The use of on-line perceptual invariants versus cognitive internal models for the predictive control of movement and action

An important and ongoing debate in the study of human motor behavior concerns the complexity of neural processing used to control our actions. On the one hand, neural systems could mimic geometric and dynamic laws to estimate the current and future movements of one's self and of objects within the environment (a cognitivistic viewpoint). Conversely, the nervous system may exploit perceptual invariants in sensorimotor signals to rapidly elicit actions with little computational overhead (the ecological-perception school of thought). In this paper we propose a hybrid solution to the classical problem of intercepting a falling object. We demonstrate how control strategies that rely on first-order, real-time estimates of time-to-contact can be tuned based on a priori knowledge about gravity to provide more effective control with little or no additional computations. We propose this solution as one way in which the central nervous system might implement "pretty good" internal models of laws of motion for the predictive control of motor actions.