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 nonlexical 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.

Cues to stress assignment in reading aloud.

“…Recently, Jouravlev and Lupker (2015a) have considered an alternative approach to stress assignment in reading aloud. Although this is not a processing model like those described earlier, we consider it in the present paper because it provides an alternative way of thinking about the summation of different sublexical cues in reading aloud behavior.…”

“…Although this is not a processing model like those described earlier, we consider it in the present paper because it provides an alternative way of thinking about the summation of different sublexical cues in reading aloud behavior. Jouravlev and Lupker (2015a) put forward the idea that stress assignment can be viewed as a problem of Bayesian probabilistic inference in which readers evaluate the likelihood of potential stress patterns when making a stress assignment decision. This decision is accomplished by considering prior beliefs about the likelihood of stress patterns in a language and adjusting them in the light of evidence for a specific stress pattern derived from the letter string that is being read aloud.…”

“…Using this approach, Jouravlev and Lupker (2015a) demonstrated that the posterior probabilities of stress patterns computed for Russian disyllabic words and nonwords correlated highly with the stress patterns that Russian readers assigned to these items. Here, we evaluated whether the process of stress assignment in English can be also explained within this Bayesian framework.…”

“…The data from these experiments are then used to assess the performance of three computational accounts of reading disyllables aloud—the rule-based disyllabic algorithm of Rastle and Coltheart (2000) , the connectionist dual process (CDP++) model ( Perry, Ziegler, & Zorzi, 2010 ), and the connectionist print-to-stress network of Ševa, Monaghan, and Arciuli (2009) . We also consider these results in the context of a Bayesian approach to understanding stress assignment ( Jouravlev & Lupker, 2015a ). In a fourth experiment, we test the influence of higher-level contextual cues on stress assignment, and critically, assess how these sentence-level cues impact on the word-level cues revealed in the first three experiments.…”

Stress assignment in reading aloud in Spanish

“…According to connectionist models (Arciuli et al, 2010;Perry et al, 2010), it is possible to include stress marks nodes linked to stress nodes representing the stress patterns. Finally, the stress mark could be considered as evidence to estimate (following the Bayes theory) the probability of a certain stress pattern (Jouravlev & Lupker, 2015). Whatever the theoretical approach, the present study suggests some type of stress mark sublexical processing should be considered.…”