Brain signatures of a multiscale process of sequence learning in humans
Brain signatures of a multiscale process of sequence learning in humans
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DOI：
10.7554/elife.41541
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发表时间：
2019-02-04
期刊：
影响因子：
7.7
通讯作者：
Meyniel, Florent
中科院分区：
文献类型：
--
作者：
Maheu, Maxime;Dehaene, Stanislas;Meyniel, Florent
提取事件序列的时间结构对于感知、决策和语言处理至关重要。在这里，我们研究了大脑获取序列知识的机制，以及连续的大脑反应反映不同时间尺度上序列统计数据的渐进提取的可能性。我们使用脑磁图测量了暴露于具有各种统计学意义的听觉序列的人类的大脑活动，并使用几种学习模型将这种活动建模为理论上的惊喜水平。连续的脑电波与不同类型的统计推断有关。早期的刺激后脑电波表示对一个简单的统计数据的敏感性，即在长时间尺度上估计的项目频率（习惯化）。中潜伏期和晚期脑电波在定性和定量上符合一个更复杂的推理的计算特性：最近的转移概率的学习。因此，我们的研究结果支持存在多个计算系统的序列处理，涉及在多个尺度的统计推断。
Extracting the temporal structure of sequences of events is crucial for perception, decision-making, and language processing. Here, we investigate the mechanisms by which the brain acquires knowledge of sequences and the possibility that successive brain responses reflect the progressive extraction of sequence statistics at different timescales. We measured brain activity using magnetoencephalography in humans exposed to auditory sequences with various statistical regularities, and we modeled this activity as theoretical surprise levels using several learning models. Successive brain waves related to different types of statistical inferences. Early post-stimulus brain waves denoted a sensitivity to a simple statistic, the frequency of items estimated over a long timescale (habituation). Mid-latency and late brain waves conformed qualitatively and quantitatively to the computational properties of a more complex inference: the learning of recent transition probabilities. Our findings thus support the existence of multiple computational systems for sequence processing involving statistical inferences at multiple scales.