Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning.

ABSTRACT: Understanding how goals 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 con

SUBMITTER: Zelinsky GJ 

PROVIDER: S-EPMC8218820 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature