Example of Implicit Prediction Models: Riding a Bicycle
Implicit prediction models is an important concept to understand in order to understand mental health. When you first learn to ride a bike, it's a challenging process that involves a lot of trial and error. Here's how implicit prediction models play a role:
Initial Experience:
The first time you get on a bike, your brain has no experience or model to predict how to balance, pedal, and steer simultaneously.
You rely on basic instincts and sensory feedback. You might wobble and fall several times.
Feedback and Learning:
Each time you fall, your brain gets feedback about what didn’t work.
When you manage to ride a few feet without falling, your brain receives positive feedback, noting what you did correctly.
Formation of Implicit Prediction Models:
Over time, with repeated practice, your brain starts to form implicit prediction models. These are automatic, unconscious processes that predict how to balance, steer, and pedal based on past experiences.
These models help you make tiny adjustments to your balance and movements without consciously thinking about them.
Consolidation into Long-Term Memory:
As you continue to practice, these experiences consolidate into long-term, non-declarative memories.
Eventually, you can ride a bike smoothly without consciously thinking about balancing or steering. Your implicit prediction models handle these tasks automatically.
Automatic Adjustments:
When you encounter different terrains, like going uphill or downhill, your implicit prediction models adjust your body’s movements to maintain balance.
You don’t have to relearn how to ride every time; your brain uses the prediction models to adapt quickly.
This process shows how implicit prediction models allow us to perform complex tasks automatically, freeing up our conscious mind to focus on other things. It’s these models that make activities like riding a bike feel second nature after enough practice.