Statistical learning: From acquiring specific items to forming general rules.

Statistical learning: From acquiring specific items to forming general rules.
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DOI:
10.1177/0963721412436806
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发表时间:
2012-06-01
影响因子:
7.2
通讯作者:
Newport EL
Newport EL
中科院分区:
心理学1区
文献类型:
--
作者:
Aslin RN;Newport EL

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统计学习是一种快速且强大的机制,它使成人和婴儿能够提取嵌入在语言和视觉领域中的刺激模式。重要的是,统计学习是隐性进行的,无需指导,仅仅通过接触一组输入刺激即可。然而,学习者必须掌握的关于一个结构化领域的很多内容是由可应用于新输入的原则或规则组成的。尽管有人声称统计学习和规则学习是不同的机制,但在这里我们回顾相关证据,并提供一个统一的观点,认为存在一种单一的统计学习机制,它既能解释输入刺激的学习,又能解释对新实例的泛化。实例学习和泛化之间的平衡基于两个因素:突出结构规律性的感知偏差的强度,以及输入中独特与重叠情境的一致性。
Statistical learning is a rapid and robust mechanism that enables adults and infants to extract patterns of stimulation embedded in both language and visual domains. Importantly, statistical learning operates implicitly, without instruction, through mere exposure to a set of input stimuli. However, much of what learners must acquire about a structured domain consists of principles or rules that can be applied to novel inputs. Although it has been claimed that statistical learning and rule learning are separate mechanisms, here we review evidence and provide a unifying perspective that argues for a single mechanism of statistical learning that accounts for both the learning of the input stimuli and the generalization to novel instances. The balance between instance-learning and generalization is based on two factors: the strength of perceptual biases that highlight structural regularities, and the consistency of unique versus overlapping contexts in the input.
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