# Project outline for 2011_9 #
**Keywords:** EEG; ERP; scalar implicature
**Overview:** Exp. 2 from Hartshorne, Snedeker, et al., 2015. An ERP investigation of scalar implicature. See paper for details.
This repository contains some of the analysis scripts. The full dataset can be found [here][1].
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**Publications:**
1. Hartshorne, Joshua K., Jesse Snedeker, Stephanie Yun-Mun Liem Azar, and Albert Kim. (2015). The neural computation of scalar implicature. Language, Cognition, & Neuroscience, 30(5), 620-634. [link](http://www.tandfonline.com/doi/abs/10.1080/23273798.2014.981195)
Experiment 2
2. Hartshorne, Joshua K., Jesse Snedeker & Albert Kim. (2012). The neural computation of scalar implicature. Architectures and Mechanisms in Language Processing (AMLaP), Riva del Garda, Italy.
**Team:**
1. Joshua Hartshorne
2. Al Kim
2. Jesse Snedeker
3. A whole bunch of KimLab RAs
**Data Collection:**
Boulder, 2011
**Notes:**
Primary analysis scripts are in clusteranalysis_t.R, which does the cluster analyses described in the paper. (clusternalaysis.R is an older verison based on a different statistic -- ANOVAs, I think.). ClusterPlotting.R should help create the visuals of cluster analyses shown in the paper.
The preprocessing was conducted using (I think) Neuroscan software. The relevant processing files appear to be in the folder ProcessingScripts.
Note that the permutation analyses used permute (separately) both the within-subjects and between-subjects factors. This is somewhat controversial, in that permutation theory does not justify this analysis, but simulations suggest it is unproblematic (we are working on a paper covering this issue). In this particular case, permuting only the within-subjects factor would probably have been preferred. Based on the size of the effect, however, this is unlikely to matter.
[1]: https://bitbucket.org/l3atbc/2011_9
