Albert Einstein College of Medicine, Bronx, NY.
The Hebrew University of Jerusalem.
J Cogn Neurosci. 2017 Dec;29(12):2114-2122. doi: 10.1162/jocn_a_01181. Epub 2017 Aug 29.
The theory of statistical learning has been influential in providing a framework for how humans learn to segment patterns of regularities from continuous sensory inputs, such as speech and music. This form of learning is based on statistical cues and is thought to underlie the ability to learn to segment patterns of regularities from continuous sensory inputs, such as the transition probabilities in speech and music. However, the connection between statistical learning and brain measurements is not well understood. Here we focus on ERPs in the context of tone sequences that contain statistically cohesive melodic patterns. We hypothesized that implicit learning of statistical regularities would influence what was held in auditory working memory. We predicted that a wrong note occurring within a cohesive pattern (within-pattern deviant) would lead to a significantly larger brain signal than a wrong note occurring between cohesive patterns (between-pattern deviant), even though both deviant types were equally likely to occur with respect to the global tone sequence. We discuss this prediction within a simple Markov model framework that learns the transition probability regularities within the tone sequence. Results show that signal strength was stronger when cohesive patterns were violated and demonstrate that the transitional probability of the sequence influences the memory basis for melodic patterns. Our results thus characterize how informational units are stored in auditory memory trace for deviance detection and provide new evidence about how the brain organizes sequential sound input that is useful for perception.
统计学习理论在为人类如何从连续的感官输入（如语音和音乐）中学习分割有规律的模式提供框架方面具有影响力。这种学习形式基于统计线索，被认为是从连续的感官输入中学习分割有规律的模式的能力的基础，例如语音和音乐中的转换概率。然而，统计学习和大脑测量之间的联系还没有得到很好的理解。在这里，我们关注的是在包含有统计一致性旋律模式的音序背景下的 ERPs。我们假设，对统计规律的隐性学习将影响听觉工作记忆中存储的内容。我们预测，在一个有凝聚力的模式（模式内偏差）中出现一个错误的音符，会比在两个有凝聚力的模式之间（模式间偏差）出现一个错误的音符产生更大的大脑信号，尽管从全局音序来看，这两种偏差类型出现的可能性是相等的。我们在一个简单的马尔可夫模型框架内讨论了这个预测，该模型学习了音序中的转换概率规律。结果表明，当有凝聚力的模式被违反时，信号强度更强，这表明序列的转移概率影响了旋律模式的记忆基础。因此，我们的结果描述了信息单元如何在听觉记忆痕迹中存储以供偏差检测，并提供了关于大脑如何组织对感知有用的连续声音输入的新证据。
