Our brains are programmed to keep wanting more – even if it leads to unhappiness, study shows 

From shoes to clothing, vinyl records and the latest smartphone, people have a seemingly insatiable desire for the latest products.

Now researchers have used computer models to try to explain why we constantly crave more and more material things — even if they make us feel miserable.

According to the findings, when we become ‘accustomed’ to a higher standard of living and compare ourselves to different standards, we strive for more rewards.

The new study was led by researchers from the Department of Psychology at Princeton University in New Jersey.

“From ancient religious texts to modern literature, human history is teeming with stories describing the struggle to achieve everlasting happiness,” they say in their paper.

“Paradoxically, happiness is one of the most desired human emotions, but achieving it in the long run remains an elusive goal for many people.

“Our results help explain why we tend to get caught up in a cycle of endless wants and desires, and can shed light on psychopathologies such as depression, materialism and overconsumption.”

According to the experts, two psychological phenomena cause our brains to relentlessly pursue material goods.

First, human happiness is influenced by a phenomenon called “relative comparisons.”

This means that we often worry about the difference between what we have and a desired level that we want to achieve.

Second, what it takes to be happy depends on our previous expectations, but these expectations can change over time.

For example, if we’ve had a particularly enjoyable experience, such as a cruise, we judge our happiness by the expectation of having a similar experience again.

Lead study author Rachit Dubey of Princeton told MailOnline: “Our article was inspired by findings about human happiness (particularly our tendency to want more and more) and we wanted to provide an explanation for this behavior.”

In their experiments, the team created computer-simulated agents to represent real human “brains” and how people think, and taught them “reinforcement learning.”

Dubey said, “Reinforcement learning methods focus on training an agent (e.g., a robot) so that the agent learns how to map situations into actions (e.g., learning to play chess).

“The guiding principle of these methods is that they train agents using rewards – they give positive rewards for desirable behavior and/or negative rewards for undesirable behavior.”

Some brains received a simple ‘reward’, while others received an additional reward when they based decisions on previous expectations and compared their rewards with others.

Researchers found that the latter group was less happy, but learned faster than the former and outperformed them in all the tests they performed.

While we may enjoy a newly purchased car, it brings less positive feelings over time and eventually we start dreaming of the next rewarding thing to pursue, researchers say (file photo)

This suggests that we will be less happy the more we are rewarded when we compare ourselves to different standards.

Dubey told MailOnline: “Our computer simulations suggest it has advantages: if we are never satisfied, we are constantly driven to find better results.

“However, this also has drawbacks: we are constantly devaluing what we already have, which in extreme cases can lead to depression and overconsumption.”

Dubey also acknowledged the question of how reliably such computing methods can map human behavior.

“Caution should be exercised in generalizing our simulation-based results to realistic scenarios,” he told MailOnline.

The team’s paper has been published in the journal PLoS computational biology.