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Occam's Razor in sensorimotor learning

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Genewein,  T
Research Group Sensorimotor Learning and Decision-Making, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Braun,  DA
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Research Group Sensorimotor Learning and Decision-Making, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Genewein, T., & Braun, D. (2013). Occam's Razor in sensorimotor learning. Poster presented at Bernstein Conference 2013, Tübingen, Germany.


Cite as: http://hdl.handle.net/21.11116/0000-0001-4E4F-1
Abstract
Prediction is a ubiquitous phenomenon in biological systems ranging from basic motor control in animals [1] to scientific hypothesis formation in humans. A central problem in prediction systems is how to choose one's predictions if there are multiple competing hypothesis that explain the observed data equally well. Following Occam's Razor the simpler explanation requiring fewer assumptions should be preferred. An implicit and elegant way to apply Occam's Razor is Bayesian inference. In particular, a Bayesian Occam's Razor effect arises when comparing different hypothesis based on their marginal likelihood [2]. Here we investigate whether sensorimotor prediction systems implicitly apply Occam's Razor in everyday movements. This question is particularly compelling, as recent studies have found evidence that the sensorimotor system makes inferences about unobserved latent variables in a way that is consistent with Bayesian statistics [3,4]. We designed a sensorimotor task, where participants had to draw regression trajectories through a number of observed data points, representing noisy samples of an underlying ideal trajectory. The ideal trajectory was generated by one of two possible Gaussian process (GP) models—a simple model with a large length-scale, leading to smooth trajectories and a complex model with a short length-scale, leading to more wiggly trajectories. Participants were trained on the two different trajectory models and then exposed to ambiguous stimuli to see whether they showed a preference for the simpler model. In case the presented stimulus could be fit equally well by both models, we found that participants showed a clear preference for the simpler model. For general stimuli, we found that participants' behavior was quantitatively consistent with Bayesian Occam's Razor. We could also show that participants' drawn trajectories were similar to samples from the posterior predictive GP and significantly different from two non-probabilistic heuristics.