Cognitive Flexibility as a Latent Structural Operator for Bayesian State Estimation
This addresses a specific issue in state estimation for systems with structural shifts, offering an incremental improvement over existing deep stochastic state-space models.
The paper tackles the problem of Bayesian filtering in nonlinear, partially observed systems when the latent structure is mismatched, introducing Cognitive Flexibility (CF) as an operator that selects latent structures online to improve predictive accuracy under mismatch while preserving Bayesian filtering.
Deep stochastic state-space models enable Bayesian filtering in nonlinear, partially observed systems but typically assume a fixed latent structure. When this assumption is violated, parameter adaptation alone may result in persistent belief inconsistency. We introduce \emph{Cognitive Flexibility} (CF) as a representation-level operator that selects latent structures online via an innovation-based predictive score, while preserving the Bayesian filtering recursion. Structural mismatch is formalized as irreducible predictive inconsistency under fixed structure. The resulting belief--structure recursion is shown to be well posed, to exhibit a structural descent property, and to admit finite switching, with reduction to standard Bayesian filtering under correct specification. Experiments on latent-dynamics mismatch, observation-structure shifts, and well-specified regimes confirm that CF improves predictive accuracy under a mismatch while remaining non-intrusive when the model is correctly specified.