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To decide which goal to prioritize at what point in time, one has to evaluate the consequences of future actions by forward planning. However, when the goal is still temporally distant, detailed forward planning can be prohibitively costly. One way to select actions at minimal computational costs is to use heuristics. It is an open question how humans mix heuristics with forward planning to balance computational costs with goal reaching performance. To test a hypothesis about dynamic mixing of heuristics with forward planning, we used a novel stochastic sequential two-goal task. We found that participants’ decisions substantially deviated from an optimal full planning agent at the early stages of goal-reaching sequences. Only towards the end of the sequence, participant’s behavior converged to near optimal performance. Subsequent model-based analyses showed that participants used heuristic preferences when the goal was temporally distant and switched to forward planning, when the goal was close.

Dynamic Integration of Forward Planning and Heuristic Preferences in a Sequential Two-Goal Task