Iris van Rooij 💭

@irisvanrooij.bsky.social

18623 Followers 1441 Following

Professor of Computational Cognitive Science | Dept. of Cognitive Science & Artificial Intelligence | @Iris@scholar.social on 🦣 | http://irisvanrooijcogsci.com | she/they 🏳️‍🌈

Box 2 — What is makeism?
Makeism: The view that computationalism implies that (a) it is possible to (re)make cognition computation- ally; (b) if we (re)make cognition then we can explain and/or understand it; and possibly (c) explaining and/or understanding cognition requires (re)making cognition itself.
The methodology endorsed by makeists has been re- ferred to as the synthetic methodology or understand- ing by design and building (Bisig & Pfeifer, 2008; Pfeifer & Scheier, 2001); also see formal realism (Chirimuuta, 2021). A well-known quote from Feyn- man (1988), “what I cannot create, I do not under- stand”, is often used to support the idea of makeism in AI (e.g. Karpathy et al., 2016).
Note that it is especially easy for makeists to fall into map-territory confusion—mistaking their model- ing artefacts for cognition itself—due to the view that the made thing could be cognition.

Wiley Online Library Page:

“Accelerating research discovery to shape a better future”

Today’s research, tomorrow’s innovation
with a green sprout in the back.

“Have questions about this content? Try AI companion” laid over my own paper’s title page.

Graph G = (V,E), with vertex set V = {a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p} and edge set E = {(a,c), (b,c), (c,i), (a,g), (d,g), (b,e), (e.l), (f,m), (f,o), (i,k), (j,k), (k,l), (o,n), (l,m), (m,n), (m,o), (o,j), (d,p), (h,n), (i,p)}

Blue = {h}
Orange = {n}

Graph G = (V,E), with vertex set V = {a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p} and edge set E = {(a,c), (b,c), (c,i), (a,g), (d,g), (b,e), (e.l), (f,m), (f,o), (i,k), (j,k), (k,l), (o,n), (l,m), (m,n), (m,o), (o,j), (d,p), (h,n), (i,p)}

Blue = {m.o.p}
Orange = {d,f,j}

Graph G = (V,E), with vertex set V = {a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p} and edge set E = {(a,c), (b,c), (c,i), (a,g), (d,g), (b,e), (e.l), (f,m), (f,o), (i,k), (j,k), (k,l), (o,n), (l,m), (m,n), (m,o), (o,j), (d,p), (h,n), (i,p)}

Blue = {h,k,p}
Orange = {a,d,j}

Graph G = (V,E), with vertex set V = {a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p} and edge set E = {(a,c), (b,c), (c,i), (a,g), (d,g), (b,e), (e.l), (f,m), (f,o), (i,k), (j,k), (k,l), (o,n), (l,m), (m,n), (m,o), (o,j), (d,p), (h,n), (i,p)}

Blue = {o,p}
Orange = {j.f}

Graph G = (V,E), with vertex set V = {a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p} and edge set E = {(a,c), (b,c), (c,i), (a,g), (d,g), (b,e), (e.l), (f,m), (f,o), (i,k), (j,k), (k,l), (o,n), (l,m), (m,n), (m,o), (o,j), (d,p), (h,n), (i,p)}

Blue = {h,p}
Orange = {a,e}

Graph G = (V,E), with vertex set V = {a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p} and edge set E = {(a,c), (b,c), (c,i), (a,g), (d,g), (b,e), (e.l), (f,m), (f,o), (i,k), (j,k), (k,l), (o,n), (l,m), (m,n), (m,o), (o,j), (d,p), (h,n), (i,p)}

Blue = {h}
Orange = {a}

Graph G = (V,E), with vertex set V = {a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p} and edge set E = {(a,c), (b,c), (c,i), (a,g), (d,g), (b,e), (e.l), (f,m), (f,o), (i,k), (j,k), (k,l), (o,n), (l,m), (m,n), (m,o), (o,j), (d,p), (h,n), (i,p)}

Blue = {o}
Orange = {j}

Graph G = (V,E), with vertex set V = {a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p} and edge set E = {(a,c), (b,c), (c,i), (a,g), (d,g), (b,e), (e.l), (f,m), (f,o), (i,k), (j,k), (k,l), (o,n), (l,m), (m,n), (m,o), (o,j), (d,p), (h,n), (i,p)}

Blue = {p}
Orange = {d}

Graph G = (V,E), with vertex set V = {a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p} and edge set E = {(a,c), (b,c), (c,i), (a,g), (d,g), (b,e), (e.l), (f,m), (f,o), (i,k), (j,k), (k,l), (o,n), (l,m), (m,n), (m,o), (o,j), (d,p), (h,n), (i,p)}

Blue = {d}
Orange = {g}

Fig. 3. An intuitive illustration of the mathematical technique that we use for proving intractability. See main
text for an explanation.

Graph G = (V,E), with vertex set V = {a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p} and edge set E = {(a,c), (b,c), (c,i), (a,g), (d,g), (b,e), (e.l), (f,m), (f,o), (i,k), (j,k), (k,l), (o,n), (l,m), (m,n), (m,o), (o,j), (d,p), (h,n), (i,p)}

Blue = {l}
Orange = {h}

Graph G = (V,E), with vertex set V = {a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p} and edge set E = {(a,c), (b,c), (c,i), (a,g), (d,g), (b,e), (e.l), (f,m), (f,o), (i,k), (j,k), (k,l), (o,n), (l,m), (m,n), (m,o), (o,j), (d,p), (h,n), (i,p)}

screenshot of me giving a talk, and on the slide a chart showing the difference between polynomial and exponential growth

What AI Companies Are Seeing as
They Scale LLMs

costs growing exponentially as a function of tokens, from 0.10 cents to 30 dollars for 50 to 200 dollars respectively. For different models, GPT, Llama, Mistral, DeepSeek.

Figure 1: The important dimensions of CAILs across research and education; clockwise from 12 o’clock: Conceptual Clarity is the idea that terms should refer. Critical Thinking is deep engagement with the relationships between statements about the world. Decoloniality is the process of de-centring and addressing dominant
harmful views and practices. Respecting Expertise is the epistemic compact between professionals and society.
Slow Science is a disposition towards preferring psychologically, techno-socially, and epistemically healthy practices. The lines between dimensions represent how they are interwoven both directly and indirectly.

view from a restaurant's deck over the blue sea

view from same spot with road and water in distance

view from mountain top

Table 1. This table shows an overview of the two types of reflexes we describe and warn against in each column. The rows outline the common framing errors, the institutional narratives that emerge when these errors are reinforced, and what is ultimately lost in each reflex.

From:

Ray, I., & Guest, O. (2026). Contra Literacy-Laundering: Mechanistic Critical AI Literacy. Zenodo. https://doi.org/10.5281/zenodo.21222978

Formally searching for substances

Capacities are not tasks

Even if multiple realizability cannot 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).

screentshot start of section 1 in van Rooij (2008) The Tractable Cognition thesis. https://onlinelibrary.wiley.com/doi/epdf/10.1080/03640210801897856

Section P2, For whom is this book https://assets.cambridge.org/97811070/43992/frontmatter/9781107043992_frontmatter.pdf

2025

What missiles does Iran have to launch against Israel, and how far can they go?

R.Savelsberg, Den Helder: Nederlandse Defensie Academie ; Faculteit Militaire Wetenschappen.

Houthis have previously used modified Scud missiles to strike Israel: Exclusive analysis

R.Savelsberg, Den Helder: Nederlandse Defensie Academie ; Faculteit Militaire Wetenschappen.

Top: War Studies

Middle: Military Management Studies

Bottom: Sustainability & Innovation

Box 1. Intractability and exponential growth

Input-output mappings can differ in the amount of resources (e.g., time, space) needed to compute them. For instance, finding a number (output) in a list of length (input) takes on the order of steps because one needs only to inspect each position in the list at most once to find the number one is looking for. Sorting a list of numbers instead takes no more than steps because one needs to go through the list at most times to find the next smallest number. Mappings like these that are computable in , , etc., steps are said to be tractable. In contrast, some input-output mappings are intractable. For instance, when for each small increase in the number of steps doubles, then the growth is exponential (i.e., ). Such exponential growth may appear deceivingly slow at first but grows astronomically large quickly. People tend to be poor in intuiting intractable resource demands, leading them to underestimate resource demands needed for scaling up to larger . The water-lilies problem is a simple example: If a water lily doubles every 24 hours, after 60 days the lilies will already cover more than the surface of the earth (van Rooij, 2018). For implications of intractability, see the main text and Figure 1.

Towards Decolonising
Computational Sciences
By Abeba Birhane & Olivia Guest
Abstract
This article sets out our perspective on how to begin the journey of decolonising computational
fi elds, such as data and cognitive sciences. We see this struggle as requiring two basic steps:
a) realisation that the present-day system has inherited, and still enacts, hostile, conservative, and
oppressive behaviours and principles towards women of colour; and b) rejection of the idea that cen-
tring individual people is a solution to system-level problems. The longer we ignore these two steps,
the more “our” academic system maintains its toxic structure, excludes, and harms Black women and
other minoritised groups. This also keeps the door open to discredited pseudoscience, like eugenics
and physiognomy. We propose that grappling with our fi elds’ histories and heritage holds the key to
avoiding mistakes of the past. In contrast to, for example, initiatives such as “diversity boards”, which
can be harmful because they superfi cially appear reformatory but nonetheless center whiteness and
maintain the status quo. Building on the work of many women of colour, we hope to advance the
dialogue required to build both a grass-roots and a top-down re-imagining of computational sciences
— including but not limited to psychology, neuroscience, cognitive science, computer science, data
science, statistics, machine learning, and artifi cial intelligence. We aspire to progress away from
these fi elds’ stagnant, sexist, and racist shared past into an ecosystem that welcomes and nurtures
demographically diverse researchers and ideas that critically challenge the status quo.
KEYWORDS: decolonisation, computational sciences, cognitive sciences, machine learning,
artifi cial intelligence, anti-Blackness, misogynoir, tokenism

Table 1. Typology of Traps, What Goes Wrong if They Are Not Avoided, and How They Can Be Avoided

We call upon you to:

• Resist the introduction of AI in our own software systems, from Microsoft to OpenAI to Apple. It is not in our interests to let our processes be corrupted and give away our data to be used to train models that are not only useless to us, but also harmful.

thuisonderwijs zorgt voor onderwijs op maat;

uit onderzoek blijkt dat thuisonderwijs-kinderen zich sociaal, emotioneel en - cognitief goed ontwikkelen;

zij goed doorstromen naar vervolgonderwijs en werk;

thuisonderwijs voor een deel van de kinderen die thuiszitten, een oplossing kan zijn;

hoogleraren en de Onderwijsraad pleiten voor regulering in plaats van beperking;
thuisonderwijsouders en -kinderen op dit moment te maken hebben met rechtsongelijkheid en onzekerheid;

in andere landen thuisonderwijs al erkend en gewaardeerd wordt.

Hundreds of AI papers hit arXiv every day — and that's before you count NeurIPS, CVPR, MICCAI, GitHub, and HuggingFace. Nobody can keep up.

Distill AI does the reading for you. Every morning it ranks the papers, code, and models that actually matter for your research, and delivers them to your feed and inbox. Track any topic you name ("MRI reconstruction"), and get the newest work from the researchers you follow the moment it drops.