The pursuit of happiness: A reinforcement learning perspective on habituation and comparisons

In evaluating our choices, we often suffer from two tragic relativities. First, when our lives change for the better, we rapidly habituate to the higher standard of living. Second, we cannot escape comparing ourselves to various relative standards. Habituation and comparisons can be very disruptive to decision-making and happiness, and till date, it remains a puzzle why they have come to be a part of cognition in the first place. Here, we present computational evidence that suggests that these features might play an important role in promoting adaptive behavior. Using the framework of reinforcement learning, we explore the benefit of employing a reward function that, in addition to the reward provided by the underlying task, also depends on prior expectations and relative comparisons. We find that while agents equipped with this reward function are less happy, they learn faster and significantly outperform standard reward-based agents in a wide range of environments. Specifically, we find that relative comparisons speed up learning by providing an exploration incentive to the agents, and prior expectations serve as a useful aid to comparisons, especially in sparsely-rewarded and non-stationary environments. Our simulations also reveal potential drawbacks of this reward function and show that agents perform sub-optimally when comparisons are left unchecked and when there are too many similar options. Together, our results help explain why we are prone to becoming trapped in a cycle of never-ending wants and desires, and may shed light on psychopathologies such as depression, materialism, and overconsumption.