jsQuestPlus: A JavaScript implementation of the QUEST+ method for estimating psychometric function parameters in online experiments

Kuroki, Daiichiro; Pronk, Thomas

doi:10.3758/s13428-022-01948-8

Cited by 5 publications

(3 citation statements)

References 39 publications

(68 reference statements)

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“…Researchers can develop plugins that abstract an experiment at a level that makes sense for a particular paradigm and then these plugins can be shared with other researchers who want to construct similar experiments. Over the past several years, there have been a handful of researchers who have published articles describing new jsPsych plugins (Donhauser & Klein, 2023;Gibeau, 2021;Kinley, 2022;Kuroki, 2021;Kuroki & Pronk, 2022;Rajananda et al, 2018;Strittmatter et al, 2022). Our community contributions repository, launched in 2021, currently contains 22 plugins and extensions enabling a variety of experiments that would be difficult or impossible to make with the official set of plugins included in the main jsPsych repository.…”

Section: Statement Of Needmentioning

confidence: 99%

jsPsych: Enabling an Open-Source Collaborative Ecosystem of Behavioral Experiments

Leeuw¹,

Gilbert²,

Luchterhandt³

2023

JOSS

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It is common practice to research human behavior using experiments that participants can complete online. Researchers use a variety of methodological approaches to conduct these studies. Some of this research can be done with survey instruments, for which there are many software options. However, much of the research in psychology and human behavior requires tasks that rely on precise measurement of stimulus presentation and response timing, specific kinds of randomization, procedures that adjust based on the responses that are given, and interactive content. jsPsych is a JavaScript library that allows researchers to build the types of experiments that historically could only be run in a lab setting, and run them on any device that has a web browser. jsPsych was initially released in 2012 and the early design is described in de Leeuw (2015). This paper is focused on jsPsych version 7, which contains the most substantial set of changes that have been made to the library since its initial release. The software has been widely rewritten with the goals of supporting integration with modern web development tools and improving the developer experience for researchers who want to make contributions to the library. We hope this will make it easier for researchers to collaboratively develop jsPsych and tools that use jsPsych (e.g.,

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Section: Statement Of Needmentioning

confidence: 99%

jsPsych: Enabling an Open-Source Collaborative Ecosystem of Behavioral Experiments

Leeuw¹,

Gilbert²,

Luchterhandt³

2023

JOSS

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“…The push towards moving our experiments online has also resulted in a flurry of new research opportunities. The year 2022 alone saw the publication of tools to collect eye-tracking data from infants in their homes using an online webcam-linked eye tracker (OWLET; Werchan et al, 2022), adaptive methods for estimating psychometric function parameters in online experiments (jsQuestPlus; Kuroki & Pronk, 2022), methods for collecting speeded overt pronunciation responses in language production tasks (Vogt et al, 2022), mouse-tracking methods that provide an alternative eye-tracking in order to track participants' spatial attention (MouseView.js;Anwyl-Irvine et al, 2022), tools to support highly interactive coalition formation experiments (The Online Coalition Game; Wissink et al, 2022), methods to allow researchers using auditory studies to standardize sound level adjustments, detect stereo/mono playback, and assess lower frequency limits (Headphone and Loudspeaker Test (HALT); Wycisk et al, 2022), a toolbox for facilitating the collection of a range of ratings data for auditory material (Donhauser & Klein, 2022), methods for measuring sensorimotor synchronization (SMS) in online experiments using the built-in microphone and speakers of standard laptop computers (Anglada-Tort et al, 2022).…”

Section: Why Would We Want To Do Our Experiments Online?mentioning

confidence: 99%

Moving Experimental Psychology Online: How to Maintain Data Quality When We Can’t See Our Participants

Rodd¹

2023

Preprint

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The past 10 years have seen rapid growth of online (web-based) data collection across the behavioural sciences. Many researchers have well-founded concerns about the lack of experimental control that arises when research moves outside of laboratory conditions. This paper provides a broad perspective on how to safeguard data quality in online experiments. I caution researchers against viewing online experiments as being necessarily cheaper, easier or more efficient than in-person testing. Ensuring appropriate data quality for online experiments requires significant effort prior to data collection to maintain the credibility of our rapidly expanding evidence base. I present a set of recommendations for researchers based on three key aspects of online data collection: technology, recruitment, and participant performance. With such safeguards in place online experiments will continue to provide important, paradigm-changing opportunities.

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“…More and more practical experiments are undertaken online [9] (e.g., the research of driving behaviors [6,10], clinical [7,8,11], and visual perception [12][13][14]). Terefore, the challenge faced by the researchers is the computation efciency in optimizing the stimulus after collecting subject's data during typical psychophysical experiments [10].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Adaptive Estimation with Theoretical Bound: An Exploration-Exploitation Approach

Zhu

2022

Computational Intelligence and Neuroscience

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This paper investigates the theoretical bound to reduce the parameter uncertainty in Bayesian adaptive estimation for psychometric functions and proposes an exploration-exploitation (E-E) approach to improve the computation efficiency for parameter estimations. When the experimental trial goes on, the uncertainty of the parameters decreases dramatically and the space between the maximal mutual information and the theoretical bound gets narrower, so the advantage of classical Bayesian adaptive estimation algorithm diminishes. This approach tries to trade off the exploration (parameter posterior uncertainty) and the exploitation (parameter mean estimation). The experimental results show that the proposed E-E approach estimates parameters for psychometric functions with same convergence and reduces the computation time by more than 34.27%, compared with the classical Bayesian adaptive estimation.

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jsQuestPlus: A JavaScript implementation of the QUEST+ method for estimating psychometric function parameters in online experiments

Cited by 5 publications

References 39 publications

jsPsych: Enabling an Open-Source Collaborative Ecosystem of Behavioral Experiments

jsPsych: Enabling an Open-Source Collaborative Ecosystem of Behavioral Experiments

Moving Experimental Psychology Online: How to Maintain Data Quality When We Can’t See Our Participants

Bayesian Adaptive Estimation with Theoretical Bound: An Exploration-Exploitation Approach

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