PNAS Publishes the Equation of Happiness

BBC reports - (h/t: Nick Brown)

Equation ‘can predict momentary happiness’

“We can look at past decisions and outcomes and predict exactly how happy you will say you are at any point in time,” said lead author Dr Robb Rutledge from University College London.

“The brain is trying to figure out what you should be doing in the world to get rewards, so all the decisions, expectations and the outcomes are information it’s using to make sure you make good decisions in the future. All of the recent expectations and rewards combine to determine your current state of happiness,” he told BBC News.

Think of going to a restaurant for example, having low expectations may improve your dining experience if the food is better than expected. But having positive expectations may improve your happiness before the meal even starts because of your anticipation of the event.

Here is the equation -

We post the abstract of original paper below with no additional comments. One thing for sure, it will not see similar level of scrutiny as the STAP paper from Japan.

A computational and neural model of momentary subjective well- being

Significance

A common question in the social science of well-being asks, How happy do you feel on a scale of 0 to 10? Responses are often related to life circumstances, including wealth. By asking people about their feelings as they go about their lives, ongoing happiness and life events have been linked, but the neural mechanisms underlying this relationship are unknown. To investigate it, we presented subjects with a decision-making task involving monetary gains and losses and repeatedly asked them to report their momentary happiness. We built a computational model in which happiness reports were construed as an emotional reactivity to recent rewards and expectations. Using functional MRI, we demonstrated that neural signals during task events account for changes in happiness.

Abstract

The subjective well-being or happiness of individuals is an important metric for societies. Although happiness is influenced by life circumstances and population demographics such as wealth, we know little about how the cumulative influence of daily life events are aggregated into subjective feelings. Using computational modeling, we show that emotional reactivity in the form of momentary happiness in response to outcomes of a probabilistic reward task is explained not by current task earnings, but by the combined influence of recent reward expectations and prediction errors arising from those expectations. The robustness of this account was evident in a large- scale replication involving 18,420 participants. Using functional MRI, we show that the very same influences account for task-dependent striatal activity in a manner akin to the influences underpinning changes in happiness.