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Charles Han

@biocharles.bsky.social

Major Language and Human Brain Sciences. Research Interest: [Algebraic] Generative Syntax & Human Intelligence; sometimes political stuff Department of Linguistics. BLCU Website: https://sites.google.com/view/biohan/home

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Charles Han@biocharles.bsky.social · last mo.

My personal feeling is, for any theory, the best way to keep its vitality is to constantly revise it whenever new insights are systematically available, and this is exactly what Chomsky and his collegues have been doing, the traction is simply the natural consequence of this.

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Charles Han@biocharles.bsky.social · last mo.

A Unified Neural Time Course for Words, Phrases, and Sentences: MEG Evidence from Parallel Presentation direct.mit.edu/nol/article/...

A Unified Neural Time Course for Words, Phrases, and Sentences: MEG Evidence from Parallel Presentation

Abstract. Recent behavioral and neural research on reading shows that humans can extract syntactic structure from short sentences within a fraction of a second—faster than many estimates for recognizi...

direct.mit.edu

Charles Han@biocharles.bsky.social · 4mo ago

I recently encountered several youtube channels posing AI-generated videos in which some impactful public intellectuals said something they had never spitted out their mouth according to my knowledge, let me remind y'all, this is also one way of distroying knowledge and truth.

Elliot Murphy@elliot-murphy.bsky.social · 4mo ago

New paper out today — on the evolution of language and how algebraic models of grammar might offer novel constraints for theories of neural mechanisms 🧠 arxiv.org/abs/2604.00291

Frege in the Flesh: Biolinguistics and the Neural Enforcement of Syntactic Structures

Biolinguistics is the interdisciplinary scientific study of the biological foundations, evolution, and genetic basis of human language. It treats language as an innate biological organ or faculty of t...

arxiv.org

Olivia Guest · Ολίβια Γκεστ@olivia.science · 10mo ago

important on LLMs for academics: 1️⃣ LLMs are usefully seen as lossy content-addressable systems 2️⃣ we can't automatically detect plagiarism 3️⃣ LLMs automate plagiarism & paper mills 4️⃣ we must protect literature from pollution 5️⃣ LLM use is a CoI 6️⃣ prompts do not cause output in authorial sense

5 Ghostwriter in the Machine A unique selling point of these systems is conversing and writing in a human-like way. This is imminently understandable, although wrong-headed, when one realises these are systems that essentially function as lossy2 content-addressable memory: when input is given, the output generated by the model is text that stochastically matches the input text. The reason text at the output looks novel is because by design the AI product performs an automated version of what is known as mosaic or patchwork plagiarism (Baždarić, 2013) — due to the nature of input masking and next token prediction, the output essentially uses similar words in similar orders to what it has been exposed to. This makes the automated flagging of plagiarism unlikely, which is also true when students or colleagues perform this type of copypaste and then thesaurus trick, and true when so-called AI plagiarism detectors falsely claim to detect AI-produced text (Edwards, 2023a). This aspect of LLM-based AI products can be seen as an automation of plagiarism and especially of the research paper mill (Guest, 2025; Guest, Suarez, et al., 2025; van Rooij, 2022): the “churn[ing] out [of] fake or poor-quality journal papers” (Sanderson, 2024; Committee on Publication Ethics,Either way, even if the courts decide in the favour of companies, we should not allow these companies with vested interests to write our papers (Fisher et al., 2025), or to filter what we include in our papers. Because it is not the case that we only operate based on legal precedents, but also on our own ethical values and scientific integrity codes (ALLEA, 2023; KNAW et al., 2018), and we have a direct duty to protect, as with previous crises and in general, the literature from pollution. In other words, the same issues as in previous sections play out here, where essentially now every paper produced using chatbot output must declare a conflict of interest, since the output text can be biased in subtle or direct ways by the company who owns the bot (see Table 2). Seen in the right light — AI products understood as contentaddressable systems — we see that framing the user, the academic in this case, as the creator of the bot’s output is misplaced. The input does not cause the output in an authorial sense, much like input to a library search engine does not cause relevant articles and books to be written (Guest, 2025). The respective authors wrote those, not the search query!
Olivia Guest · Ολίβια Γκεστ@olivia.science · 10mo ago

New preprint 🌟 Psychology is core to cognitive science, and so it is vital we preserve it from harmful frames. @irisvanrooij.bsky.social & I use our psych and computer science expertise to analyse and craft: Critical Artificial Intelligence Literacy for Psychologists. doi.org/10.31234/osf... 🧵 1/

Cover page of Guest, O., & van Rooij, I. (2025, October 4). Critical Artificial Intelligence Literacy for Psychologists. https://doi.org/10.31234/osf.io/dkrgj_v1