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
中科院分区:
文献类型:
--
作者:
Genewein T;Braun DA
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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DOI:
10.1073/pnas.0802631105
发表时间:
2008-08-05
影响因子:
11.1
作者:
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通讯作者:
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DOI:
10.1523/jneurosci.3075-08.2009
发表时间:
2009-05-20
期刊:
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子:
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作者:
Braun DA;Aertsen A;Wolpert DM;Mehring C
通讯作者:
Mehring C
影响因子:
6.8
作者:
AKAIKE, H
通讯作者:
AKAIKE, H