The Journal of Neuroscience, April 1, 2003, 23(7):3066
System Identification Applied to a Visuomotor Task: Near-Optimal
Human Performance in a Noisy Changing Task
R. J.
Baddeley1,
H. A.
Ingram1, 2, and
R. C.
Miall2
1 Laboratory of Experimental Psychology, University of
Sussex, Brighton BN1 9QG, United Kingdom, and 2 University
Laboratory of Physiology, Oxford OX1 3PT, United Kingdom
Sensory-motor integration has frequently been studied using a
single-step change in a control variable such as prismatic lens angle
and has revealed human visuomotor adaptation to often be partial and
inefficient. We propose that the changes occurring in everyday life are
better represented as the accumulation of many smaller perturbations
contaminated by measurement noise. We have therefore tested human
performance to random walk variations in the visual feedback of hand
movements during a pointing task. Subjects made discrete targeted
pointing movements to a visual target and received terminal feedback
via a cursor the position of which was offset from the actual movement
endpoint by a random walk element and a random observation element. By
applying ideal observer analysis, which for this task compares human
performance against that of a Kalman filter, we show that the
subjects' performance was highly efficient with Fisher efficiencies
reaching 73%. We then used system identification techniques to
characterize the control strategy used. A "modified" delta-rule
algorithm best modeled the human data, which suggests that they
estimated the random walk perturbation of feedback in this task using
an exponential weighting of recent errors. The time constant of the
exponential weighting of the best-fitting model varied with the rate of
random walk drift. Because human efficiency levels were high and did not vary greatly across three levels of observation noise, these results suggest that the algorithm the subjects used exponentially weighted recent errors with a weighting that varied with the level of
drift in the task to maintain efficient performance.
Key words:
visuomotor control; human; performance; movement; ideal observer analysis; system identification
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