Speeded Reaching Movements around Invisible Obstacles

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. Author Summary: In everyday, cluttered environments, moving to reach or grasp an object can result in unintended collisions with other objects along the path of movement. Depending on what we run into (a priceless Ming vase, a crotchety colleague) we can suffer serious monetary or social consequences. It makes sense to choose movement trajectories that trade off the value of reaching a goal against the consequences of unintended collisions along the way. In the research described here, subjects made speeded movements to touch targets while avoiding obstacles placed along the natural reach trajectory. There were explicit monetary rewards for hitting the target and explicit monetary costs for accidentally hitting the intervening obstacle. We varied the cost and location of the obstacle across conditions. The task was to earn as large a monetary bonus as possible, which required that reaches curve around obstacles only to the extent justified by the location and cost of the obstacle. We compared human performance in this task to that of a Bayesian movement planner who maximized expected gain on each trial. In most conditions, but not all, movement strategies were close to optimal.