One Sentence, Huge Difference: How you label an award can effect behavior.

22 10 2011

Steve Urkel from ABC/CBS sitcom "Family Matters": A smart kid?!

Which praise do you think produced better results in American 5th graders?

You are Smart!


You worked Hard!

Carol Dweck, a psychologist at Stanford along with Claudia Mueller conducted a study across 12 New York City schools to discover that what we thought to be the obvious answer, wasn’t so obvious.  Dweck’s study showed that a simple sentence, the positioning of an extrinsic reward, had huge impact on the results of the children’s performance.  An article in Wired Magazine explained the study:

When Dweck was designing the experiment, she expected the different forms of praise to have a rather modest effect. After all, it was just one sentence. But it soon became clear that the type of compliment given to the fifth graders dramatically affected their choice of tests. When kids were praised for their effort, nearly 90 percent chose the harder set of puzzles. However, when kids were praised for their intelligence, most of them went for the easier test. What explains this difference? According to Dweck, praising kids for intelligence encourages them to “look” smart, which means that they shouldn’t risk making a mistake.

Dweck’s next set of experiments showed how this fear of failure can actually inhibit learning. She gave the same fifth graders yet another test. This test was designed to be extremely difficult — it was originally written for eighth graders — but Dweck wanted to see how the kids would respond to the challenge. The students who were initially praised for their effort worked hard at figuring out the puzzles. Kids praised for their smarts, on the other hand, were easily discouraged. Their inevitable mistakes were seen as a sign of failure: Perhaps they really weren’t so smart. After taking this difficult test, the two groups of students were then given the option of looking either at the exams of kids who did worse or those who did better. Students praised for their intelligence almost always chose to bolster their self-esteem by comparing themselves with students who had performed worse on the test. In contrast, kids praised for their hard work were more interested in the higher-scoring exams. They wanted to understand their mistakes, to learn from their errors, to figure out how to do better.

The final round of tests was the same difficulty level as the initial test. Nevertheless, students who were praised for their effort exhibited significant improvement, raising their average score by 30 percent. Because these kids were willing to challenge themselves, even if it meant failing at first, they ended up performing at a much higher level. This result was even more impressive when compared to students randomly assigned to the smart group, who saw their scores drop by nearly 20 percent. The experience of failure had been so discouraging for the “smart” kids that they actually regressed.

The problem with praising kids for their innate intelligence — the “smart” compliment — is that it misrepresents the psychological reality of education. It encourages kids to avoid the most useful kind of learning activities, which is when we learn from our mistakes. Because unless we experience the unpleasant symptoms of being wrong — that surge of Pe activity a few hundred milliseconds after the error, directing our attention to the very thing we’d like to ignore — the mind will never revise its models. We’ll keep on making the same mistakes, forsaking self-improvement for the sake of self-confidence. Samuel Beckett had the right attitude: “Ever tried. Ever failed. No matter. Try Again. Fail again. Fail better.”


Dweck’s study gives us clues in ways to present awards and achievements which encourage a deeper commitment to the system.  If a player perceives to have reached “Mastery” level, does the system encourage or discourage them from achieving more?   When designing player comparison charts and leader boards, does the game reward the most effective player motivation?  Based on Dweck’s Fixed Mindset vs Growth Mindset illustration by Nigel Holmes, guiding players towards the Growth Mind-set produces a more desirable, engaged and loyal player:

Click Image to see higher resolution chart:


Games provide an engaging environment for kids to make mistakes with a low cost of failure, so they can learn and discover a path to mastery.  James Gee, a linguist at Arizona State University talks about this, and other merits of educational video games.

“In a world full of complex systems that are interacting with each other to give us more and more disasters – like our current economic system, or our global warming – we really want that video game theory of intelligence.  You’re not intelligent because you rushed to be efficient in a goal you never rethought.  You are intelligent when you have explored thoroughly and you’ve thought laterally not just linearly, and you have rethought your goals and in modern games done so collaboratively in multiplayer and having to compare and contrast your solutions.  And also having different skill sets.  In many video games, you play in a team where everybody has a different skillset.  Much like modern science where you take big challenges…and you combine scientists with different skill sets but who learn to collaborate and learn to have some common language – that’s actually a way of playing today too.”


Engagement Flow in Gamification

2 11 2010


What dynamics are in play when we examine the path to user engagement?

In visualizing how engagement flows from extrinsic game mechanics to intrinsic motivators, let’s look first at a smaller simplified model exploring the various stages a player experiences along the way.

The image below shows 5 basic elements.

To illustrate by way of example, let’s choose a quest with location check-ins:

1.  The Game Mechanic is a quest, with an achievement delivered after check-in.  The game mechanic facilitates a mode of play.

2.  Play is freely chosen and associated with fun, pleasure and enjoyment.  The idea of a scavenger hunt is playful and may initiate certain behaviors.

3.  Behaviors refer to actions related to the game mechanics such as going to a retail store, checking in for an event, challenging friends to compete, or announcing check-ins on Facebook.  These behaviors may evoke a player’s engagement.

4. Engagement in this example is when a player becomes absorbed and the game activities, subject or brand consumes one’s attention or time.  For example, the player checks in every time they pass through a location and shares this with friends.  It creates an increased involvement and participation that produces mastery.

5. Mastery is an intrinsic motivator that stimulates a player to independently and persistently overcome a challenging skill or task.  Once the player has checked in at the quest locations, they obtain an achievement and look to the next challenge or motivator.

At this point, in order to keep the player further engaged, the game mechanic must either evolve and scale in difficultly, or shift to address another motivator.  This dynamic balance between boredom and frustration is described in Mihaly Csikszentmihalyi book “Flow: The Psychology of Optimal Experience”:

Flow activities lead to growth and discovery. One cannot enjoy doing the same thing at the same level for long. We grow either bored or frustrated; and then the desire to enjoy ourselves again pushes us to stretch our skills, or to discover new opportunities for using them.

To evolve the simple model to accommodate more complex situations, we need to introduce other dynamics, leading to 2 questions:

1. How does Self-Direction influence this model?

2. How does direct feedback relate to each area?

In the next figure, the linear engagement flow is now a circle reflecting how the game mechanic shifts or scales to accommodate the growth of the player as they seek new motivations.  This highlights the relationship between the engagement flow and Self-Direction.

Self-Direction, or independently guided actions occur by making choices in the game resulting in direct feedback.  In an optimal scenario, direct feedback should take place throughout the entire experience, giving us an ever-evolving and adapting engagement model.  It is important to note that each of the elements of the flow influence the other elements creating local feedback loops and are contributors to the dynamics of the game play.

The model also aligns with Jenova Chen’s variant of Csikszentmihalyi’s flow diagram.  In his thesis “Flow in Games”, Chen describes active flow adjustments through self-directed choices, represented by the red path of arrows (see figure below).  The Gamification Continuous Engagement Flow accounts for this meandering path through the introduction of direct user feedback.   In Chen’s drawing, the white regions represent anxiety (upper) and boredom (lower):

The significance of self direction along with varying and often changing motivators make game design with a single unvarying game mechanic impractical.  Successful designs will incorporate evolving and scaling mechanics suitable to a wide range of personalities, moods, player skill levels and interests.

Posts related to this subject:

Why Both Intrinsic and Extrinsic Motivation Matter in Gamification

Gamification, Reality TV and Reiss’s 16 Intrinsic Motivators

The Myth of the Universal Player

Gamification Design

Gamification, Reality TV, and Reiss’s 16 Intrinsic Motivators

24 10 2010

Steven Reiss is a professor of Psychology at Ohio State University and author of several books including  The Normal Personality.

Based on research from over 6,000 participants, Reiss suggests that intrinsic motivators, or those reasons people hold for initiating and performing voluntary behavior,  can be described as 16 basic desires.

These desires are:

(click on chart for higher resolution)

In this multifaceted model, these basic intrinsic desires directly motivate a person’s behavior. The unique combination and ranking of these desires determine our individuality and uniqueness.  Although people may also be motivated by non-basic desires, Reiss suggests it may be a means to achieve an even deeper basic motivation.

From a Gamification standpoint, it is especially interesting to note that Reiss models a close association between the basic desire for social contact with the need to play or to have fun.   If our social needs are genetically intertwined with play, it may add another lens to the importance of multiplayer relationships in game design.

The 16 desires give us a better understanding of variability in designing systems for engagement.  Reiss suggests that the enormous differences in what makes people happy make it unreasonable to factor out extrinsic incentives such as money or grades as effective motivators.  Different people are motivated in different ways, and as Reiss wrote in an article in Psychology Today:

I [Reiss] object to intrinsic-extrinsic motivation because it offers “one size fits all” solutions for educating children and motivating adults. I believe, for example, that some children thrive with cooperative learning, others thrive with competitive learning situations, but intrinsic-extrinsic motivation theory wants all children to grow up with cooperative learning. In the name of self-determination, undermining theory imposes its values on others believing it is for their own good. I think undermining theory could be misused to teach children who are competitive by nature that something is wrong with them for enjoying competition.

How do these 16 desires affect our taste in media?  In 2004 Reiss and James Wiltz performed a study on Why People Watch Reality TV.  According to Reis, Media events like Reality TV repeatedly allow people to experience the 16 desires and joys and suggests that people select media to fulfill certain needs. These needs vary greatly from one individual to the next, however his data showed that the largest significant motive for watching reality television was social status.  Slightly less than the need for social status was the need for vengeance, or the desire to win. These same 2 high-ranking motivators may be the reason why we find early implementations of Gamification emphasizing achievements and status levels.

Consequently, it makes sense that media or subject matter that taps into all 16 basic desires have a higher chance of attracting more people.  In the case of Reality TV, Bryant Paul, a psychology professor at Indiana University suggests “The closer someone is to you, the easier it is to empathize, and really good empathy equals really good television.”  The same holds true in game design – the more the game mechanics echo our own intrinsic needs, the better the individual’s gaming experience.

Taking the 16 desires into popular game design, this chart shows how (for example) World of Warcraft addresses each motive:

(click on chart for higher resolution)

Reiss’s 16 basic desires can provide an effective framework for measuring how successfully an application covers the range of intrinsic motivators. Using this framework should allow you to evaluate your own application’s appeal and highlight areas of improvement.