How each movement changes the next: an experimental and theoretical study of fast adaptive priors in reaching.
How each movement changes the next: an experimental and theoretical study of fast adaptive priors in reaching.
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DOI：
10.1523/jneurosci.6525-10.2011
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发表时间：
2011-07-06
期刊：
影响因子：
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通讯作者：
Sabes PN
中科院分区：
文献类型：
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作者：
Verstynen T;Sabes PN
大多数自主行为依赖于神经回路，这些神经回路将感官线索映射到适当的运动反应上。人们可能会认为，对于日常运动，比如伸手，这种映射会随着时间的推移而保持稳定，至少在没有错误反馈的情况下。在这里，我们描述了一个简单而新颖的心理物理现象，其中最近的经验形状达到的统计特性，独立于任何运动错误。具体而言，当最近移动到特定位置附近的目标时，到该位置的后续移动变得更少变化，但以增加到达其他目标的偏差为代价。这个过程展示了方差-偏差权衡，这是贝叶斯估计的标志。我们提供的证据表明，这一过程反映了一个快速的，试验的目标先验分布的学习。我们还表明，这些结果可能反映了神经回路中的联想学习的涌现属性。我们证明，添加Hebbian（联想）学习到模型网络的到达规划导致网络连接的不断修改，使网络动态偏向与最近输入相关的活动模式。这个学习过程定量地捕捉了我们在人类受试者中的实验数据的关键结果，包括最近的经验对方差-偏差权衡的影响。该网络还提供了一个很好的近似规范贝叶斯估计。这些观察结果说明了联想学习如何以统计原则的方式将最近的经验融入到正在进行的计算中。
Most voluntary actions rely on neural circuits that map sensory cues onto appropriate motor responses. One might expect that for everyday movements, like reaching, this mapping would remain stable over time, at least in the absence of error feedback. Here we describe a simple and novel psychophysical phenomenon in which recent experience shapes the statistical properties of reaching, independent of any movement errors. Specifically, when recent movements are made to targets near a particular location, subsequent movements to that location become less variable, but at the cost of increased bias for reaches to other targets. This process exhibits the variance-bias tradeoff that is a hallmark of Bayesian estimation. We provide evidence that this process reflects a fast, trial-by-trial learning of the prior distribution of targets. We also show that these results may reflect an emergent property of associative learning in neural circuits. We demonstrate that adding Hebbian (associative) learning to a model network for reach planning lead to a continuous modification of network connections that biases network dynamics toward activity patterns associated with recent inputs. This learning process quantitatively captures the key results of our experimental data in human subjects, including the effect that recent experience has on the variance-bias tradeoff. This network also provides a good approximation to a normative Bayesian estimator. These observations illustrate how associative learning can incorporate recent experience into ongoing computations in a statistically principled way.