"Just in Time" World Modeling Supports Human Planning and Reasoning
This addresses the challenge of human cognitive capacity limits in mental simulation, offering a concrete algorithmic account for efficient reasoning, though it is incremental in building on existing theories of simplified representations.
The paper tackles the problem of how humans efficiently construct simplified mental representations for simulation-based reasoning in complex environments, and demonstrates that a 'Just-in-Time' framework, which interleaves simulation and visual search, makes high-utility predictions with strong empirical support in planning and physical reasoning tasks.
Probabilistic mental simulation is thought to play a key role in human reasoning, planning, and prediction, yet the demands of simulation in complex environments exceed realistic human capacity limits. A theory with growing evidence is that people simulate using simplified representations of the environment that abstract away from irrelevant details, but it is unclear how people determine these simplifications efficiently. Here, we present a "Just-in-Time" framework for simulation-based reasoning that demonstrates how such representations can be constructed online with minimal added computation. The model uses a tight interleaving of simulation, visual search, and representation modification, with the current simulation guiding where to look and visual search flagging objects that should be encoded for subsequent simulation. Despite only ever encoding a small subset of objects, the model makes high-utility predictions. We find strong empirical support for this account over alternative models in a grid-world planning task and a physical reasoning task across a range of behavioral measures. Together, these results offer a concrete algorithmic account of how people construct reduced representations to support efficient mental simulation.