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Whenever we plan, imagine, or observe an action, the motor systems that would be involved in preparing and executing that action are similarly engaged. The way in which such common motor activation is formed, however, is likely to differ depending on whether it arises from our own intentional selection of action or from the observation of another's action. In this study, we use time-resolved event-related functional MRI to tease apart neural processes specifically related to the processing of observed actions, the selection of our own intended actions, the preparation for movement, and motor response execution. Participants observed a finger gesture movement or a cue indicating they should select their own finger gesture to perform, followed by a 5-s delay period; participants then performed the observed or self-selected action. During the preparation and readiness for action, prior to initiation, we found activation in a common network of higher motor areas, including dorsal and ventral premotor areas and the pre-supplementary motor area (pre-SMA); the more caudal SMA showed greater activation during movement execution. Importantly, the route to this common motor activation differed depending on whether participants freely selected the actions to perform or whether they observed the actions performed by another person. Observation of action specifically involved activation of inferior and superior parietal regions, reflecting involvement of the dorsal visual pathway in visuomotor processing required for planning the action. In contrast, the selection of action specifically involved the dorsal lateral prefrontal and anterior cingulate cortex, reflecting the role of these prefrontal areas in attentional selection and guiding the selection of responses. (c) 2005 Elsevier Inc. All rights reserved.

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When linear equality constraints are invariant through time they can be incorporated into estimation by restricted least squares. If, however, the constraints are time-varying, this standard methodology cannot be applied. In this paper we show how to incorporate linear time-varying constraints into the estimation of econometric models. The method involves the augmentation of the observation equation of a state-space model prior to estimation by the Kalman filter. Numerical optimisation routines are used for the estimation. A simple example drawn from demand analysis is used to illustrate the method and its application.

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The dispersion model with mixed boundary conditions uses a single parameter, the dispersion number, to describe the hepatic elimination of xenobiotics and endogenous substances. An implicit a priori assumption of the model is that the transit time density of intravascular indicators is approximated by an inverse Gaussian distribution. This approximation is limited in that the model poorly describes the tail part of the hepatic outflow curves of vascular indicators. A sum of two inverse Gaussian functions is proposed as ail alternative, more flexible empirical model for transit time densities of vascular references. This model suggests that a more accurate description of the tail portion of vascular reference curves yields an elimination rate constant (or intrinsic clearance) which is 40% less than predicted by the dispersion model with mixed boundary conditions. The results emphasize the need to accurately describe outflow curves in using them as a basis for determining pharmacokinetic parameters using hepatic elimination models. (C) 1997 Society for Mathematical Biology.

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This paper offers a defense of backwards in time causation models in quantum mechanics. Particular attention is given to Cramer's transactional account, which is shown to have the threefold virtue of solving the Bell problem, explaining the complex conjugate aspect of the quantum mechanical formalism, and explaining various quantum mysteries such as Schrodinger's cat. The question is therefore asked, why has this model not received more attention from physicists and philosophers? One objection given by physicists in assessing Cramer's theory was that it is not testable. This paper seeks to answer this concern by utilizing an argument that backwards causation models entail a fork theory of causal direction. From the backwards causation model together with the fork theory one can deduce empirical predictions. Finally, the objection that this strategy is questionable because of its appeal to philosophy is deflected.