Lexical decision (LD) – judging whether a sequence of letters constitutes a word – has been widely investigated. In a typical lexical decision task (LDT), participants are asked to respond whether a sequence of letters is an actual word or a nonword. Although behavioral differences between types of words/nonwords have been robustly detected in LDT, there is an ongoing discussion about the exact cognitive processes that underlie the word identification process in this task. To obtain data-driven evidence on the underlying processes, we recorded electroencephalographic (EEG) data and applied a novel machine-learning method, hidden semi-Markov model multivariate pattern analysis (HsMM-MVPA). In the current study, participants performed an LDT in which we varied the frequency of words (high, low frequency) and “wordlikeness” of non-words (pseudowords, random non-words). The results revealed that models with six processing stages accounted best for the data in all conditions. While most stages were shared, Stage 5 differed between conditions. Together, these results indicate that the differences in word frequency and lexicality effects are driven by a single cognitive processing stage. Based on its latency and topology, we interpret this stage as a Decision process during which participants discriminate between words and nonwords using activated lexical information.

词法判定（LD）——判断一个字母序列是否构成一个词——已被广泛研究。在典型的词汇决策任务 (LDT) 中，要求参与者回答字母序列是实际单词还是非单词。尽管在 LDT 中已经有力地检测到了单词/非单词类型之间的行为差​​异，但关于该任务中单词识别过程背后的确切认知过程的讨论仍在继续。为了获得有关底层过程的数据驱动证据，我们记录了脑电图 (EEG) 数据并应用了一种新颖的机器学习方法，即隐式半马尔可夫模型多元模式分析 (HsMM-MVPA)。在当前的研究中，参与者进行了 LDT，其中我们改变了词的频率（高频、低频）和非词的“词相似度”（伪词、随机非单词）。结果表明，具有六个处理阶段的模型在所有条件下都能最好地解释数据。虽然大多数阶段是共享的，但第 5 阶段因条件而异。总之，这些结果表明词频和词汇效应的差异是由单个认知处理阶段驱动的。基于其延迟和拓扑，我们将此阶段解释为参与者使用激活的词汇信息区分单词和非单词的决策过程。