 In this paper, we develop two quantitative evaluation procedures for saliency methods, using the fact that the Human Connectome Project (HCP) dataset contains functional magnetic resonance imaging (fMRI ) datafrom multiple tasks per subject to create ground truth saliency maps .
<meaning-changed>  In this paper, we develop two quantitative evaluation procedures for saliency methods, using the fact that the Human Connectome Project (HCP) dataset contains functional magnetic resonance imaging (fMRI ) datafrom multiple tasks per subject to create ground truth saliency maps .
 In this paper, we develop two quantitative evaluation procedures for saliency map methods in fMRI, applicable whenever a DNN or linear model is being trained to decode some information from imaging data. We evaluate the procedures using a synthetic dataset where the complex activation structure is known, and on saliency maps produced for DNN and linear models for task decoding in the fact that the Human Connectome Project (HCP) dataset contains functional magnetic resonance imaging (fMRI ) datafrom multiple tasks per subject to create ground truth saliency maps .