# Data repository: Anti-locality effect without head-final dependencies
This repository contains the data and code associated with two psycholinguistic experiments that were conducted to test the predictions of memory- and expectation-based parsing models on the basis of a syntactic dependency relation that does not involve a head-argument relation. Details on the study can be found in the accompanying paper: Schwab, J., Xiang, M., and Liu, M. (accepted). Anti-locality effect without head-final dependencies. *Journal of Experimental Psychology: Learning, Memory, and Cognition*.
The repository contains separate folders for each experiment. The label (e.g. **Experiment 1**) corresponds to the label the experiment is given in the paper.
For each experiment, the repository contains the following folders:
The list of stimulus material used for the experiment. The stimuli are provided in a .xlsx file that contains two tabs (sheet 1: target items, sheet 2: filler items). Because our experiments used the self-paced reading methodology, sentences were split into regions that were presented one after the other. The applied 'chunking' of sentences into regions is indicated through separate columns per region in the .xlsx file.
The (raw) data file for each experiment. All data was anonymized and stripped of any personal information about participants. The raw data files are directly read in by the code provided in the 'Code' folder.
The R code that was used to analyze the data. We provide the .Rmd file that allows everyone to run our analysis on their own device. All you need is the .Rmd file and the data file (in the 'data' folder of the experiment)
Additionally, we provide the .html output of the .Rmd file allowing everyone to see the output from our analysis without having to run it themselves, including descriptive statistics, figures, and statistical models.
The code directly reads in the raw data files from the 'Data' folder. It includes all pre-processing steps, model specifications, and results.
This folder contains the figures that appear in the paper, along with supplementary figures.
## Prospective power analysis
In this folder, we provide the code that was used for the prospective power analysis. We used a simulation-based approach based on the data of Experiment 1. Details on the procedure can be found in the paper.