Neuroadaptive bayesian optimization – Implications for the cognitive sciences
File(s)
Author(s)
Lorenz, Romy
Type
Thesis
Abstract
Cognitive neuroscientists are often interested in broad research questions, yet use overly narrow experimental designs by considering only a small subset of possible experimental conditions. This limits the generalizability and reproducibility of many research findings. In this thesis, I propose, validate and apply an alternative approach that resolves these problems by building upon neuroadaptive experimental paradigms, and combines real-time analysis of functional neuroimaging (fMRI) data with a branch of machine learning, Bayesian optimization. Neuroadaptive Bayesian optimization is a powerful strategy to efficiently explore more experimental conditions than is currently possible with standard methodology. In the first study (Chapter 3), I demonstrate the validity of the approach in a proof-of-principle study involving audio-visual stimuli with varying perceptual complexity. In a subsequent study (Chapter 4), I test the generalizability of the framework to paradigms with lower effect sizes and investigate how automatic stopping criteria could further boost the efficiency of the approach. This is followed by three studies in which I apply neuroadaptive Bayesian optimization to tackle different research questions within the cognitive neurosciences. In the first application study (Chapter 5), I employ the approach to identify the exact cognitive task conditions that optimally dissociate between two frontoparietal brain networks. For the second application (Chapter 6), I use neuroadaptive Bayesian optimization in a study involving non-invasive brain stimulation in order to find the stimulation parameters that elicit optimal network coupling in a frontoparietal network. In the third application study (Chapter 7), I show how adaptive Bayesian optimization can be used beyond the field of cognitive neuroimaging; I investigate the phenomenon of phosphene perception caused by non-invasive brain stimulation by optimizing based on preference ratings given by the participants. As a whole, this thesis provides evidence that neuroadaptive Bayesian optimization can be used to formulate new and exciting research questions within cognitive neuroscience. I argue that the approach could broaden the hypotheses considered in cognitive neuroscience, thereby improving the generalizability of findings. In addition, Bayesian optimization can be combined with preregistration to cover exploration, mitigating researcher bias more broadly and improving reproducibility.
Version
Open Access
Date Issued
2017-04
Date Awarded
2017-08
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Leech, Robert
Faisal, Aldo
Publisher Department
Department of Medicine
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)