Max Planck Institute for Biological Cybernetics, , Tübingen, Germany, Max Planck Institute for Intelligent Systems, , Tübingen, Germany, Graduate Training Centre of Neuroscience, Tübingen, Germany.
Proc Biol Sci. 2014 Mar 26;281(1783):20132952. doi: 10.1098/rspb.2013.2952. Print 2014 May 22.
A large number of recent studies suggest that the sensorimotor system uses probabilistic models to predict its environment and makes inferences about unobserved variables in line with Bayesian statistics. One of the important features of Bayesian statistics is Occam's Razor--an inbuilt preference for simpler models when comparing competing models that explain some observed data equally well. Here, we test directly for Occam's Razor in sensorimotor control. We designed a sensorimotor task in which participants had to draw lines through clouds of noisy samples of an unobserved curve generated by one of two possible probabilistic models-a simple model with a large length scale, leading to smooth curves, and a complex model with a short length scale, leading to more wiggly curves. In training trials, participants were informed about the model that generated the stimulus so that they could learn the statistics of each model. In probe trials, participants were then exposed to ambiguous stimuli. In probe trials where the ambiguous stimulus could be fitted equally well by both models, we found that participants showed a clear preference for the simpler model. Moreover, we found that participants' choice behaviour was quantitatively consistent with Bayesian Occam's Razor. We also show that participants' drawn trajectories were similar to samples from the Bayesian predictive distribution over trajectories and significantly different from two non-probabilistic heuristics. In two control experiments, we show that the preference of the simpler model cannot be simply explained by a difference in physical effort or by a preference for curve smoothness. Our results suggest that Occam's Razor is a general behavioural principle already present during sensorimotor processing.
大量近期研究表明，感觉运动系统使用概率模型来预测其环境，并根据贝叶斯统计学对未观察到的变量进行推断。贝叶斯统计学的一个重要特征是奥卡姆剃刀——在比较同样能很好地解释一些观测数据的竞争模型时，对于更简单的模型有一种内在的偏好。在这里，我们直接在感觉运动控制中测试奥卡姆剃刀。我们设计了一个感觉运动任务，参与者必须在由两个可能的概率模型之一生成的未观察到的曲线的噪声样本云中画线——一个具有大长度尺度的简单模型，导致平滑的曲线，和一个具有短长度尺度的复杂模型，导致更弯曲的曲线。在训练试验中，参与者被告知生成刺激的模型，以便他们可以学习每个模型的统计信息。在探测试验中，参与者随后暴露于模棱两可的刺激。在探测试验中，当模棱两可的刺激可以被两个模型同样好地拟合时，我们发现参与者明显更喜欢更简单的模型。此外，我们发现参与者的选择行为与贝叶斯奥卡姆剃刀的定量一致性。我们还表明，参与者绘制的轨迹与轨迹的贝叶斯预测分布中的样本相似，与两个非概率启发式方法显著不同。在两个对照实验中，我们表明，更简单模型的偏好不能简单地用物理努力的差异或对曲线平滑度的偏好来解释。我们的结果表明，奥卡姆剃刀是一种在感觉运动处理过程中已经存在的通用行为原则。
