Creating spaghetti plots of eye-tracking data in R

I’ve been working on consolidating all the different R functions I’ve written over the years for plotting my eye-tracking data and creating just one amazing super-function (based on the ggplot2 package) that can do it all. Here’s a first attempt that anybody with the right kind of dataset should be able to use to create plots like the ones below (generated from fake data. The R code that generates the data is included at the end of the post). If you find this code helpful, please consider acknowledging it via the following URL in your paper/presentation to spread the word:

http://hlplab.wordpress.com/2012/02/27/creating-spaghetti-plots-of-eye-tracking-data-in-r/

Left: Empirical means with error bars indicating standard error for four experimental conditions. Contrast presence is coded in color, adjective type in line type. The first vertical line indicates adjective onset, the second ones indicate mean noun onset in each contrast condition. Right: Smoothed model estimates of proportions in each condition, with ribbons indicating 95% confidence intervals. Data from different subjects is plotted in different panels.

Using pyjamas to program external Mechanical Turk experiments

I recently set up my first external Mechanical Turk study. My greatest friend and foe in this process was pyjamas, a Python-to-Javascript compiler and Widget Set API. THE great advantage of using pyjamas: you can program your entire experiment in python, and pyjamas will create the browser-dependent javascript code. If you already know javascript, writing your experiment in python without having to worry about browser-dependent issues will save you time. And if you don’t, you don’t have to go through the frustrating process of learning javascript. On the downside, the documentation for pyjamas is currently not very good, so figuring out how to get things to work can take a while.

That’s why I’m providing the (commented) code that I generated to create my MechTurk experiment. A short demo version of the experiment can be found here.

If you find this code helpful, please consider acknowledging it via the following URL in your paper/presentation to spread the word:
http://hlplab.wordpress.com/2011/12/25/using-pyjamas-to-program-external-mechanical-turk-experiments/

A screenshot of the experiment. Participants were asked to rate on a 7-point scale how natural the statement they heard was as a description of the scene.

Extracting speaker meta-data from the BNC

This post is looong overdue… – in January of 2009 Ben Van Durme and I reparsed the BNC with the Charniak-Johnson parser and encoded it for TGrep2, with the cool extra feature that each sentence node is now linked to a set of meta-data about the text itself and about the speaker/author. We wrote a little python script that takes TGrep2 match IDs and gives you a file with the meta-data for all sentences that match the TGrep2 IDs. For the written part of the BNC, the following information is available: medium, genre, domain, age and sex of intended audience, size of the text in words, date of publication, and age and gender of the author. For the spoken part: age, sex, social class, dialect/accent, first language, level of education of the speaker, as well as whether or not the speaker was recruited.
This makes it a neat resource for psycho- and sociolinguistic research. If you’re interested in using it (and have a license for the BNC), let us know.

R code for LaTeX tables of lmer model effects

Here’s some R code that outputs text on the console that you can copy-paste into a .tex file and creates nice LaTeX tables of fixed effects of lmer models (only works for family=”binomial”). Effects <.05 will appear in bold. The following code produces the table pasted below. It assumes the model mod.all. prednames creates a mapping from predictor names in the model to predictor names you want to appear in the table. Note that for the TeX to work you need to include \usepackage{booktabs} in the preamble.

hlplab adirondacks retreat

Here are some pictures from the HLPlab’s Adirondacks retreat 2009.
We spent a fun & productive paper-writing week at the beautiful Upper Saranac lake…

The view from work.

Peter and Paper.