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Inclusion of neural effort in cost function can explain perceptual decision suboptimality

Published online by Cambridge University Press:  10 January 2019

Yury P. Shimansky  and
Natalia Dounskaia
Yury P. Shimansky
Affiliation:
Kinesiology Program, Arizona State University, Phoenix, AZ 85004. yury.shimansky@asu.edunatalia.dounskaia@asu.edu
Natalia Dounskaia
Affiliation:
Kinesiology Program, Arizona State University, Phoenix, AZ 85004. yury.shimansky@asu.edunatalia.dounskaia@asu.edu
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Abstract

A more general form of optimality approach applied to the entire behavioral paradigm should be used instead of abandoning the optimality approach. Adding the cost of information processing to the optimality criterion and taking into account some other recently proposed aspects of decision optimization could substantially increase the explanatory power of an optimality approach to modeling perceptual decision making.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 41 , 2018 , e242
Copyright
Copyright © Cambridge University Press 2018 

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References

Diedrichsen, J., Shadmehr, R. & Ivry, R. B. (2010) The coordination of movement: Optimal feedback control and beyond. Trends in Cognitive Sciences 14:31–39.Google Scholar
Dounskaia, N. & Shimansky Y. (2016) Strategy of arm movement control is determined by minimization of neural effort for joint coordination. Experimental Brain Research 234:1335–50.Google Scholar
Goble, J., Zhang, Y., Shimansky, Y., Sharma, S. & Dounskaia, N. (2007) Directional biases reveal utilization of arm's biomechanical properties for optimization of motor behavior. Journal of Neurophysiology 98:1240–52.Google Scholar
Rand, M. K. & Shimansky, Y. P. (2013) Two-phase strategy of neural control for planar reaching movements: I. XY coordination variability and its relation to end-point variability. Experimental Brain Research 225:55–73.Google Scholar
Shimansky, Y. P. (2011) State estimation bias induced by optimization under uncertainty and error cost asymmetry is likely reflected in perception. Biological Cybernetics 104:225–33.Google Scholar
Shimansky, Y. P., Kang, T. & He, J. (2004) A novel model of motor learning capable of developing an optimal movement control law online from scratch. Biological Cybernetics 90:133–45.Google Scholar
Shimansky, Y. P. & Rand, M. K. (2013) Two-phase strategy of controlling motor coordination determined by task performance optimality. Biological Cybernetics 107:107–29.Google Scholar
Todorov, E. (2004) Optimality principles in sensorimotor control. Nature Neuroscience 7:907–915.Google Scholar