Search Results (3)

Planning a multi-joint minimum-effort coordinated human movement to achieve a visual goal is computationally difficult: (i) The number of anatomical elemental movements of the human body greatly exceeds the number of degrees of freedom specified by visual goals; and (ii) the mass–inertia mechanical load about each elemental movement varies not only with the posture of the body but also with the mechanical interactions between the body and the environment. Given these complications, the amount of nonlinear dynamical computation needed to plan visually-guided movement is far too large for it to be carried out within the reaction time needed to initiate an appropriate response. Consequently, we propose that, as part of motor and visual development, starting with bootstrapping by fetal and neonatal pattern-generator movements and continuing adaptively from infancy to adulthood, most of the computation is carried out in advance and stored in a motor association memory network. From there it can be quickly retrieved by a selection process that provides the appropriate movement synergy compatible with the particular visual goal. We use theorems of Riemannian geometry to describe the large amount of nonlinear dynamical data that have to be pre-computed and stored for retrieval. Based on that geometry, we argue that the logical mathematical sequence for the acquisition of these data parallels the natural development of visually- guided human movement. Full article

Bringing together a Riemannian geometry account of visual space with a complementary account of human movement synergies we present a neurally-feasible computational formulation of visuomotor task performance. This cohesive geometric theory addresses inherent nonlinear complications underlying the match between a visual goal and an optimal action to achieve that goal: (i) the warped geometry of visual space causes the position, size, outline, curvature, velocity and acceleration of images to change with changes in the place and orientation of the head, (ii) the relationship between head place and body posture is ill-defined, and (iii) mass-inertia loads on muscles vary with body configuration and affect the planning of minimum-effort movement. We describe a partitioned visuospatial memory consisting of the warped posture-and-place-encoded images of the environment, including images of visible body parts. We depict synergies as low-dimensional submanifolds embedded in the warped posture-and-place manifold of the body. A task-appropriate synergy corresponds to a submanifold containing those postures and places that match the posture-and-place-encoded visual images that encompass the required visual goal. We set out a reinforcement learning process that tunes an error-reducing association memory network to minimize any mismatch, thereby coupling visual goals with compatible movement synergies. A simulation of a two-degrees-of-freedom arm illustrates that, despite warping of both visual space and posture space, there exists a smooth one-to-one and onto invertible mapping between vision and proprioception. Full article

We present a Riemannian geometry theory to examine the systematically warped geometry of perceived visual space attributable to the size–distance relationship of retinal images associated with the optics of the human eye. Starting with the notion of a vector field of retinal image features over cortical hypercolumns endowed with a metric compatible with that size–distance relationship, we use Riemannian geometry to construct a place-encoded theory of spatial representation within the human visual system. The theory draws on the concepts of geodesic spray fields, covariant derivatives, geodesics, Christoffel symbols, curvature tensors, vector bundles and fibre bundles to produce a neurally-feasible geometric theory of visuospatial memory. The characteristics of perceived 3D visual space are examined by means of a series of simulations around the egocentre. Perceptions of size and shape are elucidated by the geometry as are the removal of occlusions and the generation of 3D images of objects. Predictions of the theory are compared with experimental observations in the literature. We hold that the variety of reported geometries is accounted for by cognitive perturbations of the invariant physically-determined geometry derived here. When combined with previous description of the Riemannian geometry of human movement this work promises to account for the non-linear dynamical invertible visual-proprioceptive maps and selection of task-compatible movement synergies required for the planning and execution of visuomotor tasks. Full article