Category Archives: Online User Experience

The Psychology of Usefulness: How Our Brains Judge What is Useful

To-Do-ListDid you know that “task” and “tax” have the same linguistic roots? They both come from the Latin “taxare” – meaning to appraise. This could explain the lack of enthusiasm we have for both.

Tasks are what I referred to in the last post as an exotelic activity – something we have to do to reach an objective that carries no inherent reward. We do them because we have to do them, not because we want to do them.

When we undertake a task, we want to find the most efficient way to get it done. Usefulness becomes a key criterion. And when we judge usefulness, there are some time-tested procedures the brain uses.

Stored Procedures and Habits

The first question our brain asks when undertaking a task is – have we done this before? Let’s first deal with what happens if the answer is yes:

If we’ve done something before our brains – very quickly and at a subconscious level – asks a number of qualifying questions:

–       How often have we done this?

–       Does the context in which the task plays out remain fairly consistent (i.e. are we dealing with a stable environment)?

–       How successful have we been in carrying out this task in the past

If we’ve done a task a number of times in a stable environment with successful outcomes, it’s probably become a habit. The habit chunk is retrieved from the basal ganglia and plays out without much in the way of rational mediation. Our brain handles the task on autopilot.

If we have less familiarity with the task, or if there’s less stability in the environment, but have done it before we probably have stored procedures, which are set procedural alternatives. These require more in the way of conscious guidance and often have decision points where we have to determine what we do next, based on the results of the previous action.

If we’re entering new territory and can’t draw on past experience, our brains have to get ready to go to work. This is the route least preferred by our brain. It only goes here when there’s no alternative.

Judging Expected Utility and Perceived Risk

If a task requires us to go into unfamiliar territory, there are new routines that the brain must perform. Basically, the brain must place a mental bet on the best path to take, balancing a prediction of a satisfactory outcome against the resources required to complete the task. Psychologists call this “Expected Utility.”

Expected Utility is the brain’s attempt to forecast scenarios that require the balancing of risks and rewards where the outcomes are not known.  The amount of processing invested by the brain is usually tied to the size of the potential risk and reward. Low risk/reward scenarios require less rationalization. The brain drives this balance by using either positive or negative emotional valences, interpreted by us as either anticipation or anxiety. Our emotional balance correlates with the degree of risk or reward.

Expected utility is more commonly applied in financial decision and game theory. In the case of conducting a task, there is usually no monetary element to risk and reward. What we’re risking is our own resources – time and effort. Because these are long established evolved resources, it’s reasonable to assume that we have developed subconscious routines to determine how much effort to expend in return for a possible gain. This would mean that these cognitive evaluations and calculations may happen at a largely subconscious level, or at least, more subconscious than the processing that would happen in evaluating financial gambles or those involving higher degrees of risk and reward.  In that context, it might make sense to look at how we approach another required task – finding food.

Optimal Foraging and Marginal Value

Where we balance gain against expenditure of time and effort, the brain has some highly evolved routines that have developed over our history. The oldest of these would be how we forage for food. But, we also have a knack of borrowing strategies developed for other purposes and using them in new situations.

Pirolli and Card (1999) found, for instance, that we use our food foraging strategies to navigate digital information. Like food, information online tends to be “patchy” and of varying value to us. Often, just like looking for a food source, we have to forage for information by judging the quality of hyperlinks that may take us to those information sources or “patches.” Pirolli and Card called these clues to the quality of information that may lie on the other end of links information scent.

Cartoon_foraging_theoryTied with this foraging strategy is the concept of Marginal Value.  This was first proposed by Eric Charnov in 1976 as a evolved strategy for determining how much time to spend in a food patch before deciding to move on. In a situation with diminishing returns (ie depleted food supplies) the brain must balance effort expended against return. If you happen on a berry bush in the wild, with a reasonable certainty that there are other bushes nearby (perhaps you can see them just a few steps away) you have to mentally solve the following equation – how many berries can be gathered with a reasonable expenditure of effort vs. how much effort would it take to walk to the next bush and how many berries would be available there?

This is somewhat analogous to information foraging, with one key difference. Information isn’t depleted as you consume it. So the rule of diminishing returns is less relevant. But if, as I suspect, we’ve borrowed these subconscious strategies for judging usefulness – both in terms of information and functionality – in online environment, our brains may not know or care about the subtle differences in environments.

The reason why we may not be that rational in the application of these strategies in online encounters is that they play out below the threshold of consciousness. We are not constantly and consciously adjusting our marginal value algorithm or quantifiably assessing the value of an information patch. No, our brains use a quicker and more heuristic method to mediate our output of effort – emotions. Frustration and anxiety tell us it’s time to move onto the next site or application. Feelings of reward and satisfaction indicate we should stay right where we are. The remarkable thing about this is that as quick and dirty as these emotional guidelines are, if you went to the trouble of rationally quantifying the potential of all possible alternatives, using a Bayesian approach, for instance, you’d probably find you ended up in pretty much the same place. These strategies, simmering below the surface of our consciousness, are pretty damn accurate!

So, to sum up this post, when judging the most useful way to get a task done, we have an evaluation cascade that happens very quickly in our brain:

  • If a very familiar task needs to be done in a stable environment, our habits will take over and it will be executed with little or no rational thought.
  • If the task is fairly familiar but requires some conscious guidance, we’ll retrieve a stored procedure and look for successful feedback as we work through it.
  • If a task is relatively new to us, we’ll forage through alternatives for the best way to do it, using evolved biological strategies to help balance risk (in terms of expended effort) against reward.

Now, to return to our original question, how does this evaluation cascade impact long and short-term user loyalty? I’ll return to this question in my next post.

Google Holds the Right Cards for a Horizontal Market

First published January 9, 2014 in Mediapost’s Search Insider

android_trhoneFunctionality builds up, then across. That was the principle of emerging markets that I talked about in last week’s column. Up – then across – breaking down siloes into a more open, competitive and transparent market. I’ll come back here in a moment.

I also talked about how Google + might be defining a new way of thinking about social networking, one free of dependence on destinations. It could create a social lens through which all our online activity passes through, adding functionality and enriching information.

Finally, this week, I read that Google is pushing hard to extend Android as the default operating system in the Open Automotive Alliance – turning cars into really big mobile devices. This builds on Android’s dominance in the smartphone market (with an 82% market share).

See a theme here?

For years, I’ve been talking about the day when search transitions from being a destination to a utility, powering apps which provide very specific functionality that far outstrips anything you could do on a “one size fits all” search portal. This was a good news/bad news scenario for Google, who was the obvious choice to provide this search grid. But, in doing so, they lose their sole right to monetize search traffic, a serious challenge to their primary income source. However, if you piggy back that search functionality onto the de facto operating system that powers all those apps, and then add a highly functional social graph, you have all the makings of a foundation that will support the ‘horizontalization” of the mobile connected market. Put this in place, and revenue opportunities will begin falling into your lap.

The writing is plainly on the wall here. The future is all about mobile connections. It is the foundation of the Web of Things, wearable technology, mobile commerce – anything and everything we see coming down the pipe.  The stakes are massive. And, as markets turn horizontal in the inevitable maturation phase to come, Google seems to be well on their way to creating the required foundations for that market.

Let’s spend a little time looking at how powerful this position might be for Google. Microsoft is still coasting on their success in creating a foundation for the desktop, 30 years later.  The fact that they still exist at all is testament to the power of Windows. But the desktop expansion that happened was reliant on just one device – the PC. And, the adoption curve for the PC took two decades to materialize, due to two things: the prerequisite of a fairly hefty investment in hardware and a relatively steep learning curve. The mobile adoption curve, already the fastest in history, has no such hurdles to clear. Relative entry price points are a fraction of what was required for PCs. Also, the learning curve is minimal. Mobile connectivity will leave the adoption curve of PCs in the dust.

In addition, an explosion of connected devices will propel the spread of mobile connectivity. This is not just about smart phones. Two of the biggest disruptive waves in the next 10 years will be wearable technologies and the Web of Things. Both of these will rely on the same foundations, an open and standardized operating system and the ability to access and share data. At the user interface level, the enhancements of powerful search technologies and social-graph enabled filters will significantly improve the functionality of these devices as they interface with the “cloud.”

In the hand that will have to inevitably be played, it seems that Google is currently holding all the right cards.

Revisiting Entertainment vs Usefulness

brain-cogsSome time ago, I did an extensive series of posts on the psychology of entertainment. My original goal, however, was to compare entertainment and usefulness in how effective they were in engendering long-term loyalty. How do our brains process both? And, to return to my original intent, in that first post almost 4 years ago, how does this impact digital trends and their staying power?

My goal is to find out why some types of entertainment have more staying power than other types. And then, once we discover the psychological underpinnings of entertainment, lets look at how that applies to some of the digital trends I disparaged: things like social networks, micro-blogging, mobile apps and online video. What role does entertainment play in online loyalty? How does it overlap with usefulness? How can digital entertainment fads survive the novelty curse and jump the chasm to a mainstream trends with legs?

In the previous set of posts, I explored the psychology of entertainment extensively, ending up with a discussion of the evolutionary purpose of entertainment. My conclusion was that entertainment lived more in the phenotype than the genotype. To save you going back to that post, I’ll quickly summarize here: the genotype refers to traits actually encoded in our genes through evolution – the hardwired blueprint of our DNA. The phenotype is the “shadow” of these genes – behaviors caused by our genetic blueprints. Genotypes are directly honed by evolution for adaptability and gene survival. Phenotypes are by-products of this process and may confer no evolutionary advantage. Our taste for high-fat foods lives in the genotype – the explosion of obesity in our society lives in the phenotype.

This brings us to the difference between entertainment and usefulness – usefulness relies on mechanisms that predominately live in the genotype.  In the most general terms, it’s the stuff we have to do to get through the day. And to understand how we approach these things on our to-do list, it’s important to understand the difference between autotelic and exotelic activities.

Autotelic activities are the things we do for the sheer pleasure of it. The activity is it’s own reward. The word autotelic is Greek for “self + goal” – or “having a purpose in and not apart from itself.” We look forward to doing autotelic things. All things that we find entertaining are autotelic by nature.

Exotelic activities are simply a necessary means to an end. They have no value in and of themselves.  They’re simply tasks – stuff on our to do list.

The brain, when approaching these two types of activities, treats them very differently. Autotelic activities fire our reward center – the nucleus accumbens. They come with a corresponding hit of dopamine, building repetitive patterns. We look forward to them because of the anticipation of the reward. They typically also engage the prefrontal medial cortex, orchestrating complex cognitive behaviors and helping define our sense of self. When we engage in an autotelic activity, there’s a lot happening in our skulls.

Exotelic activities tend to flip the brain onto its energy saving mode. Because there is little or no neurological reward in these types of activities (other than a sense of relief once they’re done) they tend to rely on the brain’s ability to store and retrieve procedures. With enough repetition, they often become habits, skipping the brain’s rational loop altogether.

In the next post, we’ll look at how the brain tends to process exotelic activities, as it provides some clues about the loyalty building abilities of useful sites or tools. We’ll also look at what happens when something is both exotelic and autotelic.

The Death and Rebirth of Google+

google_plus_logoGoogle Executive Chairman Eric Schmidt has come out with his predictions for 2014 for Bloomberg TV. Don’t expect any earth-shaking revelations here. Schmidt plays it pretty safe with his prognostications:

Mobile has won – Schmidt says everyone will have a smartphone. “The trend has been mobile was winning..it’s now won.” Less a prediction than stating the obvious.

Big Data and Machine Intelligence will be the Biggest Disruptor – Again, hardly a leap of intuitive insight. Schmidt foresees the evolution of an entirely new data marketplace and corresponding value chain. Agreed.

Gene Sequencing Has Promise in Cancer Treatments – While a little fuzzier than his other predictions, Schmidt again pounces on the obvious. If you’re looking for someone willing to bet the house on gene sequencing, try LA billionaire Patrick Soon-Shiong.

See Schmidt’s full clip:

The one thing that was interesting to me was an admission of failure with Google+:

The biggest mistake that I made was not anticipating the rise of the social networking phenomenon.  Not a mistake we’re going to make again. I guess in our defense we were busy working on many other things, but we should have been in that area and I take responsibility for that.

I always called Google+ a non-starter, despite a deceptively encouraging start. But I think it’s important to point out that we tend to judge Google+ against Facebook or other social destinations. As Google+ Vice President of Product Bradley Horowitz made clear in an interview last year with Dailytech.com, Google never saw this as a “Facebook killer.”

I think in the early going there was a lot of looking for an alternative [to Facebook, Twitter, etc.],” said Horowitz. “But I think increasingly the people who are using Google+ are the people using Google. They’re not looking for an alternative to anything, they’re looking for a better experience on Google.

social-networkAnd this highlights a fundamental change in how we think about online social activity – one that I think is more indicative of what the future holds. Social is not a destination, social is a paradigm. It’s a layer of connectedness and shared values that acts as a filter, a lens  – a way we view reality. That’s what social is in our physical world. It shapes how we view that world. And Horowitz is telling us that that’s how Google looks at social too. With the layering of social signals into our online experience, Google+ gives us an enhanced version of our online experience. It’s not about a single destination, no matter how big that destination might be. It’s about adding richness to everything we do online.

Because humans are social animals our connections and our perception of ourselves as part of an extended network literally shape every decision we make and everything we do, whether we’re conscious of the fact or not. We are, by design, part of a greater whole. But because online, social originated as distinct destinations, it was unable to impact our entire online experience. Facebook, or Pinterest, act as a social gathering place – a type of virtual town square – but social is more than that. Google+ is closer to this more holistic definition of “social.”

I’m not  sure Google+ will succeed in becoming our virtual social lens, but I do agree that as our virtual sense of social evolves, it will became less about distinct destinations and more about a dynamic paradigm that stays with us constantly, helping to shape, sharpen, enhance and define what we do online. As such, it becomes part of the new way of thinking about being online – not going to a destination but being plugged into a network.

360 Degrees of Seperation

First published December 5, 2013 in Mediapost’s Search Insider

IMT_iconsIn the past two decades or so, a lot of marketers talked about gaining a 360-degree view of their customers.  I’m not exactly sure what this means, so I looked it up.  Apparently, for most marketers, it means having a comprehensive record of every touch point a customer has had with a company. Originally, it was the promise of CRM vendors, where anyone in an organization, at any time, can pull up a complete customer history.

So far, so good.

But like many phrases, it’s been appropriated by marketers and its meaning has become blurred. Today, it’s bandied about in marketing meetings, where everyone nods knowingly, confident in the fact that they are firmly ensconced in the customer’s cranium and have all things completely under control. “We have a 360-degree view of our customers,” the marketing manager beams, and woe to anyone that dares question it.

But there are no standard criteria that you have to meet before you use the term. There is no rubber-meets-the-road threshold you have to climb over. No one knows exactly what the hell it means. It sure sounds good, though!

If a company is truly striving to build as complete a picture of their customers as possible, they probably define 360 degrees as the total scope of a customer’s interaction with their company. This would follow the original CRM definition. In marketing terms, it would mean every marketing touch point and would hopefully extend through the customer’s entire relationship with that company. This would be 360-degrees as defined by Big Data.

But is it actually 360 degrees? If we envision this as a Venn diagram, we have one 360-degree sphere representing the mental model of customers, including all the things they care about. We have another 360-degree sphere representing the footprint of the company and all the things they do. What we’re actually looking at then, even in an ideal world, is where those two spheres intersect. At best, we’re looking at a relatively small chunk of each sphere.

So let’s flip this idea on its head. What if we redefine 360 degrees as understanding the customer’s decision space? I call this the Buyersphere. The traditional view of 360 degrees is from the inside looking out, from the company’s perspective. The Buyersphere moves the perspective to that of the customer, looking from the outside in. It expands the scope to include the events that lead to consideration, the competitive comparisons, the balancing of buying factors, interactions with all potential candidates and the branches of the buying path itself.  What if you decide to become the best at mapping that mental space?  I still wouldn’t call it a 360-degree view, but it would be a view that very few of your competitors would have.

One of the things that I believe is holding Big Data back is that we don’t have a frame within which to use Big Data. Peter Norvig, chief researcher for Google, outlined 17 warning signs in experimental design and interpretation. One was lack of a specific hypothesis, and the other was a lack of a theory. You need a conceptual frame from which to construct a theory, and then, from that theory, you can decide on a specific hypothesis for validation. It’s this construct that helps you separate signal from noise. Without the construct, you’re relying on serendipity to identify meaningful patterns, and we humans have a nasty tendency to mistake noise for patterns.

If we look at opportunities for establishing a competitive advantage, redefining what we mean by understanding our customers is a pretty compelling one. This is a construct that can provide a robust and testable space within which to use Big Data and other, more qualitative, approaches. It’s relatively doable for any organization to consolidate its data to provide a fairly comprehensive “inside-out” view of customer’s touch points. Essentially, it’s a logistical exercise. I won’t say it’s easy, but it is doable.  But if we set our goal a little differently, working to achieve a true “outside-in” view of our company, that sets the bar substantially higher.

360 degrees? Maybe not. But it’s a much broader view than most marketers have.

Google’s Etymological Dream Come True

First published November 14, 2013 in Mediapost’s Search Insider

Yesterday’s Search Insider column caught my eye. Aaron Goldman explained how search ads were the original native ads. He also explained why native ads work. This is backed up by research we did about 5 years ago, showing how contextual relevance substantially boosted ad effectiveness (but not, ironically, ad awareness). I did a fairly long blog post on the concept of “aligned” intent, if you really want to roll up your sleeves and dive in.

The funny thing was, I was struck by the use of the word “native” itself. For some reason, the use of the term in today’s more politically charged world struck a note of immediate uneasiness. On a gut level, it reminded me of the insensitivity of Daniel Snyder, owner of the Washington Redskins. There’s nothing immoral about the term itself, but it is currently tied to an emotionally charged issue.

As I often do, I decided to check the etymological roots of “native” and immediately noticed something different on the Google search page.  There, at the top, was an etymological time line, showing the root of “native” is the Latin “nasci” – meaning born. So, it was entirely appropriate, given Aaron’s assertion that “native” advertising was “born” on the search page. But it was at the bottom, where a downwards arrow promised “more,” that I hit etymological pay dirt.

Google showed me the typical dictionary entries, but at the bottom, it gave me a chart from it’s nGram viewer showing usage of “native” in books and publications over the past 200 years. Interestingly, the term has been in slow decline over the past 200 hundred years, with a bit of a resurgence over the last 25 years. When I clicked on the graph it broke it down further, showing that small-n “native” has been used less and less, but big-N “Native” took a jump in popularity in the mid-80’s, accounting for the mild bump.

Google’s nGram isn’t new, but its capabilities have been recently beefed up, providing a fascinating visual tool for us “wordies” out there. With it, you can plot the popularity of words over 500 years in a body of over 5 million books. For example, a blog post at Informationisbeautiful.net shows several fascinating word trend charts in the English corpus, including drug trends (cocaine was a popular topic in Victorian times, slowed down in the 20’s and exploded again in the 80’s), the battle of religion vs science (the popularity cross over was in 1930, but the trend has reversed and we’re heading for another one) and interest in sex vs. marriage (sex was barely mentioned prior to 1800, stayed relatively constant until 1910 and grew dramatically in the 70s, but lately it’s dropped off a cliff. Marriage has had a spikier history but has remained fairly constant in the last 200 years.)

I tried a few charts of my own. Since 1885, “Evolution” has beaten “Creation,” but it took a noticeable drop during the 30’s. Since 1960 both have been on the rise.  In1980, Apple got off to an initial head start, but Microsoft passed it in 1992, never to look back (although it’s had a precipitous decline since 2000.)  Perhaps the most interesting chart is comparing “radio”, “television” and “internet” since 1900. Radio started growing in the 20’s and hit its popularity peak around 1945, but the cross-over with television would take another 40 years (about 1982.) Television would only enjoy a brief period of dominance. In 1990, the meteoric rise of the Internet started and it surpassed both radio and television around 1997.

tvradiointernet

My final chart was to see how Google fared in it’s own tool. Not surprisingly, Google has dominated the search space since 2001, and done so quite handily. Currently, it’s 6 times more popular than its rivals, Yahoo and Bing.  One caveat here though – Bing’s popularity started to climb in 1830, so I think they’re talking about either the cherry, Chinese people named Bing or a German company that used to make kitchen utensils.  Either that, or Microsoft has had their search engine in development a lot longer than anyone guessed.

googleyahoobing

Whom Would You Trust: A Human or an Algorithm?

First published October 31, 2013 in Mediapost’s Search Insider

I’vmindrobote been struggling with a dilemma.

Almost a year ago, I wrote a column asking if Big Data would replace strategy. That started a several-month journey for me, when I’ve been looking for a more informed answer to that query. It’s a massively important question that’s playing out in many arenas today, including medicine, education, government and, of course, finance.

In marketing, we’re well into the era of big data. Of course, it’s not just data we’re talking about. We’re talking about algorithms that use that data to make automated decisions and take action. Some time ago, MediaPost’s Steve Smith introduced us to a company called Persado, that takes an algorithmic approach to copy testing and optimization. As an ex-copywriter turned performance marketer I wasn’t sure how I felt about that. I understand the science of continuous testing but I have an emotional stake in the art of crafting an effective message. And therein lies the dilemma. Our comfort with algorithms seems to depend on the context in which we’re encountering them and the degree of automation involved.

Let me give you an example, from Ian Ayre’s book “Super Crunchers.” There’s a company called Epagogix that uses an algorithm to predict the box-office appeal of unproduced movie scripts. Producers can retain the service to help them decide which projects to fund. Epagogix will also help producers optimize their chosen scripts to improve box-office performance. The question here is, do we want an algorithm controlling the creative output of the movie industry? Would we be comfortable take humans out of the loop completely and see where the algorithm eventually takes us?

Now, you may counter that we could include feedback from audience responses. We could use social signals to continually improve the algorithm, a collaborative filtering approach that uses the power of Big Data to guide the film industry’s creative process. Humans are still in the loop in this approach, but only as an aggregated sounding board. We have removed the essentially human elements of creativity, emotion and intuition. Even with the most robust system imaginable, are you comfortable with us humans taking our hands off the wheel?

Here’s another example from Ayre’s book. There is substantial empirical evidence that shows algorithms are better at diagnosing medical conditions than clinical practitioners. In a 1989 study by Dawes, Faust and Meehl, a diagnosis algorithmic rule set was consistently more reliable than actual clinical doctors. They then tried a combination, where doctors were made aware of the outcomes of the algorithm but were the final judges. Again, doctors would have been better off going with the results of the algorithm. Their second-guessing increased their margin of error significantly.

But, even knowing this, would you be willing to rely completely on an automated algorithm the next time you need medical attention? What if there was no doctor involved at all, and you were diagnosed and treated by an algo-driven robot?

There is also mounting (albeit highly controversial) evidence showing that direct instruction produces better learning outcomes that traditional exploratory teaching methods. In direct instruction, scripted automatons could easily replace the teacher’s role. Test scores could provide self-optimizing feedback loops. Learning could be driven by algorithms and delivered at a distance. Classrooms, along with teachers, could disappear completely. Is this a school you’d sign your kid up for?

Let’s stoke the fires of this dilemma a little. In a frightening TED talk, Kevin Slavin talks about how algorithms rule the world and offers a few examples of how algorithms have gotten it wrong in the past. The pricing algorithms of Amazon priced an out-of-print book called “The Making of a Fly” at a whopping $23.6 million dollars. Surprisingly, there were no sales. And in financial markets, where we’ve largely abdicated control to algorithms, those same algorithms spun out of control in 2012 no fewer than 18,000 times. So far, these instances have been identified and corrected in milliseconds, but there’s always a Black Swan chance that one time, they’ll crash the economy just for the hell of it.

But should we humans feel too smug, let’s remember this sobering fact: 20% of all fatal diseases were misdiagnosed. In fact, misdiagnosis accounts for about one-third of all medical error. And we humans have no one but ourselves to blame but for that.

As I said – it’s a dilemma.

What Does Being “Online” Mean?

plugged-inFirst published October 24, 2013 in Mediapost’s Search Insider

If readers’ responses to my few columns about Google’s Glass can be considered a representative sample (which, for many reasons, it can’t, but let’s put that aside for the moment), it appears we’re circling the concept warily. There’s good reason for this. Privacy concerns aside, we’re breaking virgin territory here that may shift what it means to be online.

Up until now, the concept of online had a lot in common with our understanding of physical travel and acquisition. As Peter Pirolli and Stuart Card discovered, our virtual travels tapped into our evolved strategies for hunting and gathering. The analogy, which holds up in most instances, is that we traveled to a destination. We “went” online, to “go” to a website, where we “got” information. It was, in our minds, much like a virtual shopping trip. Our vehicle just happened to be whatever piece of technology we were using to navigate the virtual landscape of “online.”

As long as we framed our online experiences in this way, we had the comfort of knowing we were somewhat separate from whatever “online” was. Yes, it was morphing faster than we could keep up with, but it was under our control, subject to our intent. We chose when we stepped from our real lives into our virtual ones, and the boundaries between the two were fairly distinct.

There’s a certain peace of mind in this. We don’t mind the idea of online as long as it’s a resource subject to our whims. Ultimately, it’s been our choice whether we “go” online or not, just as it’s our choice to “go” to the grocery store, or the library, or our cousin’s wedding. The sphere of our lives, as defined by our consciousness, and the sphere of “online” only intersected when we decided to open the door.

As I said last week, even the act of “going” online required a number of deliberate steps on our part. We had to choose a connected device, frame our intent and set a navigation path (often through a search engine). Each of these steps reinforced our sense that we were at the wheel in this particular journey. Consider it our security blanket against a technological loss of control.

But, as our technology becomes more intimate, whether it’s Google Glass, wearable devices or implanted chips, being “online” will cease to be about “going” and will become more about “being.”  As our interface with the virtual world becomes less deliberate, the paradigm becomes less about navigating a space that’s under our control and more about being an activated node in a vast network.

Being “online” will mean being “plugged in.” The lines between “online” and “ourselves” will become blurred, perhaps invisible, as technology moves at the speed of unconscious thought. We won’t be rationally choosing destinations, applications or devices. We won’t be keying in commands or queries. We won’t even be clicking on links. All the comforting steps that currently reinforce our sense of movement through a virtual space at our pace and according to our intent will fade away. Just as a light bulb doesn’t “go” to electricity, we won’t “go” online.  We will just be plugged in.

Now, I’m not suggesting a Matrix-like loss of control. I really don’t believe we’ll become feed sacs plugged into the mother of all networks. What I am suggesting is a switch from a rather slow, deliberate interface that operates at the speed of conscious thought to a much faster interface that taps into the speed of our subconscious cognitive processing. The impulses that will control the gateway of information, communication and functionality will still come from us, but it will be operating below the threshold of our conscious awareness. The Internet will be constantly reading our minds and serving up stuff before we even “know” we want it.

That may seem like neurological semantics, but it’s a vital point to consider. Humans have been struggling for centuries with the idea that we may not be as rational as we think we are. Unless you’re a neuroscientist, psychologist or philosopher, you may not have spent a lot of time pondering the nature of consciousness, but whether we actively think about it or not, it does provide a mental underpinning to our concept of who we are.  We need to believe that we’re in constant control of our circumstances.

The newly emerging definition of what it means to be “online” may force us to explore the nature of our control at a level many of us may not be comfortable with.

Losing My Google Glass Virginity

Originally published October 17, 2013 in Mediapost’s Search Insider

Rob, I took your advice.

A few columns back, when I said Google’s Glass might not be ready for mass adoption, fellow Search Insider Rob Garner gave me this advice:“Don’t knock it until you try it.”  So, when a fellow presenter at a conference I was at last week brought along his Glass and offered me a chance to try them (Or “it”? Does anyone else find Google’s messing around with plural forms confusing and irritating?), I took him up on it. To say I jumped at it may be overstating the case – let’s just say I enthusiastically ambled to it.

I get Google Glass. I truly do. To be honest, the actual experience of using them came up a little short of my expectations, but not much. It’s impressive technology.

But here’s the problem. I’m a classic early adopter. I always look at what things will be, overlooking the limitations of what currently “is.” I can see the dots of potential extending toward a horizon of unlimited possibility, and don’t sweat the fact that those dots still have to be connected.

On that level, Google Glass is tremendously exciting, for two reasons that I’ll get to in a second. For many technologies, I’ll even connect a few dots myself, willing to trade off pain for gain. That’s what early adopters do. But not everyone is an early adopter. Even given my proclivity for nerdiness, I felt a bit like a jerk standing in a hotel lobby, wearing Glass, staring into space, my hand cupped over the built-in mike, repeating instructions until Glass understood me. I learned there’s a new label for this; for a few minutes I became a “Glasshole.”Screen-Shot-2013-05-19-at-2.09.03-AM

Sorry Rob, I still can’t see the mainstream going down this road in the near future.

But there are two massive reasons why I’m still tremendously bullish on wearable technology as a concept. One, it leverages the importance of use case in a way no previous technology has ever done. And two, it has the potential to overcome what I’ll call “rational lag time.”

The importance of use case in technology can be summed up in one word: iPad. There is absolutely no technological reason why tablets, and iPads in particular, should be as popular as they are. There is nothing in an iPad that did not exist in another form before. It’s a big iPhone, without the phone. The magic of an iPad lies in the fact that it’s a brilliant compromise: the functionality of a smartphone in a form factor that makes it just a little bit more user-friendly. And because of that, it introduced a new use case and became the “lounge” device. Unlike a smartphone, where size limits the user experience in some critical ways (primarily in input and output), tablets offer acceptable functionality in a more enjoyable form. And that is why almost 120 million tablets were sold last year, a number projected (by Gartner) to triple by 2016.

The use case of wearable technology still needs to be refined by the market, but the potential to create an addictive user experiences is exceptional. Even with Glass’ current quirks, it’s a very cool interface. Use case alone leads me to think the recent $19 billion by 2018 estimate of the size of the wearable technology market is, if anything, a bit on the conservative side.

But it’s the “rational lag time” factor that truly makes wearable technology a game changer.  Currently, all our connected technologies can’t keep up with our brains. When we decide to do something, our brains register subconscious activity in about 100 milliseconds, or about one tenth of a second. However, it takes another 500 milliseconds (half a second) before our conscious brain catches up and we become aware of our decision to act. In more complex actions, a further lag happens when we rationalize our decision and think through our possible alternatives. Finally, there’s the action lag, where we have to physically do something to act on our intention. At each stage, our brains can shut down  impulses if it feels like they require too much effort.  Humans are, neurologically speaking, rather lazy (or energy-efficient, depending on how you look at it).

So we have a sequence of potential lags before we act on our intent: Unconscious Stimulation > Conscious Awareness > Rational Deliberation > Possible Action. Our current interactions with technology live at the end of this chain. Even if we have a smartphone in our pocket, it takes several seconds before we’re actively engaging with it. While that might not seem like much, when the brain measures action in split seconds, that’s an eternity of time.

But technology has the potential to work backward along this chain. Let’s move just one step back, to rational deliberation. If we had an “always on” link where we could engage in less than one second, we could utilize technology to help us deliberate. We still have to go through the messiness of framing a request and interpreting results, but it’s a quantum step forward from where we currently are.

The greatest potential (and the greatest fear) lies one step further back – at conscious awareness. Now we’re moving from wearable technology to implantable technology. Imagine if technology could be activated at the speed of conscious thought, so the unconscious stimulation is detected and parsed and by the time our conscious brain kicks into gear, relevant information and potential actions are already gathered and waiting for us. At this point, any artifice of the interface is gone, and technology has eliminated the rational lag. This is the beginning of Kurzweil’s Singularity: the destination on a path that devices like Google Glass are starting down.

As I said, I like to look at the dots. Someone else can worry about how to connect them.

Bounded Rationality in a World of Information

First published October 11, 2013 in Mediapost’s Search Insider.  

Humans are not good data crunchers. In fact, we pretty much suck at it. There are variations to this rule, of course. We all fall somewhere on a bell curve when it comes to our sheer rational processing power. But, in general, we would all fall to the far left of even an underpowered laptop.

Herbert Simon

Herbert Simon

Herbert Simon recognized this more than a half century ago, when he coined the term “bounded rationality.”  In a nutshell, we can only process so much information before we become overloaded, when we fall back on much more human approaches, typically known as emotion and gut instinct.

Even when we think we’re being rational, logic-driven beings, our decision frameworks are built on the foundations of emotion and intuition. This is not bad. Intuition tends to be a masterful way to synthesize inputs quickly and efficiently, allowing us generally to make remarkably good decisions with a minimum of deliberation. Emotion acts to amplify this process, inserting caution where required and accelerating when necessary. Add to this the finely honed pattern recognition instincts we humans have, and it turns out the cogs of our evolutionary machinery work pretty well, allowing us to adequately function in very demanding, often overwhelming environments.

We’re pretty efficient; we’re just not that rational. There is a limit to how much information we can “crunch.”

So when information explodes around us, it raises a question – if we’re not very good at processing data, what happen when we’re inundated with the stuff? Yes, Google is doing its part by helpfully “organizing the world’s information,” allowing us to narrow down our search to the most relevant sources, but still, how much time are we willing to devote to wading through mounds of data? It’s as if we were all born to be dancers, and now we’re stuck being insurance actuaries. Unlike Heisenberg (sorry, couldn’t resist the “Breaking Bad” reference) – we don’t like it, we’re not very good at it, and it doesn’t make us feel alive.

To make things worse, we feel guilty if we don’t use the data. Now, thanks to the Web, we know it’s there. It used to be much easier to feign ignorance and trust our guts. There are few excuses now. For every decision we have to make, we know that there is information which, carefully analyzed, should lead us to a rational, logical conclusion. Or, we could just throw a dart and then go grab a beer. Life is too short as it is.

When Simon coined the term “bounded rationality,” he knew that the “bounds” were not just the limits on the information available but also the limits of our own cognitive processing power and the limits on our available time. Even if you removed the boundaries on the information available (as is now happening) those limits to cognition and time would remain.

I suspect we humans are developing the ability to fool ourselves that we are highly rational. For the decisions that count, we do the research, but often we filter that information through a very irrational web of biases, beliefs and emotions. We cherry-pick information that confirms our views, ignore contradictory data and blunder our way to what we believe is an informed decision.

But, even if we are stuck with the same brain and the same limitations, I have to admit that the explosion of available information has moved us all a couple of notches to the right on Simon’s “satisficing” curve. We may not crunch all the information available, but we are crunching more than we used to, simply because it’s available.  I guess this is a good thing, even if we’re a little delusional about our own logical abilities.