System identification applied to a visuomotor task: near-optimal human performance in a noisy changing task.

Full Abstract

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.

Learn Faster Today Improve your study skills

Author information

Author/s: Baddeley, R J (RJ); Ingram, H A (HA); Miall, R C (RC);

Affiliation: Laboratory of Experimental Psychology, University of Sussex, Brighton BN1 9QG, United Kingdom.

Journal and publication information

Publication Type: Comparative Study; Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't

Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience (J Neurosci), published in United States. (Language: eng)

Reference: 2003-Apr; vol 23 (issue 7) : pp 3066-75

Dates: Created 2003/04/09; Completed 2003/05/30; Revised 2008/11/21;

PMID: 12684493, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

External Links for this article (including full text providers, if available):

Click Electronic Full-text Provider Links to see options for finding the electronic full text links to this article. Note there may be a subscription or fee required for access to the full text. See our FAQ for information on finding FREE full text articles.

This article may also be located in paper journal collections available in many libraries. Use the Journal and Publication Information above to find the full article.

MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Adult
Algorithms
Feedback
Female
Hand - physiology
Humans

These are the highest related articles currently in the database:

See 100+ related articles.