There’s lots of navel gazing about why adventure games keeled over in the first place. The problem is that most of these analyses amount to little more than unhappy augury about how the genre is doomed to niche status now that episodic gaming has returned it to some semblance of life. However, the reason that the unreflected game isn’t worth making is that there are lessons to be learned from past design mistakes. So it’s high time that whingeing about genre shortcomings be tempered with some attempt to mitigate them. However, it turns out that there’s a lot to say on the matter, so this article will be broken into two weeks. This first one will address the problems and the framework we’ll need for the solutions. We’ll hit the solutions next week.

Reinforcement As a Seed of Failure
The aptly-named Doom is a both popular and instructive scapegoat for the genre’s decline. It arrived on the tail end of adventure game popularity and heralded the arrival of the soon dominant FPS genre. Unfortunately, the only lesson to be learned there is to stop making adventure games and focus instead on FPS games, and that’s exactly what the industry did.

However, if you don’t want to leave the genre dead and forgotten, that lesson isn’t much use. Instead, we need to identify why gamers jumped ship from one to the other. Looking abstractly at the genres reveals that their reinforcement schedules have completely different pacing. Adventure games are deliberative as gamers ponder and eventually solve puzzles, while FPS games have a nearly instant feedback loop as players make kills and move on. Put another way, patience may pay off in the long run, but instant gratification always pays off right now.

And now.

And now too.

So how do we move adventure games away from the occasional shots of heroin and toward the morphine drip? The first step requires identifying the roadblocks that punctuate the fun in the first place. Consider the following statements:

In Dragon’s Lair, Dirk the Daring must press up at just the right time in the face of minimal or counterintuitive telegraphy or else be instantly killed.

In Monkey Island, Guybrush Threepwood must use the rubber chicken with a pulley in the middle on the zip line in the face of few clues or better alternative puzzle solutions, or else stare impotently at an unreachable island.

Dragon’s Lair seems like an odd waypoint when we’re discussing adventure games, but it’s easiest to see the flaw in Dragon’s Lair and then draw the parallel to the more fondly remembered Monkey Island. They share the underlying gameplay conceit of reading the designer’s mind.

Now, when you instantly intuit the puzzle solution from the available clues (or puns, in the case of Monkey Island), there’s no problem. You solve the puzzle, feel good, and clip along. However, when you guess wrong, don’t have a critical item, or, worst of all, use the right items in the wrong way, you can get locked into a frustrating cycle of failure.

Here There Be Monsters
The source of this problem is that adventure games have unbounded exploration like sandbox games, but unitary solutions like puzzle games. This means that there’s a large problem space, and only one path through it that represents the solution. When you don’t instantly grasp a puzzle’s solution, you’re stuck with a feedback problem, because you can’t read the designer’s mind, and the designer clearly didn’t read yours. In the absence of some kind of feedback, there’s no way to tell if the solution exists or not.

If you’re unfamiliar with the idea of a problem space, here’s a quick primer. You can think of a problem space as a flow chart where each box is a state and each arrow is an action. So for the Threepwood example, there’s a box somewhere in the problem space where Guybrush has the rubber chicken and is standing next to the zip line, and you can move one box closer to the end of the game by following the arrow that’s labeled “use chicken on zip line.” So if you imagine every possible combination of state and action, you have an idea of what the problem space looks like in an adventure game: a hopeless tangle of boxes and arrows with exactly one path snaking toward the goal.

Cognitive psychologists call this kind of problem unbounded search, because you don’t really know whether you’re going in the right direction to find your goal. They usually research it in terms of looking for a visual target (like your missing car keys), but examining visual search in a little more detail will reveal that it dovetails with trying to find your way through the problem space in an adventure game.

What Is Search?
At its heart, a visual search task is a very simple game. You show your player a target to look for on a monitor. Then you take that target and nestle it in a field of distractor objects that make it harder to see the target, rather like an overly simple version of Where’s Waldo? By timing how long it takes the player to find the target, you can figure out how difficult the task is, and then you can start tweaking the task to figure out ways to make it easier or harder. Fiddling with stuff like this has led psychologists to learn some truths about the way people search for objects.

One lesson learned from search tasks is that it takes longer to find a target as you add more distractors. The reason why is pure statistics. When you start your search, you don’t know where the target is, so you start in a random spot and systematically wind your way through the distractors. So adding more distractors makes it less likely that you’ll luck out and start in the right place or even start searching in the right direction.

Seating this idea in the problem space of an adventure game, the target is the action that moves the player from one state to another state that’s closer to the end of the game, and the distractors are every other possible action. So Guybrush using the rubber chicken on the zip line will get him closer to the end of the game, but using it on the nearby lake won’t, and neither will using the laundry claim ticket on the zip line. As the number of items in your pockets and in the environment increase, your chances of picking the chicken and zip line decrease.

A corollary to the distractor number rule is distractor similarity. Distractors that look similar to the target are much harder to search through than contrasting distractors. The explanation here is that you have to examine similar distractors more carefully, which wastes time and effort. Would you rather lose your car keys in a junk drawer or a bowl of M&M’s?

In a problem space, distractors are actions that look like they should solve the puzzle, but don’t actually advance the state closer to the end of the game. So if Guybrush uses a leather belt on the zip line instead of the rubber chicken with a pulley in the middle, but the “correct” solution is the rubber chicken, then he’s been foiled because the designer failed to anticipate the player action or the player intuited the wrong solution.

The last relevant thing you can learn from search tasks is that the worst possible thing is a search without a target. In a normal search task, you find the target and you move on with your life. However, to know that there is no target to find, you have to both examine every distractor and remember that each one was not the target. How many objects can you remember in detail while glancing through them? Three? Five? You can work around the memory problem with a systematic search, but how often do you honestly mount a completely systematic search (Hint: Never)?

In reality, people stop searching when they become too frustrated to continue, and although this behavior was discovered with targetless search, it applies whenever the player gets sick of struggling against the search task. If you transplant this idea from visual search and into the problem space for an adventure game, it works the same way. If a player doesn’t luck out and hit the solution immediately, he’ll randomly click items on objects in the environment until he gets lucky or quits in frustration. In a game where the design goal is to make your player feel amused or clever, you hamstring your reinforcement schedule by only reinforcing one path through the problem space and penalizing every other path.

Search isn’t just useful for framing the problem, however. Next week, we’ll see how it suggests the solutions to these problems that have already been attempted, and some left untried.

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