Elizabeth Jiwon Im

@imelizabeth.bsky.social

🌲 PhD candidate at Stanford 🧠 Intersection of developing brain, visual experience, and computational models 🐦 Johns Hopkins alum :) imelizabeth.github.io

*Sharing for our department’s trainees* 🧠 Looking for insight on applying to PhD programs in psychology? ✨ Apply by Sep 21st to Stanford Psychology's 10th annual Paths to a Psychology PhD info session/workshop to have all of your questions answered! 📝 Application: forms.gle/4nujgDd3W2tx...

Paths to a Psychology Ph.D.: An Information Session and Workshop

Join Stanford Psychology graduate students, research assistants, and faculty for a free one-day virtual information session and workshop on applying to research positions and Ph.D. programs in psychol...

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Excited to present soon @cogcompneuro.bsky.social #CCN2026! Come to my talk at 2:35 pm learn about children and teens’ visual experience in natural environments outside of traditional lab settings (“visual diet”) using a new, large-scale, egocentric scene and mobile eye-tracking data 🤓

Stanford Vision and Perception Neuroscience Lab@stanfordvpnl.bsky.social · last mo.

VPNL is at #CCN2026 !! Check out our lab members' presentations this week :)

excited to be at #CCN2026! come talk to our very new lab during the poster sessions 🌱 we're introducing novel computational frameworks to model human 3D vision + developing theoretical and empirical strategies for understanding these abilities say hello & let us know what you think!

Stephanie Fu
B89: Foveated multiview models of human 3D shape perception

Václav Knapp 
B90: Distribution shift predicts the dynamics of 3D perception

Tyler Bonnen
C78: Human-level 3D perception emerges from multi-view learning

Nathan Kong
F75: Evaluating spatial perception in humans and machines

While I'm sad to miss #CCN2026, you don't have to miss out on the cool work coming out of the lab. 😊 Super proud of the diversity of projects this year—from category and unit specialization to inversion effects, shape bias, and memorability in brains and DNNs! Stop by and check them out! #NeuroAI

Graphic showing the VCCN Lab’s contributions to CCN 2026. The design features the VCCN Lab logo on the left, a stylized neural network icon in the center, and “CCN 2026” on the right. Five posters are listed, each accompanied by a headshot of the presenting researcher and the scheduled poster session.
Jakob Winkler: Rethinking the inversion effect as a graded phenomenon across object categories (Poster Session A, Tuesday, August 4, 9:30–11:15 am).
Sule Tasliyurt-Celebi: Memorability varies with how visual information is sampled by humans and models (Poster Session C, Wednesday, August 5, 9:30–11:15 am).
Alban Flachot: More shape bias does not mean more human-like visual representations (Poster Session C, Wednesday, August 5, 9:30–11:15 am).
Lenny van Dyck: Different features shape category selectivity in human visual cortex and DNNs (Poster Session F, Thursday, August 6, 1:45–3:30 pm).
Zhengqing Miao: Functionally specialized units reflect readout pathways rather than distinct feature encoding in artificial neural networks (Poster Session F, Thursday, August 6, 1:45–3:30 pm).

This review began with the great Peter Hagoort asking after one of my talks: "What have we actually learned about the brain from brain–DNN comparisons?" 🔥 So eye-opening to survey NeuroAI work across vision and language--many questions have been answered in one field but remain open in the other!

Dota Tianai Dong@dotadotadota.bsky.social · 2mo ago

1/5 Over a decade of comparing deep neural networks to the human brain—but what have we actually learned? Our new @cp-trendscognsci.bsky.social Feature Review synthesizes a decade of brain–DNN comparisons, asking what they reveal about brain function across vision and language.

Excited to share that our paper is now out in #JNeurosci! We propose a multidimensional framework of high-level visual cortex that reconciles a longstanding debate. Thanks to @kathadobs.bsky.social, @martinhebart.bsky.social, and everyone else for the great discussions along the way. More to come 🧠🌈

Multidimensional feature tuning in category-selective areas of human visual cortex

Two prominent accounts describe the functional organization of human high-level visual cortex. A categorical view emphasizes category-selective areas, while a dimensional view highlights continuous fe...

doi.org

SfN Journals@sfnjournals.bsky.social · 2mo ago

#JNeurosci: Using a data-driven analysis of fMRI responses to natural images, @levandyck.bsky.social @martinhebart.bsky.social & @kathadobs.bsky.social identified interpretable dimensions that explain activity in face-, body-, & scene-selective areas https://doi.org/10.1523/JNEUROSCI.0038-26.2026

Diagram of brain activation patterns with labels for different stimuli.