Relevant paper: 📝 van Rooij, I., @johankwisthout.bsky.social, Blokpoel, M., Szymanik, J., Wareham, T. & Toni, I. (2011). Intentional communication: Computationally easy or difficult? Frontiers in Human Neuroscience: www.frontiersin.org/journals/hum... 9/n

Relevant paper: 📝 van de Braak, L., van Rooij, I., @dingemansemark.bsky.social, M., Toni, I., & Blokpoel, M. (2025). Understanding misunderstanding: How quick-fix solutions undermine explanation. Zenodo. doi.org/10.5281/zeno... 8/n

Relevant paper: 📝 Woensdregt, M. S, @matspike.bsky.social, de Haan, R., Wareham, T., van Rooij, I., & @blokpoel.bsky.social (2021). Why is scaling up models of language evolution hard? escholarship.org/uc/item/0217... 7/n

Relevant paper: 📝 van de Braak, L. D, @dingemansemark.bsky.social , M., Toni, I., van Rooij, I., & @blokpoel.bsky.social (2021). Computational challenges in explaining communication: How deep the rabbit hole goes. escholarship.org/uc/item/4pj1... 6/n

Relevant paper: 📝 Woensdregt, M., @blokpoel.bsky.social , van Rooij, I., & @andreaeyleen.bsky.social (2024). Challenges for a computational explanation of flexible linguistic inference. Proceedings of ICCM2024. doi.org/10.31234/osf... 5/n

Relevant paper: 📝 van de Braak, L., de Haan, R., @dingemansemark.bsky.social, Toni, I., van Rooij, I., & @blokpoel.bsky.social (2024). Intractability obstacles to explanations of communication. Proceedings of ICCM2024. osf.io/preprints/ps... 4/n

Be that as it may, I think your intuition is correct about the implications of the Reclaiming argument. Our argument is not specific to any specific cognitive capacities, but set up to argue about all and any substantive human-level (domain general) cognitive capacity. 2/n

You may also enjoy this short, accessible piece I co-authored with @olivia.science bsky.app/profile/iris...

Agreed, computational approaches fall apart at pragmatics. Every text has pragmatics, which needs to be maintained in the process of summarizing. But that is only possible if the pragmatics can be understood. Yet, pragmatics is (to date) computationally intractable. bsky.app/profile/iris...

I'm happy you mention this. I've been encouraging people who ask: how do we resist 'AI' to use your work in that space as a guide for action. But ALSO, to see cognitive science as a living research program producing insights that refute tech industry reductionism.

In case useful: www.digitalcultureandeducation.com/volume-162-p... bsky.app/profile/oliv...

Já passamos por isso antes! Como assim?, por Olivia Guest Tradução parcial de “We’ve been here before!”, de @olivia.science , 18 de fevereiro de 2026. adolfoneto.elixiremfoco.com/blog/posts/2...

* What this means is that even if we accept / assume computationalism, i.e. that cognition is computationally understandable / describable, we still may never ever be able to write those computations down. The search for them is intractable. See: bsky.app/profile/iris...

1️⃣ That's @irisvanrooij.bsky.social's tractable cognition thesis which helps constrain theories 2️⃣ Also something the dynamical systems cognitive scientists get confused about when they think they have rejected computationalism 3️⃣ Still doesn't imply we know or can ever know the computations* 1/

CAIL = prerequisite knowledge for a critical perspective, such as to tell apart nonsense hype from true theoretical computer scientific claims. For example, the idea that human-like systems are a sensible or possible goal is the result of circular reasoning and anthropomorphism.

It feels many only know my work pushing back against AI, which is very fair — but I have a broader research interest, which also forms a protective belt against such forces, as well as being what I have been thinking about since discovering "cognitive science" in ~2006: 🍃 metatheory.space 1/

On the one hand, these AI forces — what I describe above as ideas that radiate outwards — have been discussed for decades (notice extract from 1985; doi.org/10.5281/zeno...) before they became as huge a mess as we see now, see: olivia.science/ai/ and bsky.app/profile/oliv... 2/

Notice this extract from the 90s by Sheryl Hamilton which references the late 40s, which I use in doi.org/10.3390/bs16... to highlight it's nothing new. Part of doing good work, while fighting against AI, is not falling for speed, as Isabelle Stengers also warns in Another Science is Possible. 3/

On the other hand, I want to highlight my work that is about cognition, models, theory (see: olivia.science/theory/) & understanding how we think about them. Sometimes a good starting point is to zoom out: What Makes a Good Theory, and How Do We Make a Theory Good? doi.org/10.1007/s421... 4/

Also a short & snappy paper on these issues, esp. for psychologists and cognitive neuroscientists interested in modelling and AI & in how they contribute to or even hinder research, is this w/ @andreaeyleen.bsky.social @irisvanrooij.bsky.social: doi.org/10.5281/zeno... bsky.app/profile/oliv... 5/

For more on me and my work — as well as for PDFs to any paper that may not be directly accessible otherwise — see: olivia.science 6/

Published with some minor changes! Please share widely. 😊 Guest, O., Suarez, M., ... & van Rooij, I. (2026). Against the Uncritical Adoption of 'AI' Technologies in Academia. Digital Culture & Education, 16(2), 85–118. doi.org/10.5281/zeno... Journal: www.digitalcultureandeducation.com/volume-162

You can still support our message and sign the Open Letter "Stop the Uncritical Adoption of AI Technologies in Academia” Sign here: openletter.earth/open-letter-...

there are serious problems with calling non-algorithms algorithms imho, humbly some examples of misuse of terminology presented here as well as why such misuse is deployed: bsky.app/profile/oliv...

This is me expressing the finite length of, among other things, algorithm(ic explanation)s ☺️ 🎬 🍿 Video cued at the right spot: www.youtube.com/watch?v=2bdK...

“The latter demonstrates nicely the core flaw of LLMs, i.e., they cannot read and summarize, because they have no understanding or knowledge.” irisvanrooijcogsci.com/2025/08/12/a...

h/t @sonjadrimmer.bsky.social

OK, OK. Now I will have to share a different link 😂 I shared the previous version a lot.

Our position paper is out! Read it now in the following link:

Destined to be a classic

Such an excellent piece. Everyone should read this

Published version: www.digitalcultureandeducation.com/volume-162-p...

Published version: Guest, O., Suarez, M., et al. (2026). Against the Uncritical Adoption of 'AI' Technologies in Academia. Digital Culture & Education, 16(2), 85–118. doi.org/10.5281/zeno...

“We must protect and cultivate the ecosystem of human knowledge. AI models can mimic the appearance of scholarly work, but they are (by construction) unconcerned with truth — the result is a torrential outpouring of unchecked but convincing-sounding “information”. — as quoted in Guest et al. (2026)

OK, let's talk about 3 important concepts: ANNs, LLMs, & AI: "Definitions of AI and LLM are hard to pin down [&] ANN is an umbrella term for models" Black box: Really not what people think because actually you can open it (read on in the PDF to find out) by design! doi.org/10.5281/zeno... 5/

useful reference: ianparberry.com/pubs/knowled... "if a look-up table for queries of the above form were constructed, & all the pages were stacked up, they ... would require a volume of paper almost 200 feet long, 200 feet wide, and 200 feet high. [Compare the size of] the Great Pyramid of Cheops"

So pleasing and heartwarming, especially the cheering

100% bsky.app/profile/oliv...

Many people think this is no longer true for AI, but how wrong they are and how much harm this miscinception is doing ....

This hangs with the other statements in fig 1: 1️⃣ ANNs (artificial neural networks) are inspired by human organism 2️⃣ ANNs are a black box we don't understand 3️⃣ We understand neither ANNs nor cognition therefore they are similar All 3 are misplaced! We unpack this... doi.org/10.5281/zeno... 3/

Here are some flagrant examples, although the appendix is full of them of them too — basically experts and journalists alike seem to repeat this mantra that we do not understand how contemporary AI works. See appendix at the end here for more: doi.org/10.5281/zeno... 2/

Preprinted! Been working on this for ~1 year w wonderful coauthors Nancy & Mark: > Understanding Artificial Neural Networks: Mysterianism about Known Mechanism is Mysticism doi.org/10.5281/zeno... Have you noticed many experts say they don't understand AI? What's going on? 🤯 Let us tell you... 1/

Much of the difficulty in having productive conversations with friends and colleagues about the many things called “AI” is caused by the deliberate blurring of language used by the companies pushing it on us. I really appreciate the scholars who work to bring some clarity to the discussion.

I discussed this with @melhogan.bsky.social @irisvanrooij.bsky.social @altibel.bsky.social too here: bsky.app/profile/melh... 3/

And we wrote a bit about it here too @irisvanrooij.bsky.social 4/ bsky.app/profile/oliv...

Thank you! I deflate the term, so technically I also conclude it is meaningless. I do then reinflate it, like so, however! doi.org/10.3390/bs16...

This looks interesting, going right on today's to-read pile