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Honeycomb: a template for reproducible psychophysiological tasks for clinic, laboratory, and home use
Provenza, Nicole R.; Gelin, Luiz Fernando Fracassi; Mahaphanit, Wasita; McGrath, Mary C.; Dastin-van Rijn, Evan M.; Fan, Yunshu; Dhar, Rashi; Frank, Michael J.; Restrepo, Maria I.; Goodman, Wayne K.; Borton, David A..
  • Provenza, Nicole R.; Brown University School of Engineering. Providence. US
  • Gelin, Luiz Fernando Fracassi; Brown University. Center for Computation and Visualization. Providence. US
  • Mahaphanit, Wasita; Brown University. Linguistic, and Psychological Sciences. Department of Cognitive. Providence. US
  • McGrath, Mary C.; Brown University. Center for Computation and Visualization. Providence. US
  • Dastin-van Rijn, Evan M.; Brown University School of Engineering. Providence. US
  • Fan, Yunshu; Brown University School of Engineering. Providence. US
  • Dhar, Rashi; Brown University. Center for Computation and Visualization. Providence. US
  • Frank, Michael J.; Brown University. Linguistic, and Psychological Sciences. Department of Cognitive. Providence. US
  • Restrepo, Maria I.; Brown University. Center for Computation and Visualization. Providence. US
  • Goodman, Wayne K.; Baylor College of Medicine. Menninger Department of Psychiatry and Behavioral Sciences. Houston. US
  • Borton, David A.; Brown University School of Engineering. Providence. US
Braz. J. Psychiatry (São Paulo, 1999, Impr.) ; 44(2): 147-155, Apr. 2022. graf
Article in English | LILACS-Express | LILACS | ID: biblio-1374584
ABSTRACT

Objective:

To improve the ability of psychiatry researchers to build, deploy, maintain, reproduce, and share their own psychophysiological tasks. Psychophysiological tasks are a useful tool for studying human behavior driven by mental processes such as cognitive control, reward evaluation, and learning. Neural mechanisms during behavioral tasks are often studied via simultaneous electrophysiological recordings. Popular online platforms such as Amazon Mechanical Turk (MTurk) and Prolific enable deployment of tasks to numerous participants simultaneously. However, there is currently no task-creation framework available for flexibly deploying tasks both online and during simultaneous electrophysiology.

Methods:

We developed a task creation template, termed Honeycomb, that standardizes best practices for building jsPsych-based tasks. Honeycomb offers continuous deployment configurations for seamless transition between use in research settings and at home. Further, we have curated a public library, termed BeeHive, of ready-to-use tasks.

Results:

We demonstrate the benefits of using Honeycomb tasks with a participant in an ongoing study of deep brain stimulation for obsessive compulsive disorder, who completed repeated tasks both in the clinic and at home.

Conclusion:

Honeycomb enables researchers to deploy tasks online, in clinic, and at home in more ecologically valid environments and during concurrent electrophysiology.


Full text: Available Index: LILACS (Americas) Type of study: Practice guideline Language: English Journal: Braz. J. Psychiatry (São Paulo, 1999, Impr.) Journal subject: Psychiatry Year: 2022 Type: Article Affiliation country: United States Institution/Affiliation country: Baylor College of Medicine/US / Brown University School of Engineering/US / Brown University/US

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Full text: Available Index: LILACS (Americas) Type of study: Practice guideline Language: English Journal: Braz. J. Psychiatry (São Paulo, 1999, Impr.) Journal subject: Psychiatry Year: 2022 Type: Article Affiliation country: United States Institution/Affiliation country: Baylor College of Medicine/US / Brown University School of Engineering/US / Brown University/US