The consequences of our choices can echo far into the future. We often must curb our immediate desires so that something may be left for our future counterparts.
But what leads us to delay gratification?
Many explanations describe the external circumstances that lead us to prefer outcomes now or later. For example, financial theories consider the interest that can accumulate over time from immediate rewards. Actuarial theories consider the risk that future rewards might not arrive. Cultural theories consider the influence of other people in one’s society. These are all meaningful pieces of the puzzle.
And yet, there is a crucial part of our experience which only a psychological lens brings into focus: we act more patiently when we imagine the future benefits to be gained. From a psychological perspective, distant rewards have limited impact on us because our minds can only imperfectly represent future outcomes. When we put effort into prospection, we enhance the mental weight of the future, and this helps us to delay gratification.
Even though this is obviously an important process, we don’t have a clear picture of what’s going on. When do we engage in this prospection, and how does it even affect patience?
In a new article in Nature Communications, Sam Gershman and I develop and test a mathematical theory that addresses these questions. Our theory is based on the idea that people efficiently spend their limited mental effort to make their simulations of the future more vivid and precise, and thus more impactful.
We show how this mechanism can lead to a classic anomaly in intertemporal choice called the “magnitude effect,” in which people are disproportionately patient when dealing with larger rewards. As we posit, high magnitude rewards may lead us to reduce the random variation in our mental representations, making our behavior both more patient and less random.
The theory has two components. First, delayed outcomes carry less weight because our mental representations of the future are imprecise. Here we were inspired by (and formally rely on) a Bayesian model of temporal discounting proposed by economists Xavier Gabaix and David Laibson, in which noisy mental simulations are rationally combined with one’s prior beliefs about outcome value.
Second, we suppose this precision can be increased by spending mental effort, and it’s more worthwhile to do so when evaluating larger magnitudes. We build this idea into the Bayesian model using information theory to capture limited processing capacity.
This proposal fits with a range of evidence. For instance, recent work showed that when people are asked to explicitly write down reasons for their judgment, patience is enhanced only for lower magnitude rewards (around $20), as if people are already putting in effort at higher magnitudes (like $2000). We find that randomness in their judgment follows a similar pattern, revealing a link between patience, reward, and internal uncertainty.
Our results demonstrate how even puzzling dimensions of patience can make sense when we recognize both the psychological constraints that we face and the ways in which we manage to cope with them. In doing so, we help uncover the adaptive mechanisms that may underlie one of our most vital faculties.