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首页> 外文期刊>The European Journal of Neuroscience >Mixed-complexity artificial grammar learning in humans and macaque monkeys: evaluating learning strategies
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Mixed-complexity artificial grammar learning in humans and macaque monkeys: evaluating learning strategies

机译：人类和猕猴的混合复杂性人工语法学习：评估学习策略

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Artificial grammars (AG) can be used to generate rule-based sequences of stimuli. Some of these can be used to investigate sequence-processing computations in non-human animals that might be related to, but not unique to, human language. Previous AG learning studies in non-human animals have used different AGs to separately test for specific sequence-processing abilities. However, given that natural language and certain animal communication systems (in particular, song) have multiple levels of complexity, mixed-complexity AGs are needed to simultaneously evaluate sensitivity to the different features of the AG. Here, we tested humans and Rhesus macaques using a mixed-complexity auditory AG, containing both adjacent (local) and non-adjacent (longer-distance) relationships. Following exposure to exemplary sequences generated by the AG, humans and macaques were individually tested with sequences that were either consistent with the AG or violated specific adjacent or non-adjacent relationships. We observed a considerable level of cross-species correspondence in the sensitivity of both humans and macaques to the adjacent AG relationships and to the statistical properties of the sequences. We found no significant sensitivity to the non-adjacent AG relationships in the macaques. A subset of humans was sensitive to this non-adjacent relationship, revealing interesting between- and within-species differences in AG learning strategies. The results suggest that humans and macaques are largely comparably sensitive to the adjacent AG relationships and their statistical properties. However, in the presence of multiple cues to grammaticality, the non-adjacent relationships are less salient to the macaques and many of the humans.
机译：人造语法（AG）可用于产生基于规则的刺激序列。其中一些可用于调查可能与人类语言有关但不是独特的非人动物中的序列处理计算。在非人动物中的先前AG学习研究已经使用不同的AGS来单独测试特定的序列处理能力。然而，鉴于自然语言和某些动物通信系统（特别是歌曲）具有多种复杂程度，需要混合复杂性AGS来同时评估对AG的不同特征的敏感性。在这里，我们使用混合复杂性听觉AG测试人和恒河猴，其中包含相邻（本地）和非相邻（更长距离）关系。在暴露于Ag产生的示例性序列之后，用与Ag或违反特定的相邻或非相邻关系一致的序列单独测试人和Macaques。我们观察到在人类和猕猴的敏感度对相邻的AG关系和序列的统计性质中相当大的跨物种对应。我们发现对猕猴中的非相邻AG关系没有显着敏感性。一种人类的子集对这种非相邻关系敏感，揭示了AG学习策略的差异与物种内部差异。结果表明，人类和猕猴对相邻的AG关系和统计学性质大大敏感。然而，在对语法性的多个提示存在下，非相邻的关系对猕猴和许多人的突出性不太突出。

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