Multifaceted aspects of chunking enable robust algorithms
Multifaceted aspects of chunking enable robust algorithms
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DOI：
10.1152/jn.00028.2014
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发表时间：
2014-10-15
影响因子：
2.5
通讯作者：
Kording, Konrad P.
中科院分区：
文献类型：
--
作者：
Acuna, Daniel E.;Wymbs, Nicholas F.;Kording, Konrad P.
序列生产任务是分析运动学习、巩固和习惯化的标准工具。当序列被学习时，运动通常被分组为子集或块。例如，大多数美国人记忆电话号码的方式是两组三位数字，一组四位数字。研究通常使用响应时间或错误率来估计受试者如何分块，这些估计通常与生理数据有关。在这里，我们表明，组块是同时反映在反应时间，错误，以及它们的相关性。这种多模态结构使我们能够提出一种贝叶斯算法，更好地估计块，同时避免过拟合。我们的算法揭示了以前未知的行为结构，如增加的错误与训练的相关性，并承诺一个有用的工具，许多形式的顺序运动行为的表征。
Sequence production tasks are a standard tool to analyze motor learning, consolidation, and habituation. As sequences are learned, movements are typically grouped into subsets or chunks. For example, most Americans memorize telephone numbers in two chunks of three digits, and one chunk of four. Studies generally use response times or error rates to estimate how subjects chunk, and these estimates are often related to physiological data. Here we show that chunking is simultaneously reflected in reaction times, errors, and their correlations. This multimodal structure enables us to propose a Bayesian algorithm that better estimates chunks while avoiding overfitting. Our algorithm reveals previously unknown behavioral structure, such as an increased error correlations with training, and promises a useful tool for the characterization of many forms of sequential motor behavior.