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Undergraduates participated in 4 speeded naming experiments investigating 2 criteria to initiate articulation-initial phoneme (IP) or whole word (WW). These criteria make different response latency and IP duration predictions for words with regular versus irregular vowel pronunciations (e.g., "pump" vs. "pint"). The IP criterion predicts no latency differences but longer IP durations for irregulars, whereas the WW criterion predicts no IP duration differences but longer latencies for irregulars. The latencies and IP durations of words beginning with plosives are measured (a) indirectly by exploiting the conflation of latency and IP duration in the standard naming task and (b) directly by determining when closure begins and ends in the postvocalic naming task (participants say "uuhhh" until responding). Results support both criteria: Response latencies and IP durations are longer for irregular words compared with regular words.

Ambiguous words are processed more quickly than unambiguous words in a lexical decision task despite the fact that each sense of an ambiguous word is less frequent than the single sense of unambiguous words of equal frequency or familiarity. In this computer simulation study, we examined the effects of different assumptions of a fully recurrent connectionist model in accounting for this processing advantage for ambiguous words. We argue that the ambiguity advantage effect can be accounted for by distributed models if (a) the least mean square (LMS) error-correction algorithm rather than the Hebbian algorithm is used in training the network and (b) activation of the units representing the spelling rather than the meaning is used to index word recognition times.An important advantage of computational models is that the underlying assumptions of the model must be explicitly formulated. This explicit formulation allows comparison of assumptions that are highly similar. In some cases, virtually identical assumptions can give rise to qualitative differences rather than merely quantitative differences. In this article, we consider just such a situation. Two different connectionist learning algorithms lead to opposite predictions about the time required to recognize ambiguous and unambiguous words when activation of the spelling units is used as the index of word recognition. In particular, ambiguous words are incorrectly predicted to have a processing disadvantage compared with unambiguous words when the Hebbian learning algorithm is used, but correctly predicted to have a processing advantage when the least mean square (LMS) error-correction algorithm is used. Ambiguity Advantage EffectAmbiguous words pose a special challenge in modeling word recognition. Not only must a way to represent the multiple meanings of a word be found, but the manner in which these meanings affect processing must also be considered. Nowhere is this problem more clearly manifested than in accounting for relative processing times of ambiguous words and unambiguous words. For one particular task-lexical decision-ambiguous words are processed more quickly than unambiguous words, despite the fact that the frequency of the different senses of an ambiguous word is less than the frequency of the single sense of an unam-

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This study reports 4 experiments that investigated the locus of temporal effects of printed word frequency in speeded-naming tasks. Response latencies and onset durations are shorter for high-frequency words compared with low-frequency words, but there is no effect of frequency on rime durations. These results can only be accounted for if (a) phonemes are activated in parallel and not sequentially from left to right and (b) the criterion to initiate pronunciation is based on the initial phoneme and not the whole word. In addition, the effect of word-initial phoneme characteristics on acoustic latency was investigated. The acoustic latency of words beginning with voiceless sibilants was less than that of words beginning with plosives, a pattern opposite that reported by R. Treiman, J. Mullennix, R. Bijeljac-Babic, and E. E. Richmond-Welty (1995). This difference was attributed to the lower sensitivity of voice keys compared with measures based on digitized responses.