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We used Chinese prenominal relative clauses (RCs) to test the predictions of two competing accounts of sentence comprehension difficulty: the experience-based account of Levy () and the Dependency Locality Theory (DLT; Gibson, ). Given that in Chinese RCs, a classifier and/or a passive marker BEI can be added to the sentence-initial position, we manipulated the presence/absence of classifiers and the presence/absence of BEI, such that BEI sentences were passivized subject-extracted RCs, and no-BEI sentences were standard object-extracted RCs. We conducted two self-paced reading experiments, using the same critical stimuli but somewhat different filler items. Reading time patterns from both experiments showed facilitative effects of BEI within and beyond RC regions, and delayed facilitative effects of classifiers, suggesting that cues that occur before a clear signal of an upcoming RC can help Chinese comprehenders to anticipate RC structures. The data patterns are not predicted by the DLT, but they are consistent with the predictions of experience-based theories.
Linear mixed-effects models have increasingly replaced mixed-model analyses of variance for statistical inference in factorial psycholinguistic experiments. Although LMMs have many advantages over ANOVA, like ANOVAs, setting them up for data analysis also requires some care. One simple option, when numerically possible, is to fit the full variance covariance structure of random effects (the maximal model; Barr, Levy, Scheepers & Tily, 2013), presumably to keep Type I error down to the nominal a in the presence of random effects. Although it is true that fitting a model with only random intercepts may lead to higher Type I error, fitting a maximal model also has a cost: it can lead to a significant loss of power. We demonstrate this with simulations and suggest that for typical psychological and psycholinguistic data, higher power is achieved without inflating Type I error rate if a model selection criterion is used to select a random effect structure that is supported by the data. (C) 2017 The Authors. Published by Elsevier Inc.
In two self-paced reading experiments, we investigated the effect of changes in antecedent complexity on processing times for ellipsis. Pointer- or “sharing”-based approaches to ellipsis processing (Frazier & Clifton 2001, 2005; Martin & McElree 2008) predict no effect of antecedent complexity on reading times at the ellipsis site while other accounts predict increased antecedent complexity to either slow down processing (Murphy 1985) or to speed it up (Hofmeister 2011). Experiment 1 manipulated antecedent complexity and elision, yielding evidence against a speedup at the ellipsis site and in favor of a null effect. In order to investigate possible superficial processing on part of participants, Experiment 2 manipulated the amount of attention required to correctly respond to end-of-sentence comprehension probes, yielding evidence against a complexity-induced slowdown at the ellipsis site. Overall, our results are compatible with pointer-based approaches while casting doubt on the notion that changes antecedent complexity lead to measurable differences in ellipsis processing speed.