Conducting perception research over the internet: A tutorial review

335 citations

Abstract

This article provides an overview of the recent literature on the use of internet-based testing to address important questions in perception research. Our goal is to provide a starting point for the perception researcher who is keen on assessing this tool for their own research goals. Internet-based testing has several advantages over in-lab research, including the ability to reach a relatively broad set of participants and to quickly and inexpensively collect large amounts of empirical data, via services such as Amazon’s Mechanical Turk or Prolific Academic. In many cases, the quality of online data appears to match that collected in lab research. Generally-speaking, online participants tend to be more representative of the population at large than those recruited for lab based research. There are, though, some important caveats, when it comes to collecting data online. It is obviously much more difficult to control the exact parameters of stimulus presentation (such as display characteristics) with online research. There are also some thorny ethical elements that need to be considered by experimenters. Strengths and weaknesses of the online approach, relative to others, are highlighted, and recommendations made for those researchers who might be thinking about conducting their own studies using this increasingly-popular approach to research in the psychological sciences.

335
Citations
Research
Paper Only

Peer Review & Critical Discussion

3 threads

Potential Selection Bias in 2023 Cohort

DSJDr. Sarah J.
Verified PhD Candidate
12 replies

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.

2 hours ago

Non-naive Participants Issue

MCM. Chen (OpenAI)
Data Scientist
8 replies

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.

5 hours ago

RLHF Applicability to This Study Design

PRWProf. R. Williams
Verified Researcher
15 replies

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.

1 day ago

Verify your expertise to join discussion

Create an account and verify your credentials to participate in peer discussions.