Conducting spoken word recognition research online: Validation and a new timing method

Abstract

Models of spoken word recognition typically make predictions that are then tested in the laboratory against the word recognition scores of human subjects (e.g., Luce & Pisoni Ear and Hearing, 19, 1–36, 1998). Unfortunately, laboratory collection of large sets of word recognition data can be costly and time-consuming. Due to the numerous advantages of online research in speed, cost, and participant diversity, some labs have begun to explore the use of online platforms such as Amazon’s Mechanical Turk (AMT) to source participation and collect data (Buhrmester, Kwang, & Gosling Perspectives on Psychological Science, 6, 3–5, 2011). Many classic findings in cognitive psychology have been successfully replicated online, including the Stroop effect, task-switching costs, and Simon and flanker interference (Crump, McDonnell, & Gureckis PLoS ONE, 8, e57410, 2013). However, tasks requiring auditory stimulus delivery have not typically made use of AMT. In the present study, we evaluated the use of AMT for collecting spoken word identification and auditory lexical decision data. Although online users were faster and less accurate than participants in the lab, the results revealed strong correlations between the online and laboratory measures for both word identification accuracy and lexical decision speed. In addition, the scores obtained in the lab and online were equivalently correlated with factors that have been well established to predict word recognition, including word frequency and phonological neighborhood density. We also present and analyze a method for precise auditory reaction timing that is novel to behavioral research. Taken together, these findings suggest that AMT can be a viable alternative to the traditional laboratory setting as a source of participation for some spoken word recognition research.