Olivia Guest

about

Hi! I am an Assistant Professor of Computational Cognitive Science. I work in the Department of Cognitive Science and Artificial Intelligence in the Donders Centre for Cognition and the School of Artificial Intelligence at Radboud University in the Netherlands.

My research interests comprise (meta)theoretical, critical, and radical perspectives on the neuro-, computational, and cognitive sciences broadly construed. See my list of publications for more.

I am committed to equity, diversity, and inclusion in (open) science, promoting access to technical skills training — including the broader decolonisation of cognitive and computational sciences. Relatedly, Christina Bergmann and I maintain a list of underrepresented cognitive computational scientists.

biography

I emigrated from Cyprus to the United Kingdom in 2006 to pursue an undergraduate degree in Computer Science (2009; University of York, UK). After that, I moved on to an MSc in Cognitive and Decision Sciences (2010; University College London, UK). I then undertook a PhD in Psychological Sciences (2014; Birkbeck, UK), specifically on computational models for semantic memory.

Since obtaining my PhD, I have worked in labs at the University of Oxford, University College London, and as an independent scientist at an EU-funded research centre in Cyprus. In 2020, I moved to the Netherlands to work with Andrea E. Martin, before starting as an Assistant Professor at the Radboud in 2021 where I still work.

icon

Pygmalion lens (Table 1, Erscoi et al., 2023)

icon

Guest and Martin (2024; original: Hay et al., 1960)

mentoring

If you are interested in working with me, feel free to contact me. Prior to that, it might be worth taking a look at the following themes of research that currently interest me:

news

icon

Modern Alchemy: Neurocognitive Reverse Engineering

Abstract: The cognitive sciences, especially at the intersections with computer science, artificial intelligence, and neuroscience, propose `reverse engineering' the mind or brain as a viable methodology. We show three important issues with this stance: 1) Reverse engineering proper is not a single method and follows a different path when uncovering an engineered substance versus a computer. 2) These two forms of reverse engineering are incompatible. We cannot safely reason from attempts to reverse engineer a substance to attempts to reverse engineer a computational system, and vice versa. Such flawed reasoning rears its head, for instance, when neurocognitive scientists reason about what artificial neural networks and brains have in common using correlations or structural similarity. 3) While neither type of reverse engineering can make sense of non-engineered entities, both are applied in incompatible and mix-and-matched ways in cognitive scientists' thinking about computational models of cognition. This results in treating mind as a substance; a methodological manoeuvre that is, in fact, incompatible with computationalism. We formalise how neurocognitive scientists reason (metatheoretical calculus) and show how this leads to serious errors. Finally, we discuss what this means for those who ascribe to computationalism, and those who do not.

Reverse Engineering Proper
Porcelain, substance ЕС ЭВМ, computer
1) Computation performed none or identity function (universal) Turing machine
2) Equivalence sought structural functional
3) Multiple realisation minimally or uniquely realisable massively or infinitely realisable
4) Search duration thousands or hundreds of years single digit number of years
5) Search strategy industrial espionage, alchemy industrial espionage, engineering
6) Solution instances very few, one to three infinite
7) Verification method correlation principles of computation

"Comparison of the differences between two historical cases of true reverse engineering. [...] In both cases, to both limit the search space (for both it is unbounded) and search time (row 4) complexities, peeking at the solution (row 5) was a necessary part of reverse engineering." (Guest et al, 2025, table 1)

Are Neurocognitive Representations 'Small Cakes'?

icon

"Cartoon depiction of the methodology scientists deploy when they investigate neurocognitive phenomena or capacities." (Guest & Martin, 2025, figure 1)

Abstract: In order to understand cognition, we often recruit analogies as building blocks of theories to aid us in this quest. One such attempt, originating in folklore and alchemy, is the homunculus: a miniature human who resides in the skull and performs cognition. Perhaps surprisingly, this appears indistinguishable from the implicit proposal of many neurocognitive theories, including that of the 'cognitive map,' which proposes a representational substrate for episodic memories and navigational capacities. In such 'small cakes' cases, neurocognitive representations are assumed to be meaningful and about the world, though it is wholly unclear who is reading them, how they are interpreted, and how they come to mean what they do. We analyze the 'small cakes' problem in neurocognitive theories (including, but not limited to, the cognitive map) and find that such an approach a) causes infinite regress in the explanatory chain, requiring a human-in-the-loop to resolve, and b) results in a computationally inert account of representation, providing neither a function nor a mechanism. We caution against a 'small cakes' theoretical practice across computational cognitive modelling, neuroscience, and artificial intelligence, wherein the scientist inserts their (or other humans') cognition into models because otherwise the models neither perform as advertised, nor mean what they are purported to, without said 'cake insertion.' We argue that the solution is to tease apart explanandum and explanans for a given scientific investigation, with an eye towards avoiding van Rooij's (formal) or Ryle's (informal) infinite regresses.

A Metatheory of Classical and Modern Connectionism

Abstract: Contemporary AI models owe much of their success and discontents to connectionism, a framework in cognitive science that has been (and continues to be) highly influential. Herein, we analyze artificial neural networks (ANNs): a) when used as scientific instruments of study; and b) when functioning as emergent arbiters of the zeitgeist in the cognitive, computational, and neural sciences. Building on our previous work with respect to analogizing between ANNs and cognition, brains, or behaviour (Guest & Martin, 2023), we use metatheoretical analysis techniques (Guest, 2024), including formal logic, to characterise two distinct tendencies within connectionism that we dub classical and modern, with divergent properties, e.g. goals, mechanisms, scientific questions. We also demonstrate how we, as a field, often fail to follow important lines of argument to their end — this results in a paradoxical praxis. By engaging more deeply with (meta)theory surrounding ANNs, our field can obviate the cycle of AI winters and summers, which need not be inevitable.

icon

"A cartoon depiction of the simplified differences between C- and M-connectionism with respect to postulating mechanisms and building models (collectively M), and relating them to the cognitive and neural systems (collectively S)." (Guest & Martin, 2024/2025, figure 2)

Publications

(selected)
icon