No abstract

Native English speakers were instructed to detect instances of /√p/ in spoken sentences by pressing a button as soon as they hear /√p/ regardless of whether it is inside another word. We observe that detection of the particle up is slower when the frequency of the verb+up collocation is low or extremely high than when it is medium. In addition, /√p/ is more difficult to detect in high-frequency words than medium-frequency or lowfrequency words. Thus word frequency has a monotonic effect on detectability of word parts while the effect of phrase frequency is U-shaped. These results support the hypotheses that lexical units compete with their parts during speech perception and that words and ultra-high-frequency phrases are stored in the lexicon.

This article reports on an experiment with miniature artificial languages that provides support for a synthesis of ideas from USAGE-BASED PHONOLOGY (Bybee 1985, 2001, Nesset 2008) and HARMONIC GRAMMAR (Legendre et al. 1990, Smolensky & Legendre 2006). All miniature artificial languages presented to subjects feature velar palatalization ( k → tſ ) before a plural suffix, - i . I show that (i) examples of - i simply attaching to a [tſ]-final stem help palatalization (supporting t → tſi over t → ti and p → tſi over p → pi ), a finding that provides specific support for product-oriented schemas like 'plurals should end in [tſi]'; (ii) learners tend to perseverate on the form they know, leveling stem changes, which provides support for paradigm-uniformity constraints in favor of retaining gestures composing the known form, for example, 'keep labiality'; and (iii) the same plural schema helps untrained singular-plural mappings more than it helps trained ones. This result is accounted for by proposing that schemas and paradigm-uniformity constraints clamor for candidate plural forms that obey them. Given that competition is between candidate outputs, the same schema provides more help to candidates that violate strong paradigm-uniformity constraints and are therefore weak relative to competitor candidates. A computational model of schema extraction is proposed.

In spontaneous speech, speakers sometimes replace a word they have just produced or started producing by another word. The present study reports that in these replacement repairs, low-frequency replaced words are more likely to be interrupted prior to completion than high-frequency words, providing support to the hypothesis that the production of high-frequency words is more automatic than the production of low-frequency words (Bybee, 2002; Logan, 1982). Frequency appears to have an effect on interruptibility even when word duration is statistically controlled. In addition, the frequency of the replaced word is positively correlated with the frequency of the word it is replaced by, supporting the hypothesis that high-frequency words are easier to access in production (Kittredge, Dell, Verkuilen, & Schwartz, 2008): the more frequent the target, the more frequent an inappropriate word needs to be to overcome the target and be uttered, only to be replaced.

A multimodel inference approach to categorical variant choice: construction, priming and frequency effects on the choice between full and contracted forms of am, are and is Abstract: The present paper presents a multimodel inference approach to lin guistic variation, expanding on prior work by Kuperman and Bresnan (2012). We argue that corpus data often present the analyst with high model selection uncer tainty. This uncertainty is inevitable given that language is highly redundant: ev ery feature is predictable from multiple other features. However, uncertainty in volved in model selection is ignored by the standard method of selecting the single best model and inferring the effects of the predictors under the assumption that the best model is true. Multimodel inference avoids committing to a single model. Rather, we make predictions based on the entire set of plausible models, with contributions of models weighted by the models' predictive value. We argue that multimodel inference is superior to model selection for both the ILanguage goal of inferring the mental grammars that generated the corpus, and the ELanguage goal of predicting characteristics of future speech samples from the community represented by the corpus. Applying multimodel inference to the classic problem of English auxiliary contraction, we show that the choice between multimodel inference and model selection matters in practice: the best model may contain predictors that are not significant when the full set of plau sible models is considered, and may omit predictors that are significant consid ering the full set of models. We also contribute to the study of English auxiliary contraction. We document the effects of priming, contextual predictability, and specific syntactic constructions and provide evidence against effects of phono logical context.