A cross-linguistic view on the obligatory insertion of additive particles — Maximize Presupposition vs. Obligatory Implicatures

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Abstract

Presupposition triggers, such as the additive particle too, the iterative particle again, and the definite determiner the, are obligatory if their presuppositions are satisfied in the context. This observation is accounted for in the literature by two theories: one based on Maximize Presupposition (e.g., Heim 1991; Percus 2006; Chemla 2008), the other based on Obligatory Implicatures (Bade 2016). In this paper, we report on two experiments in two typologically unrelated languages, Ga (Kwa) and German, which were designed to test the predictions of these two approaches for the insertion of additive particles. The results show that in both languages the insertion of additives is regulated by Obligatory Implicatures, posing challenges for Maximize Presupposition. Following Bade (2016), we assume a division of labor between the two theories in explaining obligatory presupposition effects.

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Fitting Linear Mixed-Effects Models Using lme4

Douglas M Bates, Martin Mächler, Ben Bolker … (2015)

Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660

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Random effects structure for confirmatory hypothesis testing: Keep it maximal.

Dale Barr, Roger Levy, Christoph Scheepers … (2013)

Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades. Through theoretical arguments and Monte Carlo simulation, we show that LMEMs generalize best when they include the maximal random effects structure justified by the design. The generalization performance of LMEMs including data-driven random effects structures strongly depends upon modeling criteria and sample size, yielding reasonable results on moderately-sized samples when conservative criteria are used, but with little or no power advantage over maximal models. Finally, random-intercepts-only LMEMs used on within-subjects and/or within-items data from populations where subjects and/or items vary in their sensitivity to experimental manipulations always generalize worse than separate F 1 and F 2 tests, and in many cases, even worse than F 1 alone. Maximal LMEMs should be the 'gold standard' for confirmatory hypothesis testing in psycholinguistics and beyond.