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，这些能力往往局限于某些领域，发展阶段或行为环境。 例如，几乎所有的人都获得一个或多个复杂的语言系统，也就是语言，但不是所有的人都获得复杂的音乐系统。 同样，非人类灵长类动物非常擅长学习搜寻和分类不同类型的食物，但在获得复杂的通信系统方面受到严重限制。 此外，人类和非人类灵长类动物似乎在发育的早期阶段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。 了解学习的限制将有助于基础研究，占领域和物种的专业化，并应用研究，通过完善我们的理解，哪些领域，年龄和上下文是最佳的humanlearners.The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）学习快速顺序事件的能力。 快速顺序事件的一个主要例子是语言，其中声音组合成单词，单词组合成句子。 最近的研究结果表明，人类成年人和婴儿可以快速提取和记住非常详细的语言输入“统计数据”，例如一个音节跟随另一个音节的频率和概率。 在我们提出的研究中，我们将采用微型人工“语言”，它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）建立等效结构。 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。 它们对材料中元素的分布和高阶结构是否同样敏感？ 不同年龄、不同种族、不同结构和不同领域的学习者的学习能力是否存在差异？除了人类和非人类灵长类动物的行为实验，一系列的计算研究将使我们能够研究学习机制的形式属性，以询问什么样的结构和神经差异可能是这种学习能力差异的基础。什么样的计算架构可以学习人类婴儿学习的语言类型和模式？ 成年人或非人类灵长类动物无法学习某些类型的复杂序列事件，是因为缺乏专门针对该领域的学习设备，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?