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An Incomplete Loop: A Review of Nir Eyal's Hooked
In the last decade, the business and technology worlds have become obsessed with the science of the mind. The thinking goes as follows: if we understand the mind, in all its intricacy, we can manipulate and control it to produce the business objectives we want. While this may seem like a new movement, made possible by recent advances in psychology, “big data”, and neuroscience, it is in fact the rebirth of a line of psychological fascination that dates back at least 70 years.
In the aftermath of World War II, psychology became obsessed with behavior and mind control. The question was: How could terrible people like Mussolini and Hitler manipulate so many people? This led to research on hypnosis, brainwashing, and, in general, the dark side of humanity. When the Cold War began, this research fully blossomed, resulting in now famous experiments like the Milgram Authority Experiment and the Stanford Prison Experiment. While some of this research was intriguing to marketers, it wasn’t really applicable to concrete business problems.
However, now, in the early 2000s, the rise of Behavioral Economics has ushered in a new phase of business minded behavior science, and psychology books with tangible applications are popular once again. The moral and good side of behavior design is focused on in these works, and the triumph of the psychological sciences in cultivating positive outcomes has been called out: increasing the number of organ donors, increasing medication compliance, increasing consumer savings, and so on. One of the latest books in this line of inquiry is Nir Eyal’s “Hooked: How to Build Habit-Forming Products”. In the book, he outlines what he calls “The Hook Model”. It’s a simple four-phase model that explains the basic process of habit formation:
Step 1: Trigger Behavior
Step 2: Perform Action
Step 3: Variable Reward for Action
Step 4: Commitment to Product
Over 200 pages, Nir clearly explains and elaborates upon each of these elements and how they elegantly fit together to form a (hopefully) never-ending cycle. The more frequently a company is able to bring users through this loop, the more likely it is that they’ll successfully induce a habit in their users or customers.
Students of psychology might recognize the basic three-step process of Operant Conditioning, also called the “Habit Loop” in Charles Duhigg’s “The Power of Habit”, as the core of the Hook Model. To this core, Nir has added a fourth element: Commitment. The process works as follows: First, a user is prompted to use your product or service by a trigger of some sort. The user then performs the behavior. The easier and simpler the behavior is, the better. Next, the user receives a reward from engaging with the product. Preferably this is a reward they weren’t expecting – a “variable reward”. Finally, the user invests in the product by adding info, effort, time, etc. This not only causes them to value the product more, but it also helps them tailor the product to their needs and preferences – which makes future uses faster and more rewarding. That, in a nutshell, is the Habit Loop.
Nir makes sure to delve into each segment of the loop to provide further clarification and plenty of examples. In the trigger section, Nir outlines five different types of triggers – four types of “external triggers” and one type of “internal trigger”. In the reward section he talks about the three primary types of variable rewards, which he calls rewards of “The Tribe” (social), “The Hunt” (food, clothing, shelter, etc.), and “The Self” (intrinsic). Each of these sections is peppered with great examples of technology products that exemplify the concept being covered. When speaking about variable rewards in technology products, he appropriately turns the reader’s attention to slot machines, the Twitter feed, and the Pinterest feed. When speaking about products that won because of ease-of-use and simplicity in the “Action” (step 2) section, he compares Google’s minimalist homepage to Yahoo’s cluttered portal. Visual examples like these make the sometimes abstract concepts in the book crystal clear.
For someone just beginning his or her foray into applied psychology, this book is a fine start. This, however, does not mean that it doesn’t have its issues. Many of the biggest product successes in the technology world directly contradict the Hook Model. For example, one can make an extremely powerful case that Google won by violating step 3 of the Hook Model - Variable Rewards - by actually decreasing the variability of the rewards (in this case, answers) given. In experimental psychology, it’s known that animals given intermittent rewards (also called variable rewards) exhibit the reward-seeking behavior more intensely. This means that a rat that is given a pellet only 50% of the time it presses a lever will do so more intensely and frequently than a rat given a pellet each and every time it presses the lever. In this case, the rat is being variably rewarded for its behavior. This is what creates intense compulsive action. While this type of reward schedule works for creating compulsion in slot machines and in games like Farmville, it actually doesn’t make such sense to add such a reward schedule to a utility like Google. Google is all about finding answers to burning questions. If only 30% or 50% of one’s searches end up resulting in an answer, that’s not a good thing. In fact, Google won because it decreased the variability of finding an answer (and thus getting a reward). It had such better relevance than other search engines that it was able to get as close to what’s called a continuous reinforcement schedule (one in which each occurrence of a behavior results in a reward) as possible. Each search resulted in an answer - the reward in this scenario.
In addition, Uber, one of the most habit-forming apps that has ever been created, doesn’t use variable rewards. While there is some degree of variability in the Uber experience (sometimes there’s surge pricing and sometimes there isn’t), that’s not the reason the app has become a daily staple for so many. It’s heavily used because it’s immensely useful and solves a key problem/pain point. Paypal, Google Maps, and Dropbox are other examples of apps that violate the principle of Variable Reward. All of these products are quite practical and utilitarian, which perhaps hints at an addendum to the principle: “Utilities do not need to reward variability. In fact, stability and a lack of surprises might be preferred.” Who wants to be surprised upon opening Paypal?
Nir also writes that “Investment comes after the variable reward phase, when users are primed to reciprocate”. But this is not necessarily the case. Mint, one of the examples in the Investment section of the book, actually goes against the Hook Model’s process, requiring users to invest up front by linking their bank and credit card accounts, before they can ever get a reward. The hook process for a new Mint user is as follows: Trigger (hear about site) —> Investment (link bank accounts and credit cards) —> Reward (see total assets) —> Action. The rewards in the system only come after users have linked their accounts: A list of your total assets and salient “alerts” (potential problems that need to be addressed).
Nir also uses Twitter as an example of an application that exemplifies the investment phase of the model. However, a Twitter user’s first experience does not follow the hook model as it currently stands. If one looks at the Twitter sign up flow, it actually forces users to follow popular users (invest) before any reward is given. Thus, the Hook loop for Twitter also has investment before reward: Trigger (hearing about twitter) —> Investment (signing up and following users) —> Variable Reward (feed full of tweets). Action, in the model’s sense, isn’t even necessary in this process, since the site is framed as an RSS-reader type of application for passive reading - users are not encouraged or forced to send out their own first Tweet. Future loops for Twitter (post sign up) look as follows: Trigger —> Action (log into app or open app) —> Variable Reward (Read interesting content). Every once in awhile users will invest and follow a new user, but the main investment in the app is done up front in the sign-up process before any reward occurs. This is at odds with the Hook Model.
In fact, the first experience of most users signing up for most applications has them invest before they receive any reward. By definition, every app has users invest up-front by going to the app store and downloading the application. This then creates a persistent trigger (the app icon) that sits on their home screens. Then, in most applications, there is (more often than not) a sign-up process that asks users to submit information and set up their account. Uber is a great example of this. Users are asked to input their email, mobile phone number, pick a password, and input a credit card before they can use the product. Thus, Uber’s Hook flow is as follows: Trigger (hear about app) —> Investment (install app, add info and credit card) —> Action (get car) —> Reward (get to desired location). The reward is not variable, since it is the expected response to the behavior, and the investment is done up front.
While Hooked has its problems, it’s a delightful read and a thought provoking look at the psychological complexity of the simple products we use everyday. It definitely got me thinking, and I’m truly glad I was able to grapple with its ideas these last couple of weeks.
Northwell Health is using insights from website traffic to forecast COVID-19 hospitalizations two weeks in the future.
- The machine-learning algorithm works by analyzing the online behavior of visitors to the Northwell Health website and comparing that data to future COVID-19 hospitalizations.
- The tool, which uses anonymized data, has so far predicted hospitalizations with an accuracy rate of 80 percent.
- Machine-learning tools are helping health-care professionals worldwide better constrain and treat COVID-19.
The value of forecasting<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTA0Njk2OC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYyMzM2NDQzOH0.rid9regiDaKczCCKBsu7wrHkNQ64Vz_XcOEZIzAhzgM/img.jpg?width=980" id="2bb93" class="rm-shortcode" data-rm-shortcode-id="31345afbdf2bd408fd3e9f31520c445a" data-rm-shortcode-name="rebelmouse-image" data-width="1546" data-height="1056" />
Northwell emergency departments use the dashboard to monitor in real time.
Credit: Northwell Health<p>One unique benefit of forecasting COVID-19 hospitalizations is that it allows health systems to better prepare, manage and allocate resources. For example, if the tool forecasted a surge in COVID-19 hospitalizations in two weeks, Northwell Health could begin:</p><ul><li>Making space for an influx of patients</li><li>Moving personal protective equipment to where it's most needed</li><li>Strategically allocating staff during the predicted surge</li><li>Increasing the number of tests offered to asymptomatic patients</li></ul><p>The health-care field is increasingly using machine learning. It's already helping doctors develop <a href="https://care.diabetesjournals.org/content/early/2020/06/09/dc19-1870" target="_blank">personalized care plans for diabetes patients</a>, improving cancer screening techniques, and enabling mental health professionals to better predict which patients are at <a href="https://healthitanalytics.com/news/ehr-data-fuels-accurate-predictive-analytics-for-suicide-risk" target="_blank" rel="noopener noreferrer">elevated risk of suicide</a>, to name a few applications.</p><p>Health systems around the world have already begun exploring how <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315944/" target="_blank" rel="noopener noreferrer">machine learning can help battle the pandemic</a>, including better COVID-19 screening, diagnosis, contact tracing, and drug and vaccine development.</p><p>Cruzen said these kinds of tools represent a shift in how health systems can tackle a wide variety of problems.</p><p>"Health care has always used the past to predict the future, but not in this mathematical way," Cruzen said. "I think [Northwell Health's new predictive tool] really is a great first example of how we should be attacking a lot of things as we go forward."</p>
Making machine-learning tools openly accessible<p>Northwell Health has made its predictive tool <a href="https://github.com/northwell-health/covid-web-data-predictor" target="_blank">available for free</a> to any health system that wishes to utilize it.</p><p>"COVID is everybody's problem, and I think developing tools that can be used to help others is sort of why people go into health care," Dr. Cruzen said. "It was really consistent with our mission."</p><p>Open collaboration is something the world's governments and health systems should be striving for during the pandemic, said Michael Dowling, Northwell Health's president and CEO.</p><p>"Whenever you develop anything and somebody else gets it, they improve it and they continue to make it better," Dowling said. "As a country, we lack data. I believe very, very strongly that we should have been and should be now working with other countries, including China, including the European Union, including England and others to figure out how to develop a health surveillance system so you can anticipate way in advance when these things are going to occur."</p><p>In all, Northwell Health has treated more than 112,000 COVID patients. During the pandemic, Dowling said he's seen an outpouring of goodwill, collaboration, and sacrifice from the community and the tens of thousands of staff who work across Northwell.</p><p>"COVID has changed our perspective on everything—and not just those of us in health care, because it has disrupted everybody's life," Dowling said. "It has demonstrated the value of community, how we help one another."</p>
Scientists used CT scanning and 3D-printing technology to re-create the voice of Nesyamun, an ancient Egyptian priest.
- Scientists printed a 3D replica of the vocal tract of Nesyamun, an Egyptian priest whose mummified corpse has been on display in the UK for two centuries.
- With the help of an electronic device, the reproduced voice is able to "speak" a vowel noise.
- The team behind the "Voices of the Past" project suggest reproducing ancient voices could make museum experiences more dynamic.
Howard et al.<p style="margin-left: 20px;">"While this approach has wide implications for heritage management/museum display, its relevance conforms exactly to the ancient Egyptians' fundamental belief that 'to speak the name of the dead is to make them live again'," they wrote in a <a href="https://www.nature.com/articles/s41598-019-56316-y#Fig3" target="_blank">paper</a> published in Nature Scientific Reports. "Given Nesyamun's stated desire to have his voice heard in the afterlife in order to live forever, the fulfilment of his beliefs through the synthesis of his vocal function allows us to make direct contact with ancient Egypt by listening to a sound from a vocal tract that has not been heard for over 3000 years, preserved through mummification and now restored through this new technique."</p>
Connecting modern people with history<p>It's not the first time scientists have "re-created" an ancient human's voice. In 2016, for example, Italian researchers used software to <a href="https://www.smithsonianmag.com/smart-news/hear-recreated-voice-otzi-iceman-180960570/" target="_blank">reconstruct the voice of Ötzi,</a> an iceman who was discovered in 1991 and is thought to have died more than 5,000 years ago. But the "Voices of the Past" project is different, the researchers note, because Nesyamun's mummified corpse is especially well preserved.</p><p style="margin-left: 20px;">"It was particularly suited, given its age and preservation [of its soft tissues], which is unusual," Howard told <em><a href="https://www.livescience.com/amp/ancient-egypt-mummy-voice-reconstructed.html" target="_blank">Live Science</a>.</em></p><p>As to whether Nesyamun's reconstructed voice will ever be able to speak complete sentences, Howard told <em><a href="https://abcnews.go.com/Weird/wireStory/ancient-voice-scientists-recreate-sound-egyptian-mummy-68482015" target="_blank">The Associated Press</a>, </em>that it's "something that is being worked on, so it will be possible one day."</p><p>John Schofield, an archaeologist at the University of York, said that reproducing voices from history can make museum experiences "more multidimensional."</p><p style="margin-left: 20px;">"There is nothing more personal than someone's voice," he told <em>The Associated Press.</em> "So we think that hearing a voice from so long ago will be an unforgettable experience, making heritage places like Karnak, Nesyamun's temple, come alive."</p>
A new study proposes mysterious axions may be found in X-rays coming from a cluster of neutron stars.
Are Axions Dark Matter?<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="5e35ce24a5b17102bfce5ae6aecc7c14"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/e7yXqF32Yvw?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span>
New research suggests you can't fake your emotional state to improve your work life — you have to feel it.
What is deep acting?<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTQ1NDk2OS9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYxNTY5MzA0Nn0._s7aP25Es1CInq51pbzGrUj3GtOIRWBHZxCBFnbyXY8/img.jpg?width=1245&coordinates=333%2C-1%2C333%2C-1&height=700" id="ddf09" class="rm-shortcode" data-rm-shortcode-id="9dc42c4d6a8e372ad7b72907b46ecd3f" data-rm-shortcode-name="rebelmouse-image" data-width="1245" data-height="700" />
Arlie Russell Hochschild (pictured) laid out the concept of emotional labor in her 1983 book, "The Managed Heart."
Credit: Wikimedia Commons<p>Deep and surface acting are the principal components of emotional labor, a buzz phrase you have likely seen flitting about the Twittersphere. Today, "<a href="https://www.bbc.co.uk/bbcthree/article/5ea9f140-f722-4214-bb57-8b84f9418a7e" target="_blank">emotional labor</a>" has been adopted by groups as diverse as family counselors, academic feminists, and corporate CEOs, and each has redefined it with a patented spin. But while the phrase has splintered into a smorgasbord of pop-psychological arguments, its initial usage was more specific.</p><p>First coined by sociologist Arlie Russell Hochschild in her 1983 book, "<a href="https://www.ucpress.edu/book/9780520272941/the-managed-heart" target="_blank">The Managed Heart</a>," emotional labor describes the work we do to regulate our emotions on the job. Hochschild's go-to example is the flight attendant, who is tasked with being "nicer than natural" to enhance the customer experience. While at work, flight attendants are expected to smile and be exceedingly helpful even if they are wrestling with personal issues, the passengers are rude, and that one kid just upchucked down the center aisle. Hochschild's counterpart to the flight attendant is the bill collector, who must instead be "nastier than natural."</p><p>Such personas may serve an organization's mission or commercial interests, but if they cause emotional dissonance, they can potentially lead to high emotional costs for the employee—bringing us back to deep and surface acting.</p><p>Deep acting is the process by which people modify their emotions to match their expected role. Deep actors still encounter the negative emotions, but they devise ways to <a href="http://www.selfinjury.bctr.cornell.edu/perch/resources/what-is-emotion-regulationsinfo-brief.pdf" target="_blank">regulate those emotions</a> and return to the desired state. Flight attendants may modify their internal state by talking through harsh emotions (say, with a coworker), focusing on life's benefits (next stop Paris!), physically expressing their desired emotion (smiling and deep breaths), or recontextualizing an inauspicious situation (not the kid's fault he got sick).</p><p>Conversely, surface acting occurs when employees display ersatz emotions to match those expected by their role. These actors are the waiters who smile despite being crushed by the stress of a dinner rush. They are the CEOs who wear a confident swagger despite feelings of inauthenticity. And they are the bouncers who must maintain a steely edge despite humming show tunes in their heart of hearts.</p><p>As we'll see in the research, surface acting can degrade our mental well-being. This deterioration can be especially true of people who must contend with negative emotions or situations inside while displaying an elated mood outside. Hochschild argues such emotional labor can lead to exhaustion and self-estrangement—that is, surface actors erect a bulwark against anger, fear, and stress, but that disconnect estranges them from the emotions that allow them to connect with others and live fulfilling lives.</p>
Don't fake it till you make it<p>Most studies on emotional labor have focused on customer service for the obvious reason that such jobs prescribe emotional states—service with a smile or, if you're in the bouncing business, a scowl. But <a href="https://eller.arizona.edu/people/allison-s-gabriel" target="_blank">Allison Gabriel</a>, associate professor of management and organizations at the University of Arizona's Eller College of Management, wanted to explore how employees used emotional labor strategies in their intra-office interactions and which strategies proved most beneficial.</p><p>"What we wanted to know is whether people choose to engage in emotion regulation when interacting with their co-workers, why they choose to regulate their emotions if there is no formal rule requiring them to do so, and what benefits, if any, they get out of this effort," Gabriel said in <a href="https://www.sciencedaily.com/releases/2020/01/200117162703.htm" target="_blank">a press release</a>.</p><p>Across three studies, she and her colleagues surveyed more than 2,500 full-time employees on their emotional regulation with coworkers. The survey asked participants to agree or disagree with statements such as "I try to experience the emotions that I show to my coworkers" or "I fake a good mood when interacting with my coworkers." Other statements gauged the outcomes of such strategies—for example, "I feel emotionally drained at work." Participants were drawn from industries as varied as education, engineering, and financial services.</p><p>The results, <a href="https://psycnet.apa.org/doiLanding?doi=10.1037%2Fapl0000473" target="_blank" rel="noopener noreferrer">published in the Journal of Applied Psychology</a>, revealed four different emotional strategies. "Deep actors" engaged in high levels of deep acting; "low actors" leaned more heavily on surface acting. Meanwhile, "non-actors" engaged in negligible amounts of emotional labor, while "regulators" switched between both. The survey also revealed two drivers for such strategies: prosocial and impression management motives. The former aimed to cultivate positive relationships, the latter to present a positive front.</p><p>The researchers found deep actors were driven by prosocial motives and enjoyed advantages from their strategy of choice. These actors reported lower levels of fatigue, fewer feelings of inauthenticity, improved coworker trust, and advanced progress toward career goals. </p><p>As Gabriel told <a href="https://www.psypost.org/2021/01/new-psychology-research-suggests-deep-acting-can-reduce-fatigue-and-improve-your-work-life-59081" target="_blank" rel="noopener noreferrer">PsyPost in an interview</a>: "So, it's a win-win-win in terms of feeling good, performing well, and having positive coworker interactions."</p><p>Non-actors did not report the emotional exhaustion of their low-actor peers, but they also didn't enjoy the social gains of the deep actors. Finally, the regulators showed that the flip-flopping between surface and deep acting drained emotional reserves and strained office relationships.</p><p>"I think the 'fake it until you make it' idea suggests a survival tactic at work," Gabriel noted. "Maybe plastering on a smile to simply get out of an interaction is easier in the short run, but long term, it will undermine efforts to improve your health and the relationships you have at work. </p><p>"It all boils down to, 'Let's be nice to each other.' Not only will people feel better, but people's performance and social relationships can also improve."</p>
You'll be glad ya' decided to smile<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="88a0a6a8d1c1abfcf7b1aca8e71247c6"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/QOSgpq9EGSw?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span><p>But as with any research that relies on self-reported data, there are confounders here to untangle. Even during anonymous studies, participants may select socially acceptable answers over honest ones. They may further interpret their goal progress and coworker interactions more favorably than is accurate. And certain work conditions may not produce the same effects, such as toxic work environments or those that require employees to project negative emotions.</p><p>There also remains the question of the causal mechanism. If surface acting—or switching between surface and deep acting—is more mentally taxing than genuinely feeling an emotion, then what physiological process causes this fatigue? <a href="https://www.frontiersin.org/articles/10.3389/fnhum.2019.00151/full" target="_blank">One study published in the <em>Frontiers in Human Neuroscience</em></a><em> </em>measured hemoglobin density in participants' brains using an fNIRS while they expressed emotions facially. The researchers found no significant difference in energy consumed in the prefrontal cortex by those asked to deep act or surface act (though, this study too is limited by a lack of real-life task).<br></p><p>With that said, Gabriel's studies reinforce much of the current research on emotional labor. <a href="https://journals.sagepub.com/doi/abs/10.1177/2041386611417746" target="_blank">A 2011 meta-analysis</a> found that "discordant emotional labor states" (read: surface acting) were associated with harmful effects on well-being and performance. The analysis found no such consequences for deep acting. <a href="https://doi.apa.org/doiLanding?doi=10.1037%2Fa0022876" target="_blank" rel="noopener noreferrer">Another meta-analysis</a> found an association between surface acting and impaired well-being, job attitudes, and performance outcomes. Conversely, deep acting was associated with improved emotional performance.</p><p>So, although there's still much to learn on the emotional labor front, it seems Van Dyke's advice to a Leigh was half correct. We should put on a happy face, but it will <a href="https://bigthink.com/design-for-good/everything-you-should-know-about-happiness-in-one-infographic" target="_self">only help if we can feel it</a>.</p>
Archaeologists discover a cave painting of a wild pig that is now the world's oldest dated work of representational art.