Publications by authors named "D Paape"

Eukaryotic chromosomes segregate faithfully prior to nuclear division to ensure genome stability. If segregation becomes defective, the chromosome copy number of the cell may alter leading to aneuploidy and/or polyploidy, both common hallmarks of cancers. In eukaryotes, aurora kinases regulate chromosome segregation during mitosis and meiosis, but their functions in the divergent, single-celled eukaryotic pathogen Trypanosoma brucei are less understood.

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Cue-based retrieval theories of sentence processing assume that syntactic dependencies are resolved through a content-addressable search process. An important recent claim is that in certain dependency types, the retrieval cues are weighted such that one cue dominates. This cue-weighting proposal aims to explain the observed average behavior, but here we show that there is systematic individual-level variation in cue weighting.

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What is the processing cost of being garden-pathed by a temporary syntactic ambiguity? We argue that comparing average reading times in garden-path versus non-garden-path sentences is not enough to answer this question. Trial-level contaminants such as inattention, the fact that garden pathing may occur non-deterministically in the ambiguous condition, and "triage" (rejecting the sentence without reanalysis; Fodor & Inoue, 2000) lead to systematic underestimates of the true cost of garden pathing. Furthermore, the "pure" garden-path effect due to encountering an unexpected word needs to be separated from the additional cost of syntactic reanalysis.

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Local coherence effects arise when the human sentence processor is temporarily misled by a locally grammatical but globally ungrammatical analysis (). It has been suggested that such effects occur either because sentence processing occurs in a bottom-up, self-organized manner rather than under constant grammatical supervision, or because local coherence can disrupt processing due to readers maintaining uncertainty about previous input. We report the results of an eye-tracking study in which subjects read German grammatical and ungrammatical sentences that either contained a locally coherent substring or not and gave binary grammaticality judgments.

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We present computational modeling results based on a self-paced reading study investigating number attraction effects in Eastern Armenian. We implement three novel computational models of agreement attraction in a Bayesian framework and compare their predictive fit to the data using k-fold cross-validation. We find that our data are better accounted for by an encoding-based model of agreement attraction, compared to a retrieval-based model.

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