主讲人：Felix Wang （美国内华达大学拉斯维加斯分校心理学系）
题目：A rhythm account of statistical learning
报告摘要：Even from a brief exposure to an artificial spoken language, infants and adults can identify candidate word forms and other adjacent and non-adjacent dependencies present in the acoustic input. Among the influential theories resulting from this research is the proposal that humans compute conditional probabilities of adjacent and non-adjacent elements to discover underlying structure. However, there exist findings in the literature, including the results presented here, that are not consistent with such accounts. We propose that the perception of rhythm played an important role in prior studies, and in general that rhythm perception plays a critical role in determining which dependencies listeners can identify. We created a novel experimental paradigm to introduce a rhythm in the language stream that allowed us to manipulate the rhythm systematically. We showed that even learning adjacent dependencies with conditional probabilities of 1.0 depends on rhythm perception. We also developed a computational model to explain word segmentation in terms of rhythm perception, and we present simulations showing that the model captures patterns of human data from multiple experiments. We argue that the perception of rhythm holds explanatory power not only to our experiments and other dependency learning studies, but it is likely to be a fundamental mechanism that is ubiquitous in language and other auditory domain.
主讲人简介：Felix Wang received his Ph.D. in Psychology at the University of Southern California in 2016 with Dr. Toben Mintz and Jason Zevin. After graduation, he worked as a postdoctoral researcher in the Department of Psychology at the University of Pennsylvania, with Dr. Lila Gleitman and John Trueswell. He is currently an assistant professor in the Department of Psychology at the University of Nevada, Las Vegas. His work focuses on two broad themes, statistical learning of how elements are structured in a sequence and the learning of the meaning of words. His work on statistical learning uses artificial language studies to simulate how children discover the structure of their language, when properties of the language are controlled. His work on word learning focuses on two Quinian problems: the problems of referential ambiguity and semantic ambiguity. Methodologically, he builds computational models of language acquisition and conduct behavioral experiments with infants, children, and college students. His goals in these work is to try to identify the cognitive mechanisms that underlie the process of language acquisition.