MTS is the department’s Mind, Technology, and Society speaker series. It is hosted by a different faculty member each semester. Founded by a generous gift from Professors Robert Glushko and Pamela Samuelson, MTS brings researchers and industry professionals from across the globe to present a variety of interdisciplinary work in cognitive science. See our UCMerced CogSci youtube channel for videos of past MTS talks!
CIS graduate students, faculty, and staff, and all who are interested are invited! Members of other departments at UC Merced as well as the general public are encouraged to attend. (Note: current CIS Ph.D. students are required to attend MTS each semester in residence, to fulfill their COGS 250 course requirement).
Dr, Kevin Brown talk "Extending a formal model of predictive coding to human spoken word recognition" will be from 2-3:30pm in SSM 104.
Abstract: There is a general consensus in theories of human speech recognition that humans engage in predictive processing during online speech processing. There are also claims (e.g., Blank and Davis, 2016; Gagnepain et al., 2012) that predictive processing is indicative of the operation of a predictive coding (PC) mechanism (Rao and Ballard, 1999). PC is a generative, hierarchical feedback framework where feedback signals consist of input predictions, while feedforward signals consist primarily of prediction errors (PE). Some researchers have taken decreased neural signals when inputs conform to expectations as evidence for PC (e.g., Blank and Davis, 2016) and claim that other possible explanatory frameworks (e.g., interactive activation, cf. TRACE; McClelland and Elman, 1986) are incompatible with reported reductions in PE. However, these claims have been advanced using narrow-scope computational implementations of PC without known abilities to adequately (i.e., plausibly; Magnuson et al. , 2020) simulate broader human spoken word recognition (SWR) behavioral phenomena. Here, we present the first known neurally and behaviorally adequate, mathematically formal neural-network PC model of time-dependent human SWR. After demonstrating that the new model is able to emulate a fundamental, empirical signature of human SWR behavior (timecourse of lexical activation and competition; Allopenna et al., 1998), we compare model dynamics to neural performance targets that have been touted as hallmarks of PC in SWR. While the new model readily exhibits predictive processing (anticipatory activation of phonemes consistent with lexical knowledge) and exhibits reduced neural activity when inputs match expectations (a necessary component of PC; Gagnepain et al., 2012), more subtle patterns of neural activity under priming and noise conditions, previously proposed as diagnostic of PC (Blank and Davis, 2016), do not emerge. This suggests that such patterns may not be hallmarks of PC. We discuss implications for PC-based theories of SWR.
Bio: Dr. Kevin Brown is an associate professor in Pharmaceutical Sciences and Chemical, Biological, and Environmental Engineering at Oregon State University. He received his B.S. in physics and B. A. in mathematics from Louisiana State University and his PhD in theoretical physics from Cornell University. He was a Helen Hay Whitney Foundation fellow in Molecular and Cellular Biology at Harvard University, a postdoctoral fellow in Physics at the University of California, Santa Barbara, and an assistant professor in Biomedical Engineering at the University of Connecticut prior to coming to Oregon State University in 2018. He is a complex systems scientist who studies biological systems, particularly those arising in cognitive science, systems neuroscience, and systems biology. He is the originator of “Sloppy Models,” a theory of parameter space geometry in large nonlinear models with many underdetermined degrees of freedom. He has studied networks in molecular biology, neuroimaging, and cognitive science and employs a mix of data-driven and model-driven approaches. His work is tightly connected to experimental data, and he has many productive collaborations with experimentalists.
For more information or to sign up for email announcements, please contact the talk series organizer: cis-mts-lead@lists.ucmerced.edu.
