# How attention simplifies mental representations for planning

arXiv: https://arxiv.org/abs/2506.09520 · HTML: https://mlsanity.com/paper/2506.09520/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 85 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 1 of 1)

- [People construct simplified mental representations to plan](https://mlsanity.com/paper/2105.06948/) (2021)

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

- [MomaGraph: State-Aware Unified Scene Graphs with Vision-Language Model for Embodied Task Planning](https://mlsanity.com/paper/2512.16909/) (2025)
- [Estimating cognitive biases with attention-aware inverse planning](https://mlsanity.com/paper/2510.25951/) (2025)

## Abstract (arXiv)

Human planning is efficient--it frugally deploys limited cognitive resources to accomplish difficult tasks--and flexible--adapting to novel problems and environments. Computational approaches suggest that people construct simplified mental representations of their environment, balancing the complexity of a task representation with its utility. These models imply a nested optimisation in which planning shapes perception, and perception shapes planning--but the perceptual and attentional mechanisms governing how this interaction unfolds remain unknown. Here, we harness virtual maze navigation to characterise how spatial attention controls which aspects of a task representation enter subjective awareness and are available for planning. We find that spatial proximity governs which aspects of a maze are available for planning, and that when task-relevant information follows natural (lateralized) contours of attention, people can more easily construct simplified and useful maze representations. This influence of attention varies considerably across individuals, explaining differences in people's task representations and behaviour. Inspired by the 'spotlight of attention' analogy, we incorporate the effects of visuospatial attention into existing computational accounts of value-guided construal. Together, our work bridges computational perspectives on perception and decision-making to better understand how individuals represent their environments in aid of planning.