You are invited to the next talk of the Cognitive Science of Language lecture series organized by McMaster’s Department of Linguistics and Languages. The lecture will be delivered online by Dr. Victor Kuperman. Dr. Kuperman is an Associate Professor in the Department of Linguistics and Languages and the Director of the Reading Lab at McMaster University. He received his PhD in psycholinguistics from Radboud University, Nijmegen, Netherlands in 2008 and held a post-doctoral appointment at the Department of Linguistics at Stanford University. Dr. Kuperman specializes in several areas of psycholinguistics and quantitative linguistics, including experimental and corpus-based approaches to morphology, and probabilistic models of visual comprehension. He is also interested in cognitive, oculomotor, and computational aspects of eye-movement behavior in reading, as well as in individual differences in literacy acquisition and text comprehension. The research paradigms of the Reading Lab that Dr. Kuperman leads include eye-tracking and other behavioral studies, large-scale norming studies, and quantitative analyses of written and spoken corpora.
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Title: Personal narratives by older adults before and during the pandemic: First from the Cognitive and Social Well-Being (CoSoWELL) corpus
Abstract: Dr. Kuperman will present the Cognitive and Social Well-Being (CoSoWELL) corpus, i.e., a large collection of narratives written by North American older adults (55+ years old), supplemented by demographic and psychological participant data. The current release of the CoSoWELL corpus (version 1.0) consists of over 1.2 million words produced by over 1,000 participants over five test sessions, with a pre-pandemic baseline in March 2019 and four sessions conducted during the pandemic. The corpus contains personal narratives about events in the distant past, recent past, and future, tapping into distinct facets of autobiographical memory, and administered extensive questionnaires on loneliness, social isolation, and memory functioning. I will report results of computational topic modeling and linguistic analyses of the narratives that enable to track the time-locked impact of the COVID-19 pandemic on the content of autobiographical memories and representation of the self through language. One set of results identified the most popular topics and topical shifts throughout the pandemic. Other analyses revealed linguistic markers of distress – lower optimism, increased anxiety, increased abstractness of ideation, heightened levels of fear, anger, and disgust – and charted their temporal trajectory of emotional resilience from the pre-pandemic baseline throughout the first year of the global lockdown. The findings are discussed in the framework of research on aging and autobiographical memories under stress.
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