Individual differences in artificial and natural language statistical learning.
Individual differences in artificial and natural language statistical learning.
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DOI：
10.1016/j.cognition.2022.105123
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发表时间：
2022-08
期刊：
影响因子：
3.4
通讯作者：
Christiansen, Morten H.
中科院分区：
文献类型：
--
作者：
Isbilen, Erin S.;McCauley, Stewart M.;Christiansen, Morten H.
统计学习（SL）被认为是认知的基石。虽然几十年的研究已经揭示了参与者可以从实验环境中的统计模式中学习到的结构的显着广度，但这种能力如何与现实世界的认知现象相结合仍然没有定论。这些混合的结果可能源于这样一个事实：SL通常被视为一种在所有领域中均匀运行的一般能力，通常假设对一种规则性的敏感性意味着对其他规则性的敏感性相同。在一项预先注册的研究中，我们试图通过调整每个任务中处理的结构类型来澄清SL和语言之间的联系。我们专注于使用人工和自然语言统计学来学习三元组模式，以评估SL是否预测对自然语音中可比结构的敏感性。成年人接受了训练和测试的人工语言，包括语音定义的音节三元组。然后，我们使用多词组块任务评估了他们对自然语言中类似统计结构的敏感性，该任务检查了高频词三角形的连续回忆-语言的构建块之一。参与者学习人工音节三元组的能力与他们对自然语言中高频单词三元组的敏感性呈正相关，这表明类似的计算跨越了两项任务的学习。当统计结构和用于处理它们的计算具有可比性时，短期SL会进入长期语言习得的关键方面。因此，更好地调整特定的统计模式的任务可能会提供一个重要的垫脚石阐明SL和认知之间的关系。
Statistical learning (SL) is considered a cornerstone of cognition. While decades of research have unveiled the remarkable breadth of structures that participants can learn from statistical patterns in experimental contexts, how this ability interfaces with real-world cognitive phenomena remains inconclusive. These mixed results may arise from the fact that SL is often treated as a general ability that operates uniformly across all domains, typically assuming that sensitivity to one kind of regularity implies equal sensitivity to others. In a preregistered study, we sought to clarify the link between SL and language by aligning the type of structure being processed in each task. We focused on the learning of trigram patterns using artificial and natural language statistics, to evaluate whether SL predicts sensitivity to comparable structures in natural speech. Adults were trained and tested on an artificial language incorporating statistically-defined syllable trigrams. We then evaluated their sensitivity to similar statistical structures in natural language using a multiword chunking task, which examines serial recall of high-frequency word trigrams—one of the building blocks of language. Participants’ aptitude in learning artificial syllable trigrams positively correlated with their sensitivity to high-frequency word trigrams in natural language, suggesting that similar computations span learning across both tasks. Short-term SL taps into key aspects of long-term language acquisition when the statistical structures—and the computations used to process them—are comparable. Better aligning the specific statistical patterning across tasks may therefore provide an important steppingstone toward elucidating the relationship between SL and cognition at large.
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影响因子：
3.7
作者：
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