NDLI: Estimation of psychomotor delay from the Fitts’ law coefficients

Content Provider	Springer Nature Link
Author	Beamish, Dan Bhatti, Shabana Chubbs, C. Scott MacKenzie, I. Scott Wu, Jianhong Jing, Zhujun
Copyright Year	2009
Abstract	An intrinsic property of human motor behavior is a trade-off between speed and accuracy. This is classically described by Fitts’ law, a model derived by assuming that the human body has a limited capacity to transmit information in organizing motor behavior. However, Fitts’ law can also be realized as an emergent property of movements generated by delayed feedback. In this article, we describe the relationship between the Fitts’ law coefficients and the physiological parameters of the underlying delayed feedback circuit: the relaxation rate or time constant, and the psychomotor delay of the feedback process. This relationship is then used to estimate the motor circuit delay of several tasks for which Fitts’ law data are available in the literature. We consistently estimate the delay to be between 0 and 112 ms. A further consequence of this model is that not all combinations of slope and Y-intercept in Fitts’ law are possible when movements are generated by delayed feedback. In fact, it is only possible for an observed speed–accuracy trade-off to be generated by delayed feedback if the Fitts’ law coefficients satisfy −0.482 ≤ a/b ≤ 3.343 [bits] where b represents the slope in bits per second and a represents the Y-intercept in seconds. If we assume human movement is generated by delayed feedback, then the Fitts’ law coefficients should always be restricted to this range of values.
Starting Page	279
Ending Page	296
Page Count	18
File Format	PDF
ISSN	03401200
Journal	Biological Cybernetics
Volume Number	101
Issue Number	4
e-ISSN	14320770
Language	English
Publisher	Springer-Verlag
Publisher Date	2009-10-28
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Fitts’ law Psychomotor delay Motor performance Motor control Neurodynamics Statistical Physics, Dynamical Systems and Complexity Neurobiology Computer Application in Life Sciences Neurosciences Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Computer Science Biotechnology

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