How attention simplifies mental representations for planning

TL;DR

This study investigates how spatial attention influences the construction of simplified mental representations during planning, using virtual maze navigation to reveal individual differences and enhance computational models.

Contribution

It demonstrates how attention controls which environmental aspects enter awareness, affecting planning and representation, and integrates these effects into computational models.

Findings

Spatial proximity determines which maze aspects are available for planning.

Natural contours of attention facilitate simplified maze representations.

Individual differences in attention influence task representations and behavior.

Abstract

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.