Fig 1. Distances from a decision boundary through activation space can be used to predict reaction times (RT).

(A) A hypothetical 2D activation space for human IT representing animate and inanimate object exemplars. Activation patterns for individual exemplars are projected onto a discriminant axis, which differentiates patterns based on animacy. A decision boundary placed along the axis allows for classification of animate and inanimate exemplars. Gaussian distributions along the discriminant axis reflect “decision noise”. Exemplar representations closer to the boundary produce more ambiguous evidence compared to exemplar representations far from the boundary. An implication of classic signal detection theory is that RTs will correlate negatively with distance from the boundary. (B) A hypothetical emergent activation space for animate vs. inanimate object exemplars as would be revealed using MEG decoding methods. Stimulus onset is the time the stimulus is presented. The decoding onset (dashed line) is the first time point that a classifier trained to discriminate between animate and inanimate examplars performs significantly above chance. Peak decoding (gray box) is the optimal time point to read out information about stimulus category. Clusters depict the hypothetical 2D activation spaces at notable points in the decoding time-course.