Sensorimotor synchronization (SMS), the rhythmic coordination of perception and action, is a fundamental human skill that supports many behaviors, from daily repetitive routines to the most complex behavioural coordination, including music and dance (Repp 2005; Repp & Su, 2013). Research on SMS has been mostly conducted in the laboratory using finger tapping paradigms, where participants typically tap with their index finger to a rhythmic sequence of auditory stimuli. However, these experiments require equipment with high temporal fidelity to capture the asynchronies between the time of the tap and the corresponding cue event. Thus, SMS is particularly challenging to study with online research, where variability in participants’ hardware and software can introduce uncontrolled latency and jitter into recordings. Here we present REPP (Rhythm ExPeriment Platform), a novel technology for measuring SMS in online experiments that can work efficiently using the built-in microphone and speakers of standard laptop computers. The audio stimulus (e.g., a metronome or a music excerpt) is played through the speakers and the resulting signal is recorded along with participants’ responses in a single channel. The resulting recording is then analyzed using signal processing techniques to extract and align timing cues with high temporal accuracy. This analysis is fully automated and customizable, enabling researchers to monitor online experiments in real time and to implement a wide variety of SMS paradigms. In this paper, we validate REPP through a series of calibration and behavioural experiments. We demonstrate that our technology achieves high temporal accuracy (latency and jitter within 2 ms on average), high test-retest reliability both in the laboratory (r = .87) and online (r = .80), and high concurrent validity (r = .94). We also suggest methods to ensure high data quality in online SMS experiments using REPP while minimizing recruitment costs. REPP can therefore open new avenues for research on SMS that would be nearly impossible in the laboratory, reducing experimental costs while massively increasing the reach, scalability and speed of data collection.
bioRxiv Subject Collection: Neuroscience