# A dynamical scan path model for task-dependence during scene viewing

arXiv: https://arxiv.org/abs/2112.11067 · HTML: https://mlsanity.com/paper/2112.11067/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2021.

## Built on by (most cited first, 2 of 2)

- [Modeling Saliency Dataset Bias](https://mlsanity.com/paper/2505.10169/) (2025)
- [SEAM: An Integrated Activation-Coupled Model of Sentence Processing and Eye Movements in Reading](https://mlsanity.com/paper/2303.05221/) (2023)

## Abstract (arXiv)

In real-world scene perception human observers generate sequences of fixations to move image patches into the high-acuity center of the visual field. Models of visual attention developed over the last 25 years aim to predict two-dimensional probabilities of gaze positions for a given image via saliency maps. Recently, progress has been made on models for the generation of scan paths under the constraints of saliency as well as attentional and oculomotor restrictions. Experimental research demonstrated that task constraints can have a strong impact on viewing behavior. Here we propose a scan path model for both fixation positions and fixation durations, which includes influences of task instructions and interindividual differences. Based on an eye-movement experiment with four different task conditions, we estimated model parameters for each individual observer and task condition using a fully Bayesian dynamical modeling framework using a joint spatial-temporal likelihood approach with sequential estimation. Resulting parameter values demonstrate that model properties such as the attentional span are adjusted to task requirements. Posterior predictive checks indicate that our dynamical model can reproduce task differences in scan path statistics across individual observers.