Doctor of Philosophy
Stephen J. Lupker
The goal of the present thesis was to introduce a Bayesian model of stress assignment in reading. According to this model, readers compute probabilities of stress patterns by assessing prior beliefs about the likelihoods of stress patterns in a language and combining that information with non-lexical evidence for stress patterns provided by the word. The choice of a response is thought of as a random walk-type process which takes the system from a starting point to a response boundary. The calculated Bayesian probabilities determine the drift rate towards each boundary such that the probability of an error and the response latency are related to the posterior probabilities of the stress patterns.
The Bayesian model of stress assignment was implemented for Russian disyllabic words. In Study 1, the distribution of stress patterns in a corpus of Russian disyllabic words (reflecting prior beliefs about the likelihoods of stress patterns) was analyzed. Further, non-lexical sources of evidence for stress in Russian were investigated. In Study 2, the effect of spelling-to-stress consistency of word endings on naming performance was examined. Study 3 was a binary logistic regression analysis of a set of predictors of stress patterns (length, log frequency, grammatical category, word onset complexity, word coda complexity, and spelling-to-stress consistency of six orthographic components) in a corpus of disyllabic words. In Study 4, a generalized linear mixed effects model with the same variables as predictors of stress assignment performance was applied to word naming data. Based on the combination of the results, it was concluded that there are three sources of evidence for stress in Russian: the first syllable, the second syllable, and the ending of the second syllable.
The model was tested in two simulations. In Study 5, the predictions of the model were compared with stress assignment performance of speakers of Russian naming words. In Study 6, the model was tested on its ability to simulate stress assignment performance of readers naming nonwords. The model managed to predict not only the most frequent stress pattern that readers assigned, but also the relative ratio of trochaic versus iambic responses given by the participants.
Jouravlev, Olessia, "A Bayesian Model of Stress Assignment in Reading" (2014). Electronic Thesis and Dissertation Repository. 1913.