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Players pursue skills with high perceived value over skills with low perceived value
Play is, perhaps counter intuitively, a deeply pragmatic activity. Our impulses to engage in play are instinctual, selected for by evolution because it provides us with the safe opportunity to learn behaviors that improve our lot in life without the threat of life threatening failure. We play because we are built to expect the eventual harvesting of utility from our apparently useless actions. We stop playing when we fail to find that utility.
The perception of value is more important than an objective measurement value. Humans are not creatures of pure logic. We know people exhibit consistent biases in how they weight their actions. For example, they’ll often undertake bizarre risks because they are unable to properly evaluate statistical odds. We’ve also realized that people have substancial limits on how much information they can take into account when making any one decision. Many decisions are made based off highly predictable ‘gut’ reactions that have their own subconscious rules.
With our player model in hand, we can describe how the player interacts with the game.
The basic ingredients of a game are, if not standardized, at least well described in a variety of books and rambling by designers across the past decade or two. I’ve taken the basic ingredients of tokens, verbs, rules, aesthetics, etc and remixed them into a self contained atomic feedback loop called a skill atom. Each unit describes how the player gains a new skill.
Diagram 4: The player follows clues to the acquisition of a new skill
A skill atom feedback loop is composed of four main elements:
-Action:The player performs an action. For a skill atom encounter by a new player, the action might involve pressing a button. More advanced atoms might instead require the player execute a batched set of actions such as navigating a complex maze.
- Simulation: Based off the action, an ongoing simulation is updated. A door might open.
- Feedback:The game provides some form of feedback to the player to let them know how the simulation has changed state. This feedback can be auditory, visual, or tactile. It can be visceral in the form of an exploding corpse or it can be symbolic in the form of a block of text.
- Modeling: As the final step, the player absorbs the feedback and updates their mental models on the success of their action. If they feel that they have made progress, they feel pleasure. If they master a new skill or other tool, they experience an even greater burst of joy. If they feel that their action has been in vain, they feel boredom or frustration.
A shorthand diagram that I find useful for recording atoms is as follows:
Diagram 5: Our canonical skill atom
For example, let’s dissect the act of jumping in Mario
Diagram 6: The skill atom of the player learning how to make Mario jump
· Action: An inexperienced player pushes a button.
· Simulation: The simulation notes the action and starts the avatar of Mario on the screen moving in an arc.
· Feedback: The screen shows the user an animation of Mario jumping.
· Modeling: The user forms a mental model that pressing the button results in jumping.
Implicit in this model is that the atom is often looped through multiple times before the user understand what it teach. The first pass may only clue the user that something vaguely interesting happened. The user then presses the button again to test their theory and Mario once again bounces up into the air. At this point, the player smiles since they realize they’ve acquired an interesting skill that may be of use later on.