Running online behavioral experiments using R: Implementation of a response-time decision making task as an R-Shiny app
Online experiments allow for fast, massive, cost-efficient data collection. However, uncontrolled conditions in online experiments can be problematic, particularly when inferences hinge on response-times (RTs) in the millisecond range. To address this challenge, we developed a mobile-friendly open-s...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Ubiquity Press
2022-01-01
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| Series: | Journal of Cognition |
| Subjects: | |
| Online Access: | https://www.journalofcognition.org/articles/200 |
| _version_ | 1819278851703832576 |
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| author | Agustín Perez Santangelo Guillermo Solovey |
| author_facet | Agustín Perez Santangelo Guillermo Solovey |
| author_sort | Agustín Perez Santangelo |
| collection | DOAJ |
| description | Online experiments allow for fast, massive, cost-efficient data collection. However, uncontrolled conditions in online experiments can be problematic, particularly when inferences hinge on response-times (RTs) in the millisecond range. To address this challenge, we developed a mobile-friendly open-source application using R-Shiny, a popular R package. In particular, we aimed to replicate the numerical distance effect, a well-established cognitive phenomenon. In the task, 169 participants (109 with a mobile device, 60 on a desktop computer) completed 116 trials displaying two-digit target numbers and decided whether they were larger or smaller than a fixed standard number. Sessions lasted ~7-minutes. Using generalized linear mixed models estimated with Bayesian inference methods, we observed a numerical distance effect: RTs decreased with the logarithm of the absolute difference between the target and the standard. Our results support the use of R-Shiny for RT-data collection. Furthermore, our method allowed us to measure systematic shifts in recorded RTs related to different OSs, web browsers, and devices, with mobile devices inducing longer shifts than desktop devices. Our work shows that precise RT measures can be reliably obtained online across mobile and desktop devices. It further paves the ground for the design of simple experimental tasks using R, a widely popular programming framework among cognitive scientists. |
| first_indexed | 2024-12-24T00:18:35Z |
| format | Article |
| id | doaj.art-e44480fc2ea74854b5cc0a8454dae918 |
| institution | Directory Open Access Journal |
| issn | 2514-4820 |
| language | English |
| last_indexed | 2024-12-24T00:18:35Z |
| publishDate | 2022-01-01 |
| publisher | Ubiquity Press |
| record_format | Article |
| series | Journal of Cognition |
| spelling | doaj.art-e44480fc2ea74854b5cc0a8454dae9182022-12-21T17:24:39ZengUbiquity PressJournal of Cognition2514-48202022-01-015110.5334/joc.200219Running online behavioral experiments using R: Implementation of a response-time decision making task as an R-Shiny appAgustín Perez Santangelo0Guillermo Solovey1Laboratorio de Neurociencia, Universidad Torcuato Di Tella, Buenos AiresInstituto de Cálculo, Universidad de Buenos Aires, Buenos AiresOnline experiments allow for fast, massive, cost-efficient data collection. However, uncontrolled conditions in online experiments can be problematic, particularly when inferences hinge on response-times (RTs) in the millisecond range. To address this challenge, we developed a mobile-friendly open-source application using R-Shiny, a popular R package. In particular, we aimed to replicate the numerical distance effect, a well-established cognitive phenomenon. In the task, 169 participants (109 with a mobile device, 60 on a desktop computer) completed 116 trials displaying two-digit target numbers and decided whether they were larger or smaller than a fixed standard number. Sessions lasted ~7-minutes. Using generalized linear mixed models estimated with Bayesian inference methods, we observed a numerical distance effect: RTs decreased with the logarithm of the absolute difference between the target and the standard. Our results support the use of R-Shiny for RT-data collection. Furthermore, our method allowed us to measure systematic shifts in recorded RTs related to different OSs, web browsers, and devices, with mobile devices inducing longer shifts than desktop devices. Our work shows that precise RT measures can be reliably obtained online across mobile and desktop devices. It further paves the ground for the design of simple experimental tasks using R, a widely popular programming framework among cognitive scientists.https://www.journalofcognition.org/articles/200online experimentsr-shinyresponse timenumerical cognition |
| spellingShingle | Agustín Perez Santangelo Guillermo Solovey Running online behavioral experiments using R: Implementation of a response-time decision making task as an R-Shiny app Journal of Cognition online experiments r-shiny response time numerical cognition |
| title | Running online behavioral experiments using R: Implementation of a response-time decision making task as an R-Shiny app |
| title_full | Running online behavioral experiments using R: Implementation of a response-time decision making task as an R-Shiny app |
| title_fullStr | Running online behavioral experiments using R: Implementation of a response-time decision making task as an R-Shiny app |
| title_full_unstemmed | Running online behavioral experiments using R: Implementation of a response-time decision making task as an R-Shiny app |
| title_short | Running online behavioral experiments using R: Implementation of a response-time decision making task as an R-Shiny app |
| title_sort | running online behavioral experiments using r implementation of a response time decision making task as an r shiny app |
| topic | online experiments r-shiny response time numerical cognition |
| url | https://www.journalofcognition.org/articles/200 |
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