# Modeling Task Effects in Human Reading with Neural Network-based Attention

arXiv: https://arxiv.org/abs/1808.00054 · HTML: https://mlsanity.com/paper/1808.00054/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2018.

## Built on by (most cited first, 9 of 9)

- [Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading](https://mlsanity.com/paper/2304.10784/) (2023)
- [ScanDL: A Diffusion Model for Generating Synthetic Scanpaths on Texts](https://mlsanity.com/paper/2310.15587/) (2023)
- [Fine-Grained Prediction of Reading Comprehension from Eye Movements](https://mlsanity.com/paper/2410.04484/) (2024)
- [Decoding Reading Goals from Eye Movements](https://mlsanity.com/paper/2410.20779/) (2024)
- [Human Attention during Goal-directed Reading Comprehension Relies on Task Optimization](https://mlsanity.com/paper/2107.05799/) (2021)
- [Longer Fixations, More Computation: Gaze-Guided Recurrent Neural Networks](https://mlsanity.com/paper/2311.00159/) (2023)
- [Eye Tracking Based Cognitive Evaluation of Automatic Readability Assessment Methods](https://mlsanity.com/paper/2502.11150/) (2025)
- [Decoding Open-Ended Information Seeking Goals from Eye Movements in Reading](https://mlsanity.com/paper/2505.02872/) (2025)
- [Eye-Tracking-while-Reading: A Living Survey of Datasets with Open Library Support](https://mlsanity.com/paper/2602.19598/) (2026)

## Abstract (arXiv)

Research on human reading has long documented that reading behavior shows task-specific effects, but it has been challenging to build general models predicting what reading behavior humans will show in a given task. We introduce NEAT, a computational model of the allocation of attention in human reading, based on the hypothesis that human reading optimizes a tradeoff between economy of attention and success at a task. Our model is implemented using contemporary neural network modeling techniques, and makes explicit and testable predictions about how the allocation of attention varies across different tasks. We test this in an eyetracking study comparing two versions of a reading comprehension task, finding that our model successfully accounts for reading behavior across the tasks. Our work thus provides evidence that task effects can be modeled as optimal adaptation to task demands.