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The paper introduces a "Just-in-Time" (JiT) framework for simulation-based reasoning, positing that humans construct simplified environment representations online to overcome computational limitations. The JiT model interleaves simulation, visual search, and representation modification, using ongoing simulations to guide visual attention and identify relevant objects for encoding. Empirical validation in grid-world planning and physical reasoning tasks demonstrates the model's ability to make accurate predictions while encoding only a small subset of objects, supporting the idea that humans create reduced representations for efficient mental simulation.
Humans don't laboriously simulate every detail; instead, they construct simplified world models "just in time" by tightly interleaving simulation with visual search to focus on what matters.
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.