Log Book

Design: Explorations I

22 July 2016
Coupling the target groups’ Needs defined in my last blog as well as the theories of emotions I’ve decided to use as a basis for my research, I began the the design iteration process.


I started with a quick variations on a Radar Charts. What’s tricky about charting Emotion data is of course, that it is very subjective and qualitative. So for the purpose of first iterations I will assume that Valence, Intensity, Control, Arousal, and Conduciveness are all quantifiable.

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The trouble with each of these variables having the same “zero” is that we’re in a way saying that the following are all equal:

  • Negative Valence
  • Inability to Control
  • Low Intensity
  • Low Arousal
  • Low Conduciveness (or Obstructive) to Goals

Perhaps we can agree that Emotions with Negative Valence (Unpleasantness) is typically unwanted and therefore we’d want to be further away from 0. Calm, however, is an Emotion that has been used to determine Low Arousal (Reisenzein, 1994). If we could agree that Calm, therefore stability, as an Emotion that is desired, then we would want to be closer to 0 in the recording of the Arousal variable.

A further exploration of using color and color blends within a Radar Chart to reflect Plutchik’s model of conceiving the primary emotions as analogous to hues. Could the visual system’s color language be easily memorized, thusly allowing a patient to look at their “Emotion Shape” and recognize it’s intensity or valence? In this case, such a system could be used in reverse: define the Emotion’s variables and then through the resulting shape, name the Emotion.

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The next set of drafts are based on the Need to track and see the variables’ variability over time. Patients who have strong physical manifestations of their emotions could benefit from seeing something like Arousal overtime. Arousal is high when a patient experiences (or notices the experience of) the physiological changes in the body. We recognize these chemical processes overtly as increases in heart rate, sweating, tense feelings, and certain forms of body language.

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In the above drawing, each line, created by the sum of the recorded variables, represents the major emotion felt that day. The more days charted on the graph quickly decreases the readability. It is also difficult to understand the progress of a week, should there have been a trend. In the chart below to the right, it is also clear that an Area Chart could fail to highlight the nuance in the tracked Emotions.

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This visualization still leaves something to be desired. In the bottom section of the above drawing, I’ve mapped a week of my Emotions beginning on July 1. Each line represents a variable, the first point in every line together represents one day, the second points representing the second day, and so on from top to bottom. Left is less of that variable (less Control, less Intense, less Positive) and right the more of that variable.
It might be interesting, but not surprising, to see that Valence and Conductivity tend to positively correlate - and increase as the week went on. From a weekly perspective, such a visualization could be valuable. Though impressive in looks, viewing large amounts of tracked data (say, months) might not be very valuable.

Next Post

Artefact: Defining Needs

11 - 19 July 2016
As a framework for the Visual System, I defined the Needs as they exist from all target groups involved: Psychology Professionals, Patients, Field of Emotion Research, and Linguistics. I expect this list to change accordingly as my research and eventually user-testing progresses.