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Why not Amazon? For one, illustrated non-fiction isn’t well-suited for the Kindle format. Also, at a €14.99 price point, Amazon’s cut amounts to €9.75. Well, no.
Level Two: Interactivity
Let’s begin with a deeper look at the dynamics that tie this level’s territory, Interactivity, to the just-right amount of challenge, its associated element from the interactive playing experience model that we developed in Level One: Integral Perspectives I.
It’s the Goldilocks principle—a challenge that is neither too hard nor too easy, but just right. And we have a model for that, also introduced in Level One: Integral Perspectives I: the concept of flow.
If you’ve read more than zero books on game design, chances are you’ve seen a version of the following diagram before:
This illustration (Fig.4.7) is adapted from Csíkszentmihályi Mihály’s own illustrations in Flow: The Psychology of Optimal Experience. To get one thing out of the way quickly, the often foregrounded aspect of becoming oblivious of time is a result and a benefit, but it’s not its mechanism. Thus, the model’s familiarity is often deceiving.
The letter “A” represents “Alex,” who is learning to play a game (tennis, in Csíkszentmihályi’s original case), and Alex wants to avoid both anxiety and boredom because neither are positive experiences. To avoid these negative experiences, Alex’s only available choice (barring the decision to quit playing altogether) is to return to the flow state. Thus, Alex is highly motivated to do just that.
This is acutely relevant for game design. Following the flow model, you won’t have to (or shouldn’t) design your game in such a way that it perpetually analyzes the player’s performance and tries to wrap the optimal playing experience around them, so to speak. Instead, you have to follow three related design patterns:
The up-and-down recommendation from the second design pattern is based on Qin, Rau, and Salvendy’s study “Effects of Different Scenarios of Game Difficulty on Player Immersion.” Its results found their way into the self-determination model and also informed Nicole Lazzaro’s integrated concept of flow and fiero from “Understanding Emotions” and “Games and the Four Keys to Fun.” (Both the self-determination model and the concept of fiero were introduced in Level One: Integral Perspectives I). Reflecting these patterns, the flow model looks like this:
With these design patterns in place, the player will do the rest because they’re highly motivated to stay, or get back into, their flow channel.
As a serious collateral benefit, these design patterns will give players significantly more freedom in creating their own experiences. (You may help them along with a little dynamic difficulty adjustment, but DDA contributes its own colorful problems, to be examined later.) Moreover, these dynamics lead inevitably to growth and discovery because—to stay within the flow channel—players must a) constantly improve their skills and b) constantly explore new opportunities for putting their improved skills to use. That’s why, according to Csíkszentmihályi, the experience of flow is always related to growth, and not just any old growth, but growth of the self—a strong tie-in with our three components “growth,” “insight,” and “experience” from the design-driven goal that we discussed in the Procedure Phase’s Level Three: Tracing the Goal.
But sometimes players leave the flow channel, at least temporarily. Where do they go? There’s an illustration for that, too:
This illustration (Fig.4.9) is adapted from two related sources. The first source is Csíkszentmihályi’s illustration in Finding Flow: Psychology of Engagement with Everyday Life, which was in turn adapted from the second source, Fausto Massimini & Massimo Carli’s illustration in “The Systematic Assessment of Flow in Daily Experience.”
It shows flow within a Cartesian coordinate system of positive and negative experiences, where the magnitude of “Challenge” constitutes the vertical, the magnitude of “Skill” the horizontal axis. Flow is achieved when the values for both Skill and Challenge correspond and are both high. If Challenge is higher than Skill, the player (or performer, but let’s stick with player for the purpose of designing games) will leave the flow state toward “Arousal,” “Anxiety,” or “Worry,” depending on current skill levels. If Skill is higher than Challenge, the player will leave the flow state toward “Control,” “Relaxation,” or “Boredom,” depending on current challenge levels. At the lower end of the scale, where skill and challenge again correspond but are very low, players will experience “Apathy.”
Interestingly, in Massimini & Carli’s earlier version of this model, the placement of “Relaxation” and “Boredom” was reversed. It was an initial hypothesis that made sense intuitively: without sufficient challenge, players at higher skill levels should be more easily bored than players at lower skill levels. Empirically, that didn’t turn out to be the case at all. A good number of studies since have shown that, when players are not sufficiently challenged, boredom is more likely to occur at lower skill levels, while players with higher skill levels tend to fall back to a “relaxed” state when challenge decreases. It might not be as intuitive as the initial hypothesis, but it matches the data (and personal observations as well).
The takeaway is to bake into the game’s challenges the highest possible degree of scalability. The player can then match their current skill level with the appropriate level of challenge and with the appropriate mechanic to meet that challenge. This keeps the player motivated to constantly push forward and stay within their flow channel.
How can that be accomplished? There is a general principle that you can see at work in almost every game that has a great flow channel. This general principle puts players in control by designing the game’s challenge structure in ways that concurrently escalate risk, relief, and reward, and not just over time, but “stacked” at any given gameplay moment.
Greater risks that experienced players are willing to take are met by greater relief, i.e., more opportunities to restock staples like health, ammo, and so on, and greater rewards of any kind, from loot to experience points. Applying this principle means providing an interesting variety of interactive mechanics with different levels of execution difficulty, among which the player can choose in keeping with proficiency or preference.
You can see this design principle at work in many games, with numerous imaginative variations. It’s what makes Mario and similar games equally enjoyable for players who widely differ in experience and expertise. For a detailed and comprehensive study of scalable difficulty in Mario games, Daniel Johnson’s Game Design Companion: A Critical Analysis of Wario Land 4 cannot be recommended highly enough. At every single moment, these games provide choice—about the preferred level of challenge and the preferred means to meet that challenge. Which puts the player in control of their flow channel and on the track for great gaming experiences with high replay values and opportunities for personal growth.
As a final remark, feedback isn’t covered in this territory but in the Plurimediality territory (except feedback loops, which are discussed in Beat 3. Revolutions). Certainly, feedback is an integral part of interactivity and vital for learning success of any kind. But for a game, feedback consists first and foremost of visual, auditory, and kinesthetic elements that demand design decisions for the game as a whole along its various interfaces. Accordingly, feedback is discussed throughout Level Three: Plurimediality.
This phase’s reading order is fixed for the Introduction, Level One and Level Six.
Level Two through Five are non-linear: you can tackle them in any order you like.