We then compute the minimum squared error between the normalized eye-tracking measure and the normalized attention score.
in multi-task learning, we optimize each task for a fixed number of parameter updates (or mini-batches) before switching to the next task (Dong et al., 2015); in our case, we optimize for a target task (for a fixed number of updates), then improve our attention function based on human attention (for a fixed number of updates), then return to optimizing for the target task and continue iterating.