Statistics & Methodology
In a recent PLoS one article, Healey, Purver, and Howes (2014) investigate syntactic priming in conversational speech, both within speakers and across speakers. Healey and colleagues follow Reitter et al (2006) in taking a broad-coverage approach to the corpus-based study of priming. Rather than to focus on one or a few specific structures, Healey and colleagues assess lexical and structural similarity within and across speakers. The paper concludes with the interesting claim that there is no evidence for syntactic priming within speaker and that alignment across speakers is actually less than expected by chance once lexical overlap is controlled for. Given more than 30 years of research on syntactic priming, this is a rather interesting claim. As some folks have Twitter-bugged me (much appreciated!), I wanted to summarize some quick thoughts here. Apologies in advance for the somewhat HLP-lab centric view. If you know of additional studies that seem relevant, please join the discussion and post. Of course, Healey and colleagues are more than welcome to respond and correct me, too.
First, the claim by Healey and colleagues that “previous work has not tested for general syntactic repetition effects in ordinary conversation independently of lexical repetition” (Healey et al 2014, abstract) isn’t quite accurate.
(This is another guest post by Klinton Bicknell.)
This is an update to my previous blog post, in which I observed that post-version-1.0 versions of the lme4 package yielded worse model fits than old pre-version-1.0 versions for typical psycholinguistic datasets, and I gave instructions for installing the legacy lme4.0 package. As I mentioned there, however, lme4 is under active development, the short version of this update post is to say that it seems that the latest versions of the post-version-1.0 lme4 now yield models that are just as good, and often better than lme4.0! This seems to be due to the use of a new optimizer, better convergence checking, and probably other things too. Thus, installing lme4.0 now seems only useful in special situations involving old code that expects the internals of the models to look a certain way. Life is once again easier thanks to the furious work of the lme4 development team!
[update: Since lme4 1.1-7 binaries are now on CRAN, this paragraph is obsolete.]
One minor (short-lived) snag is that the current version of lme4 on CRAN (1.1-6) is overzealous in displaying convergence warnings, and displays them inappropriately in many cases where models have in fact converged properly. This will be fixed in 1.1-7 (more info here). To avoid them for now, the easiest thing to do is probably to install the current development version of lme4 1.1-7 from github like so:
Read on if you want to hear more details about my comparisons of the versions.
I recently was pointed to this nice and very accessible tutorial on linear mixed effects regression and how to run them in R by Bodo Winter (at UC Merced). If you don’t have much or any background in this type of model, I recommend you pair it with a good conceptual introduction to these models like Gelman and Hill 2007 and perhaps some slides from our LSA 2013 tutorial.
There are a few thing I’d like to add to Bodo’s suggestions regarding how to report your results:
- be clear how you coded the variables since this does change the interpretation of the coefficients (the betas that are often reported). E.g. say whether you sum- or treatment-coded your factors, whether you centered or standardized continuous predictors etc. As part of this, also be clear about the direction of the coding. For example, state that you “sum-coded gender as female (1) vs. male (-1)”. Alternatively, report your results in a way that clearly states the directionality (e.g., “Gender=male, beta = XXX”).
- please also report whether collinearity was an issue. E.g., report the highest fixed effect correlations.
(This is a guest post by Klinton Bicknell.)
update 2014-06-24: Using lme4.0 probably isn’t necessary anymore. See post here.
The lme4 package‘s major 1.0 release was back in August. I and others have noticed that for typical psycholinguistic datasets, the new >=1.0 versions of lme4 often yield models with substantially poorer fits to the data than the old pre-1.0 versions (sometimes worse by many points of log likelihood), which suggests that the new lme4 isn’t as reliably converging to the actual maximum likelihood (or REML) solution. Since unconverged models yield misleading inferences about model parameters, it’s useful to be able to fit models using the old pre-1.0 lme4.
Happily, the lme4 developers have created a new package (named “lme4.0”), which is a bugfix-only version of the old pre-1.0 lme4. This allows for the installation of both old and new versions of lme4 side-by-side. As of this posting, lme4.0 is not yet on CRAN, but is installable by performing the following steps: Read the rest of this entry »
A few days ago, I posted a summary of some recent work on syntactic alignment with Kodi Weatherholtz and Kathryn Campell-Kibler (both at The Ohio State University), in which we used the WAMI interface to collect speech data for research on language production over Amazon’s Mechanical Turk.
The first step in our OSU-Rochester collaboration on socially-mediated syntactic alignment has been submitted a couple of weeks ago. Kodi Weatherholtz in Linguistics at The Ohio State University took the lead in this project together with Kathryn Campbell-Kibler (same department) and me.
We collected spoken picture descriptions via Amazon’s crowdsourcing platform Mechanical Turk to investigate how social attitude towards an interlocutor and conflict management styles affected syntactic priming. Our paradigm combines Read the rest of this entry »
Thanks to Scott Jackson, Daniel Ezra Johnson, David Morris, Michael Shvartzman, and Nathanial Smith for the recommendations and pointers to the packages mentioned below.
- The maps, mapsextra, and maptools packages provide data and tools to plot world, US, and a variety of regional maps (see also mapproj and mapdata). This, combined with ggplot2 is also what we used in Jaeger et al., (2011, 2012) to plot distributions over world maps. Here’s an example from ggplot2 with maps.