TRIBE v2 is a multimodal AI model that predicts human brain activity more accurately than linear encoding models and recovers established neuroscientific findings through in-silico testing.
A foundation model to predict and capture human cognition.Nature, 644: 1002–1009
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
LLM-simulated ADHD student personas show stable self-reported traits but behavioral drift in unscripted interactions that explicit task prompts fully eliminate.
LLMs perceive cities through a culturally uneven baseline that favors Europe and Northern America over other regions in both open descriptions and structured judgments.
LLMs support decision prediction and rationale generation but lack evidence for genuine decision explanation, requiring stricter standards to avoid over-crediting.
citing papers explorer
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A foundation model of vision, audition, and language for in-silico neuroscience
TRIBE v2 is a multimodal AI model that predicts human brain activity more accurately than linear encoding models and recovers established neuroscientific findings through in-silico testing.
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LLM-Based Educational Simulation: Evaluating Temporal Student Persona Stability Across ADHD Profiles
LLM-simulated ADHD student personas show stable self-reported traits but behavioral drift in unscripted interactions that explicit task prompts fully eliminate.
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Large language models perceive cities through a culturally uneven baseline
LLMs perceive cities through a culturally uneven baseline that favors Europe and Northern America over other regions in both open descriptions and structured judgments.
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LLMs Should Not Yet Be Credited with Decision Explanation
LLMs support decision prediction and rationale generation but lack evidence for genuine decision explanation, requiring stricter standards to avoid over-crediting.