Department of Psychology, City, University of London, London, UK.
Dev Sci. 2024 Jul;27(4):e13487. doi: 10.1111/desc.13487. Epub 2024 Feb 19.
In many domains, learners extract recurring units from continuous sequences. For example, in unknown languages, fluent speech is perceived as a continuous signal. Learners need to extract the underlying words from this continuous signal and then memorize them. One prominent candidate mechanism is statistical learning, whereby learners track how predictive syllables (or other items) are of one another. Syllables within the same word predict each other better than syllables straddling word boundaries. But does statistical learning lead to memories of the underlying words-or just to pairwise associations among syllables? Electrophysiological results provide the strongest evidence for the memory view. Electrophysiological responses can be time-locked to statistical word boundaries (e.g., N400s) and show rhythmic activity with a periodicity of word durations. Here, I reproduce such results with a simple Hebbian network. When exposed to statistically structured syllable sequences (and when the underlying words are not excessively long), the network activation is rhythmic with the periodicity of a word duration and activation maxima on word-final syllables. This is because word-final syllables receive more excitation from earlier syllables with which they are associated than less predictable syllables that occur earlier in words. The network is also sensitive to information whose electrophysiological correlates were used to support the encoding of ordinal positions within words. Hebbian learning can thus explain rhythmic neural activity in statistical learning tasks without any memory representations of words. Learners might thus need to rely on cues beyond statistical associations to learn the words of their native language. RESEARCH HIGHLIGHTS: Statistical learning may be utilized to identify recurring units in continuous sequences (e.g., words in fluent speech) but may not generate explicit memory for words. Exposure to statistically structured sequences leads to rhythmic activity with a period of the duration of the underlying units (e.g., words). I show that a memory-less Hebbian network model can reproduce this rhythmic neural activity as well as putative encodings of ordinal positions observed in earlier research. Direct tests are needed to establish whether statistical learning leads to declarative memories for words.
在许多领域，学习者从连续的序列中提取重复的单元。例如，在未知的语言中，流畅的语音被视为连续的信号。学习者需要从这个连续的信号中提取潜在的单词，然后记忆它们。一个突出的候选机制是统计学习，学习者通过这种机制来跟踪音节（或其他项目）之间的预测性。同一词内的音节彼此之间的预测性比跨越词边界的音节更好。但是，统计学习是否会导致对潜在单词的记忆，还是仅仅导致音节之间的成对关联？电生理结果为记忆观点提供了最强有力的证据。电生理反应可以与统计单词边界（例如 N400）时间锁定，并显示具有单词持续时间周期性的节奏活动。在这里，我使用一个简单的赫布网络重现了这样的结果。当暴露于具有统计结构的音节序列中（并且潜在单词的长度不过度长时），网络激活具有单词持续时间的周期性，并且在单词末尾的音节上具有激活最大值。这是因为单词末尾的音节比在单词中较早出现的不太可预测的音节从与之相关的较早音节接收更多的兴奋。该网络还对其电生理相关性用于支持单词内有序位置编码的信息敏感。因此，赫布学习可以在没有单词的任何记忆表示的情况下解释统计学习任务中的节奏神经活动。学习者可能因此需要依靠超出统计关联的线索来学习母语的单词。研究亮点：统计学习可用于识别连续序列（例如，流畅语音中的单词）中的重复单元，但可能不会生成单词的显式记忆。暴露于具有统计结构的序列会导致具有潜在单元（例如，单词）持续时间的周期的节奏活动。我表明，无记忆赫布网络模型可以重现这种节奏神经活动，以及在早期研究中观察到的潜在位置编码。需要进行直接测试以确定统计学习是否会导致单词的陈述性记忆。
