Haptic Discrimination of Fields and Surfaces
Northwestern University, LIMS
Graduate Student:
Advisors:
Funding sources:
NINDS Grant NS35673
NSF Grant IIS-0082957
The ability to discriminate an object’s mechanical properties from touch is one of the most fundamental somatosensory functions. When exploring mechanical properties of an object such as stiffness, viscosity, and hysteresis, humans probe the surface and obtain information from the many sensory receptors in their upper limbs. This sensory information is implicitly incorporated into the actions of the human. For example, when navigating a room in total darkness, one may run their hand along the surface of a wall as they move through the room so as to obtain a general idea of their direction of movement and any obstacles in their path.
Experiments were performed to reveal how humans acquire information about the shape and mechanical properties of surfaces through touch and how this information affects the execution of trajectories over the surface. These experiments involved subjects executing trajectories while holding a planar manipulandum that renders virtual surfaces with variable shape and mechanical properties. Subjects were instructed to make goal directed reaching movements between points lying on the boundary of a curved virtual surface of varying stiffness (200, 400, 800, 1200, 1600, 2000 N/m). The start and goal position, and a cursor corresponding to their hand position were visible to the subject during testing. The boundary of the virtual surface was not visible to the subject.
It was found that subjects’ adaptation to object mechanics varied depending on surface stiffness. When a surface exceeded a threshold stiffness, subjects adapted by learning to produce a smooth trajectory on the surface, while at lower stiffness they adapted by recovering their original kinematic pattern of movement in free space. Further, repeated movements of the hand over high stiffness curved surfaces lead to an adaptive process as reveled by a change in movement trajectories. This adaptation implies an autonomic and implicit learning of object surface geometry.
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Trajectories from a typical subject. Green squares represent the start position, red squares the goal position. |
Area reaching deviation for all subjects’ catch trials. Trajectories shown are for all subjects. |
