The annual Quinn Memorial Lecture brings together the UBC Psychology community and friends for an evening of dialogue and a chance to learn first-hand ab out some of the most innovative psychology research from renowned experts. The lecture is made possible by a generous gift to our department from prom inent alumnus\, Dr. Michael J. Quinn (1927-2004).
On April 16\, 2019\, we are thrilled to prese nt Dr. Richard Aslin\, Distinguished Research Scientist at t he Haskins Laboratories\, as our guest speaker.
This event is free but registration is required.
I will review three lines of research from my lab that have implications for the normative course of development and for the diagnosis of deficits or delays in development among special population s. (1) Statistical learning is a rapid form of implicitly extracting inform ation from the environment. It has been shown to be robustly present in inf ants\, children\, and adults. Children with Specific Language Impairment an d adults with Autism Spectrum Disorder show different patterns of statistic al learning. It may\, therefore\, serve as both a diagnostic tool and as a potential mechanism that underlies some developmental disorders. (2) The al location of attention to gather information via statistical learning is con trolled by both low-level stimulus salience and by predictive mechanisms. I nfants allocate their attention to visual and auditory events so that they ignore both overly simple and overly complex information\, while focusing m ostly on information of medium complexity. Deviations from this normative p attern of allocating attention may contribute to some developmental disorde rs. (3) The infant brain must make predictions about upcoming stimuli. We h ave shown using a brain imaging technique called functional near-infrared s pectroscopy (fNIRS) that an auditory cue can predict a visual stimulus\, an d even in the absence of the visual stimulus this prediction will elicit a brain response in the visual cortex. A follow-up study of prematurely born infants revealed that this brain signature of prediction is absent\, despit e these at-risk infants (tested at their corrected age) showing predictions at the behavioral level.
Dr. Richard Aslin is a Distingu ished Research Scientist at the Haskins Laboratories in New Haven CT. Prior to joining Haskins in 2017 he was on the faculty at the University of Roch ester for 33 years\, where he established the Rochester BabyLab. Aslin has published widely in several sub-areas of infant development\, including per ceptual and motor systems\, language acquisition\, and statistical learning . His work on statistical learning with Jenny Saffran and Elissa Newport de monstrated the remarkable ability of infants to extract xstructure from rap id streams of speech by mere exposure. Subsequent work with Jozsef Fiser ex panded the scope of statistical learning to the visual domain. And his work with Celeste Kidd and Steven Piantadosi documented that infants deploy the ir attention to auditory and visual sequences that have an intermediate (Go ldilocks) level of complexity.
In the past decade\, Aslin has focused on extending the statistical learning approach to grammatical category lea rning with Patricia Reeder and Elissa Newport\, and on gathering neural mea sures of learning using fMRI\, EEG\, and fNIRS. With Lizz Karuza he has exp lored the neural correlates of statistical learning in both visual and audi tory domains using fMRI. His fNIRS work with Lauren Emberson and Ben Zinsze r has shown that the infant brain deploys predictive signals to encode expe cted events and that fNIRS has sufficient fidelity to “decode” stimulus con ditions (e.g.\, word meanings) on a trial-by-trial basis. With Elika Bergel son he has shown that infants’ earliest words are organized on the basis of semantic relatedness.
Aslin has been the recipient of several major awards\, including the APA Distinguished Scientific Contributions Award (20 14) and the APS Mentor Award for Lifetime Achievement (2015)\, and several honors\, including election to the American Academy of Arts and Sciences (2 006) and the National Academy of Sciences (2013).
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