Temporal Depth and Intrinsic Dominance: A Dynamical Completion of Integrated World Modeling Theory
Vaitheeswaran Ranganathan
PhilPapers (PhilPapers Foundation) March 3, 2026 preprint DOI: 10.5281/zenodo.18846919 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper |
|---|---|
| Keywords | Generative grammar Representation politics Consciousness Embodied cognition Function biology Generative model Dynamical systems theory Perspective graphical Artificial intelligence Cognitive science Qualia Abstraction Computational model Theoretical computer science Connectionism |
| Key points | Argues that conscious world modeling requires physical instantiation within intrinsically dominant, temporally deep dynamical regimes, and proposes Temporal Depth (TD) and the Intrinsic Dominance Ratio (IDR) as conjunctive necessary conditions for conscious instantiation, generating empirically testable predictions across anesthesia, disorders of consciousness, and artificial systems. |
Abstract
Integrated World Modeling Theory (IWMT) proposes that consciousness arises from integrated generative architectures capable of constructing spatially, temporally, and causally coherent world models. While IWMT specifies the representational structure required for conscious modeling, it does not explicitly constrain the dynamical regime under which such modeling becomes phenomenally instantiated. This paper introduces a regime-level completion. I distinguish representational temporal coherence from dynamical temporal depth and argue that conscious world modeling requires physical instantiation within intrinsically dominant, temporally deep dynamical regimes. Temporal Depth (TD) quantifies the contribution of slow intrinsic modes to overall system dynamics, while Intrinsic Dominance Ratio (IDR) partitions slow persistence into endogenous versus externally driven components. Together, TD and IDR function as conjunctive necessary conditions for conscious instantiation. This framework refines IWMT’s physical grounding and generates empirically testable predictions across anesthesia, disorders of consciousness, and artificial systems.