Building momentum: A computational model of persistence in long-term goals

Extended goals necessitate extended commitment. We address how humans select between multiple goals in a temporally-extended setting. We probe whether humans are likely to engage in prospective valuation of goals by estimating which goals are likely to yield future success and choosing those, or whether they rely on a less optimal retrospective strategy by favoring goals with greater accumulated progress even if less likely to result in success. To address this, we introduce a novel task where goals need to be persistently selected until a set target is reached to earn an overall reward. In a series of experiments, we show that human goal selection involves a mix of both prospective and retrospective influences, with an undue bias in favor of retrospective valuation. We show that a model of goal valuation that utilizes the concept of `momentum', where progress accrued toward a goal builds value and persists across trials, successfully explains human behavior better than alternative frameworks. Our findings thus suggest an important role for momentum in explaining the valuation process underpinning human goal selection.