Title: Perceptual-motor learning and control in humans

Over the course of a lifetime, people acquire numerous
perceptual-motor skills, many of which involve a tight coupling
between continuously available information in sensory arrays and
continuously controlled movements of the body.  People learn to steer
bicycles, catch fly balls, drive automobiles, pilot aircraft, and so
on.  It is well established that behavior in these kinds of tasks can
be characterized in terms of control strategies that map information
in sensory flow fields to movements of the body (or an input device,
as in the case of vehicle and aircraft control).  For example, control
strategies have been proposed and tested for tasks such as steering,
braking, catching fly balls, and intercepting moving targets.
However, these models are far too rigid to capture the remarkable
flexibility that humans exhibit when they perform these tasks in the
real world.  In this talk, I will summarize my research on how people
adapt to changes in the dynamics of their bodies and the systems whose
movements they control.  I will also present a reinforcement learning
model that provides a potentially powerful framework for capturing
perceptual-motor adaptation and control.  Finally, I will discuss some
broader issues concerning the influence that robotics has had on the
study of perception and action in humans.



by

Brett Fajen
http://www.cogsci.rpi.edu/pandalabs/people/fajen.html