# Spatial statistics and attentional dynamics in scene viewing

arXiv: https://arxiv.org/abs/1405.3270 · HTML: https://mlsanity.com/paper/1405.3270/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2014.

## Built on by (most cited first, 8 of 8)

- [State-of-the-Art in Human Scanpath Prediction](https://mlsanity.com/paper/2102.12239/) (2021)
- [Look Twice: A Generalist Computational Model Predicts Return Fixations across Tasks and Species](https://mlsanity.com/paper/2101.01611/) (2021)
- [TPP-Gaze: Modelling Gaze Dynamics in Space and Time with Neural Temporal Point Processes](https://mlsanity.com/paper/2410.23409/) (2024)
- [Modeling Saliency Dataset Bias](https://mlsanity.com/paper/2505.10169/) (2025)
- [Gazing at Rewards: Eye Movements as a Lens into Human and AI Decision-Making in Hybrid Visual Foraging](https://mlsanity.com/paper/2411.09176/) (2024)
- [Raising the Ceiling: Better Empirical Fixation Densities for Saliency Benchmarking](https://mlsanity.com/paper/2605.03885/) (2026)
- [Pathformer3D: A 3D Scanpath Transformer for 360deg Images](https://mlsanity.com/paper/2407.10563/) (2024)
- [Signals of Success and Struggle: Early Prediction and Physiological Signatures of Human Performance across Task Complexity](https://mlsanity.com/paper/2603.18798/) (2026)

## Abstract (arXiv)

In humans and in foveated animals visual acuity is highly concentrated at the center of gaze, so that choosing where to look next is an important example of online, rapid decision making. Computational neuroscientists have developed biologically-inspired models of visual attention, termed saliency maps, which successfully predict where people fixate on average. Using point process theory for spatial statistics, we show that scanpaths contain, however, important statistical structure, such as spatial clustering on top of distributions of gaze positions. Here we develop a dynamical model of saccadic selection that accurately predicts the distribution of gaze positions as well as spatial clustering along individual scanpaths. Our model relies on, first, activation dynamics via spatially- limited (foveated) access to saliency information, and, second, a leaky memory process controlling the re-inspection of target regions. This theoretical framework models a form of context-dependent decision-making, linking neural dynamics of attention to behavioral gaze data.