The Shallow

Preface

The Net seizes our attention only to scatter it.
“How do users read on the web?” he asked then. His succinct answer: “They don’t.”

The book, “The Shallow”, raises a common problem we all suffer nowadays: “we lost our deep reading ability.” The author studied on this issue from different aspects, which provides enough knownledge for us to understand why this happens. Although the book may not mention any specific solution, we can deal with it by bring up our own self-awareness for the first step.
In this post, I am sharing three different clips from the book. And, I think, they all answered some question in today’s tech world by descibing accurate insights.

AI

After AlphaGo won the Korean player, some people started to worried that the robots will take over the world. Some medias spreaded out articles with scary titles saying the robots may not be controllable in the near future. However, I totally disagree with these ideas, which I think they are barely based on the lack of knownledge. AI or robots are still machines, which do whatever we ask them to do, and they will never going to do “something out of control” if we don’t ask them to do so. It’s common and nature that people put emotions into the machine, but the machine eventually is still the machine. I believe, the way to build a real AI with its own emotion, mind, etc is to fully understand the human brain from Biological aspect. There’s the clip from the book that shares the same idea as mine.
The first academic conference dedicated to the pursuit of artificial intelligence was held back in the summer of 1956 - on the Dartmouth campus - and it seemed obvious at the time that computers would soon be able to replicated human thought. The mathematicians and engineers who convened the month-long conclave sensed that, as they wrote in a statement,“ every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” It was just a matter of writing the right programs, of rendering the conscious processes of the mind into the steps of algorithms. But despite years of subsequent effort, the workings of human intelligence have eluded precise description. In the half century since the Dartmouth conference, computers have advanced at lightning speed, yet they remain, in human terms, as dumb as stumps. Out “thinking” machines still don’t have the slightest idea what they’re thinking. Lewis Mumford’s observation that “no computer can make a new symbol out of its own resources” remains as true today as when he said it in 1967.
But the AI advocates haven’t given up. They’ve just shifted their focus. They’ve largely abandoned the goal of writing software programs that replicate human learning and other explicit features of intelligence. Instead, they’re trying to duplicate, in the circuitry of a computer, the electrical signals that buzz among the brain’s billions of neurons, in the belief that intelligence will then “emerge” from the machine as the mind emerges from the physical brain. If you can get the “overall computation” right, as Page said, then the algorithms of intelligence will write themselves. In a 1996 essay on the legacy of Kubrick’s 2001, the inventor and futurist Ray Kurzweil argued that once we’re able to scan a brain in sufficient detail to “ascertain the architecture of interneuronal connections in different regions,” we’ll be able to “design simulated neural nets that will operate in a similar fashion.” Although “we can’t yet build a brain like HAL’s,” Kurzweil concluded, “we can describe right now how we could do it.”
There’s little reasons to believe that this new approach to incubating an intelligent machine will prove any more fruitful than the old one. It, too, is built on reductive assumptions. It takes for granted that the brain operates according to the same formal mathematical rules as a computer does - that, in other words, the brain and the computer speak the same language. But that’s a fallacy born of our desire to explain phenomena we don’t understand in terms we do understand. John von Neumann himself warned against falling victim to this fallacy. “When we talk about mathematics,” he wrote toward the end of his life, “we may be discussing a secondary nervous system.” Whatever the nervous system’s language may be, “ it cannot fail to differ considerably from what we consciously and explicitly consider as mathematics.”

Modern Brain

Although I’ve spent lots of time on studying, my ability of memorizing things isn’t increased for years. I started to think that “do we really need to memorize things if there’s Internet?” The following clip from the book nicely explains the reason why people lose their momerizing skills.
What determines what we remember and what we forget? The key to memory consolidation is attentiveness. Storing explicit memories and, equally important, forming connections between them requires strong mental concentration, amplified by repetition or by intense intellectual or emotional engagement. The sharper the attention, the sharper the memory. “For a memory to persist,” writes Kandel, “the incoming information must be thoroughly and deeply processed. This is accomplished by attending to the information and associating it meaningfully and systematically with knowledge already well established in memory.” If we’re unable to attend to the information in our working memory, the information lasts only as long as the neurons that hold it maintain their electric charge - a few seconds at best. Then it’s gone, leaving little or no trace in the mind.
Attention may seem ethereal - a “ghost inside the head,” as the developmental psychologist Bruce MacCandliss says - but it’s a genuine physical state, and it produces material effects throughout the brain. Recent experiments wit mice indicate that the act of paying attention to an idea or an experience sets off a chain reaction that crisscrosses the brain. Conscious attention begins in the frontal lobes of the cerebral cortex, with the imposition of top-down, executive control overt the mind’s focus. The establishment of attention leads the neurons of the cortex to send signals to neurons in the midbrain that produce the powerful neurotransmitter dopamine. The axons of these neurons reach all the way into the hippocampus, providing a distribution channel for the neurotransmitter. Once the dopamine is funneled into the synapses of the hippocampus, it jump-starts the consolidation of explicit memory, probably by activating genes that spur the synthesis of new proteins.
The influx of competing messages that we receive whenever we go online not only overloads our working memory; it makes it much harder for our frontal lobes to concentrate our attention on any one thing. The process of memory consolidation can’t even get started. And, thanks once again to the plasticity of our neuronal pathways, the more we use the Web, the more we train our brain to be distracted - the process information very quickly and very efficiently but without sustained attention. The helps explain why many of us find it hard to concentrate even when we’re aways from out computers. Our brains become adept tat forgetting, inept at remembering. Our growing dependence on the Web’s information stores may in fact be the product of a self-perpetuating, self amplifying loop. As our use of the Web makes it harder for us to lock information into our biological memory, we’re forced to rely more and more on the Net’s capacious and easily searchable artificial memory, even if it makes us shallower thinkers.

Why CLI (Command Line Interface)

When I code, I prefer using CLI, the command line interface. The reason behind is that CLI brings me a clearer view of the whole system. The author mentioned that helpful interfaces aren’t always benefits the users, which is the part I would like to refer to the comparison between CLI and GUI.
In the early stages of solving the puzzle, the group using the helpful software made correct moves more quickly that the other group, as would be expected. But as the test proceeded, the proficiency of the members of the group using the blare-bones software increased more rapidly. In the end, those using the unhelpful program were able to solve the puzzle more quickly and with fewer wrong moves. They also reach fewer impasses - states in which no further moves were possible - than did the people using the helpful software. The findings indicated, as van Nimwegen reported, that those using the unhelpful software were better able to plan ahead and lot strategy, while those using the helpful software tended to rely on simple trial and error. Often, in fact, those with the helpful software were found “to aimlessly click around” as they tried to crack the puzzle.
Eight months after the experiment, van Nimwegen reassembled the groups and had them again work on the colored-balls puzzle as well as a variation on it. He found that the people who had originally used the unhelpful software able to solve the puzzles nearly twice as fast as those who had used the helpful software. In another test, he had a different set of volunteers use ordinary calendar software to schedule a complicated series of meetings involving overlapping groups of people. Once again, one group used helpful software that provided lots of on-screen cues, and another group used unhelpful software. The results were the same. The subjects using the unhelpful program “solved the problems with fewer superfluous moves and in a more straightforward manner,” and the demonstrated greater “plan-based behavior” and “smarter solution paths.”
In his report on the research, van Nimwegen emphasized that he controlled for variations in the participants’ fundamental cognitive skills. It was the differences in the design of the software that explained the differences in performance and learning. The subjects using the bare-bones software consistently demonstrated “more focus, more direct and economical solutions, better strategies, and better imprinting of knowledge.” The more that people depended on explicit guidance from software programs, the less engaged they were in the task and the less they ended up learning. The findings indicate, van Nimwegen concluded, that as we “externalize” problem solving and other cognitive chores to our computers, we reduce our brain’s ability “to build stable knowledge structures” - schemas, in other words - that can later “be applied in new situations.” A polemicist might put it more pointedly: The brighter the software, the dimmer the user.

Use Unconscious Thoughts for Complex Problems (Paper Reading Note)

Preface

While I was reading “The Shallow”: What the Internet Is Doing to Our Brains, one paper was mentioned. As it suggests that unconscious thoughts offer better decision on complex problem, I am curious about it. Here is my study note of the paper, “Think Different: The Merits of Unconscious Thought in Preference Development and Decision Making”.

Conclusion: unconscious is good at making complex decisions.

• The problem is that it feels wrong to make such an important decision so quickly.
• Both conscious and unconscious systems can be very fast, slow, smart, or stupid. It all depends on what they are asked to do.
• One needs enough processing capacity to deal with large amount of information, and one needs skills sophisticated enough to integrate information in a meaningful and accurate way.

Processing Capacity

• Maximum amount of information thatch be kept under conscious scrutiny at any given time is about seven units (40₆₀ bits per second), which is low.
• The capacity of the entire human system is about 11,200,000 bits (including visual system, etc).
• More elaborate, normative strategies only work well when all information is taken into account.

The Skills to Think

• That is the integration of information in a meaningful way.
• Consciousness may suffer from a power cut when too much pressure is put on its limited capacity, but as long as its capacity is enough to deal with a particular problem, it is likely to be a good thinker.
• Researchers have long recognized the importance of incubation, the process whereby a problem is consciously ignored for a while, after which the unconscious offers a solution.
  • We put things to rest for a while and then suddenly, “Bing,” we feel we know it.
  • Not thinking about a problem for a while may lead people to forget wrong heuristics or inappropriate strategies in general.
  • Successive guesses converged, and the unconscious seemed to be closing in on the right answer quite a while before the answer was accessible to consciousness.

Experiment

• A brief period of unconscious thought will lead to a better decision relative conditions under which unconscious thought is prevented.
• When making complex decisions, conscious thought is inferior relative to unconscious thought.
• Experiment 1₃: proofing that unconscious thinking provides better decision in some cases; experiment 4₅: testing the reason behind

Polarization Hypothesis

• Distraction can lead to the change of a “mental set”, so, the role of the unconscious is proposed to be passive: putting a problem aside for a while allows for a fresh, unbiased new start.
• Look into different options.

Clustering Hypothesis

• Unconscious thought is expected to turn an initial, disorganized set of information into a clearer and more integrated representation of information in memory.

The Hook Model

Preface

The book, “Hooked: How to Build Habit-Forming Products”, is one of my favorites as it provides a well-structured model in building something people addict to. Impressively, for me, it pointed out the problems I had faced while building new things, and explained reasons behind.

This post is my study note of reading the book.

Why Habit

Habits are “behaviors done with little or no conscious thought”. By forming a habit for users in using the product, the company can gain following benefits:

• increasing customer lifetime value (CLTV)
• providing pricing flexibility
• supercharging growth: people are more likely to share
• sharpening the competitive edge: products that change customer routines are less susceptible to attacks from other companies

Habit Zone

• frequency: how often the user us the product
• preceived utility: how useful and rewarding the behavior is in the user’s minde over alternative solutions

Habits cannot form outside the habit zone.

“Are you building a vitamin or painkiller?”

Answer: “Successful products or services seem at first to be offering nice-to-have vitamins, but once the habit is established, they provide an ongoing pain remedy.”

Notice: not all the products or services need to form habits, ex. insurance products.

1. Trigger

External

External triggers are embedded with information which tells the user what to do next.

• paid triggers: advertising / search engine marketing (habit-forming companies tend not to rely on paid triggers for very long)
• earned triggers: favorable press mentions, hot viral videos, and featured app store placement (require investment in the form of time spent on public and media relations)
• relationship triggers: one person telling others about a product or serivce (a highly effective external trigger for action)
• owned triggers: app icon, email newsletter, subscribe (prmopt repeat engagement until a habit is formed)

Internal

Internal triggers are attached to existing behaviors and emotions.

• when users form habits, they are cued by internal triggers
• designer must know their user’s internal trigger (the pain they seek to solve)
• “we often think the Internet enables you to do new things, but people just want to do the same things they’ve always done.”
• what people say they want (declared preferences) are far from what they actually do (revealed preferences).
• “user narratives”: “5 Whys Method” (keep asking whys)
• negative emotions frequently serve as internal triggers

2. Action

The Fogg Behavior Model

B = MAT

• [M] motivation: the energy for action (seek pleasure or avoid pain), and the right motivators create action by offering the promise of desirable outcomes
• [A] ability: simply start removing steps until you reach the simplest possible process
• [T] Trigger: mentioned above

To pick from M, A, T: always start with ability.

Ease / Simple

Influence simplicity:

• time
• money
• physical
• brain cycles
• social deviance (how accepted the behavior is by others)
• non-routine (how much the action matches or disrupts existing routines)

Good examples

• logging in with facebook
• sharing with the twitter button
• searching with google
• taking photos with the apple iphone
• scrolling with pinterest

Brain Biases (Heuristics and Perceptions)

Heuristics are shortcuts we take to make quick decisions.

• the scarcity effect: the appearance of scarcity affected their perception of value (“n items left” tags on Amazon)
• the framing effect: the mind takes shortcuts informed by our surrounding to make quick and sometines erroneous judgments (ex. famous violin player at subway)
• the anchoring effect: people often anchor to one piece of information when making a decision (buy more not always cheaper)
• the endowed progress effect (profile strength interface on Stack Overflow)

Note: Mental Notes help designers build better products through heuristics.

3. Variable Award

Without variability, we are like children in that once we figure out what will happen next, we become less excited by the experience.

Many habit forming products offer multiple types of variable rewards:

Type: The Tribe

“Fueled by connectedness with other people”
Our brains are adapted to seek rewards that make us feel accepted, attractive, important, and included.
Ex.

• Facebook like / comment / share
• Stack Overflow: contributing to a community (no one knows how many will be received from the community when responding to a question)

Type: The Hunt

“The search for material resources and information”
The need to acquire physical objects, such as food and other supplies that aid our survival, is part of our brain’s operating system.
Ex.

• machine gambling
• Twitter (users scroll and scroll and scroll to search for variable rewards in the form of relevant tweets)
• Pinterest (cut-off pictures at bottom)

Type: The Self

“The search for intrinsic rewaords of mastery, competence, and completion”
Their self-determination theory espouses that people desire, among other things, to gain a sense of competency (peosonal form of gratification).
Ex.

• video games: master the skills, desire for competency by showing progression and completion
• email: mastery, completion, and competence moves users to habitual and sometimes mindless actions

Important Considerations

Variable Rewards Are Not a Free Pass

Mahalo.com: found that people didn’t want to use a Q&A site to make money; however, Quora: social rewards work.

Only by understanding what truly matters to users can a company correctly match the right variable rewrds to their intended behavior.

Maintain a Sense of Autonomy

“But you are free to accept or refuse”

• If failed: Reactance, the hair-trigger response to threats to your autonomy.
• Too many companies build their products betting users will do what they make them do instead of letting them do what they want to do.
• Companies that successfully change behaviors present users with an implicit choise between their old way of doing things and a new, more convenient way to fulfill existing needs

Beware of Finite Variability

“Predictable after use”
Experiences with finit variability become less engaging because they eventually become predictable.
Ex.

• FarmVille: CityVille, ChefVille, FrontierVille, … failed (new games were not really new at all)

4. Investment

The more users invest (time, data, effort, social capital, money, etc.) into a product or service, the more they value it. The reasons are:

• we irrationally value our efforts (IKEA effect: people tend to like things they build on their own)
• we seek to be consistent with our past behavior
• we avoid cognitive dissonance

Notice: Asking users to do a bit of work comes “after” users have received variable rewards, not before.

Storing Value

• content: memories and experiences, ex. itunes collection
• data: information generated, collected, created by users, ex. songs, photos, news clippings
• followers: ex. Twitter
• reputation: monetizable, ex. vender reputation on eBay, TaskRabbit, Yelp, Airbnb, etc
• skill: once users have invested the effort to acquire a skill, they are less likely to switch to a competing product, ex. photoshop

If users are not doing what the designer intended in the investment phase, the designer may be asking them to do too much.

Loading the next trigger

Habit-forming technologies leverage the user’s past behavior to initiate an external trigger in the future (reengage the user).

The Morality of Manipulation

Instead of asking “can I hook my users?”, we should ask: “should I attempt to?”

The Facilitator

Healthy habit.
“If you find yourself squiming as you ask yourself these queations or need to qualify or justify your answers, stop! you failed.”
In building a habit for a user other than you, you can not consider yourself a facilitator unless you have experienced the problem firsthand.
“Build the change they want to see in the world”

The Peddler

Altruistic.
“Would I actually find this useful?” the answer to this uncomfortable question is nearly always no, so they twist their thing until they caan image a user they believe might find the ad valuable.
Peddlers tend to lack the empathy and insights needed to create something users truly want.
Often the peddler’s project results in a time-wasting failure because the designers did not fully understand their users.
Beware of the hubris and inauthenticity

The Entertainer

Art is often fleeting; products that form habits around entertainment tend to fade quickly from users’ lives.
Entertainment is a hits-driven business because the brain reacts to stimulus by wanting more and more of it ever hungry for continuous novelty.

The Dealer

The only reason the designer is hooking users is to make a buck.
Ex. casinos and drug dealers.

Habit Testing (Existing Products)

Building a habit-forming product is an iterative process and requires user-behavior analysis and continuous experimentation.

Step 1: Identify

Dig into the data to identify how people are using the product.
“Who are the product’s bahitual users?” (the more frequently your product is used, the more likely it is to form a user habit)
Don’t come up with an overly aggressive prediction.

Step 2: Codify

Codify these findings in search of habitual users to generate new hypotheses, study the actions and paths taken by devoted users.
You are looking for a Habit Path - a series of similar actions shared by our most loyal users.

Step 3: Modify

Modify the product to influence more users to follow the same path as your habitual users, and then evaluate results and coninue to modify as needed.

Discovering Habit-forming Opportunities

Creating a product the designer uses and believes materially improves people’s lives increases the odds of delivering something people want. Pual Graham advises entrepreneurs to leave the sexy-sounding business ideas behind and instead build for their own needs: “Instead of asking ‘what problem should I solve?’ ask ‘what problem do I wish someone else would solve for me?’”

Enabling technologies

Wherever new technogolies suddenly make a behavior easier, new possibilities are born.

Interface Change

Many companies have found success in driving new bahit formation by identifying how changing user interactions can create new routines.
“Live in the future”: Google Glass, Oculus Rift, Pebble watch, etc.

Quotes

Some nice or interesting sentences I found in the book:

• Viral cycle time is the amount of time takes a user to invite another user, and it can have a messive impact
• If you only build for fame or fortune, you will likely find neither. Build for meaning, though, and you can’t go wrong.
• There’s a pain for the fear of missing out (FOMO).
• 26 percent of mobile apps in 2010 were downloaded and used only once.
• Behaviors are LIFO - “last in, first out.”

My Own Thoughts

1. Timing

In the last chapter of the book, the author suggest that we should keep eyes on emerging technologies and seek oppertunities. This remind me a TED Talk, “Bill Gross: The single biggest reason why startups succeed”, where the speaker claimed “timing” is the most important factor of a successful startup company. Both of them hold the same view on believing “timing” is the key of a successful startup.

2. Facilitator for All Team Members?

An interesting question popped up while I was reading the manipulation matrix part. The author suggested that it’s better to work as facilitator, so the maker himself/herself will have a deeper understanding of the product.

However, in real experiences, this might hold true only in the beginning - when there’s only one person on the team. As the project grows, people join into the team, and it’s impossible that everyone suffers from original problem that the team is trying to solve. That is, an designer of a video game startup company doesn’t have to like playing video games.

So, I raised the confliction I found here to one of my friends. She replied that, in a product group, some are facilitators while some are peddlers. For those peddlers, it’s okay that they haven’t experienced the problem the group is trying to solve as long as they agree with the team. That is, the point is whether the team has a same milestone instead of all being facilitators.