Having a grasp of the different kinds of learning will be useful to learn how to arrange the different systems in your game to incentivize or avoid certain behaviors from your players, which can easily result in your player enjoying your game more and understanding it easily.

Immediate vs Iterative learning

• Learning can be immediate, this type of learning is easy to acquire and it’s deeply related to conditionals, punishment and rewards:
  • Pressing X will make my character jump
  • Grabbing a coin will increase my score
  • Falling through a hole will result in losing one life
• Learning can also require iteration or practice, this type of learning is the one that occurs when practicing skill or memorizing information:
  • Parrying attacks
  • Controlling the drift of my racing car in extreme curves
  • Arranging my chess pieces in a strategic way

Immediate learning involves a superficial understanding on how the mechanics of the game works, while iterative learning involves mastering those mechanics to reach a better outcome.

Information retention

Related to the article on how to teach the player without words, it’s important to understand how the player can learn in a more effective manner, so the player retains the knowledge as much as possible. If you see the pyramid above, you will see that the more the player gets involved in what’s being taught, the more they retain the information.

Audiovisual teaching is better than plain text, but making the player learn with their own actions and experiences is a much more effective way. Level design (giving the player safe-zones to slowly experiment and learn a new mechanic) is a great way to make the player learn without reading or seeing, but by doing.

Types of learning

Non-associative learning

This type of learning involves an increase or decrease of response based on repeated exposure of the stimulus.

• Habituation: the response decreases with the amount of times you get exposed to the same stimulus.
  • You get less and less scared on each jump scare
  • You get less and less worried about a certain type of enemy once you already know how to deal - with them after many confrontations
• Sensitization: your response increases
  • Your anger increases when losing a battle against a boss again and again
  • Your happiness increases after getting top tier items in loot boxes in a row

This type of learning can be controlled by spacing the events that causes the response.

Habituation and sensitization can be reduced by pulling apart the events in time (3 jump scares in less than 30 seconds will have less of an individual impact than 3 jump scares in 2 hours.)

Associative Learning

This type of learning involves linking or associating events or stimuli.

• Operant conditioning: when a reward or a punishment changes a behavior:
  • (Positive punishment, presents unfavorable event) If the player dies, they will need to hear a really loud and annoying sound
  • (Negative punishment, removal of favorable event) If the player falls, he won’t be able to get that special item anymore
  • (Positive reinforcement, presents a favorable event) If the player has the highest score in this level, he will receive a fun bonus level.
  • (Negative reinforcement, removes an unfavorable event) If the player defeats this mini-boss in a certain way, they won’t need to confront another boss.
• Classical conditioning: when the player links a previously neutral stimuli to naturally linked events:
  • A music starts to sound, the player needs to kill a bunch of difficult enemies, the player gets tense, the player having to confront enemies and the player getting tense are naturally linked, next time the player hears that music, he will get tense, the music was previously the neutral stimuli, now it’s linked with the player getting tense/having to confront enemies.

It’s important to make clear to the player what causes something to happen, if not, you run the risk of a player linking two unrelated events together, something which might confuse the player, there’s also the risk of the player not linking two events, which will make it seem random and possibly unfair.

Observational learning

It doesn’t require a punishment nor a reinforcement, instead it requires a source to observe and learn from. An example would be that the player watches a deer step over a trigger in the floor and the deer explodes, that’s a way for the player to learn that they shouldn’t step on those triggers.

Tangential learning

This type of learning might not be directly related to learning the game itself, but it’s related to some games.

When the will/intention to learn is caused by a certain topic being presented in a context that the player enjoys. An example would be a player choosing to learn to play the guitar or directly learning (inside your game) to play the guitar because of that being presented in a fun way. Motivating the player to learn inside or outside your game can be really useful for the player and can generate great sentiments towards your game. Another example might be including real world information or an encyclopedia of events or structures related to the game, like Assassin’s Creed or Civilization.

Transferring

Transferring is taking previously acquired knowledge and adapting it to a new context, this new context can be similar or very different, a similar context will (most likely) be easier to transfer to, and a different context might require more effort.

Transferring is related to Rote learning and Meaningful learning.

• Roto learning is memorizing a bunch of information to repeat it or perform it in that exact order
• Meaningful learning is actually understanding how what’s being learned works and how it relates to other systems

Transferring happens a lot, an obvious example on when transferring happens is puzzle games.

In a puzzle game you’re presented with rules, hopefully individually and initially in a way that’s easy for the player to understand and solve that first puzzle. Then the context changes, a new puzzle (a new context) is presented to you, and you need to apply your knowledge of the rule from your previous context to this new context. With a mix of rules in a singular puzzle, multiple previously learnt contexts can used by the player to solve this new puzzle/context.

Meaningful learning is the type of learning that will aid the player the most when transferring, as it involves an actual grasp of the rules at play and their relationships. If you were to roto learn a puzzle, you will be able to solve only that one, as you won’t be able to apply the underlying mechanics and relationships to a new context or puzzle. You would have to brute force it.

When designing puzzle games (and any kind of game), make sure that you present new rules and mechanics in a way that the player can understand how it relates to the other mechanics, this way he will be able to solve future problems not by trial and error, but by means of knowledge and/or skill.

Here's more about transferring.