Abstract
We analyze the problem of obstacle avoidance from a Bayesian decision-theoretic perspective using an experimental task in which reaches around a virtual obstacle were made toward targets on an upright monitor. Subjects received monetary rewards for touching the target and incurred losses for accidentally touching the intervening obstacle. The locations of target-obstacle pairs within the workspace were varied from trial to trial. We compared human performance to that of a Bayesian ideal movement planner (who chooses motor strategies maximizing expected gain) using the Dominance Test employed in Hudson et al. (2007). The ideal movement planner suffers from the same sources of noise as the human, but selects movement plans that maximize expected gain in the presence of that noise. We find good agreement between the predictions of the model and actual performance in most but not all experimental conditions.
Highlights
Imagine that you are sitting at your desk with a nice, hot cup of coffee in front of you and your laptop keyboard roughly behind it
We developed a model of obstacle avoidance within the framework of Bayesian decision theory and tested that model experimentally
We frame the problem as a tradeoff among possible value-weighted outcomes with the motor system able to select among movement plans that assign probabilities to those outcomes [15]
Summary
Imagine that you are sitting at your desk with a nice, hot cup of coffee in front of you and your laptop keyboard roughly behind it. In reaching out to hit the return key, you plan a trajectory that takes into account the possibility that you might jostle the cup and spill your coffee – that is, you plan a movement trajectory that you would not pick if there were no coffee cup in the way. Will typically deviate from the one that you planned due to noise/uncertainty in the neuro-motor system. This noise has two important consequences: a risk of inadvertently spilling your coffee, and a risk of missing the key altogether. The motor system, in planning any speeded movement, is selecting a stochastic ‘‘bundle’’ of possible trajectories [1,2] and the particular bundle chosen determines the probabilities of favorable and unfavorable outcomes. We consider the problem of obstacle avoidance within the framework of Bayesian decision theory
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