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Can we use the internet to study speech production? Yes we can! Evidence contrasting online versus laboratory naming latencies and errors

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  • Amie Fairs
  • Kristof Strijkers

Abstract

The closure of cognitive psychology labs around the world due to the COVID-19 pandemic has prevented in-person testing. This has caused a particular challenge for speech production researchers, as before the pandemic there were no studies demonstrating that reliable overt speech production data could be collected via the internet. Here, we present evidence that both accurate and reliable overt articulation data can be collected from internet-based speech production experiments. We tested 100 participants in a picture naming paradigm, where we manipulated the word and phonotactic frequency of the picture names. We compared our results to a lab-based study conducted on different participants which used the same materials and design. We found a significant word frequency effect but no phonotactic frequency effect, fully replicating the lab-based results. Effect sizes were similar between experiments, but with significantly longer latencies in the internet-collected data. We found no evidence that internet upload or download speed affected either naming latencies or errors. In addition, we carried out a permutation-style analysis which recommends a minimum sample size of 40 participants for online production paradigms. In sum, our study demonstrates that internet-based testing of speech production is a feasible and promising endeavour, with less challenges than many researchers (anecdotally) assumed.

Suggested Citation

  • Amie Fairs & Kristof Strijkers, 2021. "Can we use the internet to study speech production? Yes we can! Evidence contrasting online versus laboratory naming latencies and errors," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-21, October.
  • Handle: RePEc:plo:pone00:0258908
    DOI: 10.1371/journal.pone.0258908
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    References listed on IDEAS

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    1. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
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