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Anna Seydell, Brian McCann, Julia Trommershaeuser, David Knill; Learning to behave optimally in a probabilistic environment. Journal of Vision 2008;8(6):544. doi: 10.1167/8.6.544.
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© ARVO (1962-2015); The Authors (2016-present)
Recent studies have shown that humans effectively take into account task variance caused by intrinsic motor noise when planning fast hand movements. However, previous evidence suggests that humans have greater difficulty to account for arbitrary forms of stochasticity in their environment - both in economic decision making and sensorimotor tasks. We hypothesized that humans can learn to optimize movement strategies when environmental randomness mimics the kinds for which they might have generative models. We tested the hypothesis using a task in which subjects had to rapidly point at a target region partly covered by three stochastic penalty regions introduced as “defenders”. At movement completion, each defender jumped to a new position drawn randomly from fixed Gaussian probability distributions. Subjects earned points when they hit the target, unblocked by a defender, and lost points otherwise. Results indicate that after about 600 trials, subjects approached optimal behavior maximizing gain. We further tested whether subjects simply learned a set of stimulus-contingent motor plans or the statistics of defenders' movements by training subjects with one penalty distribution and then testing them on a new penalty distribution. Subjects immediately changed their strategy to achieve the same average reward as subjects who had trained with the second penalty distribution. These results indicate that subjects learned the parameters of the defenders' jump distributions and used this knowledge to optimally plan their hand movements under conditions involving stochastic rewards and penalties.
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