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How do we learn to perform complex skills like programming, physics, or piloting a plane? What changes in our brain allow us to perform these skills?
These are hard questions. Most experiments only attempt to address narrow slices of the problem.
This is what makes John Anderson’s ACT-R theory so ambitious. It’s an attempt to synthesize a huge amount of work in psychology to form a broad picture of how we learn complicated skills.
All scientific theories are built on paradigms .A paradigm is an example that you take as central for describing a phenomenon.
Newton had falling apples and orbiting planets. Obviously, Newton didn’t restrict his theory to tumbling fruit. Yet, these were the examples they used to lay the foundations for their broader theories.
Theories of the mind aren’t like this. Nobody believes we’ve found some unified theory that fully explains how the mind works. Yet we know a lot more than nothing.
ACT-R argues that we have two different memory systems: declarative and procedural.
The procedural system consists of everything you can do.
ACT-R explains complex skills as an ongoing interaction between these two systems.
Why two separate systems? A single system would be simpler. But there’s an impressive range of evidence arguing that these systems are distinct in the brain:
The basic unit of declarative memory is the chunk. This is a structure that binds approximately three pieces of information.
The idea is that, through experience, we connect these chunks into elaborate networks. We can then traverse these networks to get information as we need it.
The declarative memory structure is vast, but only a few chunks are active at any one time.
This reflects the distinction between conscious awareness and memory. When we need to remember something, we search through the network.
How do nodes get activated?
The declarative system, with its vast hidden network of long-term memory and briefly active nodes corresponding to our conscious awareness is impressive. But, according to ACT-R, it’s also inert. Something else must transform it into action. That’s where the procedural system comes in.
The basic unit of the procedural system is the production. This is an IF -> THEN pattern.
Think of productions like the atomic thinking steps involved in solving a problem.
Unlike the sprawling, interlinked declarative memory, productions are modular. Each one acts as an isolated unit that is learned and strengthened independently. Solving complex problems involves more productions than simple puzzles, but the basic ingredients are the same.
In the ACT-R theory, learning skills is thought to be a process of acquiring and strengthening productions.
Initially, productions are learned via analogy. We search our long-term declarative memories for a similar problem. Then we try to match this to our current representation of the problem. When we have a match, we create a production.
ACT-R argues that we don’t learn by explicit instruction, only by example.
When we appear to learn via instruction, we first generate an example based on the instruction and then use this example to create a new production.
Once created, productions are strengthened through use. Every time a production is used to solve a problem, it becomes more likely to be chosen again in similar circumstances. The strengthening process is incredibly slow.
This is why it can take so much practice to be good at complicated skills.
There are a few general implications we can tease out:
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