Computational Cognitive Science

@compcogsci.bsky.social

Account of the Computational Cognitive Science Lab at Donders Institute, Radboud University

@olivia.scienceReposted by @compcogsci.bsky.social

ok, as promised my take (part 1 of N) on (scholarly) reading skills:

> so you must understand that you are entering another world. Think of it a little like stepping into another person’s house. You cannot open random doors and expect the kitchen to be where yours is.

critical.computer/reading/

@irisvanrooij.bsky.socialReposted by @compcogsci.bsky.social

"Of course, we have to teach our students about AI technologies. Teaching about AI technologies should be just like how we teach ‘no smoking’ or the causal links between lung cancer and cigarette smoke; yet, we do not teach students how to roll cigarettes and smoke them." zenodo.org/records/2008...

@olivia.scienceReposted by @compcogsci.bsky.social

💯

> The rebranding is also a distraction, redirecting public attention away from resource-pillaging data centers, a fragile economic bubble and rising safety concerns.

This is a core MO of the industry to avoid critique and escape regulation. doi.org/10.5281/zeno...

intelligence.” It shows that since the start imprecise jargon was used to make exaggerated promises with the goal to please investors, claims that fundamentally remain promises to this day. Although some differentiate AI and non-AI systems by appealing to generative models versus other types of AI, we are convinced that this does not bring clarity to the discussion, and we can fall head first into misuse of terminology and fostering industry hype (see Table 1 and Figure 1; cf. Guest 2025). Granting that distinction, for example, makes classifiers such as Bernoulli naive Bayes, formally a generative model, AI technology similar to hyped applications (Efron 1975; Jebara 2004; Mitchell 1997; Ng and Jordan 2001; Xue and Titterington 2008). However, a statistical model like the Bernoulli naive Bayes classifier, which is used to analyse data, is unrelated to industry hype. By the same token, spellcheck software (which has not historically been composed of generative nor stochastic models) also falls under AI in some definitions, but not under others. Similar terminological problems also hold for phrases like ‘agentic AI’ (Chawla et al. 2024; Hosseini and Seilani 2025) — systems that can autonomously adapt their environment, make context-sensitive ‘decisions’, and act upon them without any human intervention. Formally, systems like thermostats, microwave ovens, and traffic lights are agentic in this sense and fall under AI, but are not products we wish to proscribe. Informally, however, ‘agentic’ is misused to induce anthropomorphisation that we cannot endorse (cf. Bandura 2001; Barrow 2024; Helfrich 2024). There is nothing privileged about generative AI, nor any AI technology, in any pedagogical or sociotechnical sense (see Table 1). Importantly, all technology under our purview as academics, ought to fall under the same critical considerations. Thus, to frame generative AI as unique or dramatically different to other AI systems appears anti-intellectual. Recall that gene… The Euler diagram in Figure 1 demonstrates that such hyped labels — Large Language Model (LLM), Artificial Neural Network (ANN), generative model, chatbot — are interwoven in complicated ways such that their referents remain ambiguous (cf. Guest 2025). Importantly, this holds under any rearrangement of the sets and elements: one cannot use these words without encountering additional problems and fostering ambiguity (Alkhatib 2024). We cannot escape this quagmire with more jargon. And so, we are convinced that when the technology industry presents use cases for the label generative AI (such as for LLMs, but not Bernoulli naive Bayes; Figure 1), this is a strategy to elicit confused responses against ‘generative AI,’ deflecting attention from other problematic AI systems (Table 1). This feeds into the hype and obstructs a more general critique of similar unethical or unwanted AI systems (cf. Guest 2025). Should we continue down this road, the technology industry will slip through such a distinction, skipping from terminology to terminology, consolidating its power. It has indeed done so many times before by using previous phrases as buzzwords like big data, machine learning
@olivia.scienceReposted by @compcogsci.bsky.social

Happy Friday! Here's a site I've built because I've been thinking about it & want to see more of it in the world: a space for describing and sharing scholarly skills that I've picked up and nurtured over the years. Also this is a no AI zone in at least two ways: no talking about it and no using it.

@irisvanrooij.bsky.socialReposted by @compcogsci.bsky.social

"[While] the cognitive revolution seemed to be a radical breakaway from behaviorism (...) several similarities between behaviorism and CogSci [remain] today such as the hope for a reductionist account of the mind, and a neglect of the use of phenomenological introspection." -- Pléh & Gurova (2013)

EXISTING AND WOULD-BE ACCOUNTS OF THE HISTORY OF COGNITIVE SCIENCE: AN INTRODUCTION1 Csaba Pléh and Lilia Gurova Science [...] is the source of a deep tension in the modern world. On the one hand, we have been formed by the traditions of our culture. [...] On the other hand, modern empirical sciences have reshaped our world and the way this whole world is interpreted. (Hans- Georg Gadamer 1983: 209) In this introductory chapter, the authors have three aims: 1) To summarize the accepted vision of the history of cognitive science, and somehow characterize the different weights put on different historical and substantial parts of the story, according to the sometimes diverging constituting disciplines. 2) To show that in (especially continental) European philosophy and psychology, there is an alternative prehistory of cognitive science, along the lines introduced by Brook (this volume). 3) Finally to indicate how some of the recent developments can be seen as replies to the traditional challenges regarding the supposedly reductionist theories of the mind. PDF: https://ia601405.us.archive.org/12/items/historyof-cog-sci/HistoryofCogSci.pdf
@olivia.scienceReposted by @compcogsci.bsky.social

I gave a version of this talk just now to PhD candidate students and it was such a cool and great experience! I am always so blown away by how ethical, caring, curious, etc., they are and especially so compared to certain colleagues. ☺️

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