Institute of Neuroscience, and Centre for Behaviour and Evolution, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom, Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, United Kingdom, and School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, EH8 9AD, United Kingdom.
J Neurosci. 2013 Nov 27;33(48):18825-35. doi: 10.1523/JNEUROSCI.2414-13.2013.
Artificial grammars (AG) are designed to emulate aspects of the structure of language, and AG learning (AGL) paradigms can be used to study the extent of nonhuman animals' structure-learning capabilities. However, different AG structures have been used with nonhuman animals and are difficult to compare across studies and species. We developed a simple quantitative parameter space, which we used to summarize previous nonhuman animal AGL results. This was used to highlight an under-studied AG with a forward-branching structure, designed to model certain aspects of the nondeterministic nature of word transitions in natural language and animal song. We tested whether two monkey species could learn aspects of this auditory AG. After habituating the monkeys to the AG, analysis of video recordings showed that common marmosets (New World monkeys) differentiated between well formed, correct testing sequences and those violating the AG structure based primarily on simple learning strategies. By comparison, Rhesus macaques (Old World monkeys) showed evidence for deeper levels of AGL. A novel eye-tracking approach confirmed this result in the macaques and demonstrated evidence for more complex AGL. This study provides evidence for a previously unknown level of AGL complexity in Old World monkeys that seems less evident in New World monkeys, which are more distant evolutionary relatives to humans. The findings allow for the development of both marmosets and macaques as neurobiological model systems to study different aspects of AGL at the neuronal level.
人工语法（AG）旨在模拟语言结构的某些方面，AG 学习（AGL）范式可用于研究非人类动物的结构学习能力。然而，不同的 AG 结构已被用于非人类动物，并且难以在研究和物种之间进行比较。我们开发了一个简单的定量参数空间，用于总结以前的非人类动物 AGL 结果。这用于突出一个研究不足的具有前向分支结构的 AG，旨在模拟自然语言和动物歌曲中词过渡的非确定性的某些方面。我们测试了两种猴子物种是否可以学习这个听觉 AG 的某些方面。在使猴子习惯 AG 后，对视频记录的分析表明，普通狨猴（新世界猴）主要基于简单的学习策略，将形成良好的、正确的测试序列与违反 AG 结构的序列区分开来。相比之下，猕猴（旧世界猴）表现出更深入的 AGL 证据。一种新的眼动追踪方法在猕猴中证实了这一结果，并证明了更复杂的 AGL 证据。这项研究为旧世界猴的未知的 AGL 复杂性提供了证据，而这种复杂性在与人类进化关系更远的新世界猴中则不太明显。这些发现允许将狨猴和猕猴作为神经生物学模型系统来研究神经元水平上 AGL 的不同方面。
