In many real life decisions, options are distributed in space and time, making itnecessary to search sequentially through them, often without a chance to return to arejected option. The optimal strategy in these tasks is to choose the first option that isabove a threshold that depends on the current position in the sequence. The implicitdecision making strategies by humans vary but largely diverge from this optimalstrategy. The reasons for this divergence remain unknown. We present a new model ofhuman stopping decisions in sequential decision making tasks based on a linearthreshold heuristic. The first two studies demonstrate that the linear threshold modelaccounts better for sequential decision making than existing models. Moreover, we showthat the model accurately predicts participants’ search behavior in differentenvironments. In the third study, we confirm that the model generalizes to a real-worldproblem, thus providing an important step towards understanding human sequentialdecision making.