Computational models of thinking really help us understand consciousness better. They give us organized ways to look at how our minds work. These models use math and specific ways of thinking to break down different parts of conscious experience.

1. How Consciousness Works:
One thing we learn from these models is how consciousness might come from simpler thinking processes. For example, models like recurrent neural networks show how simple parts can work together to create self-awareness and the ability to perceive things. When we see how these networks handle time and connection, it gives us clues that our brain may work in similar ways to create consciousness.

2. Understanding Knowledge:
These computational models also clarify how we keep knowledge and experiences. They suggest that our conscious experience might come from complex collections of information within our brains. It’s interesting to think that consciousness isn’t just one thing. Instead, it could be a mix of changing representations that interact and grow over time.

3. Predicting Our World:
Another important idea is predictive coding. This means our brain is always making guesses about what’s happening around us to avoid surprises. This ongoing process helps us understand not only our awareness but also things like attention and intention. When we see something, it often depends on how well our brain's understanding matches the new information we receive. This leads to a deeper appreciation of consciousness as something that adapts.

4. What We Don't Know:
Of course, these models also show us the limits of what we know about consciousness. While they can replicate some parts of conscious thought, they struggle to explain personal experiences. This struggle is often called the "hard problem" of consciousness.

In short, computational models provide valuable insights into how consciousness works, how we represent knowledge, and how it adapts. They also remind us that understanding consciousness fully is a complicated challenge.