https://github.com/abrg-models/linetask2014
Data and analysis code for the paper 'Target-distractor Synchrony Affects Performance in a Novel Motor Task for Studying Action Selection'
https://github.com/abrg-models/linetask2014
Last synced: 5 months ago
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Data and analysis code for the paper 'Target-distractor Synchrony Affects Performance in a Novel Motor Task for Studying Action Selection'
- Host: GitHub
- URL: https://github.com/abrg-models/linetask2014
- Owner: ABRG-Models
- Created: 2016-10-21T15:23:45.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2017-05-03T14:12:14.000Z (about 9 years ago)
- Last Synced: 2025-05-17T12:37:21.407Z (about 1 year ago)
- Language: Mathematica
- Size: 73.5 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# The Line Task
A repo for the 2014 Line task 3rd year project analysis and paper, entitled:
Target-distractor Synchrony Affects Performance in a Novel Motor Task for
Studying Action Selection
## Data
The data for the experiment are found in analysis/AllData/
There are sub-directories there for each experimenter, and within each
of these, sub-directories for each participant. In each particpant's
directory there is a directory called 'line' which contains three
files, one file for each condition of the task (no distractor,
synchronous distractor and asynchronous distractor).
## Latency extraction
To repeat the extraction of latencies and errors - that is to create
the file analysis/AllData/fnames.mat do the following:
Install Octave (ideally version 3.8.1 or 3.8.2).
change directory into analysis
call the octave script lt_analyse_all
This will take a few minutes to process the data, a lot of text will
fly past the screen!
For a more interesting look at the latency extraction, try just
lt_analyse. This will provide a dialog box allowing you to open one of
the data files: analysis/AllData/Experimenter/Participant/line/*.txt
Analysing just one trial will show graphs of the data, along with the
extracted latencies.
## Statistical analysis
The statistical analysis can be carried out by running the ipython
notebook called Anova.ipynb
This makes sub-calls to R scripts and displays pertinent results.