The HLJT is a behavioural paradigm aiming to assess the ability to manipulate movement imagery. If you are interested in assessing Movement Imagery ability, visit this Task Platform Project for an overview of open-source behavioural tasks.
The HLJT has been extensively used in cognitive and clinical neuroscience. The traditional 'reaction time' paradigm (Cooper and Shepard 1975, Parsons 1987) faces limitations to study information processing because: 1) it is subject to speed-accuracy trade-offs because two inherently related dependent variables are used on a trial-by-trial basis (reaction time and accuracy); 2) only the end-point of the processing time-course is available when analysing reaction times. Conversely, the 'forced response' paradigm (for general applications see Haith et al. 2016, Hardwick et al. 2019) overcomes those limitations by controlling the amount of time the participant has to respond on a trial-by-trial basis, as an independent variable. In this paradigm, the evolution of accuracy throughout a range of times used by the participant to process the stimulus is analysed through speed-accuracy trade-offs directly.
This repository contains a template of an open-source (and user-friendly) HLJT-FR for local use. This repository contains a 'general' version of the code used in Moreno-Verdú et. al 2025 (Publication | Preprint | OSF).
Subsequent updates in native software (PsychoPy) may need adjustments. As developers, we are not responsible for implementing these in every use case.
If you want to contribute to this repository by providing a language translation, or want to run the task in your own language, expansions can be done relatively easily thanks to the implementation of language localisations (please read each Readme to understand how to implement these). You can also see this demo showing how to implement a language localisation in PsychoPy with virtually no code (for local use only).
If you are interested in running the experiment online through Pavlovia, you will need to make adjustments to the code. This includes the modification of the language settings, because language localisations as implemented locally (through Python and Excel sheets) are not directly possible when working online (through JavaScript). However the adjustments required to make the experiment run only should be minimal!