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A computational model for task inference in visual search.

Amin Haji-Abolhassani¹, James J Clark

¹Department of Electrical and Computer Engineering, McGill University, Montreal, Quebec, Canada.

Journal of Vision

|September 28, 2013

Summary

This summary is machine-generated.

We developed a probabilistic framework using Hidden Markov Models (HMMs) to infer tasks during visual search. This method analyzes eye movements to understand if a search is easy or difficult, revealing the subject's focus.

Keywords:

Hidden Markov Model attention cognitive model eye movement visual search visual-task inference

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Area of Science:

Cognitive Science
Computational Neuroscience
Psychology

Background:

Visual search is a fundamental cognitive process.
Understanding the underlying task is crucial for interpreting search behavior.
Current methods often lack the ability to dynamically infer task types during search.

Purpose of the Study:

To develop a probabilistic framework for inferring the ongoing task in visual search.
To differentiate between easy and difficult search tasks based on cognitive dynamics.
To model the human cognitive process directing gaze during visual search.

Main Methods:

Utilized Hidden Markov Models (HMMs) to model the human cognitive process and gaze control.
Developed task-specific HMMs: single-state for easy tasks, multi-state for difficult tasks.
Employed Bayesian inference to estimate task likelihood from eye trajectory data.

Main Results:

The framework successfully infers task type based on eye movement patterns.
Easy tasks are characterized by focus of attention (FOA) on targets.
Difficult tasks involve frequent attention deployment on both target and non-target objects.

Conclusions:

The proposed probabilistic framework effectively distinguishes between easy and difficult visual search tasks.
HMMs provide a viable model for cognitive processes underlying gaze control in visual search.
This approach offers insights into the dynamics of attention during complex visual search scenarios.