Lexical stress assignment as a problem of probabilistic inference.

Abstract

A new conceptualization of the process of stress assignment, couched in the principles of (Bayesian) probabilistic inference, is introduced in this paper. According to this approach, in deciding where to place stress in a polysyllabic word, a reader estimates the posterior probabilities of alternative stress patterns. This estimation is accomplished by adjusting a prior belief about the likelihoods of alternative stress patterns (derived from experience with the distribution of stress patterns in the language) by using lexical and non-lexical sources of evidence for stress derived from the orthographic input. The proposed theoretical framework was used to compute probabilities of stress patterns for Russian disyllabic words and nonwords which were then compared with the performance of readers. The results showed that the estimated probabilities of stress patterns were reflective of actual stress assignment performance and of naming latencies, suggesting that the mechanisms that are involved in the process of stress assignment might indeed be inferentially-based.