How Do We Learn Complex Skills? Understanding ACT-R Theory - Scott H Young

ACT-R argues that we have two different memory systems: declarative and procedural.

• The declarative system includes all your memories of events, facts, ideas and experiences. Everything you consciously experience is part of this system.

The procedural system consists of everything you can do.

ACT-R explains complex skills as an ongoing interaction between these two systems.

• The declarative system represents the outside world, your inner thoughts and intentions.
• The procedural system acts on those representations to make overt actions or internal adjustments that move you closer to your goals.

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:

• Amnesiacs can learn through the procedural system, but not the declarative system .
• Procedural memory is unidirectional . This is why saying the alphabet forward is so much easier than saying it backward.
• Declarative memory shows fan effects .
• Neuroscientific studies suggest different locations for the two systems .

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.

• For practiced memories, this is relatively easy as most related ideas will only be a step or two away.
• For new ideas, this is much harder since they’re less well integrated into our other knowledge and therefore require more effortful search processes

How do nodes get activated?

1. First, you perceive things from the outside world that automatically activate nodes in memory .
2. Second, you can rehearse things internally to maintain them in memory
3. Third, nodes can activate connected nodes. This is what happens when one thought leads to another.

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.

1. You form a representation of the problem in your declarative system . .
2. Productions compete with each other based on this current representation .
3. As each production is executed, it changes your present state .
4. The process repeats itself until the problem is solved .

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:

1. Most skills will be highly specific . A microscopic analysis of any two skills should, in principle, tell us how much transfer is possible.
2. Transfer should look smaller on tests of problem solving than on tests of future learning . To solve a problem you need all of the productions.
3. Practice makes perfect, but many types of practice can be wasteful . Anderson favors intelligent tutoring systems that immediately correct students when they make a mistake.
4. Complicated skills have simple learning mechanisms .