Computational Cognitive Science PhD @JHUCogSci | BS @Stanford |
Joined June 2020
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What dimensions organize how humans see social interactions?
We learn them using the first large-scale dimensional account of over 53,000 odd-one-out similarity judgments of naturalistic social scenes. The learned dimensions explain over twice the variance of theory-driven, pre-labeled annotations and better predict visual cortex and regions like the superior temporal sulcus.
New preprint w/ Leyla Isik 🧵
osf.io/preprints/psyarxiv/8c…
Kathy Garcia retweeted
Stanford Digital Brain Project has launched! We're hoping to make a seriously useful model of the human brain. Please come join us! news.stanford.edu/stories/20…
📄 Preprint: osf.io/preprints/psyarxiv/8c…
💾 Data + code: github.com/garciakathy/simil…
🔎 Interactive project page: garciakathy.github.io/latent…
Questions and comments very welcome!
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Related: we these human judgments to fine-tune video foundation models toward human social perception in complementary paper:
arxiv.org/abs/2510.01502
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Kathy Garcia retweeted
So proud of this effort! Give RABBiT a try for your language fMRI data 🐰
Foundation models in cog neuro still have to prove their utility but the potential to bridge data across tasks, imaging modalities, and systems & cell neuro is really exciting.
The future is bright ☀️
🚨 Super excited to share our latest work:
RABBiT: a foundation model for speech- and language-evoked brain activity.** 🧠🎧
W/ @mtoneva1
One model enables:
✅ accurate zero-shot brain prediction
✅ efficient few-shot adaptation with only 5-10 mins of subject data
✅ convenient in silico neuroscience experiments
and it’s so lightweight that it enables real-time, in-browser inference (feel free to try for yourself!)
Website: bridge-ai-neuro.github.io/ra…
Here's the story. 🧵👇
Kathy Garcia retweeted
🚨 Super excited to share our latest work:
RABBiT: a foundation model for speech- and language-evoked brain activity.** 🧠🎧
W/ @mtoneva1
One model enables:
✅ accurate zero-shot brain prediction
✅ efficient few-shot adaptation with only 5-10 mins of subject data
✅ convenient in silico neuroscience experiments
and it’s so lightweight that it enables real-time, in-browser inference (feel free to try for yourself!)
Website: bridge-ai-neuro.github.io/ra…
Here's the story. 🧵👇
Kathy Garcia retweeted
🦀New preprint! (w/@najoungkim)🦞
Is grammaticality a major organizing principle of NLM representations? We show that many NLMs exhibit abstract rep. separation for grammaticality. We believe this work addresses debates about confounds in measuring model gram. knowledge.
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Kathy Garcia retweeted
JHU CS alum @jieneng_chen, @HopkinsEngineer ’26 (PhD) has been named an inaugural @sciam Young American Scientist, an award given to scientists whose work is poised to change the world. Congratulations, Dr. Chen! 🎉 cs.jhu.edu/news/cs-alum-reco…
Kathy Garcia retweeted
Happy to share that I’ve been appointed Professor Angela Goh Career Development Professor in Computer Science & Engineering at NTU! This professorship recognizes outstanding female faculty and academic excellence. Grateful for the recognition and support!
Kathy Garcia retweeted
I’m very happy to share our paper, Revisiting Rainbow, accepted in the Deep RL workshop @Neurips_Conf. I’ve been working with @pcastr for a few months, and I wanted to share my story, and how this work came to be, in the hope it motivates others in similar situations to mine.
Happy to share "Revisiting Rainbow" w/ @JS_Obando where we argue small/mid-scale envs can promote more insightful & inclusive deep RL research.
📜Paper: arxiv.org/abs/2011.14826
✍️🏾Blog: bit.ly/2VkPJ4r
🐍Code: bit.ly/3mrCyuD
📽️Video: bit.ly/2I0BAXk
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Kathy Garcia retweeted
🔵🔴 Join us for the UniReps Workshop: Unifying Representations in Neural Models at @NeurIPSConf 2025!
📍 Ballroom 20D, San Diego Convention Center
Dec 6
Don’t forget to fill out the participation form. Joining in person or remotely? We welcome your questions for the panel.
🔗 unireps.org/
Excited to share that I’m at #NeurIPS2025 this year! 🧠
I’ll be presenting at the UniReps Workshop on my work on Social Tokens with @su1001v and @thisismyhat, where we explore how to inject socially relevant visual features into language models to improve social reasoning and alignment.
Super excited to connect with folks working at the intersection of multimodal models and human cognition.
If you’re around, come say hi! And feel free to DM me for a coffee chat ☕️
Kathy Garcia retweeted
🚀 The “Dormant Neuron” [1] work showed that deep RL agents lose capacity when neurons stop activating.
But what if neurons look dormant yet are still learning?
Our #NeurIPS2025 paper revisits this question; using gradients instead of activations to measure real neuronal health. GraMa offers a clearer view of how deep RL agents actually learn, leading to more adaptive and higher-performing agents.
🧵👇Check out the great thread by @JasonLiu106968
#NeurIPS2025 #AgenticAI
🚀 We're thrilled to share our #NeurIPS2025 paper:
Measuring Gradients, Not Activations! Enhancing Neuronal Activity in Deep Reinforcement Learning
We identify a flaw in measuring "inactive" neurons in various complex networks and propose focusing on how neurons learn, not their outputs.
Paper: arxiv.org/abs/2505.24061
Code: github.com/torressliu/grad-b…
Thread on why gradients reveal true neuronal health in RL agents👇:
