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Conducting spoken word recognition research online: Validation and a new timing method
Joseph Slote1, Julia F Strand2
1Department of Psychology, Carleton College, Northfield, MN, 55057, USA.
Behavior Research Methods
|May 20, 2015
Summary
Online platforms like Amazon Mechanical Turk (AMT) can be used for spoken word recognition research. This method shows strong correlations with lab data, offering a faster, more cost-effective alternative for collecting word identification and lexical decision data.
Area of Science:
- Cognitive Psychology
- Psycholinguistics
- Human-Computer Interaction
Background:
- Traditional laboratory methods for collecting spoken word recognition data are time-consuming and expensive.
- Online research platforms offer advantages in speed, cost, and participant diversity.
- Previous online studies have successfully replicated classic cognitive psychology findings, but auditory tasks remain under-explored.
Purpose of the Study:
- To evaluate the efficacy of Amazon Mechanical Turk (AMT) for collecting spoken word identification and auditory lexical decision data.
- To compare data collected online with traditional laboratory data for spoken word recognition.
- To assess the viability of online platforms for auditory research in psycholinguistics.
Main Methods:
- Participants completed spoken word identification and auditory lexical decision tasks via an online platform (AMT).
- Data collected online were compared with data from a traditional laboratory setting.
- Correlations between online and lab data were analyzed, along with their relationship to word frequency and phonological neighborhood density.
Main Results:
- Online participants were faster but less accurate than laboratory participants.
- Strong correlations were found between online and laboratory measures for both word identification accuracy and lexical decision speed.
- Online and lab data showed equivalent correlations with established predictors of word recognition, such as word frequency and phonological neighborhood density.
Conclusions:
- Amazon Mechanical Turk (AMT) is a viable alternative for collecting spoken word recognition data, complementing traditional laboratory methods.
- Online data collection is suitable for tasks involving auditory stimuli, expanding the scope of remote behavioral research.
- The findings support the use of online platforms for large-scale data collection in psycholinguistic research, enhancing efficiency and accessibility.
