aether

[Reposted from QS]
Deep mysteries of human nature will be exposed by self-tracking, aspects of our behavior so disconcerting and bizarre that they will lead us to question whether we understand ourselves at all. I know this is true because such disconcerting results are already being produced at a rapid pace by experimental psychologists, and self-tracking brings the methods of experimental psychology into our daily lives; if, that is, we think we can stand to learn the lessons they teach.

Watch this video published from a story in New Scientist by Lars Hall and Petter Johansson.

[I]n an early study we showed our volunteers pairs of pictures of faces and asked them to choose the most attractive. In some trials, immediately after they made their choice, we asked people to explain the reasons behind their choices.

Unknown to them, we sometimes used a double-card magic trick to covertly exchange one face for the other so they ended up with the face they did not choose. Common sense dictates that all of us would notice such a big change in the outcome of a choice. But the result showed that in 75 per cent of the trials our participants were blind to the mismatch, even offering “reasons” for their “choice”.

This is troubling enough, but there’s more. When people are fooled into thinking they made a different choice than the one they actually made, and then articulate their “reasons” for this supposed choice, they then may actually change their future preferences to conform to their confabulated preference.

Importantly, the effects of choice blindness go beyond snap judgments. Depending on what our volunteers say in response to the mismatched outcomes of choices (whether they give short or long explanations, give numerical rating or labeling, and so on) we found this interaction could change their future preferences to the extent that they come to prefer the previously rejected alternative. This gives us a rare glimpse into the complicated dynamics of self-feedback (”I chose this, I publicly said so, therefore I must like it”), which we suspect lies behind the formation of many everyday preferences.

Lars Hall and Petter Johansson lead the Choice Blindness Laboratory at Lund University, Sweden. At the end of their New Scientist piece, they suggest that learning about this experiment should make people better at understanding their own choices.

In everyday decision-making we do see ourselves as connoisseurs of our selves, but like the wine buff or art critic, we often overstate what we know. The good news is that this form of decision snobbery should not be too difficult to treat. Indeed, after reading this article you might already be cured.

Unfortunately, this is not convincing. It is common for biases persist even when we are warned about them. I suspect we are in no position to stand guard over our judgments without the help of machines to keep us steady. Assuming, that is, that deliberative consistency is a value we care to protect.

[reposted from QS]
I’ve been meaning to link to this post for a couple of weeks. Nathan Yau over at Flowing Data has been writing personal data collection projects quite a bit. In this post, A Perfect Personal Data Collection Application, he talks about what is missing from current tools and about his dream system for personal data collection.

The number of Web applications to collect data and information about yourself continues to grow; if you want to track something, most likely there’s an online tool to do it. This is great - especially since a lot of the applications seem to have a lot of users, which means an interest in data… However, as users, developers, and designers, we shouldn’t be satisfied too quickly with what we have. Want more. Demand more. It’s interesting and oftentimes fun to log data about your life - whether it be when you go the bathroom, your sugar levels, or your mood. You get some nice graphs and charts, it looks cool, and maybe you learn something about yourself.

But all the self-surveillance tools so far are mostly about a single dataset or two at most. You track your weight and what you eat, but it’s more complex than that. Life is complicated and data is an abstraction of life after all. Do you eat when you’re depressed or are you depressed when you eat? Do you feel better if you exercise? What about sleep? How much sleep and exercise is best for you? What days should you exericse and how many days in a row and for how long? What truly makes you happy? I want my self-surveillance application to not only give me the ability to find these answers but to give them to me with very little effort on my part.

Nathan argues that any good solution for part of the problem ought to at least aspire to solve all of it. He wants the tools to include some data processing, and to be ubiquitous, so that you can post from anywhere.

In the end, I want all of my data in one place with some machine learning in the background and the ability to analyze and visualize easily and thoroughly. We’re not quite there yet, but I’m looking forward to when we do. Information overload? No. Better-educated decisions and a completely different view of ourselves and our surroundings? Definitely.

Nathan is building his own multi-tracker at your.flowingdata.com. Right now it is by invitation only, but you can follow yfd on Twitter to connect to the next wave of invites.

At the second QS Show&Tell, Joe Betts-Lacroix gave a short talk about his dream system: a website that could receive data and put it into a database, with data would be gathered by little devices that could beam it to the web using simple protocols. (The picture below is of Dan Brown, not Joe Betts-Lacroix; Dan happened to be in the first frame of this segment of video, which is automatically used for reference. Joe shows up a few seconds in.)

In this version of the dream the ideal Web site would have some
simple graphic tools, the ability to export data, good security, and some sharing and privacy options. Since then, there have been quite a few demonstrations of various ideas at the QS Show&Tell meetings, as well as a steady stream of products and, naturally, announcements of products (cf. Fitbit) that aim to achieve some parts of what Nathan, Joe, and other self-trackers have called for.

My own vision is slightly different. I think we are inevitably going to see a bunch of competing solutions, most of which will seem pretty good to some people and deeply flawed to others. People come at self-tracking with different goals and values. Daytum, which is mainly about self-expression, will be nifty for the person who uses data mainly as a feature of personal identity. Daytum’s origin is in the Feltron Annual Report by Nicholas Felton; an annual report serves many purposes, but data analysis is not one of them. Meanwhile, Zume Life is designed for people tracking serious health conditions, who are often trying to manage complex prescription drug regimens. Zume Life has made the data entry process almost as easy as imaginable until the day arrives when we can beam our data from monitoring devices without intervening at all. Along with an iPhone app, there is a voice transcription service. Just press a button, say your number (or food eaten, or exercise accomplished) and a person will transcribe it into a database. This is going forward by going backward, and there is a kind of genius to it. The type of person who likes Daytum is not going to bother with Zume Life, and for the user of Zume Life, Daytum is pointless.

We are headed into a messy, confusing, and interesting period in self-tracking, when lots of new solutions emerge, each claiming a piece of territory and pushing up against neighbors. There will be no ideal system, but a bunch of different sytesms, and then a bunch of different solutions for gluing different parts of different systems together. Some of the people who manage to aggregate lots of users will find excuses for holding on to them. (This may not always be a bad thing: see Dead ends and walled gardens.) But others will see that making the data collected on their system available in standard form will speed adoption.

But what is the standard? You can insert your own favorite standards horror story here. But after you’ve given yourself the shivers, you can recover with the realization that this conflict over standards occurs because everybody can finally see the prize they are wrestling for, which means that a substantial amount of agreement has been won. Once important people start making highly emotional arguments for how quotidian personal data (QPD - now it’s official) should be represented, you can start celebrating.

Note that I said “quotidian personal data” and not “healthcare information.” QPD is the type of thing you are willing transmit in a text message, and SMS is an easy bet for the ubiquitous medium for QPD. But if you call it healthcare information, I’m out. They fight dirty.

Self Quantifiers in New York now have their own QS Show&Tell Meetup, courtesy of Steve Dean, who has been following QS almost since its inception.

Steve was out here in the Bay Area for the second Show&Tell we had last year, and he has arranged to have the first meeting at Parsons, The New School for Design, where he teaches.

This is a great downtown location and convinient from all directions.

If you are a New York area reader of the QS blog, please come. The Show&Tell format will be the same as the SF Bay Area meetings, which is the same as it was in first grade. There will be brief presentations followed by questions and discussion about our self-tracking projects, ideas, tools, and experiments. You can message Steve from the Meetup page if you’d like to present. You can also make a spur of the moment decision when you show up.

To get more information and RSVP for the QS Show&Tell, New York Edition, join here:

NY Quantified Self Meetup

PS: I’m going to head out to New York for the first meeting, and I look forward to seeing you there!

[Reposted from QS]

The dead end. The cul-de-sac. The walled garden. These are three different ways (using 2.5 different metaphors) to refer to services that allow you to communicate and display information but not to copy, transfer, or share your data with outsiders. It’s an internet dogma that dead ends, culs-de-sac, and walled gardens are bad.

I subscribe to this dogma. The belief that we should be able to use a service, such as a mobile phone, a social networking site, or a hospital, without being taken hostage, has intuitive appeal. The favorite reference point for the failure of walled gardens is the stagnation of the online pioneer AOL, which, in the Great Internet Creation Myth, represents the morally-pleasing downfall of an arrogant oligarch. Every new service that holds on to its users by making it hard for them to export data inevitably earns comparison to AOL.

At a recent QS Show&Tell we heard a good presentation from Brandon, who works at A&D Weighing about new devices that will automatically upload biometrics to a web site. (Video here.) Brandon was good humored about the hard time he got when he revealed that the data will not exportable, will not be shareable, and will be accessible only through Actihealth Monitoring System, a walled garden (dead end? cul-de-sac?) of data owned by a biometrics/fitness company called  Fitlinxx. Biometrics belong to users - don’t they?

But here is a counter-example. Earlier in the week I visited the innovation research lab of Kaiser Permanente, where they are very keen on biometrics, and heard one of the docs talk about KP Connect, the online medical records system internal to Kaiser. This system is somewhat legendary, and not always for good reasons. Reported to cost upwards of 4 billion dollars, KP Connect is extraordinarily ambitious, combining what we ordinarily think of as medical records (lab results, prescriptions, physical complaints, observed symptoms, etc.) with appointment reminders, at-home biometrics, and expert-systems for advising doctors on treatment options.

It struck me, as I listened, that the Kaiser garden has very high walls. Most of the information in the system is government by privacy regulations, making it exportable and shareable only upon verified patient request. But that’s not really what makes these walls high. Kaiser, as an HMO, is both an insurer and a medical care provider - it knows more about your health than anybody else, and almost certainly more than you know yourself. The user experiences just the outward surface of KP Connect, the place where they see whether a child’s immunizations are up to date, or make an appointment to get a flu shot. But Kaiser can use the system to send reminders about medication, to suggest changes in treatment based on new research, and to aggregate data from which they can draw new inferences. They have begun to integrate the data on KP Connect with large genomic studies using volunteers who are Kaiser members - this will give ever more depth and complexity to the processes that control life in the walled garden. You can always choose to leave; but it may not be as verdant on the outside.

With these advantages, it is hardly necessary to keep individual data hostage. In fact, the biometrics gathered on KP Connect can be exported to Microsoft Health Vault, and from there they can be exported into files in standard formats. You can carry it around with you on your little flash drive, on your iPhone. You can publish your own little personal reports. But to make it meaningful, you may end up planting it somewhere.  

[Cross posted from QS]
Self-tracking is about self knowledge. But what if the knowledge you want is contained in the minds of others? Here are two recent videos from the last QS Show&Tell that bear on this question.

 We unfortunately didn’t capture the lovely talk Joe Betts-LaCroix and Lisa Betts-LaCroix gave at the recent QS on self-tracking in a relationship, but the Q&A is here, and it includes some really thoughtful questions and discussion, including Joe’s description of his simple web interface to Google docs, Lisa’s description of her analog self-tracking method, and an intriguing mention of social tracking, following up a suggestion by Paul Sas. (See below)