title: Neuroadaptive Bayesian Optimization and Hypothesis Testing.
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. Here, we propose an alternative approach that resolves these problems by taking advantage of recent developments in real-time data analysis and machine learning. Neuroadaptive Bayesian optimization is a powerful strategy to efficiently explore more experimental conditions than is currently possible with standard methodology. We argue that such an approach could broaden the hypotheses considered in cognitive science, 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.
authors.affiliation: The Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London, W12 0NN, UK; Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK.
authors.forename: Romy
authors.initials: R
authors.lastname: Lorenz
authors.affiliation: The Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London, W12 0NN, UK.
authors.forename: Adam
authors.initials: A
authors.lastname: Hampshire
authors.affiliation: The Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London, W12 0NN, UK. Electronic address: r.leech@imperial.ac.uk.
authors.forename: Robert
authors.initials: R
authors.lastname: Leech
mesh_terms: D001499:Bayes Theorem; D003071:Cognition; D005793:Generalization, Psychological; D006801:Humans; D015233:Models, Statistical; D015203:Reproducibility of Results