On Logical Inference over Brains, Behaviour, and Artificial Neural Networks

16 December 2021 – 15:00 GMT
Olivia Guest, Donders Institute for Brain, Cognition and Behaviour and the School of Artificial Intelligence, Radboud University, Netherlands

Meeting ID: 910 1088 2328  | Passcode: 305010

In the cognitive, computational, and neuro- sciences, we often reason about what models (viz., formal and/or computational) represent, learn, or “know”, as well as what algorithm they instantiate. The putative goal of such reasoning is to generalize claims about the model in question to claims about the mind and brain. This reasoning process typically presents as inference about the representations, processes, or algorithms the human mind and brain instantiate. Such inference is often based on a model’s performance on a task, and whether that performance approximates human behaviour or brain activity. The model in question is often an artificial neural network (ANN) model, though the problems we discuss are generalizable to all reasoning over models. Arguments typically take the form “the brain does what the ANN does because the ANN reproduced the pattern seen in brain activity” or “cognition works this way because the ANN learned to approximate task performance.” Then, the argument concludes that models achieve this outcome by doing what people do or having the capacities people have. At first blush, this might appear as a form of modus ponens, a valid deductive logical inference rule. However, as we explain in this article, this is not the case, and thus, this form of argument eventually results in affirming the consequent – a logical or inferential fallacy. We discuss what this means broadly for research in cognitive science, neuroscience, and psychology; what it means for models when they lose the ability to mediate between theory and data in a meaningful way; and what this means for the logic, the metatheoretical calculus, our fields deploy in high-level scientific inference.

Download preprint: https://psyarxiv.com/tbmcg/ Biography: I am a computational cognitive modeler. My main scientific interests comprise a) a metascientific understanding of the contribution of computational and formal modelling, specifically to the neuro- and cognitive sciences, as well as to the computational sciences broadly construed — and b) computationally modelling the cognitive capacities that give rise to categorization and conceptual organization. I emigrated to the UK from Cyprus 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 a research centre in Cyprus. I am currently an Assistant Professor in Computational Cognitive Science at the Donders Institute for Brain, Cognition and Behaviour in the Netherlands. See my website for more: https://oliviaguest.com/