Active Dynamical Prospection: Modeling Mental Simulation as Particle Filtering for Sensorimotor Control during Pathfinding

TL;DR

This paper introduces a novel computational model called Active Dynamical Prospection that simulates human-like pathfinding behavior by integrating visual exploration and mental simulation as particle filtering, validated against human data.

Contribution

It presents a new model of mental simulation in navigation, combining behavioral analysis and computational implementation, advancing understanding of planning and exploration.

Findings

Model replicates human pathfinding patterns

Distal attention predicts task performance

Delay before first move correlates with success

Abstract

What do humans do when confronted with a common challenge: we know where we want to go but we are not yet sure the best way to get there, or even if we can. This is the problem posed to agents during spatial navigation and pathfinding, and its solution may give us clues about the more abstract domain of planning in general. In this work, we model pathfinding behavior in a continuous, explicitly exploratory paradigm. In our task, participants (and agents) must coordinate both visual exploration and navigation within a partially observable environment. Our contribution has three primary components: 1) an analysis of behavioral data from 81 human participants in a novel pathfinding paradigm conducted as an online experiment, 2) a proposal to model prospective mental simulation during navigation as particle filtering, and 3) an instantiation of this proposal in a computational agent. We show that our model, Active Dynamical Prospection, demonstrates similar patterns of map solution rate, path selection, and trial duration, as well as attentional behavior (at both aggregate and individual levels) when compared with data from human participants. We also find that both distal attention and delay prior to first move (both potential correlates of prospective simulation) are predictive of task performance.