Reliability-guided meta-control in intuitive physics

Intuitive physical reasoning is an important part of daily life, but the computations underlying it remain debated. Some prominent accounts propose intuitive physics relies on mental simulation, with people evolving mental scenes forward through step-by-step. However, representing every intermediate mental state in detail can be computationally costly. We suggest that intuitive physical reasoning combines mental simulation with learned predictive abstractions, and that the trade-off between these two is determined by the prospective reliability of an abstraction. We implemented the abstraction part of our proposal as a recurrent neural network model that learns a predictive abstraction by directly predicting the object's future position from a short visual history. This network bypasses intermediate states, while also estimating its own prediction error. Our network produced simplified linear trajectories that shortcut ground-truth physical trajectories (in line with human behavior), and its error estimates were associated with human pupil size during a physical prediction task. We further developed a meta-control model that used the network's error estimate to arbitrate between abstraction (direct prediction by the network) and simulation (running a mental simulation). The meta-control model captures key patterns in human response time and accuracy, and its predicted abstraction and simulation phases corresponded to distinct eye-movement signatures, over and above an earlier blended model that uses a hand-designed abstraction. Our results suggest that efficient physical reasoning can emerge from an interaction between learned abstractions, estimates of how reliable these abstractions are, and selective engagement of detailed simulation based on reliability estimates.