Tootfinder

Brains and language models converge on a shared conceptual space across different languages
Zaid Zada, Samuel A Nastase, Jixing Li, Uri Hasson
https://arxiv.org/abs/2506.20489

Brains and language models converge on a shared conceptual space across different languages
Human languages differ widely in their forms, each having distinct sounds, scripts, and syntax. Yet, they can all convey similar meaning. Do different languages converge on a shared neural substrate for conceptual meaning? We used language models (LMs) and naturalistic fMRI to identify neural representations of the shared conceptual meaning of the same story as heard by native speakers of three languages: English, Chinese, and French. We found that LMs trained on entirely different languages co…

FieldAI, which is developing robot "brains", raised $405M at a $2B post-money valuation from Nvidia and others; FieldAI raised an initial $91M in late 2024 (Dan Primack/Axios)
https://www.axios.com/2025/08/20/fieldai-raises-over-400-million-to-…

FieldAI raises over $400 million to make robot "brains"
Investors include funds affiliated with Jeff Bezos and NVIDIA.

MovieCORE: COgnitive REasoning in Movies
Gueter Josmy Faure, Min-Hung Chen, Jia-Fong Yeh, Ying Cheng, Hung-Ting Su, Yung-Hao Tang, Shang-Hong Lai, Winston H. Hsu
https://arxiv.org/abs/2508.19026

MovieCORE: COgnitive REasoning in Movies
This paper introduces MovieCORE, a novel video question answering (VQA) dataset designed to probe deeper cognitive understanding of movie content. Unlike existing datasets that focus on surface-level comprehension, MovieCORE emphasizes questions that engage System-2 thinking while remaining specific to the video material. We present an innovative agentic brainstorming approach, utilizing multiple large language models (LLMs) as thought agents to generate and refine high-quality question-answer …

BrainSymphony: A Transformer-Driven Fusion of fMRI Time Series and Structural Connectivity
Moein Khajehnejad, Forough Habibollahi, Adeel Razi
https://arxiv.org/abs/2506.18314

BrainSymphony: A Transformer-Driven Fusion of fMRI Time Series and Structural Connectivity
Existing foundation models for neuroimaging are often prohibitively large and data-intensive. We introduce BrainSymphony, a lightweight, parameter-efficient foundation model that achieves state-of-the-art performance while being pre-trained on significantly smaller public datasets. BrainSymphony's strong multimodal architecture processes functional MRI data through parallel spatial and temporal transformer streams, which are then efficiently distilled into a unified representation by a Perceive…

Unpack AI’s environmental impact today at 11am EDT, from local vs global effects to how it stacks up against Netflix flights. Try out a new student-friendly tool brainstorm classroom ideas in this #MyFest25 webinar HT @…

Is ChatGPT Rotting Our Brains? New Study Suggests It Does https://www.404media.co/is-chatgpt-rotting-our-brains-new-study-suggests-it-does/

I think the thing people confuse re: Israel and the United States is, just because Israel is a colonial outpost of the United States, doesn’t mean the leaders of the United States aren’t *also* utter fools with fucking rocks for brains

How brains build higher order representations of uncertainty
Megan A. K. Peters, Hojjat Azimi Asrari
https://arxiv.org/abs/2506.19057 https://

How brains build higher order representations of uncertainty
Higher-order representations (HORs) are neural or computational states that are "about" first-order representations (FORs), encoding information not about the external world per se but about the agent's own representational processes -- such as the reliability, source, or structure of a FOR. These HORs appear critical to metacognition, learning, and even consciousness by some accounts, yet their dimensionality, construction, and neural substrates remain poorly understood. Here, we propose that …

"This isn’t to say that AI is uniformly bad—it’s clearly not—but that we must not fall into the trap of mistaking the outputs of writing (…) from the value of the cognitive process of writing (…).
It would be a catastrophically unwise decision for humanity to abandon a key step in training young brains simply because they can now clack a few keys and produce something that sounds intelligent even as they never become intelligent."

The Death of the Student Essay—and the Future of Cognition
One professor's reflections on the end of an era, as AI tools such as ChatGPT have murdered the student essay (RIP). Here's why that threatens the future of human cognition—and how to save ourselves.

Amortizing personalization in virtual brain twins
Nina Baldy, Marmaduke M Woodman, Viktor K Jirsa
https://arxiv.org/abs/2506.21155 https://

Amortizing personalization in virtual brain twins
Virtual brain twins are personalized digital models of individual human subject or patient's brains, allowing for mechanistic interpretation of neuroimaging data features. Training and inference with these models however presents a pair of challenges: large shared infrastructure do not allow for use of personal data and inference in clinical applications should not require significant resources. We introduce "anonymized personalization" to address both by expanding model priors to include perso…