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A new model of immediate serial recall is presented: the primacy model. The primacy model stores order information by means of the assumption that the strength of activation of successive list items decreases across list position to form a primacy gradient. Ordered recall is supported by a repeated cycle of operations involving a noisy choice of the most(More)
This study demonstrates that listeners use lexical knowledge in perceptual learning of speech sounds. Dutch listeners first made lexical decisions on Dutch words and nonwords. The final fricative of 20 critical words had been replaced by an ambiguous sound, between [f] and [s]. One group of listeners heard ambiguous [f]-final words (e.g., [WItlo?], from(More)
A Bayesian model of continuous speech recognition is presented. It is based on Shortlist (D. Norris, 1994; D. Norris, J. M. McQueen, A. Cutler, & S. Butterfield, 1997) and shares many of its key assumptions: parallel competitive evaluation of multiple lexical hypotheses, phonologically abstract prelexical and lexical representations, a feedforward(More)
The authors argue that perception is Bayesian inference based on accumulation of noisy evidence and that, in masked priming, the perceptual system is tricked into treating the prime and the target as a single object. Of the 2 algorithms considered for formalizing how the evidence sampled from a prime and target is combined, only 1 was shown to be consistent(More)
Speech is continuous, and isolating meaningful chunks for lexical access is a nontriv-ial problem. In this paper we use neural network models and more conventional statistics to study the use of sequential phonological probabilities in the segmentation of an idealized phonological transcription of the London–Lund Corpus; these speech data are representative(More)
This article presents a theory of visual word recognition that assumes that, in the tasks of word identification, lexical decision, and semantic categorization, human readers behave as optimal Bayesian decision makers. This leads to the development of a computational model of word recognition, the Bayesian reader. The Bayesian reader successfully simulates(More)
Many models of serial recall assume a chaining mechanism whereby each item associatively evokes the next in sequence. Chaining predicts that, when sequences comprise alternating confusable and non-confusable items, confusable items should increase the probability of errors in recall of following non-confusable items. Two experiments using visual(More)
Speech segmentation procedures may differ in speakers of different languages. Earlier work based on French speakers listening to French words suggested that the syllable functions as a segmentation unit in speech processing. However, while French has relatively regular and clearly bounded syllables, other languages, such as English, do not. No trace of(More)