Summary
An interaction loop is the repeatable cycle through which a player learns, tests, and refines a skill. Students usually meet this idea through questions like “how do players learn mechanics?”, “how do I teach without a tutorial pop-up?”, or “why does a mechanic stop engaging people?” The LDARF model (Learn → Decide → Action → Rules → Feedback) answers those questions by breaking every mechanic into a visible learning cycle, while skill chains show how simple loops connect into more advanced mastery. (course lecture series, see source-player-psychology-lectures)
For the broader theory behind why skill acquisition is pleasurable, see fun-as-learning.
Key ideas
The player model
The player is an entity driven — consciously or subconsciously — to learn new skills of high perceived value. They gain pleasure from successfully acquiring those skills. (see fun-as-learning, neurochemical-engagement)
This model has two critical components:
- Driven to learn: Play is instinctual. In the absence of more pressing demands, people begin playing by default. Boredom and frustration are feedback mechanisms that prod players toward more stimulating challenges.
- Perceived value: The perception of a skill’s value matters more than its objective value. Players will invest enormous effort into skills they believe will help them progress — and rapidly abandon skills that feel pointless.
The LDARF interaction loop
A game interaction loop describes the repetitive sequence through which a player learns and uses a specific skill. The five components:
| Stage | Description |
|---|---|
| Learn | Player encounters something new — a mechanic, enemy, obstacle. They begin building a mental model. |
| Decide | Player evaluates options: What can I do here? What’s the cost? What’s the benefit? What alternatives exist? |
| Action | Player executes an input — button press, movement, placement, spoken choice. |
| Rules | The game’s systems process the action. These rules are initially a black box — unknown to the player, but learnable through repetition. |
| Feedback | The game responds. This response updates the player’s mental model and feeds back into the Learn stage. |
The loop then repeats, each iteration refining the player’s understanding until mastery is achieved — what Koster calls grokking: understanding a causal link with certainty and being able to repeatedly cause the desired effect (see fun-as-learning).
Learning through cause and effect:
- Association — “When I do something, something happens” (fuzzy)
- Sampling — The more they practise, the more certain they become
- Grokking — They understand the causal link with certainty and can exploit it reliably
Skill chains
Individual interaction loops are not isolated — they form a directed graph called a skill chain. Simpler skills must be mastered before higher-complexity skills become accessible. Mastery flows down the chain.
[Jump] → [Double Jump] → [Wall Jump] → [Air Dash]
↓
[Jump Attack] → [Aerial Combo]
Case study: Zelda: Breath of the Wild skill chain atoms:
- Basic Movement: Walking → Running → Jumping
- Exploration: Climbing → Swimming → Gliding
- Puzzle Solving: Magnesis → Cryonis → Remote Bombs
- Combat: Basic Attack → Advanced Attack → Dodge/Block
- Crafting: Collecting Materials → Cooking → Making Potions
Feedback loops within the chain:
- Positive: Successfully defeating enemies with advanced combat rewards better loot, incentivising further skill development
- Negative: Failing a shrine puzzle resets it, encouraging the player to reconsider their approach
Skill status
Every skill atom in the chain has a status reflecting the player’s current relationship to it:
| Status | Description |
|---|---|
| Unexercised | The player has not yet attempted this skill |
| Active | The player is practising — building mastery (can become a grind) |
| Partially Mastered | The player has some command but not fluency |
| Mastered | The player has achieved reliable, fluent use |
| Burned Out | The player mastered it but has lost interest in using it |
Mastery must flow from foundational atoms. If a player is blocked early in the chain, they will never reach the further atoms — even if those atoms are well-designed.
Skill failure — diagnostic model
When players fail to learn a skill, the failure can be traced to a specific point in the LDARF loop:
| Loop stage | Failure mode | Common causes |
|---|---|---|
| Learn | Player didn’t build the right mental model | Incomplete prerequisite skills; insufficient learning time; wrong schema |
| Decide | Player chose not to engage | Perceived cost too high; perceived benefit too low; better alternatives existed |
| Action | Player couldn’t execute | Physical difficulty; controls unclear; required technique not yet taught |
| Rules | Causal link wasn’t learnable | Rules too dynamic (no consistent pattern); too complex to convert to cause/effect |
| Feedback | Player didn’t receive or process feedback | No feedback present; feedback ignored; multiple competing stimuli masked it |
| Burnout | Player mastered but disengaged | Skill no longer provides novelty or perceived value |
Critical insight: Learning failures are almost always design failures, not player failures. If the majority of players cannot learn a skill, the problem is in the architecture — the prerequisite chain, the feedback, or the perceived value — not in the players.
Skill trees
A skill tree is the UI manifestation of a skill chain — a graphical representation of the progression of skills, abilities, or upgrades that a player can unlock. It makes the otherwise implicit directed graph visible and navigable.
Functions:
- Customisation — players choose paths aligned to their playstyle, making each run unique
- Progression signal — unlocking nodes provides explicit acknowledgement of advancement
- Strategic depth — choices about which path to pursue create meaningful decisions
- Replayability — different build paths encourage multiple playthroughs
Player types and motivation
The course lecture series reference several player type taxonomies relevant to skill motivation:
- Bartle’s Taxonomy (Achievers, Explorers, Socialisers, Killers)
- Yee’s Gamer Motivation Model
- Lazzaro’s Four Keys to Fun
- Self-Determination Theory applied to gaming (see self-determination-theory)
Different player types will value different kinds of skill mastery — a competitive player prizes combat skill chains; an explorer prizes navigation and discovery chains.
Practice
Choose a game you know well and pick one mechanic it introduces part-way through (a new enemy, a grappling hook, a parry, a crafting station).
Part A: write the mechanic’s LDARF loop as a five-row table (Learn, Decide, Action, Rules, Feedback), describing what actually happens at each stage in this game. Underneath, note when you moved from association to sampling to grokking.
Part B: draw a skill chain of five to eight skill atoms that leads to one advanced skill, with arrows from each prerequisite to the skill that depends on it (as in the Jump → Double Jump example). Label each atom with its current skill status for you.
Success test:
- all five LDARF stages contain concrete, game-specific detail
- the Feedback row names a specific signal the game gives (a sound, an animation, a number, a state change)
- the Rules row describes something you had to learn through repetition, not something the game told you
- every arrow in the chain means “must be mastered before”
- every atom carries one of the five statuses: Unexercised, Active, Partially Mastered, Mastered or Burned Out
Extension:
- pick a skill where you or a friend got stuck, use the skill-failure diagnostic table to name the loop stage where the learning failed, and propose one design change aimed at that stage
Self-test
- What are the five stages of the LDARF loop?
- Why is the Rules stage described as a “black box” at first?
- What does Koster mean by grokking, and which two stages of cause-and-effect learning come before it?
- What is a skill chain, and why does a block early in the chain matter so much?
- A player has mastered a skill but no longer wants to use it. What is that skill’s status?
- If most players cannot learn a skill, where does this page say the problem usually lies?
Answers
- Learn, Decide, Action, Rules, Feedback.
- The game’s rules are initially unknown to the player; they become learnable only through repeated actions and the feedback those actions produce.
- Grokking is understanding a causal link with certainty and being able to cause the desired effect repeatedly; it follows association (fuzzy “I do something, something happens”) and sampling (growing certainty through practice).
- A skill chain is a directed graph in which simpler skills must be mastered before higher-complexity skills become accessible; if a player is blocked early, they never reach the later atoms, however well designed those are.
- Burned Out.
- In the design rather than the players: the prerequisite chain, the feedback, or the perceived value of the skill.
Related
-
game-atoms — the atomic loop (Action → System Response → Feedback → Understanding → Next Action) is the same cycle described from the game’s side
-
fun-as-learning — Koster’s theory that fun is the process of learning patterns and grokking them
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game-loops — the broader loop structure (core, inner, outer) that skill chains operate within
-
flow — the channel of balanced challenge and skill that skill chains help maintain
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challenge-types — Adams’ taxonomy of challenge types; skill chains generate different challenge types
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player-agency — agency requires skill — without learnable mechanics, meaningful choice collapses
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self-determination-theory — SDT’s competence need explains why skill acquisition is intrinsically motivating
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prototyping — prototyping core loops tests whether the skill chain is learnable before full development
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breath-of-the-wild — Systemic design behind the BotW skill-chain case study