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Computational Cognitive Science
@compcogsci.bsky.social
300 followers 160 following 13 posts
Account of the Computational Cognitive Science Lab at Donders Institute, Radboud University
Posts Replies Media Videos
Reposted by Computational Cognitive Science
irisvanrooij.bsky.social
Dimension 2:

CRITICAL THINKING 🤔

“Critical Thinking is deep engagement with the relationships between statements about the world.”

Guest, Suarez, & van Rooij (2025). Towards Critical Artificial Intelligence Literacies. doi.org/10.5281/zeno...

12/🧵
a square with a small square in the middle and the words critical thinking written above it
Alt: a square with a small square in the middle and the words “critical thinking” above it
media.tenor.com
December 7, 2025 at 7:17 PM
Reposted by Computational Cognitive Science
realscientists.de
Ich beende den Thread aber mit etwas Positivem.

In diesem Preprint wird diskutiert, wie KI im Bereich Psychologie unterstützen kann:

📄 van Rooij, I., & Guest, O. (2025). Combining Psychology with Artificial Intelligence: What could possibly go wrong?

🔗 philpapers.org/rec/VANCPW
January 10, 2026 at 11:56 AM
Reposted by Computational Cognitive Science
irisvanrooij.bsky.social
Relevant reading 1 📖 ✨

Guest, O. Suarez, M., et al. (2025). Against the Uncritical Adoption of 'AI' Technologies in Academia. doi.org/10.5281/zeno...

By @olivia.science @marentierra.bsky.social @altibel.bsky.social @lucyavraamidou.bsky.social @jedbrown.org @felienne.bsky.social, me & others

4/🧵
Figure 1. A cartoon set theoretic view on various terms (see Table 1) used when discussing the superset AI (black outline, hatched background): LLMs are in orange; ANNs are in magenta; generative models are in blue; and finally, chatbots are in green. Where these intersect, the colours reflect that, e.g. generative ad- versarial network (GAN) and Boltzmann machine (BM) models are in the purple subset because they are both generative and ANNs. In the case of proprietary closed source models, e.g. OpenAI’s ChatGPT and Apple’s Siri, we cannot verify their implementation and so academics can only make educated guesses (cf. Dingemanse 2025). Undefined terms used above: BERT (Devlin et al. 2019); AlexNet (Krizhevsky et al. 2017); A.L.I.C.E. (Wallace 2009); ELIZA (Weizenbaum 1966); Jabberwacky (Twist 2003); linear discrim- inant analysis (LDA); quadratic discriminant analysis (QDA).
January 16, 2026 at 8:58 PM
Reposted by Computational Cognitive Science
altibel.bsky.social
In our position paper we looked at how the core principles of the Netherlands Code of Conduct for #ResearchIntegrity applies to AI usage. @olivia.science @irisvanrooij.bsky.social @jedbrown.org @marentierra.bsky.social @lucyavraamidou.bsky.social @felienne.bsky.social
doi.org/10.5281/zeno...
Against the Uncritical Adoption of 'AI' Technologies in Academia
Under the banner of progress, products have been uncritically adopted or even imposed on users — in past centuries with tobacco and combustion engines, and in the 21st with social media. For these col...
doi.org
January 22, 2026 at 8:56 PM
Reposted by Computational Cognitive Science
irisvanrooij.bsky.social
✨ Summer School: Critical AI Literacies for Resisting and Reclaiming 📚

👉 For many more relevant resources for Critical AI literacy, check out this website maintained by @olivia.science with videos, news, opinion pieces, blogs, articles, posters, and more. 👇

olivia.science/ai

6/🧵
Critical AI
On this page are some resources for Critical AI Literacy (CAIL) from my perspective.
olivia.science
January 16, 2026 at 10:13 PM
Reposted by Computational Cognitive Science
irisvanrooij.bsky.social
Relevant reading 2 📖 ✨

Guest, O., Suarez, M., & van Rooij, I. Zenodo. Towards Critical Artificial Intelligence Literacies. Zenodo. doi.org/10.5281/zeno...

By @olivia.science @marentierra.bsky.social and me.

5/🧵
Figure 1: The important dimensions of CAILs across research and education; clockwise from 12 o’clock: Con- ceptual 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.
January 16, 2026 at 9:00 PM
Reposted by Computational Cognitive Science
irisvanrooij.bsky.social
Do you want to be able to demystify AI and recognize hyped claims? Do you worry about the social and environmental harms of AI? Do you struggle with how to protect and foster your students’ skills in the age of AI? Do you want to develop your own skills in resisting AI and critical AI literacy?

2/🧵
Critical AI Literacies for Resisting and Reclaiming | Radboud University
This course is designed to foster critical AI literacies in participants to empower them to develop ways of resisting or reclaiming AI in their own practices and social context.
www.ru.nl
January 16, 2026 at 8:38 PM
Reposted by Computational Cognitive Science
irisvanrooij.bsky.social
☀️ 📚

Summer School open for applications! www.ru.nl/en/education...

"This course is designed to foster critical AI literacies in participants to empower them to develop ways of resisting or reclaiming AI in their own practices & social context”

Learning goals 👇

3/🧵
Learning objectives 1 Gain conceptual clarity and deeper understanding of ‘AI’, its various forms and meanings 2 Understand how the science of human cognition can help resist AI hype 3 Critically uncover pseudoscientific claims about AI 4 Analyse critical intersectional theories (racism, sexism, ableism and more) to analyse social harms such as the exploitation of labor behind AI technologies 5 Critically grasp the relationship between AI technologies, sustainability, and environmental devastation 6 Perform a critical AI literacy project to start ways of resisting AI in a specific context
January 16, 2026 at 8:49 PM
Reposted by Computational Cognitive Science
olivia.science
excellent stuff, also if you need more or at a uni-level bsky.app/profile/oliv...
olivia.science Olivia Guest · Ολίβια Γκεστ @olivia.science · Sep 6
Finally! 🤩 Our position piece: Against the Uncritical Adoption of 'AI' Technologies in Academia:
doi.org/10.5281/zeno...

We unpick the tech industry’s marketing, hype, & harm; and we argue for safeguarding higher education, critical
thinking, expertise, academic freedom, & scientific integrity.
1/n
Abstract: Under the banner of progress, products have been uncritically adopted or even imposed on users — in past centuries with tobacco and combustion engines, and in the 21st with social media. For these collective blunders, we now regret our involvement or apathy as scientists, and society struggles to put the genie back in the bottle. Currently, we are similarly entangled with artificial intelligence (AI) technology. For example, software updates are rolled out seamlessly and non-consensually, Microsoft Office is bundled with chatbots, and we, our students, and our employers have had no say, as it is not considered a valid position to reject AI technologies in our teaching and research. This is why in June 2025, we co-authored an Open Letter calling on our employers to reverse and rethink their stance on uncritically adopting AI technologies. In this position piece, we expound on why universities must take their role seriously toa) counter the technology industry’s marketing, hype, and harm; and to b) safeguard higher education, critical thinking, expertise, academic freedom, and scientific integrity. We include pointers to relevant work to further inform our colleagues. Figure 1. A cartoon set theoretic view on various terms (see Table 1) used when discussing the superset AI (black outline, hatched background): LLMs are in orange; ANNs are in magenta; generative models are in blue; and finally, chatbots are in green. Where these intersect, the colours reflect that, e.g. generative adversarial network (GAN) and Boltzmann machine (BM) models are in the purple subset because they are both generative and ANNs. In the case of proprietary closed source models, e.g. OpenAI’s ChatGPT and Apple’s Siri, we cannot verify their implementation and so academics can only make educated guesses (cf. Dingemanse 2025). Undefined terms used above: BERT (Devlin et al. 2019); AlexNet (Krizhevsky et al. 2017); A.L.I.C.E. (Wallace 2009); ELIZA (Weizenbaum 1966); Jabberwacky (Twist 2003); linear discriminant analysis (LDA); quadratic discriminant analysis (QDA). Table 1. Below some of the typical terminological disarray is untangled. Importantly, none of these terms are orthogonal nor do they exclusively pick out the types of products we may wish to critique or proscribe. Protecting the Ecosystem of Human Knowledge: Five Principles
January 15, 2026 at 7:34 AM