A Generative Model for Measuring Latent Timing Structure in Motor Sequences
A Generative Model for Measuring Latent Timing Structure in Motor Sequences
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DOI：
10.1371/journal.pone.0037616
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发表时间：
2012-07-16
期刊：
影响因子：
3.7
通讯作者：
Troyer, Todd W.
中科院分区：
文献类型：
--
作者：
Glaze, Christopher M.;Troyer, Todd W.
运动的可变性通常反映了不同神经来源和外周来源的混合，这些来源在一系列时间尺度上运作。我们提出了一种序列计时的统计模型，该模型可用于测量计时变异性的三个不同分量：分布在整个序列中的全局节奏变化，例如可能源于具有广泛影响的神经调节源；快速、不相关的计时噪声，源于神经系统内的噪声成分；以及计时抖动，其不改变后续元素的计时，例如可能由电机外围的变化或测量误差引起。除了量化数据中每个潜在因素造成的变异性外，该方法还在逐个试验的基础上分配每个因素的最大似然估计。我们将该模型应用于成年斑雀鸣叫，这是一种在多个时间尺度上具有丰富结构的时间复杂行为。我们发现，单独的歌曲发声(音节)在三个组成部分中的每一个中包含大致相同的变化量，而总体歌曲长度由全球节奏变化主导。在我们的音节样本中，全局和独立的可变性随平均长度变化，而时序抖动不随平均长度变化，这一模式与Wing和Kristofferson(1973)的序列时序模型一致。我们还发现在所有三个时间源中都存在显著的逐日漂移，但仅在节奏上存在昼夜节律模式。在使用人工生成数据的测试中，该模型成功地分离出不同的成分，误差很小。该方法提供了一个通用框架，用于提取动作序列中不同的时序可变性来源，并可应用于来自广泛系统的神经和行为数据。
Motor variability often reflects a mixture of different neural and peripheral sources operating over a range of timescales. We present a statistical model of sequence timing that can be used to measure three distinct components of timing variability: global tempo changes that are spread across the sequence, such as might stem from neuromodulatory sources with widespread influence; fast, uncorrelated timing noise, stemming from noisy components within the neural system; and timing jitter that does not alter the timing of subsequent elements, such as might be caused by variation in the motor periphery or by measurement error. In addition to quantifying the variability contributed by each of these latent factors in the data, the approach assigns maximum likelihood estimates of each factor on a trial-to-trial basis. We applied the model to adult zebra finch song, a temporally complex behavior with rich structure on multiple timescales. We find that individual song vocalizations (syllables) contain roughly equal amounts of variability in each of the three components while overall song length is dominated by global tempo changes. Across our sample of syllables, both global and independent variability scale with average length while timing jitter does not, a pattern consistent with the Wing and Kristofferson (1973) model of sequence timing. We also find significant day-to-day drift in all three timing sources, but a circadian pattern in tempo only. In tests using artificially generated data, the model successfully separates out the different components with small error. The approach provides a general framework for extracting distinct sources of timing variability within action sequences, and can be applied to neural and behavioral data from a wide array of systems.