论文标题
贝叶斯对语言引起的事件相关潜力的建模
Bayesian Modeling of Language-Evoked Event-Related Potentials
论文作者
论文摘要
贝叶斯分层模型非常适合分析认知神经科学中脑电图实验的通常嘈杂数据：这些模型提供了一个直观的框架来说明数据中的结构和相关性，并且可以简单地处理不确定性。在典型的神经语言实验中，与事件相关的电位仅显示出很小的效应大小，并且频繁的数据分析方法无法确定其中某些效应的重要性。在这里，我们提出了一种贝叶斯的方法，用于分析与事件相关的电位，以实验中的示例数据，与单词惊人和神经反应有关。我们的模型能够估计单词惊奇对事件相关电位的大多数组件的影响，并提供对数据的更丰富的描述。贝叶斯框架还可以根据使用不同语言模型计算出的惊人值进行估计之间的比较。
Bayesian hierarchical models are well-suited to analyzing the often noisy data from electroencephalography experiments in cognitive neuroscience: these models provide an intuitive framework to account for structures and correlations in the data, and they allow a straightforward handling of uncertainty. In a typical neurolinguistic experiment, event-related potentials show only very small effect sizes and frequentist approaches to data analysis fail to establish the significance of some of these effects. Here, we present a Bayesian approach to analyzing event-related potentials using as an example data from an experiment which relates word surprisal and neural response. Our model is able to estimate the effect of word surprisal on most components of the event-related potential and provides a richer description of the data. The Bayesian framework also allows easier comparison between estimates based on surprisal values calculated using different language models.
