This is the code repository for Experiment One of the below-titled study. The experiment is designed to be run using psiTurk version 2.1.1 on the Amazon Mechanical Turk platform.
After calibration and instructions, the experiment consists of two separate tasks (each participant completes only one):
- Auditory Lexical Decision Task on consonant–vowel–consonant (CVC) words and phonotactilly legal nonwords (400 each)
- Identification Task on 400 CVC words
Of general interest are the following features:
- Cursor auto-hiding during experiment proper,
- Audio preloading including a progress bar pop-up,
- Fullscreen requirement to mitigate distraction (participants are asked to enter fullscreen and the experiment is paused (all input blocked) if they exit prematurely),
- Basic asynchronous flow control for transitioning between stages of the experiment,
- and Audio reCaptcha integration (you will have to input your reCaptcha keys in custom.py and task.js for this feature to function).
You are welcome to use this code for personal or academic uses. If you use all or portions of this project in an academic paper, please cite as follows:
Slote, J., & Strand, J. (2015). Conducting spoken word recognition research online: Validation and a new timing method. Behavior Research Methods. doi: 10.3758/s13428-015-0599-7.
You can find a copy of the paper here:
https://apps.carleton.edu/curricular/psyc/jstrand/assets/Slote_and_Strand_BRM.pdf
For more information about this study or the Carleton Perception Lab, please visit https://apps.carleton.edu/curricular/psyc/jstrand/research/resources/
Here's the abstract:
Joseph Slote and Julia F. Strand
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.