Leehyun Yoon

No distorted prediction error processing in depression? Pupillary responses to expected vs. unexpected emotional information in clinically depressed and healthy individuals Depression has been associated with a reduced ability to update negative beliefs, possibly driven by difficulties integrating new positive information…

🎉🥳 New article by my PhD student Alexandra Spaeth: We investigated pupillary responses to novel positive vs. negative information on interpersonal scenarios in people with major depression vs. healthy control participants. Published open access in BRAT 👇 www.sciencedirect.com/science/arti...

YouTube video by Politechnika Wrocławska Prof. Lisa Feldman Barrett: How Does the Brain Predict the World and Create Emotions? | Wrocław Tech

Does your #brain exist mainly to think? Or is its most important function something else? "Three Lessons about the Brain" is a talk I delivered at the Wrocław University of Science and Technology in Poland. www.youtube.com/watch?v=4aii...

Our Science Symposium brings together diverse perspectives on adolescent brain development, from emotion and motivation to social context and mental health. Join the conversation!

Register now: www.eventbrite.com/e/neuroscien...

#ScienceSymposium #CVL #BrainHealth #HarvardUniversity #UTD #UTSW

screenshot of article title & author team

New paper & a thread on the results 👇

‘Reward-specific learning parameters change across normative adolescent development and are blunted in youth with high risk for depression’

acamh-onlinelibrary-wiley-com.ezp3.lib.umn.edu/doi/full/10....

The end of 2nd year of our lab 🎉 We celebrated with a multicultural potluck, a gift exchange, and a conversation where everyone shared what research questions they would pursue if resources and skills were unlimited. Grateful for how far our lab has come 🙏 Merry Christmas and Happy New Year!

New release of PowerLMM.js! Browser-based power analysis for longitudinal models with dropout.

Now includes:
- Power analysis summary report
- Reproducible & shareable configs (URL/JSON)
- Calculations validated against R
- Hypothesis region visualization

powerlmmjs.rpsychologist.com

1-Minute Read. Social & Emotional Learning. Why Students Avoid Asking Questions—and How to Change Their Minds. Adapted from: 6 Essential Student Mindsets to Work On This Year

Academic peer pressure arises early: Children as young as 7 years old “begin to connect asking for help w/ looking incompetent in front of others,” researchers concluded in a 2021 study.

But fear of looking dumb is often just half of a reticent student’s calculation.

🧵1/8 #EduSky

Exploring nationwide patterns of sleep problems from late adolescence to adulthood using machine learning Sleep problems from late adolescence to adulthood have increased in the past 10 years and can be linked to complex factors.

Sleep problems among adolescents and adults in Denmark substantially increased from 2010 to 2021, according to new #ScienceAdvances research involving more than 2.2 million participants from across the nation. https://scim.ag/42aOHLm

Cognitive modeling of real-world behavior for understanding mental health A core strength of computational psychiatry is its focus on theory-driven research, in which cognitive processes are precisely quantified using computational models that formalize specific theoretical mechanisms. However, the data used in these studies often come from traditional laboratory-based cognitive tasks, which have unclear ecological validity. In this review we propose that the same theoretical frameworks and computational models can be applied to real-world data such as experience sampling, passive data, and digital-behavior data (e.g., online activity such as on social media). In turn, modeling real-world data can benefit from a theory-driven computational approach to move from purely predictive to explanatory power. We illustrate these points using emerging studies and discuss the challenges and opportunities of using real-world data in computational psychiatry.

Online Now: Cognitive modeling of real-world behavior for understanding mental health

I’ll be reviewing PhD applications for Fall 2026. Our lab investigates healthy self-development, focusing on the socioemotional and neural processes that support or hinder it across adolescence and young adulthood. Info: labs.utdallas.edu/dsanlab/join...

Our lab just completed our first pilot scanning session! 🧠
It was only possible thanks to the supportive staffs and colleagues who shared their experience with us 🙌 @cvlneuro.bsky.social

New Open dataset alert:
🧠 Introducing "Spacetop" – a massive multimodal fMRI dataset that bridges naturalistic and experimental neuroscience!

N = 101 x 6 hours each = 606 functional iso-hours combining movies, pain, faces, theory-of-mind and other cognitive tasks!

🧵below

🚨 We’re hiring! The Computational Cognitive Neuroscience Lab at Virginia Tech is looking for a postdoc to join our team studying the neural + computational mechanisms of structure learning and flexible cognition: ccnvt.github.io#positions

Disagreement drives metacognitive development Metacognition improves significantly over childhood, but the mechanisms underlying this development are poorly understood. We first review recent research demonstrating that disagreement prompts competent responses by young children across several metacognitive domains (confidence monitoring, information search, and source monitoring). We then propose a mechanistic model of how disagreement facilitates metacognition. We localize one main source of children’s metacognitive limitations in their still-developing capacities to reason about alternative possibilities, which manifest in an overly narrow focus on one hypothesis. Disagreement increases the child’s likelihood of representing alternative hypotheses, thereby promoting improved metacognitive reasoning. The broader proposal is that, through repeated experiences of disagreement, children become better at representing alternative possibilities even when reasoning on their own, leading to metacognitive development.

Online Now: Disagreement drives metacognitive development

Ruling out Latent Time-Varying Confounders in Two-Variable Multi-Wave Studies There has been considerable interest in estimating causal cross-lagged effects in two-variable, multi-wave designs. However, there does not currently exist a strategy for ruling out unmeasured time...

Betsy McCoach and I have just published a paper in my paper Multivariate Behavioral Research titled “Ruling out Latent Time-Varying Confounders in Two-Variable Multi-Wave Studies.” You can download a copy at doi.org/10.1080/0027... In this thread, I explain what we do in this paper.

The DSAN Lab joined an event organized by the Center for Children and Families and had a beautiful Saturday morning!
Kids stopped by our table to try a fun emotion recognition quiz and learn about opportunities to participate in our studies 🍀

🚨 New preprint alert! 🚨

Thrilled to share new research on teaching!
Work supervised by
@cocoscilab.bsky.social, @yaelniv.bsky.social, and @markkho.bsky.social.

This project asks:
When do people teach by mentalizing vs with heuristics? 1/3

osf.io/preprints/os...

Introducing the Neural Strategies Substack Allow me to (re)introduce myself

I've decided to start my own Substack, called Neural Strategies: russpoldrack.substack.com/p/introducin... - I will soon start releasing content from a new open-source book I'm developing, tentatively titled "Better Code, Better Science". Subscribe to receive each new section as soon as I post it!

DANS is happening!! 🎉🎉
Mark your calendars and please spread the word!

UTD Psych labs at @sansmeeting.bsky.social! So wonderful to be together with colleagues and students. Huge thanks to the organizers for making SANS 2025 such a meaningful experience!

The DSAN Lab’s first presentation at
@sansmeeting.bsky.social
! Our amazing undergraduate student, Hamshitha, presented our work on how joint trajectories of victimization and perpetration impact structural brain development in early adolescence.
@cvlneuro.bsky.social

Table 1 Summary Guidelines for Conducting Lab Experiments in Motivation and Emotion ___________________________________________________________________________ Pre-Manipulation Setup • Consider physical environment on the way to the lab room • Consider physical environment of lab room (e.g., cameras, mirror, room size, social) • Create a plausible cover story • Be mindful of the psychological state of participants at the start of experiment Experimenters/Confederates • Ensure consistency between multiple experimenters/confederates • Compare effect of multiple experimenters statistically • Keep appearance/clothing consistent • Be cognizant of informal/formal style of interacting • Note differences in physical attractiveness • Track emotion and personality variables of experimenters • Keep blind to condition Independent Variables (IVs) • Use a strong IV – aim for a psychological sledgehammer • Be careful that IV is not so strong that participants ignore DV • Ensure multiple IVs are similar in strength • Check effectiveness of manipulation and appreciate its complexity • Pre-test to insure IVs are the same psychologically in different times and places • Avoid experimenter demand • Avoid confounds Dependent Variables (DVs) • Create sensitive DVs • Consider challenges with self-reported effort • Consider challenges with self-reported emotion • Be aware that the order of presentation of variables might influence results • Pretest Applying Guidelines to Replications • Consider the benefits of conceptual replications for theory testing • Select studies for replication that have a reasonable chance of being replicated • Note that expectations of non-replication influence researchers and outcomes • Use exact original method when replicating a specific effect • Test correlations that logically follow from experimental hypotheses if possible • Insure participating labs in projects follow instructions of the study coordinators Data Management • Take steps to avoid errors i…

Very useful set of guidelines for conducting social psychology lab experiments by @eddiehj.bsky.social, @davidamodio.bsky.social, and colleagues.

Preprint: doi.org/10.31234/osf...

Few quotes follow…

What Adolescence gets right (and wrong) about teen boys’ brains It may make for a compelling drama, but young men’s minds are more complex, says one neuroscientist

I wrote an Article about the Netflix drama, Adolescence.

I’ll try to post the pdf because it’s behind a paywall (but you might be able read it for free below)

www.thetimes.com/life-style/p...

YouTube video by OHBM Australia 2024 Webinar Series: Open Science Practices for Neuroimaging

For those interested in brain visualisations and open science practices refer to this amazing webinar series by the OHBM Australia community hosted by @natashaltaylor.bsky.social.

Featured speakers:
@sidchop.bsky.social
@sinamansourl.bsky.social
@sbollmann.bsky.social

youtube.com/watch?v=RTy5...

Data Sharing - ABCD Study

FYI, if you want to be prepared to apply for the next ABCD Study data release, prepare for these new NIST security requirements 🧠: @ohbmofficial.bsky.social abcdstudy.org/scientists/d...

Thanks to everyone's feedback, we have updated our calculator to optimize sample size N & scan time T for fMRI studies: leonoqr.github.io/ORSP_Calcula...

The first new feature is that users can explore how different N & T leads to different accuracy, e.g., N=1000 & T=30min => 81% max accuracy. 🧵

Thank you so much for starting this! I would love to be added.

There's a lot of evidence of the harms of smacking children.

There's also evidence that children and young people learn more from rewards than from punishment, including this computational study by @stepalminteri.bsky.social

journals.plos.org/ploscompbiol...

Academic mentees thrive in big groups, but survive in small groups - Nature Human Behaviour Using longitudinal genealogical data on mentor–mentee relations and their publications, the authors find that mentees trained in larger groups tend to exhibit superior academic performance compared with those from smaller groups, provided they remain in academia post graduation.

Research by Zeng et al shows that mentees trained in larger groups tend to exhibit superior academic performance compared to those from smaller groups, as long as they stay in academia after graduation.
https://www.nature.c...

Coming to a Zoom near you!
The affective gradient hypothesis: An affect-centered account of motivated behavior - Dr. Amitai Shenhav