Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning

by   Gregory J. Zelinsky, et al.

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.


page 1

page 2

page 3

page 4

page 6

page 7


Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning

Being able to predict human gaze behavior has obvious importance for beh...

Learning to attend in a brain-inspired deep neural network

Recent machine learning models have shown that including attention as a ...

Target-absent Human Attention

The prediction of human gaze behavior is important for building human-co...

CNN-based search model underestimates attention guidance by simple visual features

Recently, Zhang et al. (2018) proposed an interesting model of attention...

Predicting Visual Attention and Distraction During Visual Search Using Convolutional Neural Networks

Most studies in computational modeling of visual attention encompass tas...

Curiosity-Driven Reinforcement Learning based Low-Level Flight Control

Curiosity is one of the main motives in many of the natural creatures wi...

Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior

We consider a novel application of inverse reinforcement learning which ...

Please sign up or login with your details

Forgot password? Click here to reset