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Laurence T Maloney, Julia Trommershauser, Michael S. Landy; MEGaMove: A maximum expected gain model of movement under risk. Journal of Vision 2003;3(9):386. doi: https://doi.org/10.1167/3.9.386.
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© ARVO (1962-2015); The Authors (2016-present)
We present a model for the planning of motor responses in environments where there are explicit gains and losses associated with the outcomes of actions. The goal of motor planning is the selection of a ‘motor strategy’, i.e. an algorithm that, when executed, initiates and guides movement. A strategy may incorporate visual or kinesthetic feedback to guide movement. The result of executing a motor strategy is a trajectory, but the choice of strategy does not completely determine the resulting trajectory. The gain or loss incurred is determined by the actual trajectory. The expected gain of a particular motor strategy is computed by summing the gain or loss associated with all possible trajectories weighted by the probability of their occurrence. An additional term represents the biomechanical costs to the organism. The key assumption of the MEGaMove model is that the mover will choose the motor strategy that Maximizes Expected Gain of the Movement. An immediate implication of the MEGaMove model is that the choice of motor strategy is critically dependent on the mover's motor uncertainty.
We derive predictions of the model in simple two- and three-dimensional environments containing gain and loss regions that briefly appear and disappear. Such environments provide an interesting framework for exploring human motor planning. We use the MEGaMove model to predict how optimal motor strategies should change in response to changes in the gain or loss associated with particular regions. We have tested some of these predictions in experiments and actual performance was in good agreement with the predictions of the model. Our results suggest that humans take both costs and their own movement uncertainty into account in movement planning. We compare the MEGaMove model to other motor planning models.
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