http://talklab.psy.gla.ac.uk/KeepItMaximalR2.pdf

Random effects structure for confirmatory hypothesis testing: Keep it maximal

Event-related brain potentials (ERPs) can reveal online processing differences between native speakers and second language (L2) learners during language comprehension. Using the P600 as a measure of native-likeness, we investigated processing of grammatical gender agreement in highly proficient immersed Romance L2 learners of Dutch. We demonstrate that these late learners consistently fail to show native-like sensitivity to gender violations. This appears to be due to a combination of differences from the gender marking in their L1 and the relatively opaque Dutch gender system. We find that L2 use predicts the effect magnitude of non-finite verb violations, a relatively regular and transparent construction, but not that of gender agreement violations. There were no effects of age of acquisition, length of residence, proficiency or offline gender knowledge. Additionally, a within-subject comparison of stimulus modalities (written vs. auditory) shows that immersed learners may show some of the effects only in the auditory modality; in non-finite verb violations, an early native-like N400 was only present for auditory stimuli. However, modality failed to influence the response to gender. Taken together, the results confirm the persistent problems of Romance learners of Dutch with online gender processing and show that they cannot be overcome by reducing task demands related to the modality of stimulus presentation.

/pdf/an-erp-study-on-l2-syntax-processing-when-do-learners-fail-1gpf64z9zx.pdf

An ERP study on L2 syntax processing: When do learners fail?

Balancing Type I Error and Power in Linear Mixed Models

Distributional semantic models derive computational representations of word meaning from the patterns of co-occurrence of words in text. Such models have been a success story of computational linguistics, being able to provide reliable estimates of semantic relatedness for the many semantic tasks requiring them. However, distributional models extract meaning information exclusively from text, which is an extremely impoverished basis compared to the rich perceptual sources that ground human semantic knowledge. We address the lack of perceptual grounding of distributional models by exploiting computer vision techniques that automatically identify discrete "visual words" in images, so that the distributional representation of a word can be extended to also encompass its co-occurrence with the visual words of images it is associated with. We propose a flexible architecture to integrate text- and image-based distributional information, and we show in a set of empirical tests that our integrated model is superior to the purely text-based approach, and it provides somewhat complementary semantic information with respect to the latter.

/pdf/multimodal-distributional-semantics-3789nky6kq.pdf

Multimodal distributional semantics

The analysis of experimental data with mixed-effects models requires decisions about the specification of the appropriate random-effects structure. Recently, Barr, Levy, Scheepers, and Tily, 2013 recommended fitting `maximal' models with all possible random effect components included. Estimation of maximal models, however, may not converge. We show that failure to converge typically is not due to a suboptimal estimation algorithm, but is a consequence of attempting to fit a model that is too complex to be properly supported by the data, irrespective of whether estimation is based on maximum likelihood or on Bayesian hierarchical modeling with uninformative or weakly informative priors. Importantly, even under convergence, overparameterization may lead to uninterpretable models. We provide diagnostic tools for detecting overparameterization and guiding model simplification.

/pdf/parsimonious-mixed-models-1m7ck87kby.pdf

Parsimonious Mixed Models

The complete title of this book runs ‘Analyzing Linguistic Data: A Practical Introduction to Statistics using R’ and as such it very well reflects the purpose and spirit of the book. The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models. Each of the methods is introduced in the context of concrete linguistic problems and demonstrated on exciting datasets from current research in the language sciences. In line with its practical orientation, the book focuses primarily on using the methods and interpreting the results. This implies that the mathematical treatment of the techniques is held at a minimum if not absent from the book. In return, the reader is provided with very detailed explanations on how to conduct the analyses using R [1]. The first chapter sets the tone being a 20-page introduction to R. For this and all subsequent chapters, the R code is intertwined with the chapter text and the datasets and functions used are conveniently packaged in the languageR package that is available on the Comprehensive R Archive Network (CRAN). With this approach, the author has done an excellent job in enabling researchers and students alike to reproduce the analyses and learn by doing. Another quality as a textbook is the fact that every chapter ends with Workbook sections where the user is invited to exercise his or her analysis skills on supplemental datasets. Full solutions including code, results and comments are given in Appendix A (30 pages). Instructors are therefore very well served by this text, although they might want to balance the book with some more mathematical treatment depending on the target audience. After the introductory chapter on R, the book opens on graphical data exploration. Chapter 3 treats probability distributions and common sampling distributions. Under basic statistical methods (Chapter 4), distribution tests and tests on means and variances are covered. Chapter 5 deals with clustering and classification. Strangely enough, the clustering section has material on PCA, factor analysis, correspondence analysis and includes only one subsection on clustering, devoted notably to hierarchical partitioning methods. The classification part deals with decision trees, discriminant analysis and support vector machines. The regression chapter (Chapter 6) treats linear models, generalised linear models, piecewise linear models and a substantial section on models for lexical richness. The final chapter on mixed models is particularly interesting as it is one of the few text book accounts that introduce the reader to using the (innovative) lme4 package of Douglas Bates which implements linear mixed-effects models. Moreover, the case studies included in this

Analyzing linguistic data: a practical introduction to statistics using R

Abstract Summary Xpaths is a collection of algorithms that allow for the prediction of compound-induced molecular mechanisms of action by integrating phenotypic endpoints of different species; and proposes follow-up tests for model organisms to validate these pathway predictions. The Xpaths algorithms are applied to predict developmental and reproductive toxicity (DART) and implemented into an in silico platform, called DARTpaths. Availability and implementation All code is available on GitHub https://github.com/Xpaths/dartpaths-app under Apache license 2.0, detailed overview with demo is available at https://www.vivaltes.com/dartpaths/. Supplementary information Supplementary data are available at Bioinformatics online.

Elisabeth Dévière

Papers

Analyzing linguistic data: a practical introduction to statistics using R

DARTpaths, an in silico platform to investigate molecular mechanisms of compounds