Base-State Memory and Conditional Coherence: A Layered State-Capacity Theory of Embodied Dimensional Assurance

Human observers never receive a complete object or environment in a single sensory measurement. Vision provides a projected and selective field, while touch, hearing, thermal sensing, proprioception, and movement provide different partial constraints. Nevertheless, organisms experience bodies, objects, and surroundings as persistent and controllable. This paper proposes that such experience depends on Base-State Memory: a distributed, preconscious, and continually calibrated internal state that stores or compresses bodily configurations, action transitions, expected sensory consequences, errors, and learned corrections. Sensorimotor calibration is represented through a normalised endpoint model in which requested actions are mapped onto target states and progressively corrected through prediction error, without assuming an arbitrary biological state count. The paper distinguishes conditional coherence from grounded assurance. Conditional coherence occurs when a hypothesis explains the available evidence but remains dependent on unverified assumptions that bind cues, times, surfaces, or causal sources together. Grounded assurance is operationally defined through posterior concentration, predictive fit, low residual hypothesis entropy, robustness under controlled perturbation, and low conditional burden. Four distinct forms of dimensionality are separated: intrinsic physical dimension, immediate sensory dimension, effective representational dimension, and observer-relative dimensional assurance. The state-capacity progression 2^8 → 2^16 → 2^32 is derived as a valid finite-state hierarchy in which register-width doubling squares the preceding state capacity. The paper does not claim that these bit widths are literal biological or physical dimensions. Proposed 8-, 16-, and 32-bit anchors are therefore subjected to continuous capacity sweeps and cross-task replication requirements. A reproducible computational sandbox is specified for testing simultaneous and sequential sensing, visual and nonvisual access, sensorimotor calibration, memory limitation, distance, occlusion, mirrors, environmental ambiguity, sensor noise, and arbitration between entrenched base policies, deliberation, and information-seeking. A constructed 12,000-trial computational pilot tests whether conditional burden predicts failure under causal perturbation beyond posterior confidence and residual entropy. In held-out evaluation, adding conditional burden increased area under the receiver-operating-characteristic curve from 0.840 to 0.910 while reducing log loss and Brier error. These results establish computational consistency and discriminant validity within the synthetic test environment only. They are not empirical findings concerning biological memory, human phenomenology, or universal dimensional thresholds. Blindness is treated as a task-dependent difference in available information channels and evidence timing, never as cognitive deficiency or global incoherence. The framework is explicitly falsifiable: its distinctive quantities should be rejected or revised if they fail to improve prediction, calibration, or intervention robustness beyond standard uncertainty measures in independently designed tasks. Status: Independent research manuscript; conceptual and computational methods paper; not peer reviewed. Version: 1.0