Patterns of Mercury and Methylmercury Bioaccumulation in Marine Bivalve Molluscs Oceanography

title of dissertation: Exploration of Butyrophilin Family Protein Functions in Activating γ9δ2 T Cells in Response to Phosphoantigens and Cell Stress doctoral field of study: Medicinal Chemistry, Department of Pharmaceutical Sciences, School of Pharmacy,

EEB Dissertation Defense: Insights Into Land Plant Genome Annotation, Evolution, And Conservation Through Improved Bioinformatic Approaches And Genomic Technologies Please join us for an engaging seminar that explores adaptations in land plant genomes (some of the smallest and largest will be featured!)

Shawn Cummings, in the Language & Cognition division of the Department of Psychological Sciences, will be defending his dissertation: "Linking Lexically Guided Perceptual Learning to Statistical Patterns in Speech Input". ABSTRACT: Listeners use lexical information to modify the mapping between speech acoustics and speech sound categories. Despite convention to consider lexically guided perceptual learning as a binary outcome, the magnitude of the learning effect varies in the extant literature. We hypothesize that graded learning outcomes can be linked, in part, to statistical characteristics of the to-be-learned input, consistent with the ideal adapter theory of speech adaptation. We begin with creation and analysis of a lexically guided perceptual learning corpus including stimulus sets for the /ʃ/-/s/ contrast for each of 16 talkers following standard methods (i.e., waveform averaging to create ambiguous variants), yielding variability in the statistical cues specifying this contrast across talkers (Experiment 2). We then analyze the perceptual consequences of this variability on perception prior to experimentally induced bias (Experiment 2). Finally, we (a) measure lexically guided perceptual learning for each talker, (b) identify input characteristics that are associated with learning magnitude, and (c) examine whether a computational instantiation of the ideal adapter theory can model the input-learning link (Experiment 3). Robust learning is observed for 13 of 16 talkers, with magnitudes of learning strongly convergent between behavior and model simulations. These results provide a critical and successful test of the ideal adapter framework for speech adaptation, thus informing an understanding of the mechanisms that allow listeners to solve the lack of invariance problem for speech perception.