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In multivariate pattern analysis of neuroimaging data, 'second-level'inference is often performed by entering classification accuracies into a$t$-test vs chance level across subjects. We argue that while therandom-effects analysis implemented by the $t$-test does provide populationinference if applied to activation differences, it fails to do so in the caseof classification accuracy or other 'information-like' measures, because thetrue value of such measures can never be below chance level. This constraintchanges the meaning of the population-level null hypothesis being tested, whichbecomes equivalent to the global null hypothesis that there is no effect in anysubject in the population. Consequently, rejecting it only allows to infer thatthere are some subjects in which there is an information effect, but not thatit generalizes, rendering it effectively equivalent to fixed-effects analysis.This statement is supported by theoretical arguments as well as simulations. Wereview possible alternative approaches to population inference forinformation-based imaging, converging on the idea that it should not target themean, but the prevalence of the effect in the population. One method to do so,'permutation-based information prevalence inference using the minimumstatistic', is described in detail and applied to empirical data.

Valid population inference for information-based imaging: From the second-level $t$-test to prevalence inference