A sensorimotor paradigm for Bayesian model selection.

A sensorimotor paradigm for Bayesian model selection.
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DOI:
10.3389/fnhum.2012.00291
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发表时间:
2012
影响因子:
2.9
通讯作者:
Braun DA
Braun DA
中科院分区:
医学3区
文献类型:
--
作者:
Genewein T;Braun DA

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感觉运动控制被认为依赖于预测性内部模型,以有效地科普不确定的环境。最近,研究表明,人类不仅为不同的任务学习不同的内部模型,而且还提取任务之间的共同结构。这就提出了一个问题,即当每个模型都可以与一系列不同的任务特定参数相关联时,运动系统如何在不同的结构或模型之间进行选择。在这里,我们设计了一个sensorimotor任务,需要科目,以补偿视觉位移在三维虚拟现实设置,其中一个维度可以映射到一个模型变量和其他维度的参数变量。通过引入在参数维度上是中性的试探试验,我们可以直接对模型选择进行检验。我们发现,基于贝叶斯统计的模型选择程序提供了一个更好的解释比简单的非概率统计的主题的选择行为。我们的实验设计适合在感觉运动背景下模型选择的一般性研究,因为它允许单独查询模型和参数变量。
Sensorimotor control is thought to rely on predictive internal models in order to cope efficiently with uncertain environments. Recently, it has been shown that humans not only learn different internal models for different tasks, but that they also extract common structure between tasks. This raises the question of how the motor system selects between different structures or models, when each model can be associated with a range of different task-specific parameters. Here we design a sensorimotor task that requires subjects to compensate visuomotor shifts in a three-dimensional virtual reality setup, where one of the dimensions can be mapped to a model variable and the other dimension to the parameter variable. By introducing probe trials that are neutral in the parameter dimension, we can directly test for model selection. We found that model selection procedures based on Bayesian statistics provided a better explanation for subjects' choice behavior than simple non-probabilistic heuristics. Our experimental design lends itself to the general study of model selection in a sensorimotor context as it allows to separately query model and parameter variables from subjects.
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发表时间: 2008-08-05
影响因子: 11.1
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