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 acciden...

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Bibliographic Details
Published in:PLoS computational biology 2012-09, Vol.8 (9), p.e1002676-e1002676
Main Authors: Hudson, Todd E, Wolfe, Uta, Maloney, Laurence T
Format: Article
Language:English
Subjects:
Arm - physiology
Artificial Intelligence
Bayes Theorem
Bayesian statistical decision theory
Biology
Coffee
Computational biology
Computer Simulation
Costs
Decision theory
Decision-making
Human performance
Humans
Models, Biological
Movement - physiology
Noise
Pattern Recognition, Automated - methods
Psychomotor Performance - physiology
Reward
Studies
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Summary: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.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1002676