# Task Classification Model for Visual Fixation, Exploration, and Search

## TL;DR

This paper demonstrates that it is feasible to accurately classify a user's task based solely on eye movement data, achieving over 95% accuracy with machine learning models, thus supporting Yarbus' hypothesis.

## Contribution

The study introduces a method for decoding user tasks from eye movements using feature filtering and machine learning, achieving high classification accuracy.

## Key findings

- Achieved 95.4% accuracy in task classification
- Visualized feature distinctions for different tasks
- Supported the hypothesis that eye movements encode task information

## Abstract

Yarbus' claim to decode the observer's task from eye movements has received mixed reactions. In this paper, we have supported the hypothesis that it is possible to decode the task. We conducted an exploratory analysis on the dataset by projecting features and data points into a scatter plot to visualize the nuance properties for each task. Following this analysis, we eliminated highly correlated features before training an SVM and Ada Boosting classifier to predict the tasks from this filtered eye movements data. We achieve an accuracy of 95.4% on this task classification problem and hence, support the hypothesis that task classification is possible from a user's eye movement data.