Аннотация:and shares many of its key assumptions: parallel competitive evaluation of multiple lexical hypotheses, phonologically abstract prelexical and lexical representations, a feedforward architecture with no online feedback, and a lexical segmentation algorithm based on the viability of chunks of the input as possible words.Shortlist B is radically different from its predecessor in two respects.First, whereas Shortlist was a connectionist model based on interactive-activation principles, Shortlist B is based on Bayesian principles.Second, the input to Shortlist B is no longer a sequence of discrete phonemes; it is a sequence of multiple phoneme probabilities over 3 time slices per segment, derived from the performance of listeners in a large-scale gating study.Simulations are presented showing that the model can account for key findings: data on the segmentation of continuous speech, word frequency effects, the effects of mispronunciations on word recognition, and evidence on lexical involvement in phonemic decision making.The success of Shortlist B suggests that listeners make optimal Bayesian decisions during spoken-word recognition.
