Fig. 2.

Encoding or decoding models link a feature representation of the experiment condition during functional neuroimaging to the image information. For every time point, image information is represented as a feature vector consisting of the values of each voxel. Correspondingly, the experiment condition is represented by a feature vector, either consisting of labels (house, face, etc.) or features extracted from the condition (e.g., wavelet decomposition of the image, semantic embedding of a word). Encoding models predict image features from the experiment condition features, while decoding models predict in the opposite direction