Development and validation of an open-source hand laterality judgement task for in-person and online studies.

The Hand Laterality Judgement Task (HLJT) is considered a measure of the ability to manipulate motor images. The 'biomechanical constraints' effect (longer reaction times for hand rotations towards anatomically difficult versus biomechanically easier movements) is considered the behavioural hallmark indicating motor imagery is being used. Previous work has used diverse HLJT paradigms, and there is no standardized procedure for the task. We developed an open-source, freely available version of the HLJT in PsychoPy2, which needs no programming skills and is highly customisable. Some studies suggest responding to the HLJT with the hands may interfere with performance, which would limit practical application of the task. We examined this potential issue using in-person and online versions. For the in-person version, 40 right-footed/handed individuals performed the HLJT with their feet or bimanually (N = 20 each). For the online version, 60 right-handed individuals performed the task bimanually or unimanually (N = 20 each). Bayesian mixed-effect analyses quantified the evidence for and against equivalence within and between the in-person and online versions. Both versions replicated previously described behavioural phenomena, including effects of angle, hand view, and the 'biomechanical constraints' effect. While responding with different effectors modified overall reaction times, it did not interact with other factors analysed, and did not affect accuracy or the 'biomechanical constraints' effect. There was also evidence for equivalence between in-person and online bimanual groups for all measures. We conclude that this open-source, standardized HLJT protocol (available at https://osf.io/8h7ec/) can reliably detect previously identified effects and works equally well in-person or online.