. The causal consequences of a molecule increasingly depended on the information encoded in its sequence rather than on its immediate physical identity. Natural selection therefore began to operate not only on molecular structures themselves but also on the informational patterns those structures embodied (Maynard Smith, 2000). In this sense, RNA-based representations acquired evolutionary significance: differences in sequence generated differences in function, replication, and persistence (Pattee, 1972).

Whether RNA sequences should be regarded as fully representational remains open to debate. In the present framework, they are treated as proto-representational structures because their causal role increasingly depends on the preservation and transmission of sequence organization rather than on the persistence of particular material constituents.

At this stage, however, representation and interpretation remained tightly coupled. The meaning of an RNA sequence was not determined through a separate interpretive system but arose directly from the physicochemical interactions through which replication and catalysis occurred. Representation existed, but the mechanisms that generated its functional consequences remained embedded within the same molecular substrate that carried it. The distinction between representation and interpretation, which would later become central to biological organization, had not yet fully emerged (Pattee, 1969, 1982).

The evolutionary success of RNA replicators opened a new space for variation and selection, leading to increasingly diverse and efficient molecular systems. However, RNA's limited catalytic repertoire, molecular instability, and relatively low replication fidelity constrained further increases in complexity (Joyce, 2002; Higgs & Lehman, 2015). These limitations likely created selective pressures favoring systems capable of exploiting a broader range of chemical functions.

The emergence of peptide synthesis provided such an opportunity. Proteins, assembled from amino acids with diverse physicochemical properties, greatly expanded the range of available catalytic activities. Yet realizing this potential required more than new molecular components. It required a mechanism capable of systematically relating one representational domain—the nucleotide sequence of RNA—to another—the amino acid sequence of proteins. This development gave rise to the genetic code, the next major threshold in the evolution of representation, in which interpretation became partially autonomous from the representations it acted upon (Crick, 1968; Woese, 1967).

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

The transition from an RNA world to protein-supported biology required more than the emergence of new catalytic molecules. It required a mechanism capable of reliably connecting two distinct representational domains: the nucleotide sequences of nucleic acids and the amino acid sequences of proteins (Woese, 1967; Crick, 1968). The genetic code provided this connection. By establishing a systematic relationship between codons and amino acids, it enabled information encoded in one molecular medium to be translated into functional structures realized in another (Crick, 1968). This innovation marked the next major threshold in the evolution of representation, as interpretation emerged as a partially autonomous process distinct from the representations it acted upon.

The genetic code introduced a novel organizational principle by establishing a specialized interpretive system that systematically related one representational domain to another (Barbieri, 2003). Through the coordinated action of transfer RNAs, aminoacyl-tRNA synthetases, and the translational machinery, nucleotide sequences came to specify amino acid sequences (Schimmel, 1987; Ibba & Söll, 2000). For the first time, biological systems contained a distinct mechanism capable of transforming one representational domain into another while preserving functional continuity between them (Pattee, 1982).

This development fundamentally altered the architecture of biological information processing. Nucleotide sequences no longer exerted their effects solely through their own chemical properties. Instead, their functional significance increasingly depended on an interpretive process that mapped codon sequences onto amino acid sequences (Crick, 1968; Pattee, 1982). Representation and interpretation thus became partially independent components of biological organization. A nucleotide sequence could remain unchanged while its functional effects were realized through a distinct molecular apparatus dedicated to its interpretation.

The emergence of translation created a new level of representational autonomy by separating the storage of hereditary information from many of its functional consequences. Genetic information became increasingly independent of the particular molecular mechanisms through which it acquired functional consequences. Nucleic acids served primarily as stable repositories of hereditary information, while proteins assumed a growing share of catalytic and structural functions (Maynard Smith & Szathmáry, 1995). This division of labor enabled evolutionary change to occur simultaneously within representational and functional domains, greatly expanding the capacity of biological systems to generate adaptive complexity (Maynard Smith & Szathmáry, 1997).

This separation corresponds closely to what Howard Pattee described as the epistemic cut: the distinction between symbolic description and physical dynamics (Pattee, 1969; Pattee, 1982). In the present framework, however, the significance of this transition lies not primarily in the emergence of symbolism but in the differentiation of representation from interpretation. Nucleic acids provided relatively stable representations, while the translational apparatus became the mechanism through which those representations acquired functional consequences in the material world (Pattee, 1982).

The translation system also established a self-maintaining, self-referential organizational structure. Proteins produced through translation became essential for maintaining and reproducing the very machinery required for their own synthesis. 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 (Eigen & Schuster, 1979; Pattee, 2001). The persistence of the representational system therefore depended upon the continued operation of the interpretive system, while the persistence of the interpretive system depended upon the information encoded within genetic representations (Pattee, 2001).

This reciprocal dependency created one of the first autonomous representational architectures in evolution. Genetic information could be preserved, modified, and transmitted across generations, while specialized interpretive mechanisms ensured its reliable translation into functional structures. By establishing a stable relationship between representation and interpretation, the genetic code opened a new evolutionary space in which increasingly complex forms of biological organization could emerge (Maynard Smith & Szathmáry, 1997; Koonin & Novozhilov, 2017).

The significance of this threshold extends beyond molecular biology. It introduced an organizational principle that would reappear throughout evolutionary history: increasingly specialized interpretive architectures allow representations to become progressively more autonomous from the particular physical substrates in which they are instantiated. The genetic code represents the first large-scale realization of this principle and provides the foundation upon which later representational systems—including language, culture, and artificial intelligence—would ultimately be built.

4. Language and the Social Distribution of Representation and Interpretation

The differentiation of representation and interpretation established by the genetic code provided a general organizational principle that would reappear at higher levels of biological complexity (Pattee, 1982). As nervous systems evolved, organisms acquired the capacity to construct increasingly sophisticated internal representations of their environments through patterns of neural activity (Damasio, 1999; Deacon, 1997). These representations enabled increasingly flexible behavior by allowing organisms to respond not only to immediate stimuli but also to internally generated models of external conditions.

Despite their sophistication, however, neural representations remained closely tied to the individual organism and its sensorimotor experience. Their significance remained inseparable from the biological systems that generated and interpreted them. Neural representations could guide perception, memory, and action, but they generally remained confined within the cognitive architecture of a single organism. Representation had become increasingly complex, yet its creation, storage, and interpretation remained largely individual processes.

The emergence of language transformed this condition by enabling representations to be externalized, shared, and preserved beyond the boundaries of individual minds (Donald, 1991; Deacon, 1997). Through spoken language and, later, writing, representational structures could persist independently of the immediate cognitive states that produced them (Donald, 1991). Information no longer depended exclusively on biological inheritance or individual memory. It could be transmitted socially across individuals and historically across generations (Jablonka, Lamb, & Zeligowski, 2014).

Language therefore produced a further expansion of representational autonomy. Words, narratives, concepts, and symbolic systems could be preserved, transmitted, and reinterpreted in contexts far removed from their initial production (Deacon, 1997). The continuity of representation was no longer maintained solely through biological reproduction but through social processes of communication, learning, and cultural inheritance (Tomasello, 1999). Unlike genetic representations, whose interpretation is constrained by a largely invariant molecular code, linguistic representations remain open to continual reinterpretation as communities, historical circumstances, and cultural practices change.

This transformation also altered the nature of interpretation. Whereas genetic information is interpreted through specialized molecular machinery, linguistic representations are interpreted through distributed communities of language users (Deacon, 1997). Meaning emerges through shared practices, conventions, and collective learning rather than through fixed biochemical mechanisms (Tomasello, 1999; Searle, 1995). Interpretation thus became increasingly distributed across communities of language users rather than being embodied in fixed molecular mechanisms.

The resulting representational system possessed an unprecedented capacity to generate novel representations. Language enabled the construction of representations referring not only to present objects and events but also to absent, hypothetical, future, and entirely abstract entities (Deacon, 1997). Through combinatorial syntax and recursive structure, finite vocabularies could generate an effectively unlimited range of representations (Hauser, Chomsky, & Fitch, 2002). Human cognition acquired the capacity to create models of possibilities rather than merely respond to existing conditions.

Language also enabled the emergence of metarepresentation: the capacity to represent representations themselves (Sperber, 2000). Humans could reflect upon beliefs, intentions, explanations, and symbolic systems, making representation an object of representation. This recursive capability supported abstraction, planning, scientific reasoning, and the accumulation of collective knowledge (Donald, 1991; Tomasello, 1999). Cultural evolution increasingly operated on representational structures themselves, producing systems of law, mathematics, religion, philosophy, and science that extended far beyond the capacities of individual minds (Jablonka, Lamb, & Zeligowski, 2014).

Collectively, these developments transformed representational activity into a process distributed across social networks and historical time. Knowledge could accumulate across generations, creating a form of collective memory whose growth and transformation proceeded far more rapidly than biological evolution (Donald, 1991). Human societies became increasingly organized through shared representational systems that coordinated behavior, transmitted knowledge, and generated new forms of social and technological complexity (Tomasello, 1999).

The emergence of language therefore constituted a major expansion in the autonomy of representations. Representations became increasingly independent of individual organisms, while interpretation became distributed across communities of users. Yet language also initiated a further evolutionary trajectory. As humans externalized representations through writing, mathematics, formal logic, and computation, they progressively transferred representational and interpretive functions into persistent material artifacts (Donald, 1991). These developments progressively externalized both representations and many of the interpretive practices operating upon them, preparing the conditions for the next evolutionary threshold: artificial systems capable of performing interpretive processes that had previously been restricted to biological organisms.

5. Artificial Intelligence and the Externalization of Interpretation

The externalization of representations into writing, mathematics, and computational systems created the conditions for a further evolutionary development: the externalization of interpretation itself (Donald, 1991). The next major threshold in the evolution of representation occurred when artificial systems acquired the capacity not merely to store representations but also to perform interpretive operations upon them. This development is exemplified by artificial intelligence.

Artificial intelligence systems operate through the manipulation of digital representations encoded within computational architectures (Newell & Simon, 1976; Russell & Norvig, 2021). Data structures, statistical models, symbolic expressions, and learned parameters function as representations that guide the behavior of the system. Unlike earlier informational artifacts such as books, maps, or databases, AI systems actively transform representations into new representations through computational processes that implement increasingly sophisticated forms of interpretation (Russell & Norvig, 2021).

This development extends organizational principles established during earlier evolutionary transitions. Like the genetic code, AI employs specialized interpretive systems that map one representational domain onto another. Like language, it performs these interpretive operations outside the biological organisms that originally evolved them. Just as the translational machinery maps nucleotide sequences onto amino acid sequences, AI systems map representations onto other representations through learned or programmed procedures. Inputs are transformed into classifications, predictions, decisions, plans, or linguistic outputs according to relationships encoded within computational architectures (LeCun, Bengio, & Hinton, 2015). Although the mechanisms differ fundamentally from those of biological systems, both involve the mediation of representations through specialized interpretive processes (Pattee, 1982).

The significance of AI lies not in the creation of an entirely new form of representation but in the relocation of interpretive functions beyond biological organisms. Throughout most of evolutionary history, interpretation remained dependent on living systems, whether molecular, neural, or social. Artificial intelligence introduces systems in which representational transformations operate within technological substrates rather than through ongoing biological interpretation.

This shift further increases the autonomy of representations in a manner analogous to earlier evolutionary transitions. RNA allowed representations to persist beyond particular molecules. The genetic code differentiated representation from interpretation. Language enabled representations to circulate beyond individual minds. Artificial intelligence extends this trajectory by enabling interpretive processes to occur within artificial systems that can operate across scales of time, complexity, and connectivity beyond those available to individual organisms.

Contemporary AI systems also exhibit forms of adaptive modification. Through training, optimization, and feedback, they alter internal representational structures in response to experience (LeCun et al., 2015; Goodfellow et al., 2016). The resulting models often generate representational configurations that were not explicitly specified by their designers, reflecting the capacity of complex interpretive architectures to produce novel representational configurations. 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.

The broader significance of AI lies in its role within the long-term evolution of representational systems. For the first time, interpretation itself has become implemented in non-biological substrates. Representational processes that once required molecular machinery, nervous systems, or communities of language users can now be instantiated within computational infrastructures (Hutchins, 1995; Clark & Chalmers, 1998). This development does not replace earlier representational systems; rather, it extends and reorganizes them, creating new forms of interaction between biological, cultural, and technological modes of representation.

Viewed from this perspective, artificial intelligence represents the latest major threshold in the evolution of representation. It marks the emergence of technological systems capable of performing interpretive functions that were previously confined to living organisms. The long-term evolutionary trend is therefore not toward increasing symbolic complexity alone but toward increasing representational autonomy. Across molecular, biological, cultural, and technological domains, representations and the processes that interpret them have become progressively less dependent on the particular substrates in which they originated, opening new spaces for adaptation, innovation, and organizational complexity.

6. Discussion and Outlook

The preceding analysis has examined four major transitions in the history of life, cognition, culture, and technology through the lens of representation and interpretation. Although these transitions differ substantially in their mechanisms and historical contexts, they exhibit a common organizational pattern. In each case, evolution generated increasingly capable representation–interpretation systems, whose organizational consequence was the progressive expansion of representational autonomy: the capacity of representations to persist, circulate, and influence physical processes beyond the immediate material conditions of their origin.

The concept of representational thresholds developed here may be understood as an evolutionary extension of Pattee's epistemic cut—the distinction between symbolic description and physical dynamics—emphasizing the historical emergence of increasingly differentiated representation–interpretation systems, in which representations acquire progressively greater autonomy from the physical substrates in which they originate (Pattee, 1969; Pattee, 1982).

The RNA world established the first durable representational structures by allowing molecular sequences to persist through replication despite continual material turnover. The genetic code introduced a distinct interpretive apparatus that systematically related nucleotide sequences to amino acid sequences, creating a more explicit separation between representation and interpretation. Language extended representational systems beyond individual organisms, enabling information to be preserved and transmitted through social and cultural processes. Artificial intelligence represents a further extension in which interpretive functions themselves can be implemented within technological systems operating outside biological substrates. Taken together, these four thresholds describe a coherent evolutionary trajectory: the emergence of reproducible representations, the emergence of a distinct interpretive apparatus, the social distribution of representations, and the technological externalization of interpretation. Each threshold preserves the organizational achievements of its predecessors while extending the autonomy and functional scope of representational systems.

Viewed together, these transitions suggest that the long-term evolutionary significance of information lies not simply in its storage or transmission but in the emergence of increasingly sophisticated mechanisms capable of interpreting information and acting upon it, an issue that has attracted growing attention in recent theoretical biology (Pattee, 2001; Deacon, 2012; Walker & Davies, 2013). Within this perspective, representations become evolutionarily consequential when they are embedded within systems capable of interpreting them to guide action, construction, prediction, or adaptation (Pattee, 2001). The history of evolution may therefore be understood, in part, as the history of successively more capable representation–interpretation systems through which representations become progressively more autonomous while expanding their capacity to guide and constrain physical processes.

The concept of representational autonomy provides one way of characterizing this trend. Across the thresholds examined here, representations become progressively less dependent on particular material realizations, although never independent of physical embodiment itself. Genetic information persists beyond individual molecules, linguistic information beyond individual minds, and digital information beyond particular biological or technological implementations. At the same time, interpretive processes become increasingly differentiated, distributed, and flexible, enabling representations to operate across broader spatial, temporal, and organizational scales.

The evolutionary consequences of the growing independence of representations from particular material realizations extend beyond the preservation of information. This increasing autonomy allows representations to persist over longer timescales, circulate among larger populations, and be recombined in novel contexts. As representations become progressively less constrained by particular material realizations, they can persist over longer timescales, circulate among larger populations, and be recombined in novel contexts. These properties increase the accumulation of functionally effective information and enlarge the space of potential innovations available to biological, cultural, and technological evolution. As a consequence, representation–interpretation systems become capable of coordinating increasingly complex forms of organization across multiple spatial, temporal, and social scales (Maynard Smith & Szathmáry, 1995). They also facilitate the 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.

Several limitations should also be acknowledged. The boundaries between representational thresholds are necessarily approximate, and the transitions themselves were likely gradual rather than discrete. The origins of RNA replication, the emergence of the genetic code, the evolution of language, and the development of artificial intelligence each involve complex intermediate stages that cannot be fully captured by a threshold-based analysis. Moreover, the concepts of representation and interpretation remain subjects of active debate across philosophy, biology, cognitive science, and artificial intelligence research (Hoffmeyer, 1996; Deacon, 2012; Barbieri, 2015). The framework proposed here does not seek to resolve these debates but rather to identify a common organizational pattern that appears across otherwise disparate domains.

Nevertheless, the perspective developed here suggests several directions for future research and generates comparative hypotheses that may be evaluated across biological, cognitive, cultural, and technological systems. One promising direction concerns the comparative study of interpretive architectures, including the identification of additional representational thresholds in domains such as collective intelligence, distributed cognition, and human–AI systems (Hutchins, 1995; Clark & Chalmers, 1998). The framework may also contribute to ongoing discussions concerning the origins of meaning, the evolution of cognition, and the relationship between information and causation in complex systems (Pattee, 2001; Deacon, 2012). More specifically, it predicts that major increases in representational autonomy should be associated with the emergence of increasingly capable interpretive architectures that preserve, transform, and act upon representations across broader spatial, temporal, and organizational scales. Comparative analyses across these domains may therefore help determine whether representational thresholds constitute a recurring pattern in the evolution of complex systems.

More broadly, the present analysis highlights an additional organizational dimension of evolutionary history beyond variation, selection, and adaptation: the emergence of systems capable of acting through representations. Across molecular, biological, cultural, and technological domains, evolution has repeatedly generated new ways of creating, preserving, interpreting, and transforming representations. Each threshold therefore represents not merely an increase in informational complexity but a reorganization of how causal influence is mediated, with representations playing an increasingly central role in directing physical processes.

From this perspective, the deepest continuity linking life, mind, culture, and technology lies not in any particular material substrate but in the recurring emergence of systems that increasingly mediate their interactions with the world through representations. Evolution can therefore be viewed not only as the diversification of forms but also as the progressive expansion of representational and interpretive capacities through which physical systems increasingly model and transform both their environments and themselves.