Headspace and the Nuance of Social Behavior Design

September 4th, 2015

Like most health and wellness habits, meditation has challenging behavioral characteristics:

  1. Requires a meaningful time commitment (10-30 minutes) and considerable consistency (daily)
  2. Requires a specific environment (uninterrupted, quiet, sitting, eyes closed)
  3. Yields long-term benefits (6+ months) with the potential for no appreciable short term gains

Many wellness apps use behavior design tactics to help overcome these barriers, but few are contextual to the nuances of the target behavior.  In this regard, the popular freemium meditation app Headspace stands out in its thoughtful use of social behavior design.  I’ll highlight three areas where Headspace excels and provide one recommendation for improvement.

1. A Leaderless Leaderboard

Meditation should be autotelic – done for its own sake.  A gamified, badge-laden, points-driven, force-ranked leaderboard would likely be corrosive to meditation.  However, if Headspace didn’t use any social features, its users would miss out on the benefits of peer accountability.  Headspace strikes the right balance with a low fidelity and non-competitive “buddy” feature.

Traditional Approach Headspace Approach Benefit
Avatars are self-selected photos Avatars are friendly illustrations Reduces psychological identifiability by adding distance between you and your buddies. This likely reduces the competitive drive (i.e., seeing your friend’s photo might make you want to compete with her, but it’s hard to feel competitive when you’re looking at her smiling avatar).
Add an unlimited number of friends through social media platforms Add up to 5 “buddies” by email only Incentivizes you to only add your closest companions. Meditating daily would be a lot of work to show off to just a few people, if that were your goal.
Friends (competitors!) are listed in rank-order, often with multiple metrics Buddies are displayed horizontally.  When you click on an avatar, you can only see a few metrics, such as the last time that person meditated. Minimizes competitive atmosphere and prevents showmanship.  You only know enough to check in and see how your buddies are doing.
Post comments and emoticons on a feed of activities Nudge a buddy via text or email Minimizes social praise or guilt as an extrinsic motivator for meditation.





2. Subtle Social Proof

When you swipe right from the main meditation tab, you’re taken to a screen that displays your progress.  Beneath your meditation statistics, the app displays the number of people meditating right now.  This number is a startling reminder that at this very moment, thousands of people are actually meditating.  The number is small and dynamic enough to feel credible, but substantial enough to make you feel like you’re part of a larger community with a shared experience.




If you are trying to procrastinate, that number immediately encourages you to start meditating.  If you just finished meditating, it’s a subtle congratulations for joining thousands of like-minded individuals. This is a powerful way to reinforce a habit that requires persistence in light of the fact that it yields few short-term benefits.
3. Non-Instrumental Social Rewards

Many apps incentivize the primary user for achieving the target behavior (e.g., mayorships in Foursquare/Swarm). However, providing users with an instrumental reward will likely undermine their intrinsic motivation . There’s a large body of literature suggesting that incentivizing an intrinsically-motivated behavior (e.g., helping a neighbor lift some heavy boxes out of a desire to be a nice person) with an extrinsic reward (e.g., $5) degrades performance.  In the Headspace context, you don’t want to directly incentivize habitual meditators by giving them benefits that are instrumentally valuable. Headspace thoughtfully addresses this tension by rewarding meditators who achieve 15 or 30 day streaks with a free 15 or 30 day voucher that they can only share with a friend.  Since the meditator can’t use the voucher for his own account, Headspace rewards the target behavior without corrupting the motivation, and potentially generates a referral in the process.

It is also important for users to only associate the Headspace app with the target behavior of meditation.  Headspace deftly preserves the sanctity of the app by delivering the 30 day voucher code via email, which further distances the meditation experience from any incentives.



What I’d Change: Streaks

Rewarding streaks is a powerful method to encourage consistent habits.  Unfortunately, streaks ignore the reality that life happens – flawless daily habits are nearly impossible .  Even the most dedicated flossers, exercises, or meditators simply miss a day every once in a while.


In a behavior design context, rewarding long streaks is dangerous for two reasons:

  1. You incentivize cheating.  If it’s nearing midnight and you just realized that you didn’t meditate, you might convince yourself that it’s okay to meditate while brushing your teeth.
  2. You risk demotivating users when they break long streaks.  Prospect theory reminds us that losses loom larger than gains.  When you break a long streak, everything you worked on so hard now feels like it’s thrown out the window because you forgot to meditate for just one day.  I bet that a person who breaks a 90 day streak will be subsequently much less active than someone who only ever achieves 15 or 30 day streaks.


As a result, I’d encourage Headspace to only reward streaks of up to 30 or 60 days.  You can reward 120 or 365 day streaks through surprise emails, but building it into the game dynamics distorts the core incentives.



Concluding Thoughts

Thoughtful behavior design can’t be copied.  What works in one context could backfire in another.  The tactics I’ve highlighted may be uniquely beneficial to Headspace given the nature of the target behavior. For example, you might want robust leaderboards when trying to encourage people to lose weight.  As designers of behavior, whether for ourselves or others, we must first consider the context and then build to the design.

Happy meditating!

How Foursquare Hacked Choice Confidence

July 25th, 2015

I’ve recently started using Foursquare as my default restaurant discovery app, but I couldn’t figure out why.

Picking a restaurant is a hard problem, but what had Foursquare done so well?

After reflecting on the user experience, I’ve noticed several key design choices that help Foursquare users feel more confident and make faster decisions. Below are three lessons from Foursquare that designers can apply to simplify and improve almost any challenging decision environment.
1. Convert qualitative data into quantitative insights
Foursquare highlights a specific area in which each restaurant excels. In this example, the restaurant in question is “One of the top 10 places for chicken fingers in New York.” Wouldn’t it feel good to invite your friends to the #5 burger joint in Midtown East, the #1 coffee shop in SoHo, or the #3 Italian restaurant on the Upper East Side? That’s an insight that actually helps you achieve your goal.



2. Avoid conflicting information
Conflicting information requires a degree of cognitive effort that can paralyze choice . We’re all familiar with the pattern: You read a string of positive restaurant reviews followed by a dramatic story of food poisoning at a birthday dinner. Back to the search results. You pick a new restaurant, read more reviews, and start paying attention to the credibility of each review. You uncover that the beef is popular, but at least three Yelp Elite experts point out that the appetizers are spotty and that they always overcook the vegetables. There’s so much negative information that you start making complicated comparisons on the downsides of each restaurant: Should you go to the 4 star Italian place with 107 reviews but overcooked vegetables and spotty appetizers, or the cheaper 4.5 star Indian place with only 14 reviews but which someone said has the best chana in all of Queens? Soon enough, no restaurant looks like a good option. Whoops.

Foursquare avoids the demotivating stories of diarrhea birthdays and spilled drinks on the third date. Instead of serving up reviews, Foursquare provides positive tips that make you feel like you’re getting the inside scoop. These helpful tips build confidence in a restaurant and require little cognitive effort.



3. Make it easy to solicit meaningful information
Yelp solicits long-form narrative reviews. Even the text box is ghosted with a sample review to prompt detailed stories. However, average users are unlikely to write such extensive narratives, and it’s hard to systematically extract insights from volumes of free text.

Instead of requesting a review, Foursquare asks a few simple questions that don’t require much effort, such as:

  • Did you like the restaurant? (yes, it’s okay, or no)
  • What’s good here? (select text from a list)
  • Is the restaurant romantic? (very, somewhat, not at all)
  • Is it good for families? (very, somewhat, not at all)
  • Is it good for late nights? (very, somewhat, not at all)

The questions are easy, you can respond with one tap, and you can stop at any time. By making the restaurant ranking process easy, fast, and fun, Foursquare can collect meaningful structured data from its users. This structured data allows Foursquare to generate confidence-instilling context, such as the “top 10 chicken finger” ranking for each restaurant.




Concluding Thoughts
Choices with many subjective attributes, such as picking restaurants or buying clothing, present unique challenges to the design of decision support tools. To reduce the decision space, most search and discovery or e-commerce tools only provide basic filtering functions. While it’s helpful to hide restaurants that aren’t open or perhaps prioritize restaurants within walking distance, there are usually so many options left over that it’s still hard to make a decision. By intelligently capturing, processing, and displaying relevant insights, Foursquare’s mobile app simplifies options and instills choice confidence . Designers of other subjective choice environments can look to Foursquare as a case study on behaviorally-intelligent design.

A Behavioral Lens on Being Mortal

July 4th, 2015


What would you be willing to trade to live for two more months? Would you risk paralysis of your lower body? Would you go on a feeding tube?  Or would you rather accept death?


These are the difficult choices that face many patients and their caregivers.  In Being Mortal, Dr. Atul Gawande argues that end of life care tends to focus on increasing the patient’s length of life, while sacrificing quality of life. In light of this dilemma, Dr. Gawande urges us to be more thoughtful when considering end of life treatment options. We should make deliberate choices that adhere to our personal definition of well-being. While one individual might accept a high risk of paralysis in exchange for the ability to watch football and eat ice cream, others might make radically different tradeoffs.


Looking at Being Mortal through a behavioral lens reveals a parallel argument: in order to support individual well-being, end of life care must focus on goals rather than preferences . To understand why, we can look to one of the most challenging human biases – the empathy gap.


Empathy gaps occur when we overemphasize our current “hot” emotional state and miscalculate our future “cold” state behavior (Loewenstein, 2005). The binge drinker who swears off alcohol at the height of inebriation offers a perfect example of the empathy gap in action. Overwhelmed by visceral pain and preoccupied with his emotional state, he vows to never drink again. What he doesn’t realize, however, is that his choice is heavily influenced by his “hot” state. After sobering up and returning to a “cold” state, he forgets the ill effects of inebriation and repeats the cycle at the next party.


The binge drinker in this example teaches us that a choice made in one state might not hold up in the other, because visceral factors like hunger, thirst, pleasure, and pain can cloud our judgment.

Now consider three common preferences in a living will:

  1. Do you want cardiopulmonary resuscitation if you heart ceases to function?
  2. Do you want mechanical respiration if you are unable to breathe unassisted?
  3. Do you want artificial hydration or nutrition if you are unable to eat or drink?


How can we expect a healthy individual sitting in a lawyer’s office to accurately make choices about a distant hypothetical life-threatening situation?  Even in the cold state of good health, about 1 in 3 individuals will reverse their preferences for life-sustaining interventions at least once in a two-year period (Danis et al., 1994; Ditto et al., 2003). This cold state indecisiveness makes it difficult to generate authentic living wills in times of health.  Add to that the hot state complexity of living with dementia or paralysis, and making end of life choices looks like a losing behavioral proposition.  This cold-to-hot state empathy gap represents the inverse problem of the binge drinker, yet it is just as difficult to overcome.


Now consider some of the questions mentioned in Being Mortal:

  1. What is your understanding of your situation and its potential outcomes?
  2. What are your fears and what are your hopes?
  3. What are the trade-offs you are willing to make and not willing to make?


While Dr. Gawande acknowledges the importance of assessing preferences for life-sustaining interventions, he appears to focus primarily on a patient’s goals for living. The behavioral literature supports this approach in theory, as goals are more durable than preferences.  When we set goals, we draw on our experiences in order to decide what we want to achieve. While goals shift as an individual moves through life, preferences are significantly more susceptible to empathy gaps and other context biases, such as framing.  Some of these biases are fairly minor, but others will cause us to make critical mistakes. This is especially true when we are asked to make choices about situations with which we have little to no experience, which is often the case for end of life care.


As I’ve written before, the behavioral literature can only offer heuristics and guardrails for optimizing choice and mitigating bias. Ultimately, medical professionals must negotiate these behavioral hurdles and help their patients to make the right tradeoffs.


Why Being Bad at Math is Like Having Diabetes

April 5th, 2015
Illustration by Mauricio Antón.  © 2008 Public Library of Science

Illustration by Mauricio Antón. © 2008 Public Library of Science


We’ve been hunter-gatherers for 99% of our history. We used to walk between five and ten miles a day, consume five pounds of sugar each year, and scour all day for sources of fat. We craved calorie-rich foods and our bodies figured out how to store energy to survive frequent bouts of famine.


We now live in a materially different environment. The average American now walks about one-third as far, consumes 25 times more sugar, and has ample access to caloric and fatty foods.


Our bodies are wired for famine, but we now live in abundance. As a result, we overeat and struggle to metabolize the excess sugar and fat, leading to obesity and type 2 diabetes. These chronic conditions are mismatch problems partially caused by a gap between our environment and our biology. A mismatch problem occurs when the traits that helped us thrive in our evolutionary past become inadequate in our new environment.


Mismatch problems also exist in the brain. Just as our endocrine system didn’t evolve to handle a high sugar intake, our minds didn’t specifically evolve to create art, philosophize, or make complex decisions.


Consider the Linda problem made famous by Amos Tversky and Daniel Kahneman in 1983 [PDF]:


Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.


Which is more probable?
A. Linda is a bank teller.
B. Linda is a bank teller and is active in the feminist movement.


If you’re like 85% of people, you’ll choose B because the description of Linda makes her sound like a feminist bank teller. However, basic probability reminds us that there must be more bank tellers than feminist bank tellers, as the latter is a subset of the former, so A must be more probable. This error is known as the conjunction fallacy.


Just as we can’t blame our bodies for struggling to keep our blood sugar stable after eating processed foods, we can’t fault our minds for making mistakes when asked to interpret abstract conjunctions. It’s unlikely that ancient humans needed to ruminate on conjunction problems to excel in the age of the woolly mammoth, so it’s no surprise we make these mistakes. In this way, being bad at abstract probabilities is like having diabetes — these simply aren’t problems that our evolutionary ancestors needed to handle.


How can we solve mismatch problems? We often attempt to enhance our capabilities through education. Unfortunately, the food pyramid is no match for biology programmed to devour the bulk bag of candy that awaits us in the kitchen.


Instead of trying to band-aid our biology, we should focus on shaping our local environment.  We can craft our surroundings, which we often control, to promote the desired behavior. If we don’t buy bulk bags of candy, we won’t need to exhaust willpower to fight the inevitable temptation to overindulge.


The same technique applies to the Linda problem. By reshaping the problem with natural fractions that represent our experience of the world, fewer than 15% of people violate the conjunction rule (Hertwig & Gigerenzer, 1999 [PDF]):


In an opinion poll, the 200 women selected to participate have the following features in common: They are, on average, 30 years old, single, and very bright. They majored in philosophy. As students, they were deeply concerned with issues of discrimination and social justice and also participated in anti-nuclear demonstrations.


Please estimate the frequency of the following events.
How many of the 200 women are bank tellers? ____ of 200
How many of the 200 women are bank tellers and are active in the feminist movement? ____ of 200


Humans have been evolving for about two million years, but the demands of today are a substantial shift from our past. Our diet has changed dramatically in the last few thousand years, and we started using probabilities only a few hundred years ago. Our genetics simply haven’t had time to catch up.


In the meantime, we are simply caught in a state of disequilibrium between our genes and our environment.  We shouldn’t hold ourselves to unreasonable normative standards in light of our evolutionary past. While we can’t yet easily change our genes, we can architect our environment to promote the behavior we desire.


Thanks to Kristen Berman, Miles Grimshaw, and Anastasiya Novatorskaya, who provided feedback on drafts of this article.

Everything You Thought About Behavioral Economics Is Wrong, and Why It Doesn’t Matter

January 4th, 2015

The well-known story of how the Economist magazine’s clever pricing led to a dramatic increase in premium subscriptions – an example of the asymmetric dominance or decoy effect1 – is probably incorrect. And it doesn’t really matter.



Two separate journal articles published this year have failed to replicate this classic finding after multiple attempts with thousands of participants (Frederick, Lee, & Baskin, 2014; Yang & Lynn, 2014).  These studies have helped clarify that the asymmetric dominance effect generally only occurs with salient, numerically-defined attributes in which the dominance relationship is readily encoded.  In the Economist example, the choices are defined by a jumble of words and numbers.  It is much easier to compare $59 to $125 than “Economist.com subscription” to “Print & web subscription.”  Combining these two dimensions mutes the effect.


Why did the example “work” ten years ago and why is it “not working” today?  Nobody knows.  Perhaps the concept of a print magazine is simply less relevant in 2014.  Perhaps participants in earlier studies were unintentionally biased in some unrecognized manner.  Perhaps the random perturbations of behavior flew under the arbitrary 5% level in statistical significance testing [pdf].


The true reason for the discrepancy, if it could be determined, is irrelevant.  The Economist example teaches us this crucial lesson: behavioral science research cannot tell us what will or will not translate into the real world.  Nor does it claim to.  All it can do is suggest ways of thinking about the problem and point us in some general directions for experimentation.


Taken together, the behavioral science literature represents a tour de force of how bad we are at things we think we are pretty good at.  There is not – and can never be – a grand theory that explains all of these observations.  Behavioral decision theory is not a closed-ended discipline.  We have some decent constructs and psychological models, but behavioral predictions in multifactorial decision contexts are hard to make and even harder to defend.  I admire and applaud authors who deftly translate academic research into accessible vignettes for a general audience, but stories lifted from academic journals are not designed to translate directly into the messy real world.  That’s precisely why the Economist example might not work today, and that’s why it doesn’t matter whether it works.


This reality ought not discourage.  While human behavior is complicated, the heuristic value of this research is substantial, and applications to public policy and health care are particularly encouraging.  It will simply require leaders with intellectual honesty, perseverance, and a dedication to experimentation to derive practical value from the groundwork laid by decades of research.

1 If you need a brief primer on asymmetric dominance, Wikipedia has a decent article. To be fair, this is not a pure example of the asymmetric dominance effect, as the third option completely dominates the second, but this is a distinction unimportant to the broader point.