Allostasis Machines as Continuous Cognitive Modeling

Naturalistic cognition in human performance is defined by dynamical responses to stimuli. Allostasis Machines (AMs) are characterized by an internal model and corresponding output trajectory characterizing a generalized response to stresses and sudden changes. The effects of the environment on the internal model are collectively known as perturbations, with a generalized response analogous to allostatic load. AMs consist of a sensory input, an internal model, a source of environmental perturbation, and an dynamical output that represents the response to perturbation over time. These dynamical output trajectories characterize this response either by recovering from perturbation (well-matched, ergodic), or drifting to a new stable state (accommodative, non-ergodic). We construct a quantitative model of AMs and consider their behaviors in a variety of scenarios, including isolated, serial, and new state perturbations. Control-theoretic strategies and multi-scale information processing can also be employed to provide AM models with more sophisticated feedback and control mechanisms. Understanding the difference between well-matched responses (stably matching environmental states) and allostatic drift (hysteretic responses to perturbation) clarifies how nonlinear responses produce continuous stability.