Collecting psycholinguistic response time data using Amazon mechanical Turk.

Researchers in linguistics and related fields have recently begun exploiting online crowd-sourcing tools, like Amazon Mechanical Turk (AMT), to gather behavioral data. While this method has been successfully validated for various offline measures--grammaticality judgment or other forced-choice tasks...

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Main Authors: Kelly Enochson, Jennifer Culbertson
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2015-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC4378859?pdf=render
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spelling doaj-afdca9f9a85946adb5193a839ba6f3422020-11-25T02:04:48ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01103e011694610.1371/journal.pone.0116946Collecting psycholinguistic response time data using Amazon mechanical Turk.Kelly EnochsonJennifer CulbertsonResearchers in linguistics and related fields have recently begun exploiting online crowd-sourcing tools, like Amazon Mechanical Turk (AMT), to gather behavioral data. While this method has been successfully validated for various offline measures--grammaticality judgment or other forced-choice tasks--its use for mainstream psycholinguistic research remains limited. This is because psycholinguistic effects are often dependent on relatively small differences in response times, and there remains some doubt as to whether precise timing measurements can be gathered over the web. Here we show that three classic psycholinguistic effects can in fact be replicated using AMT in combination with open-source software for gathering response times client-side. Specifically, we find reliable effects of subject definiteness, filler-gap dependency processing, and agreement attraction in self-paced reading tasks using approximately the same numbers of participants and/or trials as similar laboratory studies. Our results suggest that psycholinguists can and should be taking advantage of AMT and similar online crowd-sourcing marketplaces as a fast, low-resource alternative to traditional laboratory research.http://europepmc.org/articles/PMC4378859?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Kelly Enochson
Jennifer Culbertson
spellingShingle Kelly Enochson
Jennifer Culbertson
Collecting psycholinguistic response time data using Amazon mechanical Turk.
PLoS ONE
author_facet Kelly Enochson
Jennifer Culbertson
author_sort Kelly Enochson
title Collecting psycholinguistic response time data using Amazon mechanical Turk.
title_short Collecting psycholinguistic response time data using Amazon mechanical Turk.
title_full Collecting psycholinguistic response time data using Amazon mechanical Turk.
title_fullStr Collecting psycholinguistic response time data using Amazon mechanical Turk.
title_full_unstemmed Collecting psycholinguistic response time data using Amazon mechanical Turk.
title_sort collecting psycholinguistic response time data using amazon mechanical turk.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2015-01-01
description Researchers in linguistics and related fields have recently begun exploiting online crowd-sourcing tools, like Amazon Mechanical Turk (AMT), to gather behavioral data. While this method has been successfully validated for various offline measures--grammaticality judgment or other forced-choice tasks--its use for mainstream psycholinguistic research remains limited. This is because psycholinguistic effects are often dependent on relatively small differences in response times, and there remains some doubt as to whether precise timing measurements can be gathered over the web. Here we show that three classic psycholinguistic effects can in fact be replicated using AMT in combination with open-source software for gathering response times client-side. Specifically, we find reliable effects of subject definiteness, filler-gap dependency processing, and agreement attraction in self-paced reading tasks using approximately the same numbers of participants and/or trials as similar laboratory studies. Our results suggest that psycholinguists can and should be taking advantage of AMT and similar online crowd-sourcing marketplaces as a fast, low-resource alternative to traditional laboratory research.
url http://europepmc.org/articles/PMC4378859?pdf=render
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