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

arXiv: https://arxiv.org/abs/1907.12635 · HTML: https://mlsanity.com/paper/1907.12635/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [Infographics Wizard: Flexible Infographics Authoring and Design Exploration](https://mlsanity.com/paper/2204.09904/) (2022)
- [User-Centric Semi-Automated Infographics Authoring and Recommendation](https://mlsanity.com/paper/2108.11914/) (2021)
- [Task Decoding based on Eye Movements using Synthetic Data Augmentation](https://mlsanity.com/paper/2509.11547/) (2025)
- [Eye Gaze Estimation Model Analysis](https://mlsanity.com/paper/2207.14373/) (2022)
- [NMF-Based Analysis of Mobile Eye-Tracking Data](https://mlsanity.com/paper/2404.03417/) (2024)

## Abstract (arXiv)

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.