Your brain has 86 billion neurons. An advanced AI has over a trillionparameters. Both learn the same way: prediction, feedback, and reward. The difference is you can control your brain's âlearning rate,â and that dial is called curiosity. Science shows it triggers the same dopamine reward circuitry used by AI reinforcement learning. Lose it, and your neural network stops updating
If you spend a lot of time with AI companies, you've watched frontier models go from party tricks to systems that can reason, code, and hold complex conversations. And the more we learn about how LLMs work, the more we realize: your brain is running the same algorithm.
Consider the parallels.
Your brain has roughly 86 billion neurons connected by an estimated 100 trillion synapses. GPT-4 has approximately 1.8 trillion parameters across its mixture-of-experts architecture. Both are massive pattern-recognition networks. Both learn by prediction.
Hereâs how an LLM trains: it reads a sentence, predicts the next word, checks whether it was right, and adjusts its internal weights. Right answer? Strengthen that pathway. Wrong answer? Weaken it and try again. Billions of repetitions, trillions of adjustments.
Your brain does the same thing. Every experience is a prediction. You reach for a coffee cup and predict its weight. You start a sentence and predict how the other person will react. When reality matches your prediction, your synapses strengthen. When it doesnât, your brain recalibrates. Neuroscientists call this predictive coding, and a 2024 study in Nature Machine Intelligence by Gavin Mischler and colleagues at Columbia University found that as LLMs become more advanced, their internal representations actually become more similar to human brain activity during speech processing.
âYour brain is the original foundation model, pre-trained by evolution, fine-tuned by experience.â
But hereâs the critical difference. An LLMâs learning rate is set by engineers. They decide how aggressively the model updates its weights in response to new data. Too high and itâs unstable. Too low and it stops learning.
In your brain, that learning rate has a name. Itâs called curiosity. And unlike an LLM, you can adjust it yourself.
The word mental wellbeing combines the word âmentalâ â so to do with your mind â with the world âwellbeingâ â how well it is doing. However, when people think about their mental wellbeing, they often associate it, or conflate it, with their mood and happiness â how well they are feeling. But our mood or happiness is just one facet of our mental wellbeing. Whatâs more, when it comes to how well our mind is doing, itâs not just about how we are feeling, it also about how well we are functioning.
This more all-encompassing definition of mental wellbeing is captured nicely by the World Health Organization (WHO) who state âMental health is a state of mental well-being that enables people to cope with the stresses of life, realize their abilities, learn well and work well, and contribute to their community.â In other words, our mental wellbeing covers everything from being able to form good relationships with others; being emotionally resilient; being able to focus and make good decisions to having good energy levels.
This is also reflected in how we measure mental wellbeing in the Global Mind Project. Rather than just asking people about how happy they feel, or how satisfied they are with life, we use a tool called the MHQ assessment which asks about 47 different elements of mental wellbeing, covering our social, emotional, cognitive and physical capabilities of mind. It was developed based on a thorough analysis of 126 existing assessments of mental health and wellbeing covering 10,000 questions, to include the symptoms of 10 different mental health disorders from depression to eating disorders to PTSD, as well as positive strengths and assets.Â
With the 47 elements assessed on a scale that reflects the impact of those capabilities to oneâs life and ability to function, overall scores of mental wellbeing (MHQ scores) reflect functional productivity â the ability to get done the things you need to get done in a day. Overall, MHQ scores have a linear relationship to productive days such that the number of productive days increase or decrease equivalently as you move up or down the MHQ scale at any point.
Thus, the MHQ also serves as a proxy for functional capacity of a population and can be used to demonstrate the functional capacity of various populations from schools to companies, and countries.
Altogether, mental wellbeing as measured by the MHQ reflects the composite capability of mind that enables us to navigate lifeâs challenges and function productivity.
Our mental health and wellbeing is declining with each younger generation. This is a new phenomenon that has emerged in the past two decades and is more prominent in developed countries.
With an unparalleled global data acquisition and analytical infrastructure Sapien labs is at the forefront of tracking this decline, identifying its root causes and accelerating the path from data to insights to real-world action.
Sapien Labs is a 501(c) (3) not for profit organization founded in 2016 with a mission to understand and enable the human mind.
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