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Erik J. Schlicht, Paul R. Schrater, Charles E. Sloane; Statistical decision theory for everyday tasks: A natural cost function for human reach and grasp. Journal of Vision 2004;4(8):146. doi: https://doi.org/10.1167/4.8.146.
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People are able to pick-up objects of varying physical properties with great ease. The effortlessness with which we act upon objects suggests that we are aware of the optimal trajectory and forces needed to successfully complete a reach. Previous work (Trommershauser, et al., 2003) has demonstrated that people's mean end-point location on a pointing task is such that it maximizes the expected reward for the task. However, the reward associated with each reach (i.e., cost function) is experimentally imposed in most work of this sort. We are interested in deriving natural cost functions that may be used to predict people's actions in everyday tasks. To that end, we have developed a parametric family of cost functions for reaching tasks that is based on the physical properties of the target (e.g. object's boundaries, mass, and friction), the configuration of the object with respect to gravity and the observer, and the biological cost associated with the reach. Using this framework, we are able to make predictions about how people should reach if they were to maximize the expected reward for this cost function. To test our model, we required people to reach to objects of varying mass and orientation. Our results indicate that people are reaching in a manner that maximizes their expected reward for a natural cost function. These findings suggest that people are optimally estimating the physical constraints of the task and are aware of the motor uncertainty involved with their reach.
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