A vector-integration-to-endpoint model for performance of viapoint movements
SourceNeural Networks, 12, 1, (1999), pp. 1-29
Article / Letter to editor
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Viapoint (VP) movements are movements to a desired point that are constrained to pass through an intermediate point. Studies have shown that VP movements possess properties, such as smooth curvature around the VP, that are not explicable by treating VP movements as strict concatenations of simpler point-to-point (PTP) movements. Such properties have led some theorists to propose whole-trajectory optimization models, which imply that the entire trajectory is precomputed before movement initiation. This paper reports new experiments conducted to systematically compare VP with PTP trajectories. Analyses revealed a statistically significant early directional deviation in VP movements but no associated curvature change. An explanation of this effect is offered by extending the vector-integration-to-endpoint (VITE) model (Bullock, D., & Grossberg, S. (1988a). Neural dynamics of planned arm movements: Emergent invariants and speed-accuracy properties during trajectory formation. Psychological Review, 95, 49–90; Bullock, D., & Grossberg, S. (1988b). The VITE model: A neural command circuit for generating arm and articulator trajectories. In J.A.S. Kelso, A.J. Mandell & M.F. Schlesinger (Eds.), Dynamic patterns in complex systems (pp. 305–326). Singapore: World Scientific Publishers.), which postulates that voluntary movement trajectories emerge as internal gating signals control the integration of continuously computed vector commands based on the evolving, perceptible difference between desired and actual position variables. The model explains the observed trajectories of VP and PTP movements as emergent properties of a dynamical system that does not precompute entire trajectories before movement initiation. The new model includes a working memory and a stage sensitive to time-to-contact information. These cooperate to control serial performance. The structural and functional relationships proposed in the model are consistent with available data on forebrain physiology and anatomy.
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