The Many Faces of Fear: Univariate, Predictive and Representational Perspectives on Fearful Neuroimaging
This project explores how different fMRI analysis methods reveal distinct aspects of the neural representation of fear, including a mass univariate approach (GLM), a machine learning (decoding) approach, and representational similarity analysis (RSA) approach. While GLM identified some expected activation patterns and machine learning failed to decode fear ratings reliably, RSA revealed modest but significant structure in frontal regions, highlighting the value of methodological triangulation in cognitive neuroscience.