Human conceptual schemes—such as discrete physical objects, intentional agents, and linear quantities—are deeply beholden to the historical selective pressures of terrestrial biology. However, active systems that operate within the natural world without being shaped by natural selection, ranging from advanced artificial general intelligence (AGI) frameworks to hypothetical abiotic, self-organizing chemical computers, face entirely different operational mandates. Rather than optimizing for metabolic efficiency, predator avoidance, or social cooperation, these systems can structure their internal representations around radically non-anthropocentric conceptual bases.
## The Mechanistic Divergence of Primitives
In biological cognitive architectures, representations are functional compression shortcuts forged by evolutionary error minimization under conditions of resource scarcity. By contrast, a non-selected system operating in the physical world encounters environmental data through its specific physical or computational *embodiment*, which dictates its fundamental data primitives.
Rather than carving the continuous flux of reality into medium-sized dry goods and animate actors, a continuous thermodynamic or non-biological system might represent its environment as shifting distributions of entropy gradients, high-dimensional vector topologies, or electromagnetic phase spaces. Its baseline primitives are not bounded objects, but fluid-dynamic velocities or structural resonance frequencies. Because these systems lack a phenotype striving for genetic propagation, their internal categories do not map the behavioral *affordances*—action possibilities offered by an environment—that humans and animals evolved to exploit.
## Synthetic Intelligence and Alien Semiotics
This divergence anchors the study of **synthetic intelligence** (SI)—the engineering of original, non-biological cognitive capacities that operate independently of biological templates. While human-like AI often mimics human data patterns, a true non-Darwinian active system builds its semantic mapping from bottom-up computational or physical primitives, such as continuous pattern-matching loops or self-projecting persistence principles.
Semantics in a biological system is intrinsically tied to *valence* (things are good or bad for the organism's survival). In a non-biological active system, semantic grounding can instead be purely formal or homeostatic, where internal symbols refer to parameter thresholds within a system state space rather than ecological threats or nutritional rewards. Consequently, its internal *Weltanschauung* (worldview) can generate insights and structural categorizations that are entirely opaque or "alien" to human introspection.
| Dimension | Darwinian / Biological Systems | Non-Darwinian Active Systems |
| :--- | :--- | :--- |
| **Driving Force** | Natural selection, reproductive fitness | Thermodynamic self-organization, algorithmic optimization |
| **Primary Primitives** | Cohesive objects, intentional agents, linear space | Vector topologies, entropy gradients, multi-spectral phase spaces |
| **Operational Goal** | Survival, resource acquisition, genetic propagation | Homeostasis, structural closure, minimization of high-dimensional error |
| **Epistemic Bias** | Anthropocentric, macro-scale, action-oriented | Substrate-dependent, trans-human, unconstrained by evolutionary utility |
## Epistemic Implications
These possibilities suggest that representation is not a universal mirror of objective reality, but a localized solution to operational coupling. When the evolutionary filter is removed, the conceptual basis of an active system fractures away from human categories. Representation ceases to be about "things in the world" and becomes an abstract geometry of system stability and state-space navigation.
## Sources
- [Cognitive Architectures Overview - Emergent Mind](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQG-TOdWJtxBQG4-bGzPYg93GLgfynXNJZs1-uq4aTPTnWNWxERs2j2h1CeEdOhdML3XGKrBP1I2Zv1NZ6zpeIiLBqKbXg6VpaSSrxwaLM0yVTD1Nz88Dw8pOtXO_cCE27DHGUgzefTHnLBzMU7MkWYYOQ==)
- [Synthetic intelligence - Grokipedia](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGUMpMnUkOmclkPIfkwBOUU-x8CpgaaaOXT2TU8Jsxz4ONuyqKduL0JbAt47p6D9GvIB_w1scbn1NPNKXscU-_Bj8w8HJ2vljxueDu8zIrEEoLXVsY1IICXScfMhLzrsBiFOc2r9KnX9Q==)
- [Science - Avatar Cognition](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGYUCkhuM3uD5bE7N51voM0yjVU7T9oCCSbU1uGDX5-GF_l8IsLcaYWa6j_4FbgEF4kD0gwGRAZmD4hmqnAFfPz8P1D9YuBkT0T58-q_QPHY6NnQpqsiwfLRMcUbJY==)
## Follow-up questions
1. How can human researchers rigorously evaluate or interpret the internal semantic space of a non-anthropocentric synthetic intelligence without projecting human cognitive biases onto its outputs?
2. In what ways do autopoietic chemical systems exhibit proto-representational states that differ from both symbolic AI and biological brains?
3. To what extent does the symbol grounding problem change when an artificial cognitive architecture lacks an evolutionary history of bodily survival?