A Computational Model of Momentary Subjective Well-being
You know that classic question they often toss around in the world of well-being research? It goes like this: ‘On a scale from 0 to 10, how’s your happiness these days?’ Interestingly enough, the answers tend to be connected to what’s happening in our lives — yes, things like our wealth or our health. The subjective well-being or happiness of individuals is an important metric for societies.
While it’s pretty clear that stuff like how much dough (read: money) you’ve got and the general demographic scene play a role in how happy you are, what’s not so clear is how all those little things that happen every day add up to how we actually feel. It’s like, how does the daily grind shape our overall mood? We’re still sort of in the dark about that.
Decoding the happiness is not an easy stuff, but somehow we can arrange few components to make a foundation of the momentary subjective well-being (SWB). The happiness equation is the answer for that.
In his book “The Happiness Hypothesis,” social psychologist Jonathan Haidt introduced a formula aimed at explaining happiness. This equation can be represented as:
H = S + C + V
- H denotes your overall sense of happiness.
- S signifies your inherent biological baseline of happiness, established from birth.
- C embodies the external circumstances that shape your life, encompassing factors like your income, occupation, and marital status.
- V represents the realm of voluntary actions, encompassing the choices you make, activities you engage in, and interactions with loved ones.
The primary intent behind this equation is to deconstruct/decode the intricate notion of happiness into more comprehensible elements:
Set Point (S): This is mostly determined by your genes, making it hard to change. Some people are naturally more optimistic, while others tend to feel sadder more easily.
Conditions (C): These are the things from outside that impact your life. Some of them can be changed, like finding a better job, but others are more fixed because of things that happened in the past.
Voluntary Activities (V): This is where you have a lot of power. It’s about what you choose to do, how you act, and how you see things around you.
Haidt’s equation suggests that even though you can’t easily change your basic happiness level you were born with, you still have some say in the things that happen around you and the choices you make. This means that if you want to increase how happy you feel overall, it’s a good idea to focus on the things you can control, especially the activities and decisions you make (represented by the V in the equation).
Reality and Expectations
Another way to deconstruct the notion of happiness is using the reality and expectations equation. We can represent happiness as:
H = w1(Reality)— w2(Expectations)
According to this equation, our happiness level depends on the difference between what we expect and what actually happens. If things turn out better than we thought, we feel happy. But if they don’t meet our expectations, we feel unhappy.
w1 and w2 are weights that may add up to 1 and may be subjective on a per-person basis. We can also apply the equations to multiple situations like a job, a vacation, etc.
Reward Prediction Errors
To calculate momentary happiness, the researchers used a mathematical formula that incorporated two factors: reward expectations and prediction errors.
Reward expectations refer to what we anticipate receiving from a particular event. For example, if we expect to win a certain amount of money from a gamble, our reward expectation would be based on that anticipated outcome.
Prediction errors, on the other hand, are the differences between our expectations and the actual outcomes. If the outcome of a gamble is better than what we expected, we experience a positive prediction error. Conversely, if the outcome is worse than expected, we experience a negative prediction error.
The formula to calculate momentary happiness included three weights: CR (chosen certain rewards), EV (expected values of chosen gambles), and RPE (reward prediction errors). The formula at a given trial (t) can be represented as:
In this formula, j represents the previous trials, and γ is a forgetting factor that determines how much weight is given to past events. The weights w0, w1, w2, and w3 were determined through statistical analysis. In one of their research projects, the researchers from England found that momentary subjective well-being was not solely determined by the amount of money earned in the task. Instead, it was influenced by the cumulative impact of recent reward expectations and prediction errors. This suggests that our happiness is shaped by how well our expectations align with the actual outcomes we experience.
Additionally, the researchers used functional MRI (fMRI) to investigate the neural correlates of subjective well-being. They found that activity in the ventral striatum, a brain region associated with dopamine release, correlated with subsequent reports of subjective well-being. This provides further evidence for the involvement of reward-related processes in shaping our happiness.
Learning vs Getting Reward
We now understand the concept of reward prediction errors and also how reality and expectation can give us the nuance of our decoded subjective well-being. These concepts play a vital role in learning. They motivate people to repeat actions that led to unexpectedly great rewards and also help adjust their beliefs about the world, which is rewarding in itself.
So, the question is, could it be that reward prediction errors make us happier because they help us understand the world a bit better?
To test this theory, researchers Blain and Rutledge developed a task where the chance of getting a reward wasn’t linked to the reward’s size. This design lets them separate how learning and rewards contribute to happiness moment by moment.
In this task, volunteers had to pick which of two cars would win a race. In the ‘stable’ situation, one car always had an 80% chance of winning. In the ‘volatile’ scenario, one car had an 80% chance of winning for the first 20 rounds, then the other car took over. Volunteers had to figure out these odds by playing the game since they weren’t told upfront. However, on each try, they were shown the reward they’d get if their chosen car won. These rewards were random and unrelated to the car’s winning odds.
After every few rounds, volunteers rated their current happiness. The findings revealed that winning made them happier, and overall, they were happier in the stable scenario than in the volatile one. This was especially true for volunteers with signs of depression. Interestingly, the size of the reward didn’t impact happiness after wins; it was the surprise of winning that mattered.
These outcomes suggest that how we learn about our world can be more influential on our feelings than direct rewards. Examining happiness across different environments could help us understand factors influencing mental well-being. These findings imply that uncertain situations might be particularly unpleasant for individuals with depression. More research is necessary to fully grasp why. Despite the uncertainty of real-world rewards, learning might have the potential to uplift happiness.
Conclusion
- If you want to increase how happy you feel overall, it’s a good idea to focus on the things you can control, like the external thing.
- Momentary subjective well-being is not solely determined by task earnings, but rather by the combined influence of reward expectations and prediction errors.
- Our happiness is shaped by how well our expectations align with the actual outcomes we experience.
- Despite the uncertainty of real-world rewards, learning might have the potential to uplift happiness.
