Perceptual judgments are resistant to the advisor's perceived level of trustworthiness: A deep fake approach

2 citations

Abstract

As we navigate our environment, we frequently make spontaneous judgments about other’s characteristics. Trustworthiness is a particularly important trait, often judged instantly and used to guide decisions, especially in uncertain situations. Although the impact of trustworthiness on social behaviour is well-documented, its influence on more fundamental cognitive processes, such as perceptual decision-making, remains unclear. The present study aims to fill this gap. In the first experiment (N =  100), we validated a new trustworthiness manipulation by applying deep fake technology to create animated versions of perceptually trustworthy, untrustworthy, and neutral static computer-generated faces. In the second experiment (N =  199), the deep fake procedure was applied to a new set of trustworthy and untrustworthy faces that served as advisors during a perceptual decision-making task. Here participants had to indicate the direction of dots that were either moving coherently to the left or the right (i.e., random dot motion task). Contrary to our predictions, participants did not align more with the advice of trustworthy advisors than that of untrustworthy advisors. While participants made faster decisions and reported greater confidence when aligning with the advice, these effects were not influenced by the advisor’s perceived trustworthiness. We integrate our findings within theoretical frameworks of advice taking, domain specificity of facial trustworthiness, and task requirements.

2
Citations
Research
Paper Only

Study specs

Institution
Ghent University
Year
2022
Human Data Platform
Prolific

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

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