Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, Brazil.
Instituto de Matemática, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Sci Rep. 2021 Feb 10;11(1):3520. doi: 10.1038/s41598-021-83119-x.
Using a new probabilistic approach we model the relationship between sequences of auditory stimuli generated by stochastic chains and the electroencephalographic (EEG) data acquired while 19 participants were exposed to those stimuli. The structure of the chains generating the stimuli are characterized by rooted and labeled trees whose leaves, henceforth called contexts, represent the sequences of past stimuli governing the choice of the next stimulus. A classical conjecture claims that the brain assigns probabilistic models to samples of stimuli. If this is true, then the context tree generating the sequence of stimuli should be encoded in the brain activity. Using an innovative statistical procedure we show that this context tree can effectively be extracted from the EEG data, thus giving support to the classical conjecture.
我们使用一种新的概率方法，对由随机链生成的听觉刺激序列与 19 名参与者在暴露于这些刺激时获取的脑电图 (EEG) 数据之间的关系进行建模。生成刺激的链的结构由有根和标记的树来表征，这些树的叶子（此后称为上下文）代表过去刺激序列，决定了下一个刺激的选择。一个经典的猜想声称大脑为刺激样本分配概率模型。如果这是正确的，那么生成刺激序列的上下文树应该被编码在大脑活动中。我们使用一种创新的统计程序表明，该上下文树可以有效地从 EEG 数据中提取出来，从而支持了经典的猜想。
