class/tutorial

Presentation at CNS symposium on “Prediction, adaptation and plasticity of language processing in the adult brain”

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Earlier this week, Dave Kleinschmidt and I gave a presentation as part of a mini-symposium at Cognitive Neuroscience Conference  on “Prediction, adaptation and plasticity of language processing in the adult brain” organized by Gina Kuperberg.  For this symposium we were tasked to address the following questions:

  1. What is prediction and why do we predict?
  2. What is adaptation and why do we adapt?
  3. How do prediction and adaptation relate?

Although we address these questions in the context of language processing, most of our points are pretty general. We aim to provide intuitions about the notions of distribution, prediction, distributional/statistical learning and adaptation. We walked through examples of belief-updating, intentionally keeping our presentation math-free. Perhaps some of the slides are of interest to some of you, so I attached them below. A more in-depth treatment of these questions is also provided in Kleinschmidt & Jaeger (under review, available on request).

Comments welcome. (sorry – some of the slides look strange after importing them and all the animations got lost but I think they are all readable).

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It was great to see these notions discussed and related to ERP, MEG, and fMRI research in the three other presentations of the symposium by Matt Davis, Kara Federmeier and Eddy Wlotko, and Gina Kuperberg. You can read their abstracts following the link to the symposium I included above.

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Updated slides on GLM, GLMM, plyr, etc. available

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Some of you asked for the slides to the Mixed effect regression class I taught at the 2013 LSA Summer Institute in Ann Arbor, MI. The class covered some Generalized Linear Model, Generalized Linear Mixed Models, extensions beyond the linear model, simulation-based approaches to assessing the validity (or power) of your analysis, data summarization and visualization, and reporting of results. The class included slides from Maureen Gillespie, Dave Kleinschmidt, and Judith Degen (see above link). Dave even came by to Ann Arbor and gave his lecture on the awesome power of plyr (and reshape etc.), which I recommend. You might also just browse through them to get an idea of some new libraries (such as Stargazer for quick and nice looking latex tables). There’s also a small example to work through for time series analysis (for beginners).

Almost all slides were created in knitr and latex (very conveniently integrated into RStudio — I know some purists hate it, but comm’on), so that the code on the slides is the code that generated the output on the slides. Feedback welcome.

 

 

HLP lab will be at the LSA 2013 summer institute

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Come join us in Ann Arbor, MI for the 2013 Summer Institute of the Linguistic Society of America. You can follow the institute on facebook.

Victor Ferreira and I will be organizing a workshop on How the brain accommodates variability in linguistic representations (more on that soonish). I will be teaching a class on regression and mixed models and I am sure a bunch of other folks from the lab will be there, too.

 

 

Creating spaghetti plots of eye-tracking data in R

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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:
https://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.

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New R resource for ordinary and multilevel regression modeling

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Here’ s what I received from the Center of Multilevel Modeling at Bristol (I haven’t checked it out yet; registration seems to be free but required):

The Centre for Multilevel Modelling is very pleased to announce the addition of
R practicals to our free on-line multilevel modelling course. These give
detailed instructions of how to carry out a range of analyses in R, starting
from multiple regression and progressing through to multilevel modelling of
continuous and binary data using the lmer and glmer functions.

MLwiN and Stata versions of these practicals are already available.
You will need to log on or register onto the course to view these
practicals.

Read More...
http://www.cmm.bris.ac.uk/lemma/course/view.php?id=13

LSA 2011 class on Computational Psycholinguistics

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Due to popular demand 😉 – you can find the Computational Psycholinguistics class Roger Levy and I are currently teaching at the LSA 2011 institute at Boulder mirrored here.

LSA 2011 at Boulder: Yeah!

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Woohooo. Roger Levy and I will be teaching a class on Computational Psycholinguistics at the 2011 LSA’s Linguistics Institute to be held July 5th- August 5th next year in Boulder, CO. The class description should be available through their website soon, but here are some snippets from our proposal: Read the rest of this entry »