About the speaker
Olivia Guest is a computational cognitive modeller at RISE in Cyprus and University College London (UCL) in the UK. She emigrated to the UK to pursue a BSc in Computer Science at the University of York (2009), with an eye towards research at the intersection of AI and Psychology. She followed her undergraduate work with an MSc in Cognitive and Decision Sciences at UCL (2010) and a PhD in Psychology at Birkbeck (2014) where she focused on modelling semantic cognition using deep and shallow neural networks. Since then she has continued research in the area of categorisation, and conceptual representation broadly, using various modelling techniques — such as deep and shallow neural networks — with postdoctoral positions at the University of Oxford (2014–2016) and UCL (2016–2019). She was exposed to open communities in her teen years, e.g. open-source and open licensing, and believes modellers can have a doubly important role to play in guiding and enacting useful changes in open cognitive science: firstly, from experience with the open-source community and secondly, from experience navigating interdisciplinary settings. She is an editor-in-chief at ReScience C and a topic editor at the Journal of Open Source Software. She is committed to equity, diversity, and inclusion in (open) science, e.g., promoting access to technical skills training. In addition, Christina Bergmann and Olivia try to maintain a list of underrepresented cognitive computational scientists at compcog.science.
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About the talk
Psychology endeavours to develop theories of human capacities and behaviours based on a variety of methodologies and dependent measures. We argue that one of the most divisive factors in our field is whether researchers choose to employ computational modelling of theories (over and above data) during the scientific inference process. Modelling is undervalued, yet holds promise for advancing psychological science. The inherent demands of computational modelling guide us towards better science by forcing us to conceptually analyze, specify, and formalise intuitions which otherwise remain unexamined — what we dub “open theory”. Constraining our inference process through modelling enables us to build explanatory and predictive theories. Herein, we present scientific inference in psychology as a path function, where each step shapes the next. Computational modelling can constrain these steps, thus advancing scientific inference over and above stewardship of experimental practice (e.g. preregistration). If psychology continues to eschew computational modelling, we predict more replicability “crises” and persistent failure at coherent theory-building. This is because without formal modelling we lack open and transparent theorising. We also explain how to formalise, specify, and implement a computational model, emphasizing that the advantages of modelling can be achieved by anyone with benefit to all.
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