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On metrics for measuring scanpath similarity.

Ramin Fahimi1, Neil D B Bruce2

  • 1Computer Science, University of Manitoba, Winnipeg, Canada. fahimir@myumanitoba.ca.

Behavior Research Methods
|August 12, 2020
PubMed
Summary
This summary is machine-generated.

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This study reviews metrics for analyzing human visual attention scanpaths. It finds that sequential analysis is crucial and proposes new metrics for evaluating scanpath plausibility.

Area of Science:

  • Computational vision
  • Cognitive science
  • Human-computer interaction

Background:

  • Computational saliency models traditionally predict static maps, neglecting the sequential nature of human visual attention.
  • Existing metrics for scanpath similarity lack universal agreement and understanding of their properties.
  • Modeling human gaze patterns requires robust methods to assess sequence prediction quality.

Purpose of the Study:

  • To consolidate and analyze existing metrics for computational scanpath analysis.
  • To provide an axiomatic framework for evaluating gaze metrics.
  • To introduce and validate novel metrics for scanpath plausibility.

Main Methods:

  • Comprehensive literature review of scanpath similarity and quality metrics.
Keywords:
Eye movementSaliencyScanpathVisual attention

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  • Axiomatic analysis of gaze metrics properties.
  • Experimental validation using classic saliency models and deep neural networks.

    • Main Results:

    • Demonstrates the inadequacy of static saliency maps for predicting human scanpaths.
    • Highlights the importance of sequential analysis in visual attention modeling.
    • Identifies specific metrics, including newly proposed ones, that effectively capture scanpath plausibility.

    Conclusions:

    • Sequential modeling is essential for accurately simulating human visual attention.
    • The proposed metrics offer a promising direction for evaluating computational scanpath predictions.
    • This work provides a roadmap for future research in computational models of visual attention.