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Collecting psycholinguistic response time data using Amazon mechanical Turk.

Kelly Enochson1, Jennifer Culbertson2

  • 1Linguistics Program, Department of English, George Mason University, Fairfax, Virginia, United States of America.

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|March 31, 2015
PubMed
Summary
This summary is machine-generated.

Online crowd-sourcing tools like Amazon Mechanical Turk (AMT) can reliably collect precise response time data for psycholinguistic research. This validates AMT as a fast, low-cost alternative to traditional lab studies for behavioral data collection.

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Area of Science:

  • Psycholinguistics
  • Computational Linguistics
  • Cognitive Science

Background:

  • Online crowd-sourcing platforms are increasingly used for behavioral data collection.
  • Previous research validated these tools for offline measures but not for time-sensitive psycholinguistic tasks.
  • Concerns exist regarding the precision of web-based response time measurements.

Purpose of the Study:

  • To investigate the feasibility of using Amazon Mechanical Turk (AMT) for psycholinguistic research requiring precise response time measurements.
  • To determine if classic psycholinguistic effects can be replicated online.
  • To assess AMT as a viable alternative to laboratory-based data collection.

Main Methods:

  • Utilized AMT for participant recruitment and data collection.
  • Employed open-source software for client-side response time measurement during self-paced reading tasks.
  • Replicated three established psycholinguistic effects: subject definiteness, filler-gap dependency, and agreement attraction.

Main Results:

  • Demonstrated reliable replication of subject definiteness effects online.
  • Successfully replicated filler-gap dependency processing using AMT.
  • Showcased reliable agreement attraction effects in web-based self-paced reading tasks.
  • Achieved comparable participant and trial numbers to traditional laboratory studies.

Conclusions:

  • Online crowd-sourcing via AMT is suitable for precise response time measurements in psycholinguistic research.
  • AMT offers a fast and resource-efficient alternative to traditional lab methods.
  • Psycholinguists should consider leveraging online platforms for behavioral data acquisition.