iiinova — where your digital self keeps evolving

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 1h

DeLLMusions

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 1h

And there is also this of course mu.social/profile/iris...

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 1h

I think maybe you mean this: mu.social/profile/iris...

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 1h

"Money, again, has often been a cause of the delusion of multitudes. (...) To trace the history of the most prominent of these delusions is the object of the present pages. Men, it has been well said, think in herds; it will be seen that they go mad in herds (...)" -- Charles Mackay (1841)

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jorisvlaarhoven.bsky.social @jorisvlaarhoven.bsky.social · 03/10/2026

Mackay’s 1841 classic EXTRAORDINARY POPULAR DELUSIONS might be worth re-reading amidst all the frenzied ‘AI is the future’ rhetoric, as this excerpt from the introduction shows

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Sean Mackinnon @seanpmackinnon.bsky.social · 8h

Interesting thread under this OP My academic reading time is limited due to the demands of the job, and the primary problem for me is that it is hard to find good papers. The literature is vast and most journal articles are boilerplate I'm dying to find some truly interesting ideas/theory

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 8h

I recommend others to also do this with their work and/or others' as they feel comfortable. I have a list of books to add too (some of which I have posted about a lot before), but for now I will take a break, etc. Also here's the point from Sean (below) in all its glory!

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 8h

🍃 What happens when these ideas radiate outwards from cognitive science? What Does ‘Human-Centred AI’ Mean? doi.org/10.3390/bs16... Understanding Artificial Neural Networks: Mysterianism about Known Mechanism is Mysticism doi.org/10.5281/zeno...

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 8h

🍃 How do we use formal models to reason about cognition? Models doi.org/10.5281/zeno... On Models, Prediction, and Scientific Roles Thereof: Commentary on Sanbonmatsu et al. (2025) doi.org/10.5281/zeno...

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 8h

🍃 What does taking computationalism seriously imply? Are Neurocognitive Representations ‘Small Cakes’? philsci-archive.pitt.edu/24834/ / philpapers.org/rec/GUEANR Modern Alchemy: Neurocognitive Reverse Engineering philsci-archive.pitt.edu/25289 / philpapers.org/rec/GUEMAN

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 8h

🍃 How do we build theory in cognitive science? What Makes a Good Theory, and How Do We Make a Theory Good? doi.org/10.1007/s421... / philpapers.org/rec/GUEWMA How computational modeling can force theory building in psychological science doi.org/10.1177/1745... / philpapers.org/rec/GUEHCM

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 8h

Inspired by @seanpmackinnon.bsky.social on "curating reading lists for others", here's a thread with some of mine for when people want to collaborate: 1) How do we build theory? 2) What does computationalism imply? 3) How do we use models? 4) What happens when these ideas radiate outwards? 👇🏼

metatheory.space

Hello! 🍃 metatheory.space is a group that works at the foundations of cognitive science. We are interested in the intersections of psychological and computational neurocognitive theorising with critic...

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Ajuna Soerjadi 🌸 @ethicsofai.bsky.social · 5h

What better place to think about deep ecology than in the forest 🌳🪾💭

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 12h

So whenever I see these takes, I know they don't actually want to know what academics actually say — you wanna know how I know this? They would read the papers and not (just) the social media posts.

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 03/10/2026

"Capacities are not tasks" -- van Rooij & Guest (2026) journals.sagepub.com/doi/10.1177/...

be ruled out, it may seem that AI systems that can
(be made to) perform like humans on cognitive tasks
provide at least possible theories of how cognition could
work. However, this inference is not licensed either.
Computational models of tasks, and task performance,
are not yet theories of cognition (Guest & Martin, 2021;
Morrison & Morgan, 1999). Theories of cognition should
at a minimum provide possible explanations of one or
more substantive human cognitive capacities, such as
vision, decision-making, reasoning, concept formation,
learning, or communication (Cummins, 2000; Egan, 2018;
van Rooij & Baggio, 2021). “A capacity is a more or less
reliable ability (or disposition or tendency) to transform
some initial state (or ‘input’) into a resulting state (‘out-
put’)” (van Rooij & Baggio, 2021, p. 684). A cognitive
capacity is thus the ability to transform cognitive states
(e.g., percepts, preferences, beliefs) into other cognitive
states (e.g., beliefs, plans, decisions, actions). Although it
is true that in (hyperempiricist) psychology cognitive
capacities are typically studied by having people perform
various tasks, computationally (or, more often, statisti-
cally) modeling task performance does not yield explan-
atory theories of the full-blown capacities. This is no

only because tasks do not map one-to-one, or in any
other straightforward way, to substantive cognitive capac-
ities, but also because even if they could, the models
would not be able to scale up to situations of real-world
complexity.
AI systems cannot scale. For computational models of
cognition to be able to scale up from toy scenarios (such
as studied in the psychological labs or such as form the
bases of training AI systems through machine learning),
these models should minimally be computationally trac-
table (van Rooij, 2008). That is, the input-output map-
pings hypothesized by the computational models should
in principle be computable using a realistic amount of
resources (Box 1). However, when existing computa-
tional models are mathematically analyzed for their
resource demands it turns out that such models are either
tractable but limited in their input domains, or they are
domain-general but then the models are intractable (for
a textbook introduction to this methodology, see van
Rooij et al., 2019). Consequently, no computationally
tractable account exists to date for substantive and
domain-general cognitive capacities, such as reasoning,
communication, decision-making, planning, analogizing,
categorization, and concept formation (van Rooij et al.,
2019). Moreover, there is no good reason to believe that
such tractable accounts will be forthcoming via machine
learning (van Rooij, Guest, et al., 2024) or otherwise
(Rich et al., 2021). Hence, if someone claims their con-
crete AI system is a “theory” of human cognition, they are
more likely than not overstating the scope and capacities
of the system (van Rooij et al., 2019) and obfuscating the
system’s limits, including the human in the loop needed
to make such systems “work” (Guest & Martin, 2025

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 29/09/2026

Dear @erc.europa.eu , we DO already have common standards & frameworks. 'AI' likes to pose itself as 'special' but we do not need to reinvent the wheel. We already have established codes of conduct for research integrity. We just need to apply them. See below + more resources in thread🧵

Protecting the Ecosystem of Human Knowledge: Five Principles
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 “informa-
tion”. At best, such output is accidentally true, but generally citationless, divorced from
human reasoning and the web of scholarship that it steals from. At worst, it is confidently
wrong. Both outcomes are dangerous to the ecosystem.
Olivia Guest, Iris van Rooij, Marcela Suarez, et al. (2025, n.p.)
Knowledge production is supposed to be safeguarded by (inter)national codes of conduct for research
integrity (ALLEA 2023; KNAW et al. 2018). Such codes forbid, for instance, fabrication of data,
falsification of results, plagiarism, and, generally, distortion of the scientific record. Many argue that
new rules are required to regulate academic AI use, but pre-existing guidelines fit the bill (cf. Tafani

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Anthony Moser @anthonymoser.com · 03/10/2026

claiming we need to understand the latest ai performance of some task *is a rhetorical technique* it defines ai as something to be understood by the performance of measured tasks. but doesn't acknowledge that it's doing that

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Asheesh Kapur Siddique @asheeshks.org · 03/10/2026

Higher ed leaders are worried about the "decline of public trust"; what are they doing about the fact that faculty, students, and staff at their own institutions do not trust them to lead effectively, defend academic freedom, and meet the challenges of the moment?

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Chanda Prescod-Weinstein 🌌 @chanda.blacksky.app · 03/10/2026

I’m gonna make a general comment here: every person who comes into a Black woman’s mentions to say she doesn’t know her intellectual place thinks that race and gender has nothing to do with it. BUT: The decision not to consider how Black women are targeted before commenting is itself misogynoir.

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 03/10/2026

> For 26-year-old [...] Roz, the politics of language do not begin with those who bring the dialect to the stage or the page; they begin in classrooms. > “Is it not a linguistic crime to tell a child that the way they learned to say ‘I love you’ is grammatically wrong?”

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Ajuna Soerjadi 🌸 @ethicsofai.bsky.social · 03/10/2026

Spotted in a bookstore where you could buy mystery book without seeing it's cover and title, just some descriptive words 🙃 "Please don't use ChatGPT to guess to book. It ruins all the fun and we will have to publicly shame you :("

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Anthony Moser @anthonymoser.com · 03/10/2026

a lot of people into ai want to talk about how it works, but they either mean the stack inside the software or the things it "can do now" that "can't be explained" but ai is a rhetorical technology. the way to understand how it works is to understand how rhetoric works

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Anthony Moser @anthonymoser.com · 03/10/2026

but really, this post is just me reaffirming that I am not interested in whether they can do math because the primary function of LLMs is to help centralize, justify, and validate power, and they are doing that successfully already

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Grant Jacobs BioinfoTools/NotJustDNA 🔬🧬🖥️✍️📚🇳🇿 @notjustdna.bsky.social · 01/10/2026

Sharing for scientists grappling with use of 'AI'; some useful thoughts here – – Jumping in the middle to highlight key take-home points, but best to track back up, and read the thread from the top 🧵⬇️

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 29/09/2026

See, e.g., Section 4 in our position paper for an overview of how the principles can be applied to 'AI': 📝 @olivia.science , @marentierra.bsky.social et al. (2026). Against the Uncritical Adoption of 'AI' Technologies in Academia. Digital Culture & Education, 16(2), 85–118. doi.org/10.5281/zeno...

Honesty implies that we do not secretly use AI technologies without disclosure, and that one does not make unfounded claims about the presumed capabilities of AI technologies (this also follows
from Responsibility; see example from MIT Economics 2025, where perhaps too little too late was
done).

Scrupulousness demands, among other things, that scientists only use AI products whose functional-
ity is well-specified and validated for its specific scientific usage (cf. Kwisthout 2024; Kwisthout and
Renooij 2025). This includes terminological precision about what formalisms, models, and/or tech-
nologies are used (recall Figure 1) and rigorous argumentation to motivate why these technologies
are appropriate for the scientific purposes at hand.
Transparency requires that the AI technologies are open source and computationally reproducible.
Here we must recall the technology industry’s obfuscatory tactics: “the name of the current
producer of ChatGPT. ‘OpenAI’ sounds like it is engaged in open science, but as we have now
seen, ‘open’ never really means what you think it does.” (Mirowski 2023, p. 738; see also Dingemanse
2025; Hao 2025; Jackson 2024; Liesenfeld and Dingemanse 2024; Liesenfeld, Lopez, et al. 2023;
Maffulli 2023; Maris 2025; Nolan 2025; Solaiman 2023; Thorne 2009; Widder et al. 2024)
Independence means that scientists ensure that their research is unbiased by AI companies’ agendas,
and that any potential conflicts of interest are declared in publications and other public communi-
cations (this also follows from Honesty and Transparency; cf. Mohamed Abdalla and Moustafa
Abdalla 2021; Atkin 2025; Forbes and Guest 2025; Knoester et al. 2025).
Responsibility precludes scientists from using AI products whose use is irresponsible, e.g. harmful to
people, animals, and the environment, or otherwise in violation of legal guidelines (e.g. copyright,
data privacy, labour laws, Butterick 2025; Cole 2025; Rijo 2025; Tafani 2024a). Minimizing harm is
vital for both engin…

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 21/02/2025

24. van Rooij, I. (2015). How the curse of intractability can be cognitive science’s blessing. In Noelle, Dale, Warlaumont, Yoshimi, Matlock, Jennings, & Maglio (Eds.), Proceedings of CogSci2015. osf.io/preprints/ps...

Figure 1: Illustration of hypothetical space of possible theories (circles) for a given capacity φ. See text for details.

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 21/02/2025

14. Scott, A., Stege, U. & van Rooij, I. (2011). Minesweeper may not be NP-complete but is hard nonetheless. The Mathematical Intelligencer, 33(4), 5-17. link.springer.com/article/10.1...

link.springer.com

Minesweeper May Not Be NP-Complete but Is Hard Nonetheless - The Mathematical Intelligencer

The Mathematical Intelligencer -

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 13/09/2026

"Using some of my own work, I set out to clear up a few relevant concepts from theoretical computer science and computational cognitive science that can help to build conceptual hygiene and immunity against hyped claims about “AI” capabilities." irisvanrooijcogsci.com/2026/09/12/c...

irisvanrooijcogsci.com

Computational complexity for hype immunity

Using some of my own work, I set out to clear up a few relevant concepts from theoretical computer science and computational cognitive science that can help to build conceptual hygiene and immunity…

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 30/09/2026

We end with guidance on how Psych & AI can benefit Cognitive Science if these traps are avoided. This alternative approach disavows attempts to automate science—using machine learning or otherwise—and instead centers scientists’ own cognition. Download here: 📝 journals.sagepub.com/doi/epub/10....

Figure 2
A visual depiction of the connections between the Cognitive Sciences. Solid lines denote stronger interdisciplinary ties; and dashed lines denote weaker ones. This figure is derived from the original put forth by the Sloan Foundation in 1978 and reproduced from Figure 4 in Pléh and Gurova (2013). Different versions of it over time have used ‘Artificial intel- ligence’ (as above) instead of ‘Computer Science’ and vice versa (cf. Miller, 2003).

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 30/09/2026

We present a typology of traps to avoid: 1. Believing that AI systems are minds 2. Believing that AI systems are theories 3. Believing that cognitive science can be automated. Learn to recognise and avoid these traps. Failure to avoid leads to numerous problems.

Table 1: Typology of traps, what goes wrong if not avoided, and how the traps can be avoided. Note that all traps in a sense constitute category errors (Ryle & Tanney, 2009) and the success-to-truth inference (Guest & Martin, 2023) is an important driver in most, if not all, of the traps.

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 13/09/2026

"(...) no computationally tractable account exists to date for substantive and domain-general cognitive capacities (...) Moreover, there is no good reason to believe that such tractable accounts will be forthcoming via machine learning or otherwise" journals.sagepub.com/doi/10.1177/...

journals.sagepub.com

Combining Psychology With Artificial Intelligence: What Could Possibly Go Wrong? - Iris van Rooij, Olivia Guest, 2026

The current AI hype cycle combined with psychology’s various crises make for a perfect storm. Psychology, on the one hand, has a history of weak theoretical fou...

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Ajuna Soerjadi 🌸 @ethicsofai.bsky.social · 02/10/2026

I'm cracking up 🤣 read the axis labels. Excellent example of why critical AI literacy should also include basic data literacy. The marketing machine behind AI depends on us being oblivious to the ways numbers and statistics are used to mislead us

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Timnit Gebru @timnitgebru.blacksky.app · 02/10/2026

I suppose in the same way that eugenics was also the "biggest idea"?

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 02/10/2026

> Gleichschaltung [is a Nazi concept] from the German words gleich ('same') and Schaltung ('circuit', 'switching') and was derived from an electrical engineering term meaning that all switches are put on the same circuit allowing them all to be simultaneously activated with a master switch.

en.wikipedia.org

Gleichschaltung - Wikipedia

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Democracy Now! @democracynow.org · 01/10/2026

AI safety expert Timnit Gebru says tech leaders are promoting greatly exaggerated claims about "superintelligence" and "existential risk" as a way to distract from the real threat — their own unregulated companies.

democracynow.org

The Real Existential Risk Is AI CEOs Avoiding Oversight: “Alternative Nobel” Winner Timnit Gebru

We speak to the prominent AI researcher and leading critic of the AI industry, Timnit Gebru, who was recently awarded a 2026 Right Livelihood Award for her work investigating the harms and hype behind...

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 01/10/2026

Update: campus cat video 🥰

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Ask me about my cats @jimbobschaoscorral.bsky.social · 01/10/2026

Here’s some of our campus cats!

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