Empirica: a virtual lab for high-throughput macro-level experiments

被引:0
作者
Abdullah Almaatouq
Joshua Becker
James P. Houghton
Nicolas Paton
Duncan J. Watts
Mark E. Whiting
机构
[1] Massachusetts Institute of Technology,
[2] University College London,undefined
[3] University of Pennsylvania,undefined
来源
Behavior Research Methods | 2021年 / 53卷
关键词
Virtual lab; Online research; Crowdsourcing;
D O I
暂无
中图分类号
学科分类号
摘要
Virtual labs allow researchers to design high-throughput and macro-level experiments that are not feasible in traditional in-person physical lab settings. Despite the increasing popularity of online research, researchers still face many technical and logistical barriers when designing and deploying virtual lab experiments. While several platforms exist to facilitate the development of virtual lab experiments, they typically present researchers with a stark trade-off between usability and functionality. We introduce Empirica: a modular virtual lab that offers a solution to the usability–functionality trade-off by employing a “flexible defaults” design strategy. This strategy enables us to maintain complete “build anything” flexibility while offering a development platform that is accessible to novice programmers. Empirica’s architecture is designed to allow for parameterizable experimental designs, reusable protocols, and rapid development. These features will increase the accessibility of virtual lab experiments, remove barriers to innovation in experiment design, and enable rapid progress in the understanding of human behavior.
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页码:2158 / 2171
页数:13
相关论文
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  • [11] Massonnié J(2004)Human research and data collection via the Internet Annual Review of Psychology 55 803-5141
  • [12] Flitton A(2019)Cognitive model priors for predicting human decisions In Proceedings of Machine Learning Research 97 5133-130
  • [13] Kirkham N(2014)Nonnaïveté among Amazon Mechanical Turk workers: Consequences and solutions for behavioral researchers Behavior Research Methods 46 112-97
  • [14] Evershed JK(2016)oTree–an open-source platform for laboratory, online, and field experiments Journal of Behavioral and Experimental Finance 9 88-12
  • [15] Arechar AA(2015)jsPsych: a JavaScript library for creating behavioral experiments in a web browser Behavior Research Methods 47 1-409
  • [16] Gächter S(2017)From anomalies to forecasts: Toward a descriptive model of decisions under risk, under ambiguity, and from experience Psychological Review 124 369-1453
  • [17] Molleman L(2019)Best practices: Two web-browser-based methods for stimulus presentation in behavioral experiments with high-resolution timing requirements Behavior Research Methods 51 1441-224
  • [18] Awad E(2013)Data collection in a flat world: The strengths and weaknesses of Mechanical Turk samples Journal of Behavioral Decision Making 26 213-1803
  • [19] Dsouza S(2019)A thousand studies for the price of one: Accelerating psychological science with Pushkin Behavior Research Methods 51 1782-425
  • [20] Kim R(2011)The online laboratory: Conducting experiments in a real labor market Experimental Economics 14 399-521