# Attention Allocation Aid for Visual Search

## TL;DR

This paper presents a feedback-enabled attention allocation aid that uses real-time physiological data and eye-tracking to optimize visual search efficiency and accuracy in surveillance tasks.

## Contribution

It introduces a novel AAAD system that integrates physiological and eye-tracking data to improve human search performance in real-time.

## Key findings

- AAAD improves search efficiency without sacrificing accuracy.
- Experimental results show increased target detection rates.
- System effectively guides operators in sequential visual search tasks.

## Abstract

This paper outlines the development and testing of a novel, feedback-enabled attention allocation aid (AAAD), which uses real-time physiological data to improve human performance in a realistic sequential visual search task. Indeed, by optimizing over search duration, the aid improves efficiency, while preserving decision accuracy, as the operator identifies and classifies targets within simulated aerial imagery. Specifically, using experimental eye-tracking data and measurements about target detectability across the human visual field, we develop functional models of detection accuracy as a function of search time, number of eye movements, scan path, and image clutter. These models are then used by the AAAD in conjunction with real time eye position data to make probabilistic estimations of attained search accuracy and to recommend that the observer either move on to the next image or continue exploring the present image. An experimental evaluation in a scenario motivated from human supervisory control in surveillance missions confirms the benefits of the AAAD.