That’s a wrap! #spsp2026

Last day!

Today (Saturday, Feb 28) 2pm in E271 #spsp2026

Super excited to chair my first symposium with @dongwonoh.bsky.social at @spspnews.bsky.social this Saturday (2pm-3:10pm, E271)! We have talks from Abhinanda Dash, Ming Huang Teo, Bastian Weitz, and me.

If you are in the Windy City and curious about social hierarchies, join us! #SPSP2026 🌬️ 🌬️ 🌬️

*Correction: DongWon’s cultural psych preconfeference talk is on Wednesday, Feb 25 (not Tues, Feb 24)!

A presentation schedule slide titled 'Oh Lab at SPSP 2026 · Chicago' from the National University of Singapore, directed by DongWon Oh. Lists 7 presentations in chronological order: (1) Preconference Talk — DongWon Oh, Tue Feb 24; (2) Poster — Finneaz Moner, Thu Feb 26; (3) Poster — Yuqing Shi, Thu Feb 26; (4) Poster — Anqi Mao, Sat Feb 28; (5) Poster — Runquan Yu, Sat Feb 28; (6) Talk — Joy Tong (Chair & Presenter), Sat Feb 28; (7) Symposium — Finneaz Moner & DongWon Oh (co-Chairs), Sat Feb 28, featuring talks by Finneaz Moner and Ming Huang Teo. Each entry includes the presenter's photo, paper title, and location.

Hello Chicago! Oh Lab @ohlab.bsky.social at #SPSP2026 hailing from Singapore--8 presentations and 7 speakers across posters, talks, and a symposium. Come say hi! 👋

Big congratulations to Yuqing Shi @syuqing.bsky.social, a PhD student of mine, on receiving the Besample Dissertation Grant! Really exciting work on cross-cultural face perception. Can’t wait to see where this goes. besample.app/dissertation...

Now in press at SPPS. Ruled out perceiver effects, stereotypical trait structure, assumed similarity; tested generalizability; and added longitudinal data. Kudos to coauthors @finneazmoner.bsky.social, Natasha Tan, and others.

First paper as PI!

Latest preprint: osf.io/preprints/ps...

👇Last chance to sign up!👇

Congrats Joy Tong for her single-presenter talk acceptance at #SPSP2026: “Intrapersonal Factors Trump Contact Frequency in Cross-Racial Face Recognition.”

Network analysis shows that quality interactions (not mere exposure) are what matter for improving other-race recognition.

Congrats Finneaz @firdausmoner.bsky.social for his accepted symposium “The Architecture of Status Perception: Economic Status Stereotype Awareness Shapes Clothing-Based Competence Perceptions” at #SPSP2026!

He’ll present “Dressing the Part,” and Ming Huang (Ben) Teo will share “Empowering Attire."

For the first time, my trainees got their SPSP talks accepted--and 3 at once! Such a blessing.

Congratulations to Finneaz Moner @firdausmoner.bsky.social [lab manager], Ming Huang (Ben) Teo [former lab manager)], and Joy Tong [PhD student].

Proud of their hard work. See you in Chicago! #SPSP2026

I'll be in Sydney (June 16–21) for conferences and Melbourne (June 21–25) for a talk at the University of Melbourne. If you're around and want to connect, @ me! Would love to meet up 🇦🇺🦘🐨🪃

#EPC2025 #APCV2025

Year 3 comes to a close.

Grateful for steady growth and small wins—a seed grant, journal R&Rs, and new manuscripts in the works.

Thank you to the students, RAs, and lab manager for showing up with care, curiosity, and energy.

Wishing all the best to those graduating.

Oh Lab represents!!! #spsp2025

Today (Feb 20) 6-7PM in Exhibit D 🔜🚀

[211] Implicit Encoding of Social Trait Perceptions (Anqi Mao)

[214] National Identity Motivates Individuation in Other-Race Perception in Multiracial Societies (Joy Tong)

[193] Selective Halo Effect (Yuqing Shi)

[11/11] Thanks to PhD students Anqi Mao & Yuqing Shi for their work. And to @erichehman.bsky.social and Rebecca Neel @travislim.bsky.social for organizing this novel competitive collaborative approach to advancing prejudice research. Excited to see where the future research will take this research.

[10/11] The consistency in outcome is remarkable considering a wide range of groups considered, from historically marginalized groups (Black/gay/transgender people, immigrants) to high-status groups (extremely wealthy people). This suggests a "base recipe" for prejudice, regardless of target group.

[9/11] Pretty pleased to share that MAP Team placed 3rd on bias prediction and 6th on outgroup attitudes. The models explain more than half the variance in prejudice across very different groups with similar predictors. Good work, team!

[8/n] The winning approach among our internal candidates kept it focused: just 4 key stable predictors in each model, including symbolic threat, contact quality & group identification. These focused models actually outperformed more complex versions by reducing statistical noise.

[7/n] Our final models used Random Forest for bias & PLS regression for outgroup attitudes. Using the full dataset with careful variable selection based on model performance proved more effective at finding universal predictors (e.g., rather than separating data by prejudice target group or for CV).

[6/n] To predict universal prejudice patterns, our team balanced competing goals: learning from all data vs. avoiding overfitting. We tested multiple approaches - Random Forest, Elastic Net regression, PLS regression, & Principal Component Regression.

[5/n] The competition drew 90+ researchers across 40+ teams (!). Goal was to Create models predicting prejudiced attitudes toward different social groups. Team Cluster Busters won by achieving 55%+ variance explained, big improvement over the baseline. Kudos to Team Cluster Busters.

[4/n] As in the original Prejudice 1.0 model, the competition targeted two aspects of explicit prejudice: 'Bias' (ingroup vs outgroup rating difference) and 'Outgroup Attitudes' (direct outgroup ratings). Different psychological processes might drive these two outcomes.

[3/n] The approach used explicit item-level measures of key constructs (e.g., contact quality, perceived threat, group identification) rather than pre-defined latent factors. We tested these predictors across different target groups to identify the most consistent, universal prejudice predictors.

[2/n] Why important? While psychology has produced many verbal theories of prejudice over 80+ years, they rarely made precise predictions or competed head-to-head. According to the authors (and I agree) this mathematical approach lets us make specific, numerical predictions about prejudice levels.

[1/n] We recently participated in a competition to improve "Prejudice 1.0", a model by Eric Hehman and Rebecca Neel, the first mathematical formalization of universal prejudice prediction. hehmanlab.org/competition

paper [Psych Rev 2024]: psycnet.apa.org/record/2024-...

preprint: osf.io/vz6gc/

Day 2 #apcv2024

Day 1 #apcv2024

<<Thursday, July 11>>
📜 1130 Alyssa Goh
📜 1300 Ben (Huang Ming) Teo

#apcv2024
apcv2024.com