Why do we learn?
A Summary of Wozniak's Concept of the Learn Drive.
- Underlies the Learn Drive
- Based on an analogy with Shannon Entropy
- Not to be confused with entropy in Physics.
- Can't help but include fact about Johnny Von Neumann telling Shannon to use entropy
- If the information coming from some source is perfectly predictable
- You are not surprised when you are told that
- If the information coming from some source is perfectly unpredictable
- Surprise based on
- 1950s with the cognitive revolution this was applied to the brain.
- Surprisal underlies the reward of the learn drive.
Toddlers scan the environment for low probability components like colorful toys.
BIG link to the difference between information and meaning
Complexity Melanie Mitchell
Art of the Problem has an AMAZING series on information theory
What should the unit of information be? Bits? Chunks (H.A. Simon)?
Importance of Learntropy
- Information is attractive to the brain based on its learntropy
- The measure of meaning must involve the brain itself in addition to the information channel metric. Prior knowledge is essential in learning.
- New information compared with old knowledge.
- Compared for relevance, coherence and value.
- We immediately
- If the new infomation is low probability, it generates reward.
- "Wow is how the brain responds to a sudden discovery"
- The entire purpose of the learn drive is to search for wow factors.
- Sensory information enters
- Scanned for surprisal.