Persuasive tech for the perfected self

This post was inspired by two sources that came from rather different places but brought home the same point: how do we harness the convenience and power of technology to develop new cognitive habits for the better? In other words, how do we use technology to persuade ourselves to lead better lives? One of the sources was an essay from the Atlantic, titled “The Perfected Self,” which focused on the current wave of mobile health apps (weight-loss, in particular) that in effect utilize the best of psychological techniques for behavior modification.  The other was a website for the Stanford Persuasive Tech Lab which documents some very fascinating research on motivation and behavior design.

I have always viewed the highly addictive nature of mobile phones with some caution, but the flipside of this compulsiveness is a very real opportunity to shape powerful daily habits. Imagine instead of checking Facebook every 5-10minutes, you are using this impulse and slice of time to build up towards a healthier routine– is this possible? Certainly, the inkling of motivation has to be there in the first place, but the form of technological design can facilitate the ease of habit-building.

[T]echnology is radically lowering that cost barrier. Today, for absolutely nothing, would-be weight-losers can download many of the key elements of a Skinnerian behavior-modification program directly to their phones and computers…Abby King, a leading health-related behavior-change researcher at Stanford University, has studied smartphone apps that aim to get older, non-technology-savvy people to move more throughout the day. The study subjects, most of whom had never used a smartphone before, significantly increased their activity. “If it works on them, it will work on anyone,” says King. “Skinner was right-on, in terms of any sentient being from pigeons to humans responding to setting goals, tracking progress, and getting feedback. These tools can provide all that, and can reach into any population to do it.”

So far, the focus of persuasion has been on mobile health– a topic that is certainly more measurable and inherently less difficult to persuade people the benefits of. I wonder how long it will be before the technology could be extended into more complex territories, such as learning and education,  and other topics that have more complex motivation constraints as well as are more difficult to measure in progress. Imagine if it’s possible to break up the study of complex calculus or programming into something manageable daily that you can carry with you in your pockets? In fact, many of the current MOOC’s do implement some of these techniques already, but there’s certainly more to do.

Some will say that it’s not and that we will risk losing the subtle humanities in letting ourselves be measured and guided by machines. But the truth is it never has to be this zero-sum, and there is tremendous cognitive power we can harness from technology to help us toward the same goal. Measurement should not feared, if we can design a balanced algorithm with the right methodology. From Daniel Kahneman’s Thinking, Fast and Slow:

The important conclusion…is that an algorithm that is constructed on the back of an envelope is often good enough to compete with an optimally weighted formula, and certainly good enough to outdo expert judgment…

Why are experts inferior to algorithms? On reason…suspected…is that experts try to be clever, think outside the box, and consider complex combinations of features in making their predictions. Complexity may work in the odd case, but more often than not it reduces validity…Another reason for the inferiority of expert judgment is that humans are incorrigibly inconsistent in making summary judgments of complex information.

To be fair, I am not sure if I’ll ever be comfortable with having sensors around the house or on my phone to automatically detect my biometric information and give me predictions (we seem to be entering the realm of Gattaca here)– but still, let’s remember that ultimately whatever measured is still a factual reflection of whatever we are trying to improve.


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