Alan Barker

Presenting science: finding the structure

[image: Harish Krishna on Flikr]

In the previous two posts of this series, I’ve outlined some of the challenges facing scientists presenting to a non-specialist audience, and the need for a clear message. Once you've clarified your message, you need to find the structure that will work best for it.

The thoughts in this final post arise from my work with the seven Award Lecturers at this year’s British Science Festival (2018, at the University of Hull).They were consistently inspiring.

When I’m working with scientists on scicomm presentations, creating the structure is usually the most exciting part of the job. Every presentation takes its own shape, and our task is to discover that structure together.

So making rules about structure is pretty well impossible. But I think we can lay down three broad principles.

First, the presentation structures that succeed are always dynamic. They move in some way from beginning to end. That movement might be a straight line; it may be a tortuous meander; it may be a journey that suddenly changes direction.

Now, you could present your research as a journey. Simply adapt the structure of a research paper:

• Introduction (what was the problem?)
• Methodology (what did you do?)
• Results (What did you find?)
• Discussion (what did you think about it?)
• Conclusion (what have you proved?)

This is the classic IMRAD structure. And it might work for the audience in a scientific conference. That’s to say: it’s likely to make them feel comfortable and safe and maybe a bit sleepy…

But IMRAD is unlikely to work with a non-specialist audience. The problem is that the place where your research starts is unlikely to be a place that non-specialists would recognise or understand.

You have to start somewhere the audience finds familiar.

And then you have to entertain them.

Deep down, every audience wants a performance. Think of a simple tune, or a joke, or a magic trick. (Good science demos, of course, are very like magic tricks.) They all arouse expectations, and then fulfil them. Many scientific presentations are static: they’re all fulfilment.  (‘Make your point, then give the evidence.’). Your structure has to set up an expectation - and then fulfil it. For many researchers, this realisation is often an 'aha!' moment.

The structure you're looking for, then, is dynamic. It must move. It's not the structure of a paper; it's the structure of a performance.

All gripping performances contain moments of suspense and surprise. Create a mystery.  The more intriguing, the better. (Think of all those science documentaries in which, about halfway through, the narrator’s voice deepens and we hear the words: “And at that point they discovered something utterly astounding.”) It doesn’t have to be a burning controversy.  A life cycle with intriguing gaps; an ancient manufacturing process that remains a mystery to this very day; a mismatch between theory and findings; all of these can give you the hook that will capture your audience’s attention.  

Or think of a story. Every story follows a similar structure.

• Situation: which everyone in the audience recognises.
• Problem: which complicates the situation and makes in interesting, adding tension.
• Question: how is the crisis going to be resolved?
• Response: “…and they all lived happily ever after/… and the beast was slain/ … and the hero discovered something new about himself/herself/the world.”

There are plenty of models around to help you develop a narrative. Take a look at the the Freytag Triangle, ‘SPQR’; and Monroe’s Motivated Sequence. The trick is to ask what would work for your audience: what stories work for them? Look for the points of arousal in your narrative: the moments of mystery, choice, uncertainty, conflict. (“Why did that happen? Why did that fail? How can we fix this?”)

Arrange your structure around these turning points.  (I sometimes call them ‘hinges’.)

Second, narrative isn’t everything.  Scicomm practitioners and consultants can become obsessed with storytelling. But other kinds of discourse can perform. Some kinds of explanation, for example, are inherently dynamic. Think of contrast (the difference between then and now, here and there, us and them). Cause and effect, too, can be gripping, especially if the effect is surprising. (Science demos, again...) A process, by contrast, might be dynamic but it probably lacks suspense or surprise (and a process that involves conflict probably isn’t a very effective process). Lists of examples and carefully organised categories tend to be utterly boring. (Especially on slides.)

Argument, of course, is packed with drama. Make a striking or controversial claim, and your audience will be gripped.

Third, let your intuition help you.  Caroline Goyder suggests factoring in dream time.  Create a loose framework, she says, “as soon as the invitation to present goes in the diary.” “Once you have that frame,” she says, “your unconscious will get to work and the idea will grow, even while you’re doing other things.”  I’d also suggest talking your material through with a (preferably non-scientific) friend.  Where do their eyes light up?  What fascinates them?  Those moments are potential hinges.

Discovering the structure that makes a science presentation fizz is one of the most exciting parts of my job. We never know what that structure will be when we start exploring. But we always know it when we find it.

What's your message?: finding the foundation of a great science presentation

This is the second of three posts.

What makes for a zingy science presentation?

In my previous post, I highlighted the need for scicomm practitioners to answer the ‘so what?’ question. How can we produce a science presentation that’s truly meaningful for a non-specialist audience?

The sessions that I’ve seen in the last two days all delivered simple messages. It was the clarity of those messages that made them satisfying and enjoyable. We took them away with us. They were truly take-home messages.

The message depends more on your audience than it does on your subject matter. Every ordinary presentation talks about something. Every extraordinary presentation talks to its audience.

So how do you find a good message?

It's a matter of pulling focus. Start broad and narrow your thinking down.

Start with your subject. What are you talking about?

Ok. Now put that question behind you. You're not going to talk about anything. You're going to find interesting and meaningful things to say to your audience.

Now ask: who's my audience? Think about their likely demographics. Think about their general beliefs and attitudes, especially about the subject you’re tackling. (Vaccination? Climate change? Masculinity? There will be attitudes, beliefs and prejudices swirling around…) Think, too, about how they might think about you. And think about the hidden audience: on social media, in the press, or around the festival or event where you’re speaking.

You’ll be able to use all of this information in the presentation itself. For example, you can use it to help you identify – or seem to identify – with the audience. You could use information about the audience itself in the presentation. And you could use questions or statements generated by the audience themselves at some point. But all this is for later. Let’s come back to the message.

So: now identify your objective. How do you want to influence the audience? The simple answer is almost certainly that you will want to either explain or persuade. You can do both, but not at the same time! Try to decide which of these two is your overall objective.

And now, identify your topic. This is your position on the subject, where you stand in relation to it in the presentation. (The word comes from the Greek word topos, meaning ‘place’.) A quick short cut to a topic is to write down a phrase beginning with the word ‘how’ or the word ‘why’. One session today had the topic: “why our approach to obesity is wrong”. Another had the topic: “how we can strengthen our immune system’s ability to remember pathogens”.  A third was: “how brain training might help people living with Huntington’s disease”.

Now put the topic and the objective together. (They should of course make sense already in relation to each other.) Find the sentence that expresses your message, and delivers your objective, as simply as it can. In the cases I’ve mentioned, we can simply remove the initial words.

Our approach to obesity is wrong. [Persuading]

We can strengthen the immune system’s memory. [Explaining]

Brain training may be able to help people living with Huntington’s disease. [Explaining]

Your message is the foundation on which all the rest of the presentation will be built. And if you’re wondering where to put the message – At the beginning? In the middle? At the end? – then you’re ready to move on to the next stage of constructing a meaningful and entertaining science presentation: you’re thinking about structure.

And we’ll deal with that in the next post.

'So what?': the conundrum of scicomm

[image with thanks to magnoliamc.com]

This is the first of three posts. Links to the other two are at the end.

This week, I’m at the British Science Festival in Hull, which offers hundreds of exciting events creating a conversation between science – and scientists – and everyone else. It’s a great place to observe the challenges facing science communication, and the thrill when good scicomm successfully engages its audience.

This year’s festival is likely to be dominated by artificial intelligence – not least because the British Science Association’s new president, Jim Al-Khalili, will be devoting his Presidential Address to that topic.

It’s hard to keep up with the advances in AI: only three months ago, IBM unveiled Project Debater, a system capable of debating with humans on complex topics in real time. Project Debater seems to bring AI squarely into the rhetorical arena, perhaps for the first time. Does Project Debater automate persuasion?

The history of AI begins with attempts to replicate logical thinking: Simon and Newell developed Logical Theorist, widely considered the first AI program, in the mid 1950s. Classical AI, based in part on the work of Alan Turing, later developed algorithms using heuristics to make reasonable choices in pursuit of a goal. Classical AI now helps systems in logistics, in manufacturing and construction to plan and execute processes in highly controlled environments.

Machine learning – arguably the next stage in the AI story – creates systems that hunt for patterns in data. Neural networks take AI still further, mimicking to some extent the neurological structures of the brain. Systems like IBM’s Watson and AlphaGo (developed by Deep Mind) seem to be able to go beyond regurgitating knowledge and running logical deductions: they give a very good impression of discovering new strategies for solving problems and even generating new ideas.

But even these most advanced forms of AI lack the ability to think conceptually. As Professor Al-Khalili demonstrated in a recent TV documentary, we can train a program to recognise a dog, but it doesn’t know what a dog is.

So, at the heart of AI sits a conundrum. It’s known as Moravec’s paradox. AI is becoming ever more effective at the kind of complicated rational thinking that humans find hard, but it can't yet replicate the kind of perceptual and conceptual thinking that toddlers find effortless: recognizing a face, moving around in space, and catching a ball; paying attention to what’s interesting, setting goals, and planning a course of action.

Moravec famously explains the paradox in evolutionary terms:

Encoded in the large, highly evolved sensory and motor portions of the human brain is a billion years of experience about the nature of the world and how to survive in it. […] We are all prodigious olympians in perceptual and motor areas, so good that we make the difficult look easy. Abstract thought, though, is a new trick, perhaps less than 100 thousand years old. We have not yet mastered it. It is not all that intrinsically difficult; it just seems so when we do it

There’s a further paradox here. Those older, unconscious cognitive skills include the most sophisticated thinking skill of all: the ability to generate meaning. The question that AI has yet to learn to answer is: “So what?”

Scicomm faces the same paradoxical challenge. Scientists – inheritors of a rational method barely 2000 years old, which Moravec calls "the thinnest veneer of human thought" – need to be able to communicate with human brains that are highly evolved meaning-making systems. They need to engage the emotions, values, aesthetic judgements and social skills that shape our perception of reality. They need to tell us, not just what they know or how they’ve come to know it, but what it means.

In short, they need a rhetorical method to complement the scientific method.

In the next two posts, I’ll be exploring two key elements of that method: finding a message and discovering a structure for your presentation.