The neuroscience of planning has long been analogized to search algorithms in artificial intelligence (AI), which simulate future actions to guide immediate choices. We argue that advances in both neuroscience and AI suggest that planning is better understood to encompass a broader class of computations where mental simulation supports learning, often well before a decision is needed. We review three neurocomputational mechanisms that illustrate this shift. First, hippocampal replay resembles search but also often occurs prospectively or offline, likely training downstream circuits rather than directly guiding choice. Second, temporally abstract representations, such as grid cells, can enable planning without iterative search. Third, metalearning may shape how prefrontal dynamics implement task-specific planning strategies, echoing how AI systems learn to adapt across contexts. This view recasts the brain's planning machinery as a family of learning processes that leverage simulations to build representations and strategies, with forward search as one special case.
Mattar et al. (2026) studied this question.