Replication of When Seating Matters: Modeling Graded Social Attitudes as Bayesian Inference
by Wang & Jara-Ettinger (2025, Proceedings of the Annual Meeting of the Cognitive Science Society)
Introduction
Justification
I want to replicate this study because I am interested in how humans make sophisticated social relationship inferences–for instance, how one person feels about another based off of perceptual cues (i.e., seating arrangements)–from very little data. This project is ideal because it elucidates how high-level cognition (e.g., theory of mind, naive utility calculus) mediates low-level perceptual cues that give rise to social inferences. Previously, I worked on a related project that sought to determine whether children can use gaze direction and duration to infer the nature of a relationship between dyads, and I hope to continue developing my knowledge about this line of work. Finally, the authors compared the human data they collected to three computational models; one of my goals this year is to learn more about computational modeling, so this project is an ideal first step.
Stimuli & Procedures
The stimuli for this project consisted of 30 still images depicting two characters (Yellow and Purple); each still represented a unique seating configuration between these two characters. Participants were presented with a cover story and had to pass a comprehension test to move on to the test phase. Then, participants saw all 30 stills in randomized order; for each still participants had to answer, “How does Purple feel about Yellow?” using a slider where one end represents “strongly dislikes” and the other end represents “strongly likes.”
Expected Challenges
The biggest challenge will be interpreting the code and outputs for the models. I have never done modeling work first-hand, so I may grow confused and frustrated. The second biggest challenge may be hosting my study; I don’t think that I will have to code a custom interface because the stimuli are just static images and participants will use scales to provide their responses (which are very common), but if I do have to code a custom interface, a challenge will be to ensure that I adequately collect all the right data needed for my analysis and correctly set the pipeline to download and store the data, e.g., make sure that refreshing the page does not overwrite the participant’s data-frame. In any case, I have experience coding an interface from scratch (but am not super confident about it), so I know I can do it if it comes down to it.
Links
In case the links fail to load (as they are doing for me):
my repo: https://github.com/karlaeperez/Wang2025
the orig paper: https://escholarship.org/uc/item/10d930rk#main
I am still not part of the psych251 organization, but I will update the ownership of my repo once I am.
Methods
Power Analysis
Original effect size, power analysis for samples to achieve 80%, 90%, 95% power to detect that effect size. Considerations of feasibility for selecting planned sample size.
Planned Sample
Planned sample size and/or termination rule, sampling frame, known demographics if any, preselection rules if any.
Materials
All materials - can quote directly from original article - just put the text in quotations and note that this was followed precisely. Or, quote directly and just point out exceptions to what was described in the original article.
Procedure
Can quote directly from original article - just put the text in quotations and note that this was followed precisely. Or, quote directly and just point out exceptions to what was described in the original article.
Analysis Plan
Can also quote directly, though it is less often spelled out effectively for an analysis strategy section. The key is to report an analysis strategy that is as close to the original - data cleaning rules, data exclusion rules, covariates, etc. - as possible.
Clarify key analysis of interest here You can also pre-specify additional analyses you plan to do.
Differences from Original Study
Explicitly describe known differences in sample, setting, procedure, and analysis plan from original study. The goal, of course, is to minimize those differences, but differences will inevitably occur. Also, note whether such differences are anticipated to make a difference based on claims in the original article or subsequent published research on the conditions for obtaining the effect.
Methods Addendum (Post Data Collection)
You can comment this section out prior to final report with data collection.
Actual Sample
Sample size, demographics, data exclusions based on rules spelled out in analysis plan
Differences from pre-data collection methods plan
Any differences from what was described as the original plan, or “none”.
Results
Data preparation
Data preparation following the analysis plan.
Confirmatory analysis
The analyses as specified in the analysis plan.
Side-by-side graph with original graph is ideal here
Exploratory analyses
Any follow-up analyses desired (not required).
Discussion
Summary of Replication Attempt
Open the discussion section with a paragraph summarizing the primary result from the confirmatory analysis and the assessment of whether it replicated, partially replicated, or failed to replicate the original result.
Commentary
Add open-ended commentary (if any) reflecting (a) insights from follow-up exploratory analysis, (b) assessment of the meaning of the replication (or not) - e.g., for a failure to replicate, are the differences between original and present study ones that definitely, plausibly, or are unlikely to have been moderators of the result, and (c) discussion of any objections or challenges raised by the current and original authors about the replication attempt. None of these need to be long.