Catching heuristics are optimal control policies
Two seemingly contradictory theories attempt to explain how humans move to intercept an airborne ball. One theory posits that humans predict the ball trajectory to optimally plan future actions; the other claims that, instead of performing such complicated computations, humans employ heuristics to reactively choose appropriate actions based on immediate visual feedback. In this paper, we show that interception strategies appearing to be heuristics can be understood as computational solutions to the optimal control problem faced by a ball-catching agent acting under uncertainty. Modeling catching as a continuous partially observable Markov decision process and employing stochastic optimal control theory, we discover that the four main heuristics described in the literature are optimal solutions if the catcher has sufficient time to continuously visually track the ball. Specifically, by varying model parameters such as noise, time to ground contact, and perceptual latency, we show that different strategies arise under different circumstances. The catcher's policy switches between generating reactive and predictive behavior based on the ratio of system to observation noise and the ratio between reaction time and task duration. Thus, we provide a rational account of human ball-catching behavior and a unifying explanation for seemingly contradictory theories of target interception on the basis of stochastic optimal control.
This paper attempts to show that previously researched ball catching heuristics can be understood as optimal control policy strategies by modelling catching as a POMDP, and making several assumptions.   This paper also explores how varying parameters can affect the optimal strategy in a simulated catching task by varying the task length and the reaction time as well as the noise in the partially observable states.  The authors spend a good amount of effort developing a computational model of a human catching a ball with observational uncertainty. This drives their point that heuristics researched in the past may not be sufficient to model scenarios where the reaction time is a significant fraction of task time, or in scenarios where there is a substantial amount of noise.  
This is an interesting paper which explores the catching policies of reaction and planning. While the background is present, it is not presented in a way that fully justifies the experimental simulations. There is some discussion of outfielder vs. catcher catcher scenarios, but those are not fully defined or simulated. A more thorough investigation of these tasks would help to illustrate the simulated model of reaction and planning.   While the paper provides generous background and model development the experimental configuration is not fully described, and the results demand additional investigation. This is a cursory introduction to the justification of a model which includes reaction and planning, but additional experimentation would allow for this relationship to be fully elucidated.   The statistics are insufficient to show significance and the paper suffers from a lack of proof-reading and editing.   Please follow up with these references to further develop the modelling and dynamics as well as the tradeoff between planning and reaction and set ups involving robotic systems:  Kim, Seungsu, Ashwini Shukla, and Aude Billard. "Catching objects in flight." IEEE Transactions on Robotics 30.5 (2014): 1049-1065. Zago, Myrka, et al. "Visuo-motor coordination and internal models for object interception." Experimental Brain Research 192.4 (2009): 571-604. Cesqui, Benedetta, et al. "Catching a ball at the right time and place: individual factors matter." PLoS one 7.2 (2012): e31770. Kim, Seungsu, and Aude Billard. "Estimating the non-linear dynamics of free-flying objects." Robotics and Autonomous Systems 60.9 (2012): 1108-1122.