Measuring Mood – Current Research and New Ideas

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:
Circumplex.jpg
There were lots variations on this circumplex. Here is another one:

CircleOfAffect.jpgSource: (Framework of product experience, Pieter Desmet* and Paul Hekkert,
IJDesign, Vol 1, No 1 (2007)

Here is another one:

Valencia y atividade.jpg
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
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.

ESM.jpg

 

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.

NOTES
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.

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21 Responses to Measuring Mood – Current Research and New Ideas

  1. Buster McLeod says:

    Thanks for the very detailed write-up. This article is packed full of interesting insights about measuring mood and the implications of paying attention to something.
    I’ve been trying to come up with a better way to measure mood for years. From rating happiness (or morale) on a 1-10 scale, to using adjectives, to studying facial expressions (Paul Ekham’s Emotions Revealed is a great book).
    I like the circumplex model, and think that it might be the best compromise between comprehensiveness and usability. I’ve never been able to accurately report on every single emotion I’m having in a given moment as easily as taking the loudest or the sum of all emotions. Checking off from a list of 100 different possible emotions doesn’t seem like the kind of exercise I could do every day.
    What are your thoughts on the most practical and yet still accurate hypothetical tool to measure emotion?

  2. Gary Wolf says:

    Hi Buster – I currently rate my mood using a single number, on a scale from 1-5. I am not satisfied with this, however. I feel that my moods change frequently, but my ratings do not show much variability. Neither 1-5 gets used, so I am actually using a scale of 1-3. There is some variability, but I suspect that a lot of it comes from the effect of daily rhythms. I did not write about this yet, but there is good research showing predictable variation in mood over the course of the day; also, mood is affected by how long you have been by yourself. (Being alone reduces mood for most people.) The fact that there are systematic variations in mood over relatively small time scales means that you are going to get skewed results if you allowed your measurement protocol to also vary according to some systematic pattern that you didn’t recognize. (Say you tended to measure early in the morning when you didn’t work late; and later in the morning when you worked late. You are correlating mood with work. You discover that working late makes you happy! This is not necessarily the case; you are just happier by the time you get around to measuring.)
    So, I really like the idea of random queries via text messaging. This could help a lot.
    As for the scale, I think that although one number is really convenient, the circumplex is a proven instrument, and the two dimensions of the scale obviously give you a richer look. And I think you should always have a notes field, in case you want to try some analysis/experiments on the structure of your emotions.
    Finally, if you skip to the the latter part of the video, you will see Ka-Ping Yee make an interesting suggestion about using seemingly tangential questions to measure mood. What about just asking questions like: “do you think you can change the world?” This answer to this question obviously changes, and would intuitively seem to be correlated with mood. In fact, there is good research on this in the literature about “self-efficacy,” and yes, it does correlate with mood. I am going to try to review it for the blog soon.
    Thanks again for this question. Please share your own experiences/insights doing self-tracking if you think they might be helpful.

  3. Alex Chaffee says:

    >”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… But think about if the question were unconstrained: “what emotion are you experiencing right now?” Suddenly, the descriptive landscape our mood becomes accessible.”
    Guess what! You’ve just described Moodlog, a web application project I’ve had on my back burner for a year and a half now. I recently broke my leg so I’ve had time to finish it up to the point of bare minimum functionality. It’s very rough, the UI is a wireframe, and it has virtually no analytic tools, but it does the following:
    * Asks you “How do you feel?” via email, text, or web, on a daily schedule you can configure.
    * Records the first couple of words as your mood, plus an optional comment (maybe I should rename the field “context”)
    * Lets you customize the frequency and schedule of the reminders (“feelers”)
    * Works correctly across time zones (I hope)
    Appropriately enough, I have no good words to describe the emotions I’m feeling right now. Excited? Anxious? Insecure? Proud? Hopeful? I’d love to open this up to the QS community (which I’ve just discovered) but I’m nervous about the flood of suggestions and criticism…
    Ah, what the heck. Come visit at http://moodlog.org and tell me what you think. If you like the idea but it’s not good enough for you to use yet, let me know and I’ll let you know when new versions come along.
    The next area of emphasis is on the whole mechanism of inputting a mood, offering a bit more guidance than mere freeform text, but still preserving the “anything goes” style. I’ve got some neat ideas, but I’ll save them for another day…
    P.S. You missed Margaret E. Morris, Intel researcher, in your survey.
    http://www.intel.com/technology/itj/2007/v11i1/7-heart-mind/5-case-study-three.htm
    http://download.intel.com/technology/itj/2007/v11i1/v11-i1-art07.pdf

  4. Penfield says:

    Intrigued.
    We’ve been dabbling with a more ‘forced-feedback’ approach to mood experience and collaborative discovery.
    “The Penfield Mood Organ helps you to rewrite your mood. You replace your current state of mind with a new one.”
    http://www.penfieldmoodorgan.com

  5. Dan says:

    Might I add that the circumplex is actually valid according to either model. If conceptualized as positive affect/negative affect (independent mood states – can experience both happiness and sadness), then you simply rotate the axes 45 degrees to arrive at two orthogonal mood dispositions (i.e., tendency to feel energetic vs. tendency to feel distressed). Or, if using Russell’s argument, then you keep the axes positioned exactly as you have above and maintain that affect is bipolar.

  6. Hyacinth Ann says:

    Thank you for this very interesting information. this helps a lot with my psychological report about moods of people.. and by the way the scale you posted here, is it reliable and valid? can i also use it in testing my subjects mood?

  7. Jennifer says:

    Is there any way I could get a copy of your mood log/tracker? I am bi-polar among many other things and my pc does not like pdf because its stupid and it hates me. THanks Jennifer

  8. James says:

    Thanks, very interesting. I am doing a project on wether i can influence somebody’s mood through music. Do you know what test is best to do? Thanks, James

  9. Jason says:

    This is a great article. I would like to ask whether you know of any computational linguistics methods that map natural language structures or vocabulary to the different spaces that you describe.
    Best.
    Jason

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  11. Bryan Lundeen says:

    An idea that I’m not sure anyone has tried here before is limiting the samples to just simply +1 or -1 or zero if nothing feels particularly impressive at the moment. By limiting the samples taken, it uncomplicates the process of tracking mood. Just limit your focus to happy or sad or “no comment.” Add a text entry to this and you have a way to track what event is associated with the mood. Don’t write down intangible thoughts, just limit the text to what you are doing at that hour of the day.

    When i started tracking on a 1 to 10 scale, I was surprised to see how low my scores were. I thought, “jeez I thought i was much more depressed than this!” I found out I was just making it too complicated.

    I am now using an android app called “KeepTrack” which has these functions. I’ll try to let you guys know my research but I think it really can take months and maybe years to track mood and it’s causes.

  12. Marina says:

    Hi Gary,
    Thank you for writing this great post/article. I’m working on the happiness formula. So far I received feedback from about 20 testers. It is interesting that there is more preference for 1-10 scale than 1-5 (http://www.theultimateanswer.org/blog/?p=509). From my own experience I noticed that happiness is hard to quantify and measure unless someone explains exactly what it means (as it is different things to different people). And vice versa people’s reactions to the same things are different. It would be great to be able to easily manage emotions like Penfield proposes. Another interesting observation from my tests: very often when something great happens in one sphere of your life (love, work, etc.), it lightens up your perception of other spheres of your life, even though it is totally subjective.
    Marina.

  13. Marina says:

    Thank you Gary for this great article/post.

  14. David says:

    Thank you all for a wonderful discussion regarding the many different aspects of mood. My interest with this post revolves around my upcoming dissertation. I am working on my PH D in industrial/organizational psychology. Much research has been applied to how personality effects leadership. I am planning on creating a study about how mood effects leadership in an effort to enhance the trait versus state argument to a new dimension.

    To be specific, I am planning on setting up a quasi-experimental study with a treatment group and a control group. Each of these groups will take pre-tests and post-tests on their leadership abilities; however, the treatment group will have mood alterting treatment after the pre-test and prior to the post-test. The treatment will most likely involve adjusting the leaders’ affect to a postitive state. Furthermore, statistical analysis will determine if the treatment shows any statistical signficant changes to the group.

    The problem I am facing revolves around the treatment (changing of mood) upon the leaders. I don’t believe I will have any difficult measuring leadership performance due to the amount of research surrounding leadership. I am currently reviewing literature on reliable mood adjustment treatment. If anyone has any suggestions or comments that may help improve this study please let me know. I feel there is an obvious gap in the literature surrounding mood from this aspect and the implications of a study on this can move the research literature forward on many levels.

    Thank you.

    David

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  18. Ellinghaus says:

    I miss someone read and reporting about
    Luc Ciompi
    Die emotionalen Grundlagen des Denkens – Entwurf einer fraktalen Affektlogik

    also miss

    Mihály Csíkszentmihályi
    and his Flow research
    he also did allready bodytracking on moods.

    Greetings Florian

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  20. Hounaine says:

    Great. You can also add the fabulous tool SPANE – university of Illinois by Diesner and team and chinese empirical confirmation (april 2013)

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