I stumbled across this Dec. 13, 2016 essay/book announcement by Dr. Andrew Maynard and Dr. Dietram A. Scheufele on The Conversation,
Many scientists and science communicators have grappled with disregard for, or inappropriate use of, scientific evidence for years – especially around contentious issues like the causes of global warming, or the benefits of vaccinating children. A long debunked study on links between vaccinations and autism, for instance, cost the researcher his medical license but continues to keep vaccination rates lower than they should be.
Only recently, however, have people begun to think systematically about what actually works to promote better public discourse and decision-making around what is sometimes controversial science. Of course scientists would like to rely on evidence, generated by research, to gain insights into how to most effectively convey to others what they know and do.
As it turns out, the science on how to best communicate science across different issues, social settings and audiences has not led to easy-to-follow, concrete recommendations.
About a year ago, the National Academies of Sciences, Engineering and Medicine brought together a diverse group of experts and practitioners to address this gap between research and practice. The goal was to apply scientific thinking to the process of how we go about communicating science effectively. Both of us were a part of this group (with Dietram as the vice chair).
The public draft of the group’s findings – “Communicating Science Effectively: A Research Agenda” – has just been published. In it, we take a hard look at what effective science communication means and why it’s important; what makes it so challenging – especially where the science is uncertain or contested; and how researchers and science communicators can increase our knowledge of what works, and under what conditions.
At some level, all science communication has embedded values. Information always comes wrapped in a complex skein of purpose and intent – even when presented as impartial scientific facts. Despite, or maybe because of, this complexity, there remains a need to develop a stronger empirical foundation for effective communication of and about science.
Addressing this, the National Academies draft report makes an extensive number of recommendations. A few in particular stand out:
- Use a systems approach to guide science communication. In other words, recognize that science communication is part of a larger network of information and influences that affect what people and organizations think and do.
- Assess the effectiveness of science communication. Yes, researchers try, but often we still engage in communication first and evaluate later. Better to design the best approach to communication based on empirical insights about both audiences and contexts. Very often, the technical risk that scientists think must be communicated have nothing to do with the hopes or concerns public audiences have.
- Get better at meaningful engagement between scientists and others to enable that “honest, bidirectional dialogue” about the promises and pitfalls of science that our committee chair Alan Leshner and others have called for.
- Consider social media’s impact – positive and negative.
- Work toward better understanding when and how to communicate science around issues that are contentious, or potentially so.
The paper version of the book has a cost but you can get a free online version. Unfortunately, I cannot copy and paste the book’s table of contents here and was not able to find a book index although there is a handy list of reference texts.
I have taken a very quick look at the book. If you’re in the field, it’s definitely worth a look. It is, however, written for and by academics. If you look at the list of writers and reviewers, you will find over 90% are professors at one university or another. That said, I was happy to see references to Dan Kahan’s work at the Yale Law School’s Culture Cognition Project cited. As happens they weren’t able to cite his latest work [***see my xxx, 2017 curiosity post***], released about a month after “Communicating Science Effectively: A Research Agenda.”
I was unable to find any reference to science communication via popular culture. I’m a little dismayed as I feel that this is a seriously ignored source of information by science communication specialists and academicians but not by the folks at MIT (Massachusetts Institute of Technology) who announced a wireless app in the same week as it was featured in an episode of the US television comedy, The Big Bang Theory. Here’s more from MIT’s emotion detection wireless app in a Feb. 1, 2017 news release (also on EurekAlert),
It’s a fact of nature that a single conversation can be interpreted in very different ways. For people with anxiety or conditions such as Asperger’s, this can make social situations extremely stressful. But what if there was a more objective way to measure and understand our interactions?
Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Institute of Medical Engineering and Science (IMES) say that they’ve gotten closer to a potential solution: an artificially intelligent, wearable system that can predict if a conversation is happy, sad, or neutral based on a person’s speech patterns and vitals.
“Imagine if, at the end of a conversation, you could rewind it and see the moments when the people around you felt the most anxious,” says graduate student Tuka Alhanai, who co-authored a related paper with PhD candidate Mohammad Ghassemi that they will present at next week’s Association for the Advancement of Artificial Intelligence (AAAI) conference in San Francisco. “Our work is a step in this direction, suggesting that we may not be that far away from a world where people can have an AI social coach right in their pocket.”
As a participant tells a story, the system can analyze audio, text transcriptions, and physiological signals to determine the overall tone of the story with 83 percent accuracy. Using deep-learning techniques, the system can also provide a “sentiment score” for specific five-second intervals within a conversation.
“As far as we know, this is the first experiment that collects both physical data and speech data in a passive but robust way, even while subjects are having natural, unstructured interactions,” says Ghassemi. “Our results show that it’s possible to classify the emotional tone of conversations in real-time.”
The researchers say that the system’s performance would be further improved by having multiple people in a conversation use it on their smartwatches, creating more data to be analyzed by their algorithms. The team is keen to point out that they developed the system with privacy strongly in mind: The algorithm runs locally on a user’s device as a way of protecting personal information. (Alhanai says that a consumer version would obviously need clear protocols for getting consent from the people involved in the conversations.)
How it works
Many emotion-detection studies show participants “happy” and “sad” videos, or ask them to artificially act out specific emotive states. But in an effort to elicit more organic emotions, the team instead asked subjects to tell a happy or sad story of their own choosing.
Subjects wore a Samsung Simband, a research device that captures high-resolution physiological waveforms to measure features such as movement, heart rate, blood pressure, blood flow, and skin temperature. The system also captured audio data and text transcripts to analyze the speaker’s tone, pitch, energy, and vocabulary.
“The team’s usage of consumer market devices for collecting physiological data and speech data shows how close we are to having such tools in everyday devices,” says Björn Schuller, professor and chair of Complex and Intelligent Systems at the University of Passau in Germany, who was not involved in the research. “Technology could soon feel much more emotionally intelligent, or even ‘emotional’ itself.”
After capturing 31 different conversations of several minutes each, the team trained two algorithms on the data: One classified the overall nature of a conversation as either happy or sad, while the second classified each five-second block of every conversation as positive, negative, or neutral.
Alhanai notes that, in traditional neural networks, all features about the data are provided to the algorithm at the base of the network. In contrast, her team found that they could improve performance by organizing different features at the various layers of the network.
“The system picks up on how, for example, the sentiment in the text transcription was more abstract than the raw accelerometer data,” says Alhanai. “It’s quite remarkable that a machine could approximate how we humans perceive these interactions, without significant input from us as researchers.”
Indeed, the algorithm’s findings align well with what we humans might expect to observe. For instance, long pauses and monotonous vocal tones were associated with sadder stories, while more energetic, varied speech patterns were associated with happier ones. In terms of body language, sadder stories were also strongly associated with increased fidgeting and cardiovascular activity, as well as certain postures like putting one’s hands on one’s face.
On average, the model could classify the mood of each five-second interval with an accuracy that was approximately 18 percent above chance, and a full 7.5 percent better than existing approaches.
The algorithm is not yet reliable enough to be deployed for social coaching, but Alhanai says that they are actively working toward that goal. For future work the team plans to collect data on a much larger scale, potentially using commercial devices such as the Apple Watch that would allow them to more easily implement the system out in the world.
“Our next step is to improve the algorithm’s emotional granularity so that it is more accurate at calling out boring, tense, and excited moments, rather than just labeling interactions as ‘positive’ or ‘negative,’” says Alhanai. “Developing technology that can take the pulse of human emotions has the potential to dramatically improve how we communicate with each other.”
This research was made possible in part by the Samsung Strategy and Innovation Center.
Episode 14 of season 10 of The Big Bang Theory was titled “The Emotion Detection Automation” (full episode can be found on this webpage) and broadcast on Feb. 2, 2017. There’s also a Feb. 2, 2017 recap (recapitulation) by Lincee Ray for EW.com (it seems Ray is unaware that there really is such a machine),
Who knew we would see the day when Sheldon and Raj figured out solutions for their social ineptitudes? Only The Big Bang Theory writers would think to tackle our favorite physicists’ lack of social skills with an emotion detector and an ex-girlfriend focus group. It’s been a while since I enjoyed both storylines as much as I did in this episode. That’s no bazinga.
When Raj tells the guys that he is back on the market, he wonders out loud what is wrong with his game. Why do women reject him? Sheldon receives the information like a scientist and runs through many possible answers. Raj shuts him down with a simple, “I’m fine.”
Sheldon is irritated when he learns that this obligatory remark is a mask for what Raj is really feeling. It turns out, Raj is not fine. Sheldon whines, wondering why no one just says exactly what’s on their mind. It’s quite annoying for those who struggle with recognizing emotional cues.
Lo and behold, Bernadette recently read about a gizmo that was created for people who have this exact same anxiety. MIT has a prototype, and because Howard is an alum, he can probably submit Sheldon’s name as a beta tester.
Of course this is a real thing. If anyone can build an emotion detector, it’s a bunch of awkward scientists with zero social skills.
This is the first time I’ve noticed an academic institution’s news release to be almost simultaneous with mention of its research in a popular culture television program, which suggests things have come a long way since I featured news about a webinar by the National Academies ‘ Science and Entertainment Exchange for film and television productions collaborating with scientists in an Aug. 28, 2012 post.
One last science/popular culture moment: Hidden Figures, a movie about African American women who were human computers supporting NASA (US National Aeronautics and Space Agency) efforts during the 1960s space race and getting a man on the moon was (shockingly) no. 1 in the US box office for a few weeks (there’s more about the movie here in my Sept. 2, 2016 post covering then upcoming movies featuring science). After the movie was released, Mary Elizabeth Williams wrote up a Jan. 23, 2017 interview with the ‘Hidden Figures’ scriptwriter for Salon.com
I [Allison Schroeder] got on the phone with her [co-producer Renee Witt] and Donna [co-producer Donna Gigliotti] and I said, “You have to hire me for this; I was born to write this.” Donna sort of rolled her eyes and was like, “God, these Hollywood types would say anything.” I said, “No, no, I grew up at Cape Canaveral. My grandmother was a computer programmer at NASA, my grandfather worked on the Mercury prototype, and I interned there all through high school and then the summer after my freshman year at Stanford I interned. I worked at a missile launch company.”
She was like, “OK that’s impressive.” And I said, “No, I literally grew up climbing on the Mercury capsule — hitting all the buttons, trying to launch myself into space.”
She said, “Well do you think you can handle the math?” I said that I had to study a certain amount of math at Stanford for economics degree. She said, “Oh, all right, that sounds pretty good.”
I pitched her a few scenes. I pitched her the end of the movie that you saw with Katherine running the numbers as John Glenn is trying to get up in space. I pitched her the idea of one of the women as a mechanic and to see her legs underneath the engine. You’re used to seeing a guy like that, but what would it be like to see heels and pantyhose and a skirt and she’s a mechanic and fixing something? Those are some of the scenes that I pitched them, and I got the job.
I love that the film begins with setting up their mechanical aptitude. You set up these are women; you set up these women of color. You set up exactly what that means in this moment in history. It’s like you just go from there.
I was on a really tight timeline because this started as an indie film. It was just Donna Gigliotti, Renee Witt, me and the author Margot Lee Shetterly for about a year working on it. I was only given four weeks for research and 12 weeks for writing the first draft. I’m not sure if I hadn’t known NASA and known the culture and just knew what the machines would look like, knew what the prototypes looked like, if I could have done it that quickly. I turned in that draft and Donna was like, “OK you’ve got the math and the science; it’s all here. Now go have fun.” Then I did a few more drafts and that was really enjoyable because I could let go of the fact I did it and make sure that the characters and the drive of the story and everything just fit what needed to happen.
For anyone interested in the science/popular culture connection, David Bruggeman of the Pasco Phronesis blog does a better job than I do of keeping up with the latest doings.
Getting back to ‘Communicating Science Effectively: A Research Agenda’, even with a mention of popular culture, it is a thoughtful book on the topic.