Toro Juan M, Trobalón Josep B
Universitat de Barcelona, Barcelona, Spain.
Percept Psychophys. 2005 Jul;67(5):867-75. doi: 10.3758/bf03193539.
Statistical learning is one of the key mechanisms available to human infants and adults when they face the problems of segmenting a speech stream (Saffran, Aslin, & Newport, 1996) and extracting long-distance regularities (G6mez, 2002; Peña, Bonatti, Nespor, & Mehler, 2002). In the present study, we explore statistical learning abilities in rats in the context of speech segmentation experiments. In a series of five experiments, we address whether rats can compute the necessary statistics to be able to segment synthesized speech streams and detect regularities associated with grammatical structures. Our results demonstrate that rats can segment the streams using the frequency of co-occurrence (not transitional probabilities, as human infants do) among items, showing that some basic statistical learning mechanism generalizes over nonprimate species. Nevertheless, rats did not differentiate among test items when the stream was organized over more complex regularities that involved nonadjacent elements and abstract grammar-like rules.
统计学习是人类婴儿和成年人在面对分割语音流（萨夫兰、阿斯林和纽波特，1996年）以及提取远距离规律（戈麦斯，2002年；佩尼亚、博纳蒂、内斯波尔和梅勒，2002年）等问题时可用的关键机制之一。在本研究中，我们在语音分割实验的背景下探索大鼠的统计学习能力。在一系列五个实验中，我们探讨大鼠是否能够计算必要的统计量，以便能够分割合成语音流并检测与语法结构相关的规律。我们的结果表明，大鼠可以利用项目之间的共现频率（与人类婴儿不同，不是过渡概率）来分割语音流，这表明一些基本的统计学习机制在非灵长类物种中具有普遍性。然而，当语音流按照涉及非相邻元素和类似抽象语法规则的更复杂规律进行组织时，大鼠并未区分测试项目。
