adumbmoron.bsky.social
Postdoc at NYU using ECoG to study how the brain translates from thought to language. On the job market! 🏳️🌈🏳️⚧️🗳️ he/him https://adam-milton-morgan.github.io/
👤 Joined on 15 November 2024
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@adumbmoron.bsky.socialImportantly, the best bracketing decoder can be the wrong mechanism. Ordered role-filler laws decode well partly because they retain daughter order that Merge should discard. More decodable information is not automatically more mechanistic truth (similar to 'surprisal' debacle with LLM-brain work).
Show PostFor syntax, the target is unusually restrictive: preserve non-associative grouping; factor daughter order out of the composed object; remain recursively closed; keep substructures addressable; enforce type/role compatibility; and support headedness.
Show PostNAP's basic rule is simple: a neural mechanism is admissible only if it preserves the defining invariants of the computation it claims to implement. Neural data should then adjudicate among admissible mechanisms - not merely locate correlates of linguistic variables.
Show PostMuch contemporary work in language neuroscience asks which LLM features, embeddings or surprisal measures best predict brain activity. NAP starts elsewhere: formal invariants first, neural mechanisms second (see: arxiv.org/abs/2609.03160).
Show PostThis paper develops the Neural Admissibility Program (NAP): begin with what linguistic computation must preserve, then ask which neural dynamics could actually implement it. Many other domains of neuroscience have long adopted this logic, yet language has not been embraced in the same way.
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