Web-based experiments controlled by JavaScript: an example from probability learning.
Abstract: JavaScript programs can be used to control Web experiments. This technique is illustrated by an experiment that tested the effects of advice on performance in the classic probability-learning paradigm. Previous research reported that people tested via the Web or in the lab tended to match the probabilities of their responses to the probabilities that those responses would be reinforced. The optimal strategy, however, is to consistently choose the more frequent event; probability matching produces suboptimal performance. We investigated manipulations we reasoned should improve performance. A horse race scenario in which participants predicted the winner in each of a series of races between two horses was compared with an abstract scenario used previously. Ten groups of learners received different amounts of advice, including all combinations of (1) explicit instructions concerning the optimal strategy, (2) explicit instructions concerning a monetary sum to maximize, and (3) accurate information concerning the probabilities of events. The results showed minimal effects of horse race versus abstract scenario. Both advice concerning the optimal strategy and probability information contributed significantly to performance in the task. This paper includes a brief tutorial on JavaScript, explaining with simple examples how to assemble a browser-based experiment.
Publication Date: 2002-07-12 PubMed ID: 12109011DOI: 10.3758/bf03195442Google Scholar: Lookup
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- Journal Article
- Research Support
- U.S. Gov't
- Non-P.H.S.
Summary
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The researchers in this study demonstrated the utility of JavaScript in controlling web-based experiments, exemplified by a probability learning experiment. They investigated factors that could potentially enhance performance in a task that required consistently choosing the more frequent event instead of probability matching, which tends to cause suboptimal performance.
JavaScript and Web-based Experiments
- The study emphasizes the use of JavaScript programming as a tool in controlling web-based experiments. The researchers laid out an example of a probability learning experiment to illustrate the function and effectiveness of JavaScript.
Probability Learning Experiment
- The experiment tested in the study draws on a classic probability learning paradigm – a frequentist approach to probability where frequencies of events are utilized to predict future events.
- Participants were given tasks where they had to consistently choose an event with higher frequency, thereby eschewing the most common probability matching strategy, which generally leads to suboptimal performance.
Manipulations to Improve Performance
- In their attempt to boost performance over the tasks, researchers experimented with different types of advice offered to the participants. Participants were divided into ten groups, each getting varying levels of advice.
- The advice was a mix of explicit instructions about the optimal strategy, explicit advice on maximizing a monetary sum, and accurate information about the probability of events.
Comparison of Scenarios
- To understand the effect of context, researchers compared an abstract scenario used previously with a more relatable, concrete scenario: a horse race where participants were asked to predict the winning horse in a series of races.
- The results indicated that the context had minimal impact on the participants’ overall performance, putting more emphasis on the advice received.
Effects of Advice on Performance
- The results suggested that both advice concerning the optimal strategy and honest information about the probability of events significantly boosted the participants’ performances.
JavaScript Tutorial
- The researchers also provided a brief tutorial on JavaScript. They included simple examples to help the readers understand how to set up their own browser-based experimental tool.
Cite This Article
APA
Birnbaum MH, Wakcher SV.
(2002).
Web-based experiments controlled by JavaScript: an example from probability learning.
Behav Res Methods Instrum Comput, 34(2), 189-199.
https://doi.org/10.3758/bf03195442 Publication
Researcher Affiliations
- Decision Research Center, Department of Psychology H-830M, California State University, Fullerton, P. O. Box 6846, Fullerton, CA 92834-6846, USA. mbirnbaum@fullerton.edu
MeSH Terms
- Computer-Assisted Instruction
- Humans
- Internet
- Probability Learning
- Programming Languages
Citations
This article has been cited 2 times.- Anwyl-Irvine A, Dalmaijer ES, Hodges N, Evershed JK. Realistic precision and accuracy of online experiment platforms, web browsers, and devices.. Behav Res Methods 2021 Aug;53(4):1407-1425.
- Vadillo MA, Miller RR, Matute H. Causal and predictive-value judgments, but not predictions, are based on cue-outcome contingency.. Learn Behav 2005 May;33(2):172-83.
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