Speaker: Brian Dillon and Maayan Keshev Title: Encoding and accessing syntactic structure in memory: Insights from verbal agreement Time: 12:00 – 13:30, Wed, 16th June, 2021 (Beijing, Hong Kong time)Venue: https://cuhk.zoom.us/j/779556638 https://cuhk.zoom.cn/j/779556638Brian Dillon is an Associate Professor in Linguistics at the University of Massachusetts Amherst. His area of specialization is psycholinguistics. His work focuses on the real-time computation of syntactic dependencies, focusing on the processing of agreement and anaphoric dependencies in English, Mandarin, and other languages.Maayan Keshev is a Fulbright post-doctoral scholar at the University of Massachusetts Amherst. Her area of specialization is psycholinguistics. She has published extensively on Hebrew sentence processing, addressing a range of topics such as filler-gap processing, agreement attraction, grammatical islands, resumptive pronouns, and noisy-channel processing.Encoding and accessing syntactic structure in memory: Insights from verbal agreement Brian Dillon and Maayan Keshev University of Massachusetts, Amherst "Grammatical illusions" occur when speakers and listeners seem unable to faithfully apply their grammatical knowledge during the course of analyzing or producing language (Phillips, Wagers, and Lau, 2011). The distribution of grammatical illusions across constructions and across languages has led to insights into the nature of the cognitive mechanisms that speakers use during the course of routine language comprehension and production. In this talk, we will focus on one grammatical illusion: agreement attraction, the tendency for speakers to express verb agreement with nouns other than the intended agreement target (e.g. 'The key to the cabinets are rusty'; Bock & Miller, 1991). This phenomenon has proven to be a useful test case to better understand how morpho-syntactic features are bound to syntactic structure during real-time language production and comprehension, revealing the limits of how speakers can encode and maintain syntactic structure in working memory. In this talk, we will overview a range of evidence that suggests that agreement attraction likely reflects the contribution of multiple distinct underlying mechanisms, including errors in encoding syntactic structure in a noise-prone memory architecture and errors in retrieval of syntactic encodings during incremental processing. We will also overview how these basic mechanisms of encoding and retrieval interference are shaped by cross-linguistic grammatical differences, and how they impact different types of grammatical dependencies such as reflexive agreement.Virtual Psycholinguistics Forum: (https://cuhklpl.github.io/forum.html)Title: Digital Language Learning (DLL): Insights from Behavior, Cognition, and the Brain Time: 15:00 – 16:30, Wed, 30 June 2021 (Beijing, Hong Kong time)Venue: https://cuhk.zoom.us/j/779556638 https://cuhk.zoom.cn/j/779556638Prof Ping Li (李 平) currently serves as the Chair Professor of Neurolinguistics and Bilingual Studies, Dean of Faculty of Humanities and the Associate Director of the University Research Facility in Behavioral and Systems Neuroscience (UBSN) at the Hong Kong Polytechnic University. Prior to joining PolyU, he was Professor of Psychology, Linguistics, and Information Sciences, and Associate Director of the Institute for CyberScience at the Penn State University. He previously taught at the Chinese University of Hong Kong and the University of Richmond. The goal of his research is to understand the neuro-computational basis of language and cognition, and the relationships among language, culture, brain, and technology. His recent work uses brain-based, cyber-enabled and data-intensive methods to study language acquisition, bilingualism, and reading comprehension. Prof Ping Li currently serves as the Editor-in-Chief of Brain and Language, Senior Editor of Cognitive Science and Associate Editor of Frontiers in Psychology: Language Sciences. He previously served as Editor of Bilingualism: Language and Cognition and Journal of Neurolinguistics, as President of Society for Computers in Psychology, and Program Director of Cognitive Neuroscience and of Perception, Action and Cognition at the U.S. National Science Foundation. For further details, please refer to http://blclab.org/. Digital Language Learning (DLL): Insights from Behavior, Cognition, and the Brain
The Hong Kong Polytechnic UniversityAbstract: How can we leverage digital technologies to enhance language learning and bilingual representation? In an era of pervasive digital applications, our theories and practices for the learning and teaching of second languages (L2) have lagged significantly behind the pace of scientific advances and technological innovations. In this article, we outline the approach of digital language learning (DLL) for L2 acquisition and representation, and provide a theoretical synthesis and analytical framework with respect to DLL’s current and future promises. Theoretically, the DLL approach serves as the basis for understanding differences between child language and adult L2 learning, and for understanding the effects of learning context and learner characteristics. Practically, findings from learner behaviors, cognitive processing, and brain correlates can inform DLL-based language pedagogies including the design of tools and platforms for better L2 acquisition and instruction. Because of its highly interdisciplinary nature, DLL can serve as an approach to integrate cognitive, social, affective, and neural bases of L2 learning with the rapidly developing technologies including VR, AI, and big data analytics, therefore providing a gateway for multilingual research and multicultural communication.Virtual Psycholinguistics Forum: (https://cuhklpl.github.io/forum.html)本文来源：港中文语言处理实验室
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