Postdoc @ MIT @mcgovernmit @mitbrainandcog website: https://nitter.cf/t.co/DZcRQZpOBh
Cambridge, MA
Joined April 2016
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Causal reasoning lies at the core of human intelligence but few studies have tested its neural basis. In a new study, we find that despite its importance, causal reasoning is implemented not in a single unified brain system, but rather in separate domain-specific regions.
Pramod RT/ಪ್ರಮೋದ್ ರಾ ತಾ retweeted
Causal reasoning lies at the core of human intelligence but few studies have tested its neural basis. In a new study, we find that despite its importance, causal reasoning is implemented not in a single unified brain system, but rather in separate domain-specific regions.
Key questions remain: How do the Physics Network and Theory of Mind system integrate when scenarios involve both physical and social causes? And why does causal reasoning—central to human cognition—not get its own cortical machinery when so many others do?
Preprint: biorxiv.org/content/10.64898…
This study wouldn’t have been possible without the amazing Jessica Chomik-Morales, Laura Schulz and Nancy Kanwisher. @mitbrainandcog
Pramod RT/ಪ್ರಮೋದ್ ರಾ ತಾ retweeted
1/ Can brain inspired self-supervised objectives lead to more robust visual representations in DNNs?
Excited to share our preprint with @adeli_hossein, Tian Zeng, @KriegeskorteLab, Ning Qian
biorxiv.org/content/10.64898…
Pramod RT/ಪ್ರಮೋದ್ ರಾ ತಾ retweeted
1/ Governments and organizations often cut staff and overlapping roles in the name of efficiency. But what looks unnecessary on a normal day may be essential when something goes wrong. Our new preprint tests how redundancy protects human teams 🧵👇
Pramod RT/ಪ್ರಮೋದ್ ರಾ ತಾ retweeted
Have we built machines that think like humans? 🧠🤖
In a new preprint, we propose CogGym, which compares AI and human judgments across 258 commonsense-reasoning experiments from 100 cognitive-science papers.
Paper: arxiv.org/abs/2609.21259
Platform: coggym.org
ALT CogGym workflow diagram: research experiments are standardized in EML by an LLM agent with human review, then rendered for human participants and computational models. Replicated and original human data are compared with model responses in a shared analysis.
Pramod RT/ಪ್ರಮೋದ್ ರಾ ತಾ retweeted
🧠🎬 Our (w/@dwLi_Neuro) AVMoments-EEG dataset is out!
We recorded EEG from 10 participants watching 3-s naturalistic videos (8 EEG sessions and 10,368 trials per partcipant).
Preprint: doi.org/10.64898/2026.09.17.…
Dataset: zenodo.org/records/22227356
Code: github.com/ShuningTangg/AVMo…
Pramod RT/ಪ್ರಮೋದ್ ರಾ ತಾ retweeted
Today in @Nature, we report a new approach to studying human brain development and modeling disease in vivo.
Human neural #assembloids and #organoids have opened new ways to study development in vitro, but they also have important limitations. Transplantation can provide a more physiological environment, yet human cortical cells then develop within a rodent brain that matures much faster, constraining their growth and integration.
To address some of these limitations and obtain more advanced functional readouts relevant to disease biology, we generated #apallial mice, in which ~98% of the cortex and hippocampus fail to form, creating space for transplanted human cortical organoids to grow and integrate extensively.
We call these #xenocortical mice (XCX).
Nearly 7 years in the making. Congratulations to an extraordinary team and wonderful collaborators!
Link to the article below 👇
Pramod RT/ಪ್ರಮೋದ್ ರಾ ತಾ retweeted
**new paper: Superposition in Brains**
What if Barlow was wrong? In his neuron doctrine (1972), he suggested to search for the 'trigger features' of each neuron to understand cortex. But what if that is the wrong coordinate system to understand neural representations?
biorxiv.org/content/10.64898…
