https://github.com/mvdoc/pitcher_localizer
PsychoPy implementation of the dynamic face localizer from Pitcher et al., 2011, NeuroImage
https://github.com/mvdoc/pitcher_localizer
Last synced: 12 months ago
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PsychoPy implementation of the dynamic face localizer from Pitcher et al., 2011, NeuroImage
- Host: GitHub
- URL: https://github.com/mvdoc/pitcher_localizer
- Owner: mvdoc
- Created: 2017-11-03T22:21:00.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2019-11-22T18:31:32.000Z (over 6 years ago)
- Last Synced: 2025-04-09T21:49:34.643Z (about 1 year ago)
- Language: Python
- Size: 27.3 KB
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Dynamic Face/Body/Scene localizer
This repository contains a PsychoPy implementation of a dynamic localizer,
facet inspired from the paradigm by Pitcher, D., Dilks, D. D., Saxe, R. R.,
Triantafyllou, C., & Kanwisher, N. (2011). Differential selectivity for dynamic
versus static information in face-selective cortical regions.
*Neuroimage*, 56(4), 2356-2363.
## Details
Each run contains five categories
1. faces
2. objects
3. bodies
4. scenes
5. scrambled objects
which are presented with the following paradigm:
- fixation (18s)
- randomized blocks of categories with no inter-trial interval (18s * 5)
- fixation (18s)
- reversed order of categories as in previous block (18s * 5)
- fixation (18s)
Each block contains six video clips of about 3s each.
Each run lasts 234s.
Participants perform a 1-back repetition detection on the clip. In each run
there is one such repetition once for every category. By default four runs
are generated.
### Stimuli
The stimuli are not shared with this repository because I don't have the
license to release them. Please contact the authors of the original paper,
or use your own clips. Simply store them in the `stimuli` directory, with a
subdirectory for each category, e.g.
```
stimuli
├── bodies
├── faces
├── objects
├── scenes
└── scrambled_objects
```
## How To
Running the script without arguments will start a dialog where you can input
the participant's information. Alternatively the script can be run from the
command line with the following arguments
```bash
$ python run_localizer.py -h
usage: run_localizer.py [-h] [--subject SUBJECT] [--runnr {1,2,3,4}]
[--no-scanner] [--no-fullscreen]
Presentation script for a face/object/scene/bodies localizer, inspired by the
paradigm in Pitcher, D., Dilks, D. D., Saxe, R. R., Triantafyllou, C., &
Kanwisher, N. (2011). Differential selectivity for dynamic versus static
information in face-selective cortical regions. Neuroimage, 56(4), 2356-2363.
optional arguments:
-h, --help show this help message and exit
--subject SUBJECT, -s SUBJECT
subject id
--runnr {1,2,3,4}, -r {1,2,3,4}
run nr
--no-scanner do not listen to the serial port
--no-fullscreen do not run in fullscreen
```
### Extracting logs for BIDS `events.tsv` files
The script will create a logfile for each subject and run under
`res/sub-id/`. The log contains already all the information to create a BIDS
compliant `events.tsv` files. You just need to grep `BIDS`, and that's it.
For example:
```bash
$ grep BIDS res/test/sub-test_task-localizer_run-1_20171102T142349.txt | awk '{for (i=3; i