Doctor of Philosophy, The Ohio State University, 2021, Linguistics

The predictions of theories of incremental human sentence processing are often cached out in word-by-word measures, but the mind is a dynamical system that responds to language in real time. As a result, there may be a complex alignment between the properties of words in language and the influence those properties exert on measures of human cognition. One possible aspect of this alignment is temporal diffusion, whereby sentence processing effects are realized in a delayed manner (Mitchell, 1984). For example, because of real-time bottlenecks in human information processing (Mollica & Piantadosi, 2017), encountering a surprising word may increase cognitive load not only at that word, but also on subsequent words as the rest of the experiment unfolds (Smith & Levy, 2013). In this thesis, I argue that effect timecourses are of direct or indirect importance to many central questions in psycholinguistics, that failure to account for these timecourses can have large impacts on the results of scientific hypothesis tests, and that existing discrete-time approaches to estimating and controlling for effect timecourses are not well adapted to many experimental designs in psycholinguistics, which involve non-uniform time series in which events (words) have variable duration. I define and implement an analysis technique that addresses these concerns: continuous-time deconvolutional regression (CDR). CDR estimates continuous-time functions that describe the shape and extent of a predictor's influence on the response over time, thus directly illuminating and controlling for temporally diffuse effects. I show empirically that CDR accurately recovers ground-truth models from synthetic data and provides plausible and detailed estimates of temporal structure in human data that generalize better than estimates obtained using existing techniques. I apply CDR to measures of naturalistic sentence processing in order test several theoretical questions in psycholinguistics. In one st (open full item for complete abstract)

Committee: William Schuler PhD (Advisor); Micha Elsner PhD (Advisor); Paul Subhadeep PhD (Committee Member) Subjects: Computer Science; Linguistics; Neurosciences

MA, Kent State University, 2015, College of Arts and Sciences / Department of Geography

This research examines the diffusion process of the institutional development of the three major religions (i.e., Buddhism, Daoism, and Christianity) in Zhejiang Province, China since the year 1949. By utilizing analytical tools in geographic information systems and statistical analysis software, a spatio-temporal analytical approach was implemented to determine the specific diffusion process associated with the development and regional distribution of the religious establishments. The results revealed a hierarchical diffusion process as well as the explicit connections between the institutional development of the studied religions and the political events occurred during the associated time period.

Committee: Jay Lee Dr. (Advisor); James Tyner Dr. (Committee Member); Xinyue Ye Dr. (Committee Member) Subjects: Geographic Information Science; Geography; Religion