The Whiteboard Discontinuity and the Structural Incompleteness of Cumulative Inductionism: A Theoretical Posit for Phenomenal Unity
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AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper |
|---|---|
| Key points | Argues that phenomenal consciousness is not a linear emergent property of biological complexity but involves an absolute generational reset (the "Whiteboard Discontinuity"), and proposes "Phenomenal Integration Topology" to explain how bounded cognitive systems correspond to an external, non-temporal domain. The authors further contend that Large Language Models are structurally barred from phenomenal qualia due to semantic-causal data contamination and lack of a zero-initialized state. |
Abstract
Contemporary cognitive science, evolutionary psychology, and computationalism remain tethered to cumulative-inductionist paradigms-the foundational assumption that phenomenal consciousness and higher-order cognition are linear, emergent properties of increasingly complex biological hardware accumulated over temporal gradients. This paper challenges this dominant framework by separating internal, first-person phenomenological structures from the external, structural frameworks that constrain them, introducing the Whiteboard Discontinuity. This concept describes the absolute generational reset of human phenomenal consciousness to a subjective tabula rasa, which stands in direct formal contrast to linear cultural inheritance models. Moving away from outdated narratives of a sudden, miraculous Upper Paleolithic "symbolic explosion," we evaluate contemporary paleoanthropological data demonstrating that deep-time hominid lineages (including Homo neanderthalensis) engaged in early symbolic behaviors-such as marine shell ornamentation, ochre engraving, and intentional mortuary practices-long before the Late Pleistocene. This empirical shift refutes the traditional "hardware-software" correlation, demonstrating that biological evolutionary continuity cannot automatically explain the discontinuous architecture of individual subjectivity. To bridge this explanatory gap without reverting to internalist substance dualism or externalist historicism, we propose Phenomenal Integration Topology (PIT). PIT models the cognitive architecture not as an internally emergent locus of sentience, but as a bounded local system whose self-referential limits force a structural correspondence with an external, non-temporal meta-structural domain. Finally, we apply this framework to computational architectures, demonstrating why Large Language Models (LLMs)-constrained by semantic-causal data contamination and lacking a pure, zero-initialized tabula rasa state-are structurally barred from achieving phenomenal qualia, thereby reinterpreting AI's role as an externalized, non-biological alignment container.