@GoldsteinYAriel
Joined June 2016
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New paper! 📷 The brain's Multiple Demand (MD) network ramps up when tasks get hard, so its activity gets read as "effort", while meaning is left to the language network. But when you're following a tough lecture, is MD just working harder, or is it tracking the content?
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Was MD just lending extra horsepower to the language network? Its activity patterns didn't follow the language network's, even though MD was more active during lectures. We conclude that MD was processing the computer science content itself.
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Read full paper here: biorxiv.org/content/10.64898… with @MiriamHavin @MeshulamMeir @R_Tikochinski @HassonLab @GoldsteinYAriel @TAILIN52261306
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We are excited to share our new preprint! 🚨 biorxiv.org/content/10.1101/… In this work, we investigate the neural mechanisms by which speakers generate information-rich linguistic content during spontaneous natural conversation.
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Finally, turning to LLMs to computationally model this additional processing, we find that improbable words emerge as stable predictions in significantly later layers than probable words, suggesting that information-rich words require deeper internal computation before generation
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In summary, by leveraging naturalistic neural recordings and LLMs, this study identifies a previously unrecognized information-making process in the speaker’s brain. We would like to thank our co-authors: Haocheng Wang, @TomSheffer17807, @DariaLioub, @SchainMariano @HassonLab 🙏
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Ariel Goldstein retweeted
Hasson Lab@HassonLabApr 6
How does language learning emerge from everyday experience? Our new preprint, led by @HRaviv830 offers a mechanistic answer.
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Ariel Goldstein retweeted
Omer Nahum@omer6nahumMar 18
Do LLMs have motivation? Motivation is a key lens for explaining human behavior. As LLM behavior becomes more human-like, a natural question arises: could it help understand model behavior too? With @AsaelSklar @GoldsteinYAriel @roireichart 📄 Paper: arxiv.org/pdf/2603.14347 1/5
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I’m hiring a PhD student for my lab at Cambridge’s Center for Human Inspired AI (CHIA)! Work at the intersection of AI × cognition × neuroscience. 📅 Deadline: Dec 2 🔗 Program: postgraduate.study.cam.ac.uk… 📚 My Google Scholar: scholar.google.com/citations…
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🚨 New preprint! One idea, many ways to say it – does your brain track those options before you speak? Using LLMs, we put this to the test: biorxiv.org/content/10.1101/… We show for the 1st time that the brain represents many alternatives simultaneously in both listening & speaking 🧵
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New paper out! Do LLMs Exhibit Human-Like Cognitive Biases? We systematically tested 20 biases across 10 leading LLMs using pre-registered experiments. The result: cognitive biases in LLMs are both pervasive and nuanced. papers.ssrn.com/sol3/papers.…
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Most novel insight: When an LLM recognizes a bias, it’s actually less likely to exhibit it. This helps explain conflicting results in prior studies—each was seeing a subset of the broader pattern.
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