The goal of the github repository is to help you host your motor learning experiment online. For a detailed step-by-step breakdown, please visit the OnPoint Manual for Online Experiment Hosting. The experiment is in essence a website coded in Javascript/HTML/CSS and hosted on the Firebase server. Participants can be recruited using Amazon Mechanical Turk Requester, Prolific, or any other crowdsourcing platform.
Try out one of our experiments here.
- Python3
- NPM: requirement to download Firebase
- Firebase: functions needed to host your online experiment on Google's Firebase server.
- Amazon Mechanical Turk Requester & Prolific: Crowdsourcing websites used to recruit participants.
- Javascript code to make your experiment dynamic (e.g., appearance of a target):
public/index.js - HTML files to create the content (e.g., experiment instructions):
public/index.html - CSS files to style your content (e.g., color):
public/static/. - JSON target files (e.g., experiment design, with one row corresponding to one trial):
public/tgt_files/. - Downloading data from the Firebase server to your local computer:
python_scripts/db_csv.py. - Generate JSON target files:
public/tgt_files/generate_test_rot.py. - Convert CSV target files into JSON files:
python_scripts/csv_json.py. - Example JSON target file:
public/tgt_files/demo_file
If you are stuck, please make a comment on the Manual or use the github's issue tab!
J.S.T was funded by a 2018 Florence P. Kendall Scholarship from the Foundation for Physical Therapy Research. This work was supported by grant NS092079 from the National Institutes of Health.
Tsay, J. S., Ivry, R. B., Lee, A., & Avraham, G. (2021). Moving outside the lab: The viability of conducting sensorimotor learning studies online. Neurons, Behavior, Data Analysis, and Theory. https://doi.org/10.51628/001c.26985
Tsay, J. S., Kim, H. E., Saxena, A., Parvin, D. E., Verstynen, T., & Ivry, R. B. (2022). Dissociable use-dependent processes for volitional goal-directed reaching. Proceedings. Biological Sciences, 289(1973), 20220415. [supplementary experiment]
Tsay, J. S., Haith, A. M., Ivry, R. B., & Kim, H. E. (2022). Interactions between sensory prediction error and task error during implicit motor learning. PLoS Computational Biology, 18(3), e1010005.
Avraham, G., Ryan Morehead, J., Kim, H. E., & Ivry, R. B. (2021). Reexposure to a sensorimotor perturbation produces opposite effects on explicit and implicit learning processes. PLoS Biology, 19(3), e3001147. (See supplemental figure, S2)
Shyr, M. C., & Joshi, S. S. (2021). Validation of the Bayesian sensory uncertainty model of motor adaptation with a remote experimental paradigm. 2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS),