  Understanding how goal states control behavior is a question ripe for
interrogation by new methods from machine learning. These methods require large
and labeled datasets to train models. To annotate a large-scale image dataset
with observed search fixations, we collected 16,184 fixations from people
searching for either microwaves or clocks in a dataset of 4,366 images
(MS-COCO). We then used this behaviorally-annotated dataset and the machine
learning method of Inverse-Reinforcement Learning (IRL) to learn
target-specific reward functions and policies for these two target goals.
Finally, we used these learned policies to predict the fixations of 60 new
behavioral searchers (clock = 30, microwave = 30) in a disjoint test dataset of
kitchen scenes depicting both a microwave and a clock (thus controlling for
differences in low-level image contrast). We found that the IRL model predicted
behavioral search efficiency and fixation-density maps using multiple metrics.
Moreover, reward maps from the IRL model revealed target-specific patterns that
suggest, not just attention guidance by target features, but also guidance by
scene context (e.g., fixations along walls in the search of clocks). Using
machine learning and the psychologically-meaningful principle of reward, it is
possible to learn the visual features used in goal-directed attention control.
