Search Results for: achievement
Inspired how powerful a connection he developed to the tracking of his runs, Chris Lukic has built a website to display data from a Nike sensor and put it in a social context. There are badges to reward users for various achievements, and something akin to a leaderboard for seeing how your runs compare to those of your friends — a little friendly competition to increase motivation. Chris has also spent a lot of time working on the visualization of correlations, such as grouping runs of a similar length together or looking at how your performance changes by the day of the week. He has a few ideas for incorporating other data sources that might have interesting correlations, but is looking for feedback from the QS community, so check out the video below and give his site a whirl!
When was the last time you stepped back and gave yourself credit for your data-driven work? In our busy lives it is easy to forget to celebrate our accomplishments. It’s especially true when what we’re doing is heavy, like working with a medical ailment or a relationship problem.
Fortunately, treating life as an experiment – including the essential observation and measurement tasks – offers natural opportunities to mark important personal events. In one sense, life distills down to a sequence of these events, so it’s a shame not to salute them regularly.
Here are some triggers you might use to kick off a celebration, either modest or grand. I’ve included in parenthesis some specific things to honor.
- Starting an experiment (the courage of trying something new; the start of an adventure)
- Finishing an experiment (the satisfaction of completion and achievement)
- Realizing a self improvement (becoming more of yourself; strengthening character)
- Getting a great result (the fruit of hard work)
- Being surprised (the deliciousness of novelty; a chance to adjust mental models)
- Experiencing beauty (the pricelessness of the moment)
- Having the data talk to you (the joy of insight; a deeper understanding of the world)
- Encountering the serendipitous (seeing seeds sprout; the unpredictability of life)
- Moments of resiliency (the strengthening of your flexibility muscles)
- Welcoming a new character in your life (the healthiness of an expanding social circle)
- Making a goof (the boldness of being an amateur; the chance to laugh at mistakes)
- Getting help from someone (the gift of collaborators)
- Helping someone (the gratification of doing good)
And a couple of meta ones, which are thankfully guaranteed with every self-tracking effort:
- Figuring something out (a tasty bit of learning)
- Getting better at discovery (becoming more skilled at exploring the world)
How you celebrate depends on you and the event. It might be a little reward, a personal pat on the back, or sharing it with someone (even a quick IM – “Hey – guess what happened!”) A colleague suggests “bottling” the feeling and keeping a hold of it for quieter days. If you keep an experimenter’s journal, then record it there for when you need it. Or maybe just put something up on your “wall of fame” for a few days, like a good-looking graph of your data.
I’m curious: How do you celebrate your quantified self work? Any tips for triggering?
[Image from x-ray delta one]
(Matt is a terminally-curious ex-NASA engineer and avid self-experimenter. His projects include developing the Think, Try, Learn philosophy, creating the Edison experimenter’s journal, and writing at his blog, The Experiment-Driven Life. Give him a holler at firstname.lastname@example.org)
I thought this might interest QS friends who are thinking about extremely simple systems of self-assessment. It is a paragraph about the influence of Walter Mischel that comes from the autobiographical sketch of Daniel Kahneman, on the occasion of his Nobel prize. I post this simply for inspiration, in case it is useful as a starting point for some of you.
I had learned a lot in Berkeley, but I felt
that I had not been adequately trained to do research. I
therefore decided that in order to acquire the basic skills I
would need to have a proper laboratory and do regular science – I
needed to be a solid short-order cook before I could aspire to
become a chef. So I set up a vision lab, and over the next few
years I turned out competent work on energy integration in visual
acuity. At the same time, I was trying to develop a research
program to study affiliative motivation in children, using an
approach that I called a “psychology of single questions.” My
model for this kind of psychology was research reported by Walter
Mischel (1961a, 1961b) in which he devised two questions that he
posed to samples of children in Caribbean islands: “You can have
this (small) lollipop today, or this (large) lollipop tomorrow,”
and “Now let’s pretend that there is a magic man … who
could change you into anything that you would want to be, what
you would want to be?” The answer to the latter question was
scored 1, if it referred to a profession or to an
achievement-related trait, otherwise 0. The responses to these
lovely questions turned out to be plausibly correlated with
numerous characteristics of the child and the child’s background.
I found this inspiring: Mischel had succeeded in creating a link
between an important psychological concept and a simple operation
to measure it. There was (and still is) almost nothing like it in
psychology, where concepts are commonly associated with
procedures that can be described only by long lists or by
convoluted paragraphs of prose.
A good popular account
of Mischel’s career by Johan Lehrer was published last year in the
Atish Mehta presenting Happy Factor at the QS Show&Tell; video by Paul Lundahl.
This long post is an attempt to provoke QS readers to approach the question of measuring mood, and to use their unique combinations of skills to advance the cause in a practical way. It is inspired by the Facebook app Happy Factor, presented by Atish Mehta at a recent QS Show&Tell. If you watch the video above, you will see a the presentation and the very good discussion that followed. Since Atish has already built an app to measure happiness, while others here have expert knowledge of how to analyze data, and others know computer science, and still others are social scientists, journalists and ethnographers, it seems promising to drop a few annotated references on you and see if anything happens.
There is also an interesting story to tell. As self-measurers, you won’t be surprised to discover that measuring mood has been the subject of controversy. (Those of you who are also academics won’t be surprised to know that the controversy has at times become hostile.) But in it are clues to some of the problems immediately confronting anybody who is trying to track how they feel. Let’s start the story in the 1980′s, when psychologists began to increasingly use a two dimensional model to describe and measure mood. The key paper is James A. Russell’s “A Circumplex Model of Affect,” published in 1980 in the Journal of Personality and Social Psychology. [Abstract; subscription required]
Think of your emotional space as a two dimensional grid. On the x-axis is “pleasantness/unpleasantness,” sometimes called “valence.” On the y-axis is “arousal,” or “activation.” Arranged in a rough circle around this two dimensional space are the varieties of human feeling, like this:
There were lots variations on this circumplex. Here is another one:
Here is another one:
The nice thing about this model is that you can track mood with a mood checklist containing any number of terms, but whether you use more terms or fewer, this circumplex pattern is generally going to emerge. If you are only interested in the high level constructs, rather than in all the component descriptors you should be able to approximate your mood with two questions: How happy do you feel? How energetic do you feel?
Or maybe not. In 1988, David Watson, Lee Anna Clark, and Auke Tellegen published a paper introducing the Positive and Negative Affect Schedule (PANAS), based on the idea that positive and negative affect should be separately tracked because they vary independently. In other words, it is possible to feel good and bad at the same time. To force mood tracking onto a bipolar two-dimension grid, when the supposed poles are not really opposites, is to risk screwing up badly. You might get results which reflect the architecture of the model, rather than your actual moods. So, better not use the old circumplex. Use the PANAS instead.
Here is an example of a PANAS checklist, from a survey designed to measure emotions. You are asked to report to what extent you have felt this way during the time period being measured (right now, past few hours, past week, etc.).
1 2 3 4 5
very slightly a little moderately quite a bit extremely
or not at all
______ cheerful ______ sad ______ active ______ angry at self
______ disgusted ______ calm ______ guilty ______ enthusiastic
______ attentive ______ afraid ______ joyful ______ downhearted
______ bashful ______ tired ______ nervous ______ sheepish
______ sluggish ______ amazed ______ lonely ______ distressed
______ daring ______ shaky ______ sleepy ______ blameworthy
______ surprised ______ happy ______ excited ______ determined
______ strong ______ timid ______ hostile ______ frightened
______ scornful ______ alone ______ proud ______ astonished
______ relaxed ______ alert ______ jittery ______ interested
______ irritable ______ upset ______ lively ______ loathing
______ delighted ______ angry ______ ashamed ____ confident
______ inspired ______ bold ______ at ease ______ energetic
______ fearless ______ blue ______ scared ______ concentrating
______ disgusted ______ shy ______ drowsy ______ dissatisfied with self
Thus the great bipolarity controversy was launched. The details are highly technical. (Technical in a good way, I’m sure, as long as you don’t fear getting lost in the byways of this debate for weeks longer than you originally intended: you have been warned.) For a short taste of the polemics you can read Russell and James M Carroll’s The Phoenix of Bipolarity: Reply to Watson and Tellegen (1999) [PDF]. In this short essay, which served as a final salvo on the first stage of the controversy, the Forces of the Circumplex seemed to achieve victory. It turned out that under widely varying circumstances data from the PANAS showed – when analyzed – very strong evidence of bipolarity. Items from the poles of the circumplex descriptors had strong correlations (positive or negative, as the case may be), and the descriptors arranged themselves roughly around the circle. The opposition between happiness and sadness was not an artifact of the model, in general. Watson and Tellegen appeared to concede the most controversial points, and bipolarity was saved. Bring back the circumplex!
Or maybe not. Let’s bring this subject into the realm of anecdote, where journalists are comfortable. I can think of many situations in which I feel both positive and negative feelings. A good day comes to an end. A good night comes to an end. I speak a foreign language with happy enthusiasm mixed with sad awareness that lack of practice has insured my incompetence. We are having inspiring conversation which is pleasing me extremely, but the whole time I’m thinking: it’s tragic I can’t give it my whole attention because I just heard a sad story from a friend I’m worried about. I like a devastating intellectual critique as much as the next bystander; and Russell and Carroll’s reply to Watson and Tellegen seemed definitive; but in me, at least, positive and negative feelings coexist. And since we are talking about subjective experience, don’t the subjects get to have the last word?
In fact, this problem with the circumplex model has also been noticed by professionals, and looked into, and confirmed. Among the leading researchers to insist upon the independence of positive and negative feelings is John T. Cacioppo. The model Cacioppo recommends is called the Evaluative Space model; you can read more about it in his important paper: Relationship Between Attitudes and Evaluative Space; A Critical Review, With Emphasis on the Sparability of Positive and Negative Substrates. [PDF]. Cacioppo proposed an architecture of the emotional space that looks like this. Please start redesigning your mood trackers now.
Then, in 2001 paper called Can People Feel Happy and Sad at the Same Time? [PDF], Jeff T. Larsen, A. Peter McGraw, and Cacioppo used some experiments to demonstrate clearly that happiness and sadness can indeed co-occur. The frequency with which this happens is obviously something that could be further researched, but the key point is that if happiness and sadness usually correlate negatively, but sometimes correlate positively, then they cannot be polar opposites. Another researcher, Eshkol Rafaeli (whose web site contains many interesting references), has put the label affective synchrony on the phenomenon of simultaneous happiness and sadness, and has done some work which, while limited in scope, clearly suggest that all people are not alike when it comes to affective synchrony. Some people experience happiness and sadness as bipolar opposites; others have more experiences of mixed emotions. (See Rafaeli’s Affective Synchrony: Individual Differences in Mixed Emotions. [PDF]) Down with the circumplex!
Of course in your self-tracker you are welcome to measure anything you want. The controversy is relevant because what you choose to measure defines your experiment. If the circumplex is valid, you can simply measure “mood” on a simple grid. If it is not valid, you need to think more about what descriptors to present.
At this point, I hope you are now both happy and sad. Happy, that so much research on this question has been done for you. Sad, because this research remains ambiguous where it is not merely confusing. What would a good mood tracker look like? Is there a clear, promising direction? If you only look at one of the references attached to this post, I recommend Lisa Feldman Barrett’s Solving the Emotion Paradox: Categorization and the Experience of Emotion (PDF). Barrett offers both a wide-ranging review of the state of the art in modeling human emotion; more importantly, her theory of emotion offers some clear and plausible guidance for self-trackers.
Barrett divides emotion into two parts. Core affect is the totality of a person’s state that is available for emotional processing. The physiology of core affect is shared among mammals. All of us carry out some degree of assessment along the lines of good/bad; and all of us vary in our level of energy. (The traits evolved long before mammels, of course, but let’s try to keep our focus.) The fact that human descriptions of emotion clump together along the two dimensions of the circumflex reflects the structure of our core affect. This suggests that the circumplex is perfectly valid for many potential self-tracking experiments.
Let’s say we want to quit smoking, but we know that the process of quitting will make us tense and unhappy. We have been warned not to make any important life decisions during the traumatic period of our quit attempt, we know from reading the literature that the effects of smoking and smoking withdrawal on mood will show strong diurnal rhythms. Perhaps it would be prudent to have a mood meter that, based on our self-ratings of mood on a simple bipolar, two dimensional scale, can predict what times of day we ought to take ourselves off-line, as well as show us the rate of improvement and projected “finish line” after which the acute effects of quitting will be negligible. All of these are easily envisioned applications of the circumplex model. We are concerned about some basic elements of our emotional life – valence /arousal or pleasantness/activation – in which our categories do not have to do very much subtle work. “These feelings” writes Feldman, “are primitive (psychologically irreducible) and universal…” Go circumplex!
But there are other situations in which the circumplex will fail us. Wherever the aim of our experiment is to understand and/or alter the structure of our mood, we shouldn’t start with a system that assumes a structure and incorporates into its measurement protocols.
Why should we be interested in the structure our moods? Here is where the second part of Barrett’s paper becomes interesting. In her model of emotion, core affect is the material on which emotion works, but the experience of emotion – the inner experience, as well as most of the repertoire of outwardly emotional behavior – comes from the act of categorizing core affect, giving it a label such as “anger,” “sadness” or “fear.” This does not mean that the emotion is not experienced until you are conscious of putting a name on it. You don’t have to quietly mutter “anger” in order to feel anger. But it does suggest that anger is a concept that you begin learning in fancy and may continue to extend and revise throughout life. The repeated experience of labeling a combination of core affect and the context in which it occurs as “anger” trains you in how to be angry and how to recognize anger. Barrett describes emotions as simulations, in the sense that they take an experience of core affect, plus the situation in which it occurs, and compute an appropriate result:
“….conceptual knowledge about emotion constitutes expertise about how to deal with your own internal state – experienced as “an emotion” – and the situation or event that you believe caused that emotion in the first place. In this sense, emotional categorization is functional. Situated conceptualizations may be thought of as an inference about what will make for successful self-regulation or goal achievement…”
This is a theory of emotion that could be articulated by a robot, and it strikes me as entirely plausible. Here are the practical implications:
“…conceptualizing core affect as emotion, like conceptualizing in general, is a skill… This skill for wielding conceptual knowledge about emotion might be considered a core aspect of emotional intelligence… It is a skill to simulate the most appropriate or effective representation, or even to know when to inhibit a simulated conceptualization that has been incidentally primed. Presumably, this skill not only can be measured, it can also be trained.”
This suggests that we can revise our emotional architecture through experiments in description.
When I was just out of my teens I worked in a very fancy restaurant under the mad tutelage of the chef-owner. He was proud of his wine list, and asked us to be prepared to sell it to patrons; to this end he let us taste everything and trained us in the vocabulary of oenology. The obscurity of the distinctions permissible in describing wine are notorious, but the chef only tolerated my faux naïve remarks for the first five minutes of the first lesson, after which he said: “You are being asked to remember these wines. These adjectives are your labels. You are welcome to make up your own, but then I can’t instruct you, and nobody will understand you.” Suddenly, I saw that the absurd refinement of descriptors might have a useful purpose. Like emotions in the scheme Barrett describes, such phrases are simulations, bringing to mind previous experiences, triggering a cascade of associations. They were a form of knowledge, a social prediction machine; they allowed me to meet the demands of the situation, assess possibilities, invite cooperation.
Barrett’s theory of emotion opens the door for another type of self-tracking than is permitted by the circumplex, tracking that asks questions like: How many emotions do I have? What is the range of my emotions? Do I meet various experiences with well tuned emotional responses, or have my feelings become rigid and stereotyped? Her paper suggests that we can improve our emotional structure, increasing the granularity of emotional experiences by enriching our vocabulary and learning to apply it to previously unnoticed patterns in affect and context. (I am assuming for the moment that a more complex structure of emotion is a good thing. This could be questioned. But the first step in any case is mapping our emotional architecture.)
I’ll end with an idea about how this second type of mood tracking could work. First, let’s revisit two projects already underway: Ka-Ping Yee’s time allocation diary; and Atish Mehta’s Happy Factor. At the first QS Show&Tell, Ping showed us his time-allocation diary, which he keeps using a widget that stays open on his screen. He can enter some text into the box, where it automatically gets a time/date stamp. He often adds a keyword, so that he can graph his activities by category. Such a system is simple and flexible, and is not dependent on fixed categories; Ping can always start and maintain a new category simply by using a new word in his short entries.
Meanwhile, Atish has created a Facebook app that randomly queries users with a text message and asks them to rate their happiness. The ratings get a time/date stamp, and allow for the entry of a short note.
A mood tracking system to investigate emotional architecture might fruitfully combine these two methods. The ability to perform randomly timed queries is powerful. (For more on this, see “The Descriptive Experience Sampling Method” (PDF) by Russell T. Hurlburt and Sarah A. Akhter. I also discuss this method in a review of Describing Inner Experience by Hurlburt and philosopher Eric Schwitzgebel.) Right now Happy Factor asks only about happiness. Asking a constrained question such as “how happy are you” is only useful in the context of other data; to make use of Happy Factor as currently designed requires exporting the data and combining it with data about some other dimension of your life, in order to give it meaning. But think about if the question were unconstrained: “what emotion are you experiencing right now?” Suddenly, the descriptive landscape our mood becomes accessible. [Starting at about minute 12:00 in the video above, some of these ideas are batted around in the discussion, along with other interesting prospects.]
Once the structure of our moods became accessible for visualization, experiments and interventions become possible. The type of experiments or interventions that might be interesting is left as an exercise for the reader.
I have used mood, feeling, and emotion interchangeably. Though they are not the same, similar questions of measurement apply. A good recent summary of the controversy over the circumplex is given in: Causes and Consequences of Feelings (Studies in Emotion and Social Interaction) (Paperback) by Leonard Berkowitz. The book is expensive but I’m happy to share the key pages if anybody needs them.
My curiosity about real world applications of objective techniques of self-discovery and self-management let me recently to some classic work by [Albert Bandura](http://en.wikipedia.org/wiki/Albert_Bandura), who introduced the idea of [self-efficacy](http://www.des.emory.edu/mfp/self-efficacy.html) into cognitive psychology. Self-efficacy is different than self-confidence or self-esteem. It is not a personality trait, or a set of general beliefs about oneself. Rather, it is a subjective expectation of how likely you are to succeed at some specified goal. You can have high self-efficacy in one area, and low self-efficacy in another. Although these expectations are subjective, they can be measured objectively by researchers, who ask a standard set of multiple choice questions. The design of these questionnaires is itself a [research area](http://userpage.fu-berlin.de/%7Ehealth/engscal.htm) of some interest, but the important thing is that, in the three decades since Bandura first introduced the concept, he and others have proven that it can be measured, that it can be influenced, and, most importantly, correlates with the actual probability of success in tasks that require motivation and persistence.
In outline, this seems obvious. If you don’t believe you can do something – quit smoking; learn a foreign language; overcome a phobia; etc. – you are less likely to persist in the face of discouragement. Any wise counselor could tell you to pace yourself, and tackle challenges in an ascending order of difficulty, so as not to burn out too soon or fall into despair.
What’s interesting about Bandura’s work, and those who followed him, was not the confirmation of penny wisdom, but the development of objective measures of self-efficacy that allowed experimentation. At what stage of behavioral change is self-efficacy most important? How can it be effectively influenced? Research on self-efficacy has influenced treatment for addiction, chronic pain, stress, depression, and obesity; it’s also played a role in athletic training and physical rehabilitation.
For the aspiring self-quantifier, the history of self-efficacy research is full of promising hints. Once the notion of self-efficacy has been separated from the more general concept of self-esteem, it’s easier to notice specific areas where low self-efficacy may interfere with learning or achievement. Low self-efficacy leads to avoidance behavior. We don’t try things we believe we can’t do. In Bandura’s original paper, he points to research showing that this avoidance behavior can persist even when there is no conscious anxiety, no negative emotional arousal. We simply skirt the issue. Perhaps we even convince ourselves that it is not necessary, or a waste of time. Engrained habits of avoidance can become nearly invisible to our conscious reflection, due to how effectively they guard us from the bad consequences we believe will result from failure.
The research results influenced by Bandura’s paper are applied mainly by teachers, coaches, and health care providers to boost self-efficacy in training situations. But it can also be a tool of self-investigation. We can hunt for deficits in self-efficacy that create unnecessarily limits using the following question:
“If I were good at it, I would definitely want to (do/learn) [X]”
It may well turn out that some of our deficits are not due to a lack of inherent capacity, but to a failure of persistence and motivation stemming from a lack of self-efficacy. Improve self-efficacy, and previously inaccessible achievements come within reach. This is an especially promising approach when the goal is simply average proficiency. Obviously, increases in self-efficacy cannot affect inherent limits. But in areas that one has avoided do to fear of failure, average can be a truly excellent result.
Noticing and correcting low-self efficacy can be difficult. The power of the concept is linked to its specificity. Self-efficacy is efficacy for something. It has an explicit goal. Complex tasks consists of lots of different parts, and of course they are situated within the context of our whole life. So self-efficacy can get lost in the noise of other phenomenon – general self-esteem, physical tiredness, environmental stresses and distractors. That’s why instruments for measuring and tracking self-efficacy are crucial for regulating it. In a future post, I’ll do a quick pass on some of the technical possibilities for tracking self-efficacy. But for now, here’s the original Bandura paper (PDF), which boasts more than 5500 citations, according to [Google Scholar](http://scholar.google.com/scholar?q=Self+Efficacy&hl=en&lr=&btnG=Search).
> An outcome expectancy is defined as a person’s estimate that a given behavior will lead to certain outcomes. An efficacy expectation is the conviction that one can successfully execute the behavior required to produce the outcomes. Outcome and efficacy expectations are differentiated, because individuals can believe that a particular course of action will produce certain outcomes, but if they entertain serious doubts about whether they can perform the necessary activities such information does not influence their behavior.
> The principal assumption that defensive behavior is controlled by anxiety arousal is also disputed by several lines of evidence. Autonomic arousal, which constitutes the principal index of anxiety,-is not necessary for defensive learning. Because autonomic reactions take much longer to activate than do avoidance responses, the latter cannot be caused by the former. Studies in which autonomic and avoidance responses are measured concurrently indicate that these two modes of activity may be partially correlated in the acquisition phase but are not causally related (Black, 1965). Avoidance behavior, for example, can persist long after autonomic reactions to threats have been extinguished. Surgical removal of autonomic feedback capability in animals has little effect on the acquisition of avoidance responses (Rescorla & Solomon, 1967). Maintenance of avoidance behavior is even less dependent on autonomic feedback. Once defensive behavior has been learned, depriving animals of autonomic feed back does not hasten the rate at which such activities are extinguished.
> The theory presented here posits a central processor of efficacy information. That is, people process, weigh, and integrate diverse sources of information concerning their capability, and they regulate their choice behavior and effort expenditure accordingly.
> It will be recalled that efficacy expectations are presumed to influence level of performance by enhancing intensity and persistence of effort.