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Durham University

Music

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Publication details for Professor Tuomas Eerola

Armitage, James & Eerola, Tuomas (2020). Reaction Time Data in Music Cognition: Comparison of Pilot Data From Lab, Crowdsourced, and Convenience Web Samples. Frontiers in Psychology 10: 2883.

Author(s) from Durham

Abstract

Reaction time (RT) methods have been a mainstay of research in cognitive psychology for over a century. RT methods have been applied in domains as diverse as visual perception (e.g., Ando et al., 2002), personality traits (e.g., Robinson and Tamir, 2005), and social psychology (e.g., Wang et al., 2017). In music cognition, RT methods have been used as an indirect measure of several phenomena such as harmonic expectation (Bharucha and Stoeckig, 1986), melodic expectation (Aarden, 2003) cross modal priming (Goerlich et al., 2012), absolute pitch (Miyazaki, 1989; Bermudez and Zatorre, 2009), and emotional responses (Bishop et al., 2009).

Traditionally, reaction time data has been collected in a lab. However, recent years have seen the development of software capable of collecting accurate response time data online, for instance PsyToolkit (Stoet, 2010, 2017), PsychoPy (Peirce et al., 2019), Gorilla (Anwyl-Irvine et al., 2019), and Qualtrics' QRTEngine (Barnhoorn et al., 2015) amongst others. In the early days of web-based reaction time studies, there was considerable skepticism about the viability of RT data collected online. Despite the prevalence of software specifically designed to collect reaction time data online, and the increasing incidence of Web-based data collection, there remains a degree of caution around online reaction time studies. However, recent research (Barnhoorn et al., 2015; de Leeuw and Motz, 2016; Hilbig, 2016) suggests that online reaction time data is perhaps more trustworthy than was previously thought, but these studies have not yet involved music as stimuli.

Alongside the developments in software, recruitment of participants in online studies has been made easier by the prevalence of social media and crowdsourcing platforms such as Amazon's MTurk service and Prolific. Not surprisingly, the use of crowdsourced samples by researchers is growing rapidly (Stewart et al., 2017).

However, to the authors' knowledge (with the exception of de Leeuw and Motz, 2016) the comparisons of laboratory and online RT data have focused on descriptive measures of the RT distributions, and relatively little attention has been paid to the agreement between the RT distributions as a whole. Moreover, none of these studies considers phenomena associated with music cognition. Given the widespread use of RT methods in music cognition and the growth of crowdsourcing as a recruitment tool, the authors consider there to be a need to test the viability of online RT collection specifically in the case of music cognition.

The present data report offers the results of a response time task completed in three different contexts—in a standard lab setting (“Lab”), online recruited via “traditional” online techniques (“Web”) and crowdsourced vis Prolific.ac (“CS”). Below, we present summary data for the three data sets before testing the comparability of the three data sets on an item-by-item basis.