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Target-absent Human Attention.

Zhibo Yang1, Sounak Mondal1, Seoyoung Ahn1

  • 1Stony Brook University, Stony Brook, NY 11794, USA.

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Summary
This summary is machine-generated.

This study introduces a new computational model for predicting human visual search behavior when targets are absent. It accurately forecasts scanpaths and search termination, improving human-computer interaction systems.

Keywords:
Human AttentionInverse Reinforcement LearningScanpath PredictionTarget AbsentTermination PredictionVisual Search

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Cognitive Science

Background:

  • Predicting human gaze is crucial for intuitive human-computer interaction (HCI).
  • Existing models focus on target-present visual search, neglecting target-absent scenarios.
  • Understanding search termination is vital for anticipating user attention when targets are not found.

Purpose of the Study:

  • To develop a data-driven model for predicting human visual search scanpaths when targets are absent.
  • To address the problem of search termination in visual search tasks.
  • To enhance HCI systems by anticipating user attention during unsuccessful searches.

Main Methods:

  • Visual search is modeled as an imitation learning problem.
  • A novel state representation, Foveated Feature Maps (FFMs), is introduced.
  • FFMs integrate a simulated foveated retina with a pretrained ConvNet for feature extraction.

Main Results:

  • The proposed model significantly improves the state of the art in predicting human target-absent search behavior.
  • Experimental results validate the effectiveness of FFMs and inverse reinforcement learning integration.
  • The model accurately predicts scanpaths and search termination points for absent targets.

Conclusions:

  • The developed model offers a robust solution for predicting human attention in target-absent visual search.
  • FFMs provide an efficient and effective internal knowledge representation for visual search.
  • This work advances the field of computational attention modeling and HCI applications.