Vsevolod Kapatsinski

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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(More)
Speakers of morphologically-rich languages commonly face what has been called the Paradigm Cell Filling Problem: they know some form of a word but it is inappropriate to the current context, leading them to derive a form of that word they have never encountered (e.g., they know the singular form of a noun, and now need to produce the plural). We suggest(More)
This article reports on an experiment with miniature artificial languages that provides support for a synthesis of ideas from 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 [tʃ]-final stem help palatalization (supporting t → tſi(More)
What statistics do learners track? What statistics do learners track? What statistics do learners track? Rule Rule Rule Rules s s s, constraints and , constraints and , constraints and , constraints and s s s schema chema chema chemas s s s in (artificial) grammar learning in (artificial) grammar learning in (artificial) grammar learning in (artificial)(More)
All human languages have restrictions on sound sequences, called phonotactic constraints. Knowledge of phonotactic constraints is typically tested using pseudoword rating tasks, e.g., an English speaker might be asked to rate acceptability or wordlikeness of the phonotactically illegal /bnɪk/ and the phonotactically legal /blɪk/. We introduce a new method(More)
ABSRACT: Phoneme inventories of the world's languages as depicted by the UPSID database (Maddieson and Precoda 1990) are analyzed using multivariate statistical techniques of principal components analysis and k-means and hierarchical clustering. The first two meaningful principal components, representing dimensions that account for the most variance in(More)
Influences on variable behavior vary in importance. As researchers, we sometimes want to be able to quantify the importance of a particular predictor and to compare predictors on importance. Relative importance measures are useful for comparing language varieties, identification of predictors to focus on when faced with data sparseness, or to explore in(More)