Building momentum: A computational account of persistence toward 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 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, favoring goals with greater accumulated progress even if less likely to result in success. To address this, we introduce a novel task in which 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 prospective and retrospective influences, with an undue bias in favor of retrospective valuation. We show that a goal valuation model utilizing 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.Author summary: Goals take time to accomplish and require commitment over extended periods of time. However, over time, some goals may become less attainable than others, necessitating switching between goals as contexts and circumstances change. A fundamental question concerns how humans switch goals across extended intervals. One possibility is that humans prospectively commit to specific goals in a way that is maximally sensitive to the likelihood of achieving that goal in the near future. Alternatively, humans might retrospectively persist in goals that they have previously worked toward. Consistent with this latter possibility, we found evidence that humans are retrospectively biased towards goals that they have spent time building progress in, even when it is more optimal to switch. We account for such a preference for accrued progress using a computational model incorporating the concept of momentum borrowed from classical mechanics. We show that momentum steadily integrates reinforcement throughout the goal progress and maintains stable goal commitment toward the goal despite environmental shifts. Although this results in suboptimal performance in our experimental paradigm, we show that momentum computations for goal commitment can have adaptive advantages for goal pursuit in real-world scenarios.