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  • Author or Editor: Kerstin Preuschoff x
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Mael Lebreton and Kerstin Preuschoff

Neuroeconomics aims to advance our understanding of decision-making by exploring the neural basis of economic models, using physiological data such as brain images, hormones, and DNA. This chapter provides an introduction to the data analysis tools and pipelines of the most frequently acquired data modalities such as data from functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG) or electroencephalography (EEG). Aimed at non-specialists in this field (economists, psychologists), the analysis is illustrated by example studies investigating the representation of utility in the human brain. The sections on statistical analyses, which form the core of the chapter, cover the standard mass univariate regression pipeline, model-based fMRI and multivariate methods such as PCA/ICA or multi-voxel pattern (MVPA) analyses. In addition to discussing experimental designs, data processing and statistical analyses, the chapter provides practical advice for interpreting neuroimaging results, and includes links to key references and dominant analysis packages used in the neuroeconomics community.