title: Internal models of target motion: expected dynamics overrides measured kinematics in timing manual interceptions.
abstract: Prevailing views on how we time the interception of a moving object assume that the visual inputs are informationally sufficient to estimate the time-to-contact from the object's kinematics. Here we present evidence in favor of a different view: the brain makes the best estimate about target motion based on measured kinematics and an a priori guess about the causes of motion. According to this theory, a predictive model is used to extrapolate time-to-contact from expected dynamics (kinetics). We projected a virtual target moving vertically downward on a wide screen with different randomized laws of motion. In the first series of experiments, subjects were asked to intercept this target by punching a real ball that fell hidden behind the screen and arrived in synchrony with the visual target. Subjects systematically timed their motor responses consistent with the assumption of gravity effects on an object's mass, even when the visual target did not accelerate. With training, the gravity model was not switched off but adapted to nonaccelerating targets by shifting the time of motor activation. In the second series of experiments, there was no real ball falling behind the screen. Instead the subjects were required to intercept the visual target by clicking a mousebutton. In this case, subjects timed their responses consistent with the assumption of uniform motion in the absence of forces, even when the target actually accelerated. Overall, the results are in accord with the theory that motor responses evoked by visual kinematics are modulated by a prior of the target dynamics. The prior appears surprisingly resistant to modifications based on performance errors.
authors: Sezione di Fisiologia umana, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy. m.zago@hsantalucia.it
authors: Myrka
authors: M
authors: Zago
authors: Gianfranco
authors: G
authors: Bosco
authors: Vincenzo
authors: V
authors: Maffei
authors: Marco
authors: M
authors: Iosa
authors: Yuri P
authors: YP
authors: Ivanenko
authors: Francesco
authors: F
authors: Lacquaniti
mesh_terms: D000054:Acceleration; D000328:Adult; D000704:Analysis of Variance; D001696:Biomechanical Phenomena; D004215:Distance Perception / Q000502:physiology; D004576:Electromyography / Q000379:methods; D005260:Female; D006801:Humans; D008297:Male; D008960:Models, Psychological; D009039:Motion Perception / Q000502:physiology*; D009068:Movement; D018482:Muscle, Skeletal / Q000502:physiology; D017711:Nonlinear Dynamics; D011597:Psychomotor Performance / Q000502:physiology*; D011897:Random Allocation; D017603:Signal Detection, Psychological; D013997:Time Factors; D013998:Time Perception / Q000502:physiology*