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Awasthi, Bhuvanesh; Friedman, Jason; Williams, Mark A |
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Title |
Processing of low spatial frequency faces at periphery in choice reaching tasks |
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2011 |
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Neuropsychologia |
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49 |
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7 |
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2136-2141 |
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Various aspects of face processing have been associated with distinct ranges of spatial frequencies. Configural processing of faces depends chiefly on low spatial frequency (LSF) information whereas high spatial frequency (HSF) supports feature based processing. However, it has also been argued that face processing has a foveal-bias (HSF channels dominate the fovea). Here we used reach trajectories as a continuous behavioral measure to study perceptual processing of faces. Experimental stimuli were LSF–HSF hybrids of male and female faces superimposed and were presented peripherally and centrally. Subject reached out to touch a specified sex and their movements were recorded. The reaching trajectories reveal that there is less effect of (interference by) LSF faces at fovea as compared to periphery while reaching to HSF targets. These results demonstrate that peripherally presented LSF information, carried chiefly by magnocellular channels, enables efficient processing of faces, possibly via a retinotectal (subcortical) pathway. |
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Author |
Biess, A.; Flash, T.; Liebermann, D.G. |
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Title |
Riemannian geometric approach to human arm dynamics, movement optimization, and invariance |
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Journal Article |
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2011 |
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Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics |
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Phys Rev E Stat Nonlin Soft Matter Phys |
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83 |
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3 Pt 1 |
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031927 |
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Arm/*physiology; Biomechanics; Computer Simulation; Humans; Kinetics; Male; Models, Biological; Models, Statistical; Models, Theoretical; *Movement; Psychomotor Performance/*physiology; Range of Motion, Articular/physiology; Reaction Time/physiology; Space Perception/*physiology; Torque |
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We present a generally covariant formulation of human arm dynamics and optimization principles in Riemannian configuration space. We extend the one-parameter family of mean-squared-derivative (MSD) cost functionals from Euclidean to Riemannian space, and we show that they are mathematically identical to the corresponding dynamic costs when formulated in a Riemannian space equipped with the kinetic energy metric. In particular, we derive the equivalence of the minimum-jerk and minimum-torque change models in this metric space. Solutions of the one-parameter family of MSD variational problems in Riemannian space are given by (reparameterized) geodesic paths, which correspond to movements with least muscular effort. Finally, movement invariants are derived from symmetries of the Riemannian manifold. We argue that the geometrical structure imposed on the arm's configuration space may provide insights into the emerging properties of the movements generated by the motor system. |
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Bernstein Center for Computational Neuroscience, DE-37073 Gottingen, Germany. armin@nld.ds.mpg.de |
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English |
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1539-3755 |
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PMID:21517543 |
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