Hierarchical Resource Rationality Guides Reading Under Time Pressure
A hierarchical resource-rational model of reading treats eye movements and memory as optimally chosen actions under bounded resources, using word-, sentence-, and text-level POMDP controllers trained with deep reinforcement learning. The model reproduces classic reading effects, accounts for skips, regressions and rereading under time pressure, and aligns with new dataset results, showing that higher-level goals guide lower-level eye movements and that hierarchy is essential for human-like reading.