Motor planning under temporal uncertainty is suboptimal when the gain function is asymmetric

For optimal action planning, the gain/loss associated with actions and the variability in motor output should both be considered. A number of studies make conflicting claims about the optimality of human action planning but cannot be reconciled due to their use of different movements and gain/loss functions. The disagreement is possibly because of differences in the experimental design and differences in the energetic cost of participant motor effort. We used a coincident timing task, which requires decision making with constant energetic cost, to test the optimality of participant's timing strategies under four configurations of the gain function. We compared participant strategies to an optimal timing strategy calculated from a Bayesian model that maximizes the expected gain. We found suboptimal timing strategies under two configurations of the gain function characterized by asymmetry, in which higher gain is associated with higher risk of zero gain. Participants showed a risk-seeking strategy by responding closer than optimal to the time of onset/offset of zero gain. Meanwhile, there was good agreement of the model with actual performance under two configurations of the gain function characterized by symmetry. Our findings show that human ability to make decisions that must reflect uncertainty in one's own motor output has limits that depend on the configuration of the gain function.

[1]  H. Simon,et al.  Rational choice and the structure of the environment. , 1956, Psychological review.

[2]  L. Maloney,et al.  Decision-theoretic models of visual perception and action , 2010, Vision Research.

[3]  Wolfram Müller,et al.  Determinants of Ski-Jump Performance and Implications for Health, Safety and Fairness , 2009, Sports medicine.

[4]  M. Landy,et al.  Statistical decision theory and trade-offs in the control of motor response. , 2003, Spatial vision.

[5]  Alaa A. Ahmed,et al.  Learning from the value of your mistakes: evidence for a risk-sensitive process in movement adaptation , 2013, Front. Comput. Neurosci..

[6]  Daeyeol Lee Neuroeconomics: making risky choices in the brain , 2005, Nature Neuroscience.

[7]  Uta Wolfe,et al.  Speeded Reaching Movements around Invisible Obstacles , 2012, PLoS Comput. Biol..

[8]  H. Zelaznik,et al.  Motor-output variability: a theory for the accuracy of rapid motor acts. , 1979, Psychological review.

[9]  K. Adolph Learning to Move , 2008, Current directions in psychological science.

[10]  Michael X. Cohen,et al.  Different neural systems adjust motor behavior in response to reward and punishment , 2007, NeuroImage.

[11]  R A Scheidt,et al.  Learning to move amid uncertainty. , 2001, Journal of neurophysiology.

[12]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[13]  Wolfram Schultz,et al.  BOLD responses in reward regions to hypothetical and imaginary monetary rewards , 2012, NeuroImage.

[14]  O. Turnbull,et al.  Real versus facsimile reinforcers on the Iowa Gambling Task , 2003, Brain and Cognition.

[15]  K. Kudo,et al.  Compensatory Coordination of Release Parameters in a Throwing Task , 2000, Journal of motor behavior.

[16]  Shih-Wei Wu,et al.  Limits to human movement planning in tasks with asymmetric gain landscapes. , 2006, Journal of vision.

[17]  Hang Zhang,et al.  Testing Whether Humans Have an Accurate Model of Their Own Motor Uncertainty in a Speeded Reaching Task , 2011, PLoS Comput. Biol..

[18]  Konrad Paul Kording,et al.  Relevance of error: what drives motor adaptation? , 2009, Journal of neurophysiology.

[19]  Joanne Lymn,et al.  Learning on the move. , 2010, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[20]  A. Tversky,et al.  Advances in prospect theory: Cumulative representation of uncertainty , 1992 .

[21]  Y. Loewenstein,et al.  Reinforcement learning in professional basketball players , 2011, Nature communications.

[22]  L. Maloney,et al.  Economic decision-making compared with an equivalent motor task , 2009, Proceedings of the National Academy of Sciences.

[23]  Daniel A. Braun,et al.  Risk-sensitivity and the mean-variance trade-off: decision making in sensorimotor control , 2011, Proceedings of the Royal Society B: Biological Sciences.

[24]  R. J. van Beers,et al.  The role of execution noise in movement variability. , 2004, Journal of neurophysiology.

[25]  Reza Shadmehr,et al.  Learning of action through adaptive combination of motor primitives , 2000, Nature.

[26]  Kurt A. Thoroughman,et al.  Trial-by-trial transformation of error into sensorimotor adaptation changes with environmental dynamics. , 2007, Journal of neurophysiology.

[27]  A. Young Prospect Theory: An Analysis of Decision Under Risk (Kahneman and Tversky, 1979) , 2011 .

[28]  Alaa A. Ahmed,et al.  Does risk-sensitivity transfer across movements? , 2013, Journal of neurophysiology.

[29]  B. Skinner The Problem of Shot Selection in Basketball , 2011, PloS one.

[30]  Michael S Landy,et al.  Statistical decision theory and the selection of rapid, goal-directed movements. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[31]  J. Berger Statistical Decision Theory and Bayesian Analysis , 1988 .

[32]  M. Landy,et al.  Optimal Compensation for Changes in Task-Relevant Movement Variability , 2005, The Journal of Neuroscience.

[33]  Marcia C Smith Pasqualini,et al.  Stronger autonomic response accompanies better learning: A test of Damasio's somatic marker hypothesis , 2004 .