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Decoding target discriminability and time pressure using eye and head movement features in a foraging search task.

Anthony J Ries1,2, Chloe Callahan-Flintoft3, Anna Madison3,4

  • 1Humans in Complex Systems, U.S. Army DEVCOM Army Research Laboratory, 7101 Mulberry Point Rd, Aberdeen Proving Ground, MD, 21005, USA. Anthony.j.ries2.civ@army.mil.

Cognitive Research: Principles and Implications
|August 22, 2025
PubMed
Summary

Combining eye and head movement data improves the understanding of search behavior in virtual reality (VR). This approach accurately classifies changes in visual search under varying task demands like target difficulty and time pressure.

Keywords:
Cognitive stateEye trackingHead movementsMachine learningMulticlass classificationSupport vector machineTask predictionVirtual realityVisual foragingVisual search

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Area of Science:

  • Human-Computer Interaction
  • Cognitive Science
  • Virtual Reality

Background:

  • Effective decision-making in military operations relies on rapid visual search.
  • Virtual reality (VR) offers a controlled environment to study complex visual search behaviors.
  • Eye and head movements are key indicators of attentional and search strategies.

Purpose of the Study:

  • To investigate how eye and head movement metrics can infer changes in search behavior.
  • To determine the combined utility of eye and head movement data in classifying search conditions.
  • To explore the differential contributions of eye and head metrics to understanding task-induced changes.

Main Methods:

  • Thirty-one participants performed a visual search task in VR using a head-mounted display (HMD) with eye tracking.
  • Task parameters included varying target discriminability (easy/hard) and time pressure (low/high).
  • Support vector classifiers were trained using eye and head movement features to predict task conditions.

Main Results:

  • Increased discrimination difficulty and time pressure impaired performance (slower response times, reduced d-prime).
  • Combined eye and head movement features yielded the most accurate classification of search behavior.
  • Eye features (fixation) were strong for discriminability, while head features (kinematics) were important for time pressure.

Conclusions:

  • Eye and head movement metrics provide complementary information for understanding visual search.
  • Integrating both eye and head movement data enhances the classification of task-induced changes in search behavior.
  • This research offers insights into optimizing training and performance in high-pressure visual search environments.