Conducting linguistic experiments online with OpenSesame and OSWeb
Abstract
In this Methods Showcase Article, we outline a workflow for running behavioral experiments online, with a focus on experiments that rely on presentation of complex stimuli and measurement of reaction times, which includes many psycholinguistic experiments. The workflow that we describe here relies on three tools: OpenSesame/OSWeb (open source) provides a user-friendly graphical interface for developing experiments; JATOS (open source) is server software for hosting experiments; and Prolific (commercial) is a platform for recruiting participants. These three tools integrate well with each other and together provide a workflow that requires little technical expertise. We discuss, and illustrate through an example study, several challenges that are associated with running online experiments, including temporal precision, the implementation of counterbalancing, data quality, and issues related to privacy and ethics. We conclude that these challenges are real but surmountable, and that in many cases online experiments are a viable alternative to laboratory-based experiments.
Study specs
- Institution
- Ghent University,KU Leuven
- Discipline
- Psycholinguistics
- Year
- 2022
- 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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