Computational basis of hierarchical and counterfactual information processing

Humans solve complex multistage decision problems using hierarchical and counterfactual strategies. Here we designed a task that reliably engages these strategies and conducted hypothesis-driven experiments to identify the computational constraints that give rise to them. We found three key constraints: a bottleneck in parallel processing that promotes hierarchical analysis, a compensatory but capacity-limited counterfactual process, and working memory noise that reduces counterfactual fidelity. To test whether these strategies are computationally rational-that is, optimal given such constraints-we trained recurrent neural networks under systematically varied limitations. Only recurrent neural networks subjected to all three constraints reproduced human-like behaviour. Further analysis revealed that hierarchical, counterfactual and postdictive strategies-typically viewed as distinct-lie along a continuum of rational adaptations. These findings suggest that human decision strategies may emerge from a shared set of computational limitations, offering a unifying framework for understanding the flexibility and efficiency of human cognition.