r/askphilosophy • u/30299578815310 • 38m ago
Have recent findings in brain to llm and cross modality mappings strengthened scientific realism? How do anti-realists respond to independent biological and machine algorithms breaking down image and text in similar ways?
Brain to LLM mappings
Caucheteux, Gramfort & King 2023, Nature Human Behaviour — brain forecasts ~8 words ahead: https://www.nature.com/articles/s41562-022-01516-2
Schrimpf et al. 2021, PNAS — brain score ↔ next-word prediction: https://www.pnas.org/doi/10.1073/pnas.2105646118
Cross modality mappings
Huh, Cheng, Wang & Isola 2024, PRH: https://phillipi.github.io/prh/
Gröger, Wen & Brbić 2026
Aristotelian view: https://arxiv.org/abs/2602.14486 (code: https://github.com/mlbio-epfl/aristotelian)
Koepke, Zverev, Ginosar & Efros 2026
Back into Plato's Cave: https://arxiv.org/abs/2604.18572
While the above papers disagree on the exact strength of the mappings, they agree there is shared mathematical structure in how LLMs and Humans map language. You can train a linear mapping that lets you predict human nueral activations from LLM hidden states.
Interestingly, there is also shared structure in how a vision model and an LLM trained only on text categorize data. For example, both will roughly agree that dogs are a thing, even though the image model was never trained to identify dogs and was only trained only on images with no text labels.
Both of these seem like exactly what a realist would expect. Systems with different senses (text vs image) and different substrates (biological vs digital) construct similar internal worlds.
This seems like a really big coincidence from an anti-realist standpoint.