Open Call project, 2022 Visuomotor Serial Targeting Task (VSTT)
Total developer time: 2 months
Contact person: Dr. Philipp Wanner, Institute of Sports and Sports Science, Heidelberg University
Outline
Most experiments in the field of motor skill acquisition write custom scripts to generate the experiments and analyze the resulting data.
This makes it hard to share experiment setups and data, and makes it more difficult to reproduce results.
The goal of this project is to create an open source software tool with a graphical user interface for designing, running and analyzing these experiments.
Results
We developed Visuomotor Serial Targeting Task (VSTT) an open source GUI tool for designing, running and analyzing motor skill acquisition experiments.
It allows researchers to quickly and easily construct a wide range of visuomotor task experiments, run these experiments and analyse the results.
It provides a set of built-in statistics that are automatically calculated and displayed, as well as providing the raw data as a pandas dataframe or excel file for use in further analysis.
It can also import and export these experiments from/to json and excel format, making it straightforward to publish, share and reproduce experiments and analyses.
The software is easy to install and use, and supports Windows, Mac and Linux operating systems.
Testimonial by Philipp Wanner
The ability to acquire motor skills is a prerequisite for lifelong independence (e.g. learning to walk). Therefore, many researchers from multiple disciplines aim at understanding the underlying processes of motor learning, identifying learning deficits (e.g. due to neurological conditions) and evaluating strategies to improve motor learning. However, Ranganathan and colleagues (2021) showed that 97% of experiments published between 2017 and 2018 used unique task paradigms. This heterogenous use of motor learning tasks adversely affects the acquisition of knowledge and decelerates the transfer from research into practice. Thus, the urgent need for developing a common model task paradigm which could be widely used across labs has been postulated.
Dr. Liam Keegan from the Scientific Software Center provided excellent work in developing a highly flexible software to run various motor learning paradigms. The open source software with a graphical user interface and a well-structured documentation enables researchers from all over the world to conduct motor learning experiments.
We would like to thank the Scientific Software Center for this great support.
