Conducting web-based experiments in L2 psycholinguistic research
Abstract
Conclusions about adult second language (L2) learners’ representation and processing of grammatical structures are largely derived from psycholinguistic experiments. The use of web-based psycholinguistic experiments with participants sampled from crowdsourcing platforms has increased considerably in recent years, especially since the COVID-19 global pandemic. This chapter introduces representative software and libraries available for scripting experiments and crowdsourcing platforms for administering these experiments online. Through examples from studies we have conducted, this chapter illustrates the programming of three types of experiments: acceptability judgment tasks with Qualtrics, self-paced reading tasks with PennController for Ibex, and webcam-based eye-tracking with Gorilla. Meanwhile, crowdsourcing platforms, such as Prolific, are introduced for recruiting participants on a large scale. In comparison with established techniques, these software packages and platforms for designing and running experiments online will be addressed for their advantages and limitations. Best practices for conducting a web-based experiment are also suggested.
Study specs
- Discipline
- Psycholinguistics
- Year
- 2024
- Human Data Platform
- Prolific
- Source
- View Source Google Scholar
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.
Non-naive Participants Issue
I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.
RLHF Applicability to This Study Design
The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.
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