| Title |
Evaluating Amazon’s Mechanical Turk for psychological research on the symbolic control of attention
|
|---|---|
| Published in |
Behavior Research Methods, January 2017
|
| DOI | 10.3758/s13428-016-0847-5 |
| Pubmed ID | |
| Authors |
Joseph R. Pauszek, Pedro Sztybel, Bradley S. Gibson |
| Abstract |
The use of online crowdsourcing services like Amazon's Mechanical Turk (AMT) as a method of collecting behavioral data online has become increasingly popular in recent years. A growing body of contemporary research has empirically validated the use of AMT as a tool in psychological research by replicating a wide range of well-established effects that have been previously reported in controlled laboratory studies. However, the potential for AMT to be used to conduct spatial cuing experiments has yet to be investigated in depth. Spatial cuing tasks are typically very basic in terms of their stimulus complexity and experimental testing procedures, thus making them ideal for remote testing online that requires minimal task instruction. Studies employing the spatial cuing paradigm are typically aimed at unveiling novel facets of the symbolic control of attention, which occurs whenever observers orient their attention through space in accordance with the meaning of a spatial cue. Ultimately, the present study empirically validated the use of AMT to study the symbolic control of attention by successfully replicating four hallmark effects reported throughout the visual attention literature: the left/right advantage, cue type effect, cued axis effect, and cued endpoint effect. Various recommendations for future endeavors using AMT as a means of remotely collecting behavioral data online are also provided. In sum, the present study provides a crucial first step toward establishing a novel tool for conducting psychological research that can be used to expedite not only our own scientific contributions, but also those of our colleagues. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 38 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 16% |
| Student > Ph. D. Student | 5 | 13% |
| Student > Doctoral Student | 5 | 13% |
| Student > Postgraduate | 4 | 11% |
| Other | 3 | 8% |
| Other | 9 | 24% |
| Unknown | 6 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 15 | 39% |
| Computer Science | 5 | 13% |
| Neuroscience | 3 | 8% |
| Linguistics | 2 | 5% |
| Medicine and Dentistry | 2 | 5% |
| Other | 2 | 5% |
| Unknown | 9 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 November 2017.
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#20,660,571
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Outputs from Behavior Research Methods
#1,981
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Outputs of similar age
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Outputs of similar age from Behavior Research Methods
#24
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