Multivariate pattern analysis

Analysis using patterns across multiple measured features rather than a single averaged response. In neuroimaging, classification or related methods can distinguish remembered content from spatial activity patterns. Independent testing and control of nuisance structure are essential to interpreting the result.

Training a statistical predictor on one modality and evaluating it on another to test information that generalises across input or output forms. Stimulus identity, response mapping, noise and held-out evaluation affect what a successful or failed result means.

Comparison of the pairwise relationships among measured or model response patterns. The same stimuli are presented to the systems being compared, and their representational dissimilarity matrices are related. A match can constrain representation without requiring one-to-one unit correspondence or establishing the underlying learning rule.

A matrix whose entry $D_{ij}$ describes how differently a system responds to stimuli $i$ and $j$. One convention is $D_{ij}=1-\mathrm{corr}(z_i,z_j)$ when the response vectors have nonzero variance. Such a dissimilarity need not satisfy every metric axiom. Model comparison must control fitted transformations and use held-out data.