Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis
Fig 2
Three approaches to testing representational models.
(A) In encoding analysis, the distribution of activity profiles is described by the underlying features (red vectors). The direction of a feature vector determines the associated activity profile, and the length the strength of the feature encoding in the representation. (B) PCM models the distribution of the activity profiles as a multivariate Gaussian. This model is parametrized by the second moment of the activity profiles, which determines at what signal-to-noise ratio any feature is linearly decodable from the population. (C) RSA uses the representational distances (or, more generally, dissimilarities) between activity patterns as a summary statistic to describe decodability and hence the second moment of the underlying distribution.