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Crowdsourcing Empathetic Responses and Cognitive Reappraisals

22 May

At the Collective Intelligence conference in April, Rob Morris presented a paper that he and Rosalind Picard wrote, titled   “Crowdsourcing Collective Emotional Intelligence”.

It sounds crazy, I know, but they figured out how to structure micro-tasks on Amazon Mechanical Turk such that they elicited empathetic responses and cognitive reappraisals from anonymous workers with no training in psychotherapy.  For example, the system starts with a stressor text that a distressed user might enter, such as, “I’m going to flunk out of school and I’ll never get a job, I know it!”

Generating empathetic responses was fairly straightforward. They post the stressor comment and some guidelines for generating an empathetic response:

(1) address the user directly, (e.g., “Michael, I’m sorry to hear …”), (2) let the user know that his/her emotion makes sense, given the situation, and (3) share how you might feel if you were in a similar situation.

Turkers generate candidate responses and other Turkers vote on whether the candidate responses are appropriately empathetic. In an experiment, these responses were rated as much more empathetic than responses generated in response to the instruction to simply make the stressed user feel better about his/her situation (5.71 vs. 4.14 on a 7-point scale.)

Even more interestingly, the crowd could follow a structured process to generate cognitive reappraisals. They first ask some turkers to classify the stressor statement as having some cognition distortion or not. A distortion means, “logical fallacies within negative statements (Beck, 1979).” The example statement about flunking out never getting is a distortion  because there’s no way the speaker could know that s/he’ll never get a job in the future. On average, workers made this binary classification correctly 89% of the time. Using several workers to classify a single statement could increase accuracy.

When the worker marks a statement as a cognitive distortion, they are asked to give a “thought-based reappraisal” explaining the nature of the distortion. No complex training is needed for the workers: they are simply given some sample responses for inspiration.

When the work does not indicate a distortion, the worker is asked to give a “situation-based reappraisal” that suggests a different way of thinking about the situation. Workers are introduced to the concept and given a few examples of good and bad appraisals (the latter are needed to dissuade workers from offering advice or making unrealistic assumptions about the original speaker’s situation, two common errors they observed.) Some workers were asked to come up with their own reappraisal suggestions, while others were asked to try specific strategies such as finding a silver lining or taking a long-term perspective.)

Responses were limited to four sentences. In the experiment, reappraisals solicited in the way described above  were rated as better at offering a positive way to think about the situation (5.45 vs. 4.41) than when workers were asked to simply make the stressed user feel better about his/her situation.

Overall, this suggests that the crowd can, with little training, be a useful source of informational feedback and emotional support.

Running Together, At Different Paces

9 May

Florian Mueller and colleagues from Australia presented an interesting paper at the CHI conference titled, “Balancing Exertion Experiences.”

They had previously developed and reported on a system that lets people jog “together” though physically separated (even England to Australia!) They can talk with each other, but the sound is spatially located, so it sounds like your running partner is to your left (or right) and ahead of you or behind you. If they’re getting ahead of you, it can spur you to speed up, or slow down if they’re behind you.

Now they’ve gone for a “better than being there” experience. If your running pace is different than your partner’s, they can still have you run together. Instead of balancing your speed in order to stay next to your partner, you have to balance some other metric of exertion. In their version, each person picks their own maximum target heart rate, and you have to match the percentage of personal target in order to stay aurally next to your partner while jogging.

Pretty goal. Current prototypes use a little too much hardware for comfortable jogging, but I expect something like this will be available for iPhones some time.

The Eatery: Crowdsourcing Evaluation of Food Diaries

3 Nov

Previously, I suggested that other people can be a good source of feedback, among other things, to support self-regulation. Right on queue, two days ago The Eatery was released, the first iPhone app from MassiveHealth. The two biggest differences from other food logging applications out there are:

  1. You just take a photo of what you are about to eat; you don’t try to semantically tag it in a way that allows for calorie counting or ingredient analysis.
  2. Other people rate what you eat on a 10-point fit-or-fat scale. Continue reading

Where are the Recommender Systems in Tailored Health Messaging?

1 Nov

In areas like smoking cessation and cancer screening,  where the goal is to educate and get people to take the first steps toward behavior change, “tailored messaging” was developed in the 1990s to try to improve on the effectiveness of one-size-fits-all brochures that are often distributed in clinics. So far, however, the techniques of recommender systems (also called collaborative filtering) that I helped to develop, also starting in the  early 1990s (recipient of ACM Software Systems Award last year) , don’t seem to have been applied in tailored health messaging. In this post, I’ll explore what has been tried in tailored health messaging, and where the opportunities might be to incorporate the recommender system techniques that are now ubiquitous in commerce and other applications on the Internet. Continue reading

Roles for Other People in Self-Regulation

24 Oct

Self-regulation theories describe how people regulate the setting and pursuit of goals. In the realm of health behavior change, it is a useful lens for understanding what happens after someone is motivated, say, to lose weight. How can they structure a program that will actually lead to weight loss? This post examines the key elements of the theory and then considers how other people can enhance self-regulation processes. Continue reading