The Identity Landscape: A Dynamical Model of Self Inference from Memory and Prediction

"This paper proposes a formal computational framework in which self-continuity is modeled as probabilistic inference over a latent identity state. By integrating a state-space generative architecture with content-addressable memory retrieval, the model describes identity as a hidden dynamical variable reconstructed through Bayesian updating. The framework introduces an identity potential function that defines a landscape of attractor configurations, allowing for a quantitative analysis of identity stability, gradual drift, and abrupt phase transitions. Five testable empirical predictions are generated concerning memory consistency, precursor shifts, and neural correlates of identity velocity."