Literature DB >> 34032146

Crowdsourcing in Cognitive and Systems Neuroscience.

Brian P Johnson1, Eran Dayan2, Nitzan Censor3, Leonardo G Cohen1.   

Abstract

Behavioral research in cognitive and human systems neuroscience has been largely carried out in-person in laboratory settings. Underpowering and lack of reproducibility due to small sample sizes have weakened conclusions of these investigations. In other disciplines, such as neuroeconomics and social sciences, crowdsourcing has been extensively utilized as a data collection tool, and a means to increase sample sizes. Recent methodological advances allow scientists, for the first time, to test online more complex cognitive, perceptual, and motor tasks. Here we review the nascent literature on the use of online crowdsourcing in cognitive and human systems neuroscience. These investigations take advantage of the ability to reliably track the activity of a participant's computer keyboard, mouse, and eye gaze in the context of large-scale studies online that involve diverse research participant pools. Crowdsourcing allows for testing the generalizability of behavioral hypotheses in real-life environments that are less accessible to lab-designed investigations. Crowdsourcing is further useful when in-laboratory studies are limited, for example during the current COVID-19 pandemic. We also discuss current limitations of crowdsourcing research, and suggest pathways to address them. We conclude that online crowdsourcing is likely to widen the scope and strengthen conclusions of cognitive and human systems neuroscience investigations.

Entities:  

Keywords:  behavioral neuroscience; cognitive neuroscience; crowdsourcing; motor control; motor learning; systems neuroscience

Mesh:

Year:  2021        PMID: 34032146     DOI: 10.1177/10738584211017018

Source DB:  PubMed          Journal:  Neuroscientist        ISSN: 1073-8584            Impact factor:   7.235


  4 in total

1.  ReActLab: A Custom Framework for Sensorimotor Experiments "in-the-wild".

Authors:  Priscilla Balestrucci; Dennis Wiebusch; Marc O Ernst
Journal:  Front Psychol       Date:  2022-06-21

2.  Long-Term Motor Learning in the "Wild" With High Volume Video Game Data.

Authors:  Jennifer B Listman; Jonathan S Tsay; Hyosub E Kim; Wayne E Mackey; David J Heeger
Journal:  Front Hum Neurosci       Date:  2021-12-20       Impact factor: 3.169

3.  What Accounts for the Factors of Psychopathology? An Investigation of the Neurocognitive Correlates of Internalising, Externalising, and the p-Factor.

Authors:  Darren Haywood; Frank D Baughman; Barbara A Mullan; Karen R Heslop
Journal:  Brain Sci       Date:  2022-03-22

4.  Neurocognitive Artificial Neural Network Models Are Superior to Linear Models at Accounting for Dimensional Psychopathology.

Authors:  Darren Haywood; Frank D Baughman; Barbara A Mullan; Karen R Heslop
Journal:  Brain Sci       Date:  2022-08-10
  4 in total

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