This paper describes a model that learns to pronounce English words. Learning occurs in two modules: 1) a rule-based module that constructs pronunciations by phonetic analysis of the letter string, and 2) a whole-word module that leams to associate subsets of letters to the pronunciation, without phonetic analysis. In a simulation on a corpus of over 300 words the model produced pronunciation latencies consistent with the effects of word frequency and orthographic regularity observed in human data. Imphcations of the model for theories of visual word processing and reading instruction are discussed.

For over 25 years, researchers have debated whether physically salient stimuli capture attention in an automatic manner, independent of the observer's goals, or whether the capture of attention depends on the match between a stimulus and the observer's task set. Recent evidence suggests an intermediate position in which salient stimuli automatically produce a priority signal, but the capture of attention can be prevented via an inhibitory mechanism that suppresses the salient stimulus. Here, proponents from multiple sides of the debate describe how their original views have changed in light of recent research, as well as remaining areas of disagreement. These perspectives highlight some emerging areas of consensus and provide new directions for future research on attentional capture.