
Wednesday Bushong
Assistant Professor of Psychology and Cognitive & Linguistic Sciences
Links
My research is interested in the cognitive mechanisms that underlie how we use language.
My research program lies at the intersection of linguistics, computer science, and cognitive science, using behavioral and computational methods to understand language use. I am particularly interested in how humans represent perceptual information during real-time processing. My main line of research investigates how people integrate multiple cues (sources of information) across time to understand sounds and words. Historically, most research on speech perception and spoken word recognition has focused on isolated sounds and words. But in real life, we have a much richer context to draw upon: full sentences, past conversations, knowledge about our interlocutor, etc. How do we use all these pieces of information to optimally understand a speaker's meaning? My lab investigates these questions using a two-pronged approach: we develop computational models of alternative strategies and mechanisms listeners could be employing during language processing; then, we test these predictions by conducting behavioral experiments and fitting the models to the data we collect. At present my lab focuses primarily on speech and listening, but I am interested in similar processes at other levels of language processing, particularly sentence processing and pragmatics.
I primarily teach courses related to cognition and cognitive science, including PSY 217: Cognition, CLSC 110: Introduction to Cognitive Science, and CLSC 300: Seminar in Cognitive & Linguistic Sciences. My main goal in teaching is to give students the opportunity to engage critically in everything we learn. Cognitive science is a young and highly interdisciplinary field, where things change quickly and fresh perspectives are always valuable. In my courses, we focus not only on the "what" of science (what do we know about cognition), but also the "why" and "how." My courses emphasize reading, writing, and group discussion.
In addition to my primary research, I love engaging in unique collaborations with colleagues that bring together our professional and personal interests. An ongoing collaboration I'm very excited about includes Dr. EB Caron, a world-ranked speed jigsaw puzzler, Dr. Margaret Tarampi, a spatial cognition expert, Dr. Natasha Segool, a school psychologist, and Dr. Nayomi Walton, a geriatric nursing specialist. We are in the early stages of a long-term project investigating the cognitive abilities of expert speed jigsaw puzzlers and asking whether training novices on this task can be an effective intervention for improving cognitive abilities across the lifespan, from childhood to to old age.
In my free time, I love reading, hiking, and rock climbing.
Education
- B.A., University of California-San Diego
- B.S., University of California-San Diego
- M.A., University of Rochester
- Ph.D., University of Rochester
Current and upcoming courses
Cognition
PSYC217
Cognition refers to the processes and systems that enable us to perceive, attend to, represent and understand the world around us, to learn and remember information, to communicate with each other, and to reason and make decisions. This course provides a survey of research and theory in all of these domains.
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Topic for 2024-25: From Perceptrons to ChatGPT: How Computational Models Help Us Understand the Mind. Cognitive scientists have used mathematical and computational methods to understand human cognition since at least the 1940s. Similarly, the study of human neuroscience and cognition has influenced the development of artificial intelligence systems. Beginning in the early 2010s, massive increases in computational power and the accessibility of large databases have resulted in the rapid rise of human-like artificial intelligence systems, culminating in well-known public AI tools like ChatGPT. To what degree are these models a reflection of human intelligence, and can they help us understand human cognition? Are human-like cognitive biases also present in these models, and does this present ethical issues with their use? This course will cover the history of computational modeling in cognitive science, from early debates about modularity, interactivity, and the nature of representation; to the modern development of deep neural networks not only as practical systems, but as models of human cognition. (CLSC 300 and PSYC 300 are cross-listed courses.)