KDI: Statistical Learning and Its Constraints

人类和非人类灵长类动物都表现出非凡的学习能力。然而，这些能力通常局限于特定的领域、发育时期或行为背景。例如，几乎所有人都习得一种或多种复杂的语言系统——即语言——但并非所有人都习得复杂的音乐系统。类似地，非人类灵长类动物在学习寻找和分类不同种类的食物方面非常熟练，但在获得复杂的交流系统方面却受到严重限制。此外，人类和非人类灵长类动物在发育的早期似乎在几个领域学习得最好。因此，学习几乎总是以专门化为特征，而不是以通用机制为特征。理解学习的制约因素将有助于基础研究（通过考虑领域和物种的专门化）和应用研究（通过改进我们对哪些领域、年龄和环境最适合人类学习者的理解）。本研究项目的目标是探索人类成人、儿童、婴儿和非人类灵长类动物（绢毛猴）学习快速序列事件的能力。快速顺序事件的一个主要例子是语言，在语言中，声音被组合成单词，单词被组合成句子。最近的研究结果表明，成人和婴儿可以快速提取并记住语言输入的非常详细的“统计数据”，比如一个音节接另一个音节的频率和概率。在我们提出的研究中，我们将使用微型人工“语言”来模拟自然语言的一些结构特性，但可以在不同的领域（语音序列，音调序列，视觉序列，运动序列）中使用等效结构来构建。争论的焦点是人类和非人类灵长类动物从这些不同的学习材料中提取统计结构的能力。它们对材料中元素和高阶结构的分布是否同样敏感？不同年龄、不同物种、不同结构和领域的学习者的学习能力不同吗？除了对人类和非人类灵长类动物进行行为实验外，一系列的计算研究将使我们能够调查学习机制的形式属性，以便了解学习能力差异背后的结构和神经差异。什么样的计算架构可以学习人类婴儿学习的规律和模式？成人或非人类灵长类动物无法学习某些类型的复杂连续事件是由于缺乏专门用于该领域的学习设备，还是计算和/或记忆的微小差异会导致学习结果的巨大差异？

Both humans and non-human primates show remarkable learning abilities. However, these abilities are often limited to certain domains, developmental periods, or behavioral contexts. For example, nearly all humans acquire one or more complex linguistic systems-that is, languages -- but not all humans acquire complex musical systems. Similarly, non-human primates are exceptionally adept at learning to forage for and categorize different types of food, but are severely limited in acquiring complex communication systems. Also, both humans and non-human primates appear to learn best in several domains during early periods of development. Thus, learning is nearly always characterized by specializations, rather than by general-purpose mechanisms. Understanding the constraints on learning will contribute to basic research, by accounting for domain- and species-specializations, and to applied research, by refining our understanding of which domains, ages, and contexts are optimal for humanlearners.The goal of the present research project is to explore the ability of human adults, children, infants, and non-human primates (Tamarins) to learn rapid sequential events. A prime example of a rapid sequential event is language, in which sounds are combined to form words, and words are combined to form sentences. Recent findings have demonstrated that human adults and infants can rapidly extract and remember very detailed 'statistics' of linguistic input, such as the frequency and probability that one syllable will follow another. In our proposed research, we will employ miniature artificial 'languages' which simulate some of the structural properties of natural languages, but which can be built with equivalent structures across different domains (speech sequences, tone sequences, visual sequences, motor sequences). At issue is the facility humans and non-human primates show for the extraction of statistical structure from these different learning materials. Are they equally sensitive to the distributions of elements and higher order structure in the materials? Do learning abilities differ across learners of different ages and species, and across different structures and domains?In addition to behavioral experiments with humans and non-human primates, a series of computational studies will allow us to investigate the formal properties of learning mechanisms, in order to ask what architectural and neural differences might underlie such differences in learning abilities. What kinds of computational architectures can learn the types of regularities and patterns that human infants learn? Is the inability of adults or non-human primates to learn some types of complex sequential events due to the absence of a learning device specialized for that domain, or could small differences in computation and/or memory lead to large differences in learning outcome?