Long night of sciences on Saturday at the MDC in Berlin-Buch – come by for a visit

Dear friends,

Once again the MDC is participating in the Long Night of Sciences in Berlin, this Saturday, June 9, from 4-9 pm. Please come by and see my stand in the foyer of the MDCC. This year the theme is a new children’s book I have just written on the topic of evolution:

Charlie & Fitzroy
& the very strange bugs

a book about Evolution for kids

Every day Charlie and her pet beagle Fitzroy take a walk through the woods. One day they discover some strange bugs. By watching them over a few weeks, they discover the basic principles of evolution. Along the way they make friends with a strange old man with a long white beard…

I’d love to get feedback from scientists, teachers, kids, and especially anybody in the publishing business about the next steps in producing this book!



A dialog on ghosts and models in science

This is the first of several pieces in response to questions I have received about my recent lengthy article (too lengthy!) on “Ghosts, models and meaning: rethinking the role of communication in science.” It’s intended to give a quick overview of the main ideas; you’ll find the full article here.

Can you give me a succinct definition of the “ghosts” you’re talking about?

There are a lot of contexts in which science communication somehow fails because an audience doesn’t get the point or understand a message the way it was intended. The naïve view of this is that scientists just know a lot more about a specialized topic than people from other fields or the public. Of course that happens, but I’ve found it’s rarely the biggest issue in communication. And it doesn’t explain why people often have problems writing for experts in their own field.

When I began teaching scientists to write, I constantly came across content-related breakdowns that were hard to understand. This got so frustrating that I finally decided to carry out a systematic analysis of the problems. That took about four years, and “ghosts” emerged as a fundamental concept that’s helpful in understanding a lot of what goes wrong.

Ghosts originate from many things: concepts, frameworks, logical sequences, various patterns of linking ideas, theories, images and so on. What unifies them is that the author has something in mind that is essential to understanding what he means – but it’s missing or very hard to find within the message itself. Often the author is not even aware he’s thinking of something a certain way. Since it’s nowhere to be found in the message, it’s invisible. If the reader doesn’t sense its presence and go looking for it, or has too much trouble digging it out, he will probably misunderstand what the author really meant. All the words might make sense, but there’s some core idea that’s still missing.

This happens in all kinds of communication, of course. But these ghosts are particularly interesting in science because it has some very special ways of assigning meaning to things. Most scientists eventually absorb and imitate this “hidden code,” but a failure to recognize its existence causes all kinds of problems. A scientific text will be completely opaque to a lot of people not only because its meaning depends on all of these invisible things – even more because people don’t know where to look for it, or that it’s there at all.

So science has special ways of assigning meaning to things that really need to be taken into account when you’re planning a message or trying to interpret one. If you don’t, a lot of misunderstandings that could be avoided become almost inevitable.


You mention models again and again – why are they so central to misunderstanding science?

Among the most significant and disruptive ghosts in science are various models that are used in formulating a question or hypothesis and interpreting the results. Most studies engage many types and levels of models. In a single paper an author often draws on basic concepts such as the structure, organization and composition of cells, to the components and behavior of biochemical signaling pathways, to complex processes such as gene regulation, to notions like states of health and disease, evolutionary theory and so on. The way scientists describe fairly simple things usually draws on a complex, interlinked universe of models that goes from the smallest level of chemical interactions to mechanisms, organisms, species, and their evolutionary relationships.

Scientists obviously recognize this; as Theodore Dobzhansky said, “Nothing in biology makes sense except in the light of evolution.” But there is a big difference between vaguely acknowledging this and actually working out how the vast theoretical framework of evolution reaches into every single event you’re studying, and reaches into the way you understand the “simplest” things – such as the names of molecules.

And often people don’t realize that even Dobzhansky’s statement is resting on huge, invisible ghosts that he doesn’t explicitly state but are essential to understanding what he means. What I mean is that evolution itself is based on principles of science that are even more fundamental – it follows from them. So if you’re talking about the theory, you’re also engaging this deeper level. That’s really interesting because most of the “debates” over evolution I’ve witnessed are actually arguments about these even larger things. If the parties in the dialogue never articulate that deeper level of the disagreement, it makes very little sense to discuss the types of details that people go round and around about. They’re exchanging a lot of words, but they don’t fundamentally agree on what those words mean. They are arguing about whether species change, split apart or go extinct, but to get anywhere on those issues you have to agree what the term “species” means. It’s not so much that they don’t agree – more that they don’t even realize there is a problem.


What deeper ghosts have to be faced before someone can really understand evolution? 

I think there are two, which are so basic that they distinguish science from other ways of thinking about things and assigning them meaning. I call the first one the principle of local interactions, which follows from a fundamental assumption about physical laws. In science if you claim that something directly causes another thing, you are expected to prove that there is some moment of time and space where the cause and effect come into direct contact with each other, or at least to demonstrate that this is a highly reasonable assumption to make. Scientists extend this concept with a sort of shorthand: the two objects may not really bang into each other, but then they have to be linked by steps such as a transfer of energy that do follow this rule. So to make a scientific claim that a child inherits traits from its parents, you have to find some direct mechanism linking them, such as the DNA in their cells. It is directly passed to the oocyte from DNA from the reproductive cells of the parents, and gets copied into each cell, and then it gets used in the transcription of RNAs and translation into proteins through a lot of single, physical interactions. You’ll never directly see all of those things happening, but the models you use predict they are there.

The second principle applies this type of causality to entities as complex as organisms or entire ecospheres. It shows what happens when a lot of local interactions create systems that are much more complex. At that point the principle declares that the state of a system arises from its previous state through a rule-governed process. From that it follows that future states of the system will arise from the present one, following the same rules. We’re far from knowing all those rules, but scientists assume they are there, and a lot of their work is aimed at creating models that describe them.

Both of these concepts are closely tied to a style of argumentation that integrates Occam’s razor; I’ll talk about that elsewhere.

How are these fundamental principles linked to evolution? Well, you start by observing what is going on in a biological system right now and creating models that project the state into the past and future. You test those models with experiments, and then start extending them farther and farther into the past and future. You make predictions about what will happen if the model is correct in the future, and look for evidence of its activity in the past. If something in an experiment violates those predictions, you have to revise the model. This process of observation, modeling, and challenging models is the source of the Big Bang theory in astrophysics; it’s the basis of our geological understanding of the Earth’s crust, and when Darwin applied it to life he got evolution.

Other belief systems such as religious accounts don’t start from an assumption that models are works in progress that will inevitably be revised; nor do they require that their versions of things constantly be revised to conform to evidence. It leaves people free to believe whatever they like, to maintain idiosyncractic positions in the face of mounting evidence to the contrary. It leads to inconsistencies about the way they think about causes and effects in their daily lives versus how they extend their opinions to the universe. This is pretty egocentric; it leaves no place for self-doubt and encourages no respect for the potential validity of other belief systems. This very easily slides into a type of intellectual authoritarianism which is absolutely counter to the fundamentally democratic nature of science.

You can see these two principles at work in the way we distinguish “scientific models” from every other kind. Anything that violates the principle of local interactions would be considered non-scientific. That’s the case for extrasensory perception – until someone demonstrates that some energy passes from one person’s mind into another’s, you can’t make a scientific claim for its existence, so you have to look closely into whatever model of causality led you to claim it might exist. And the second principle implies that there are no discontinuities – you can’t create something from nothing. Miracles and the fundamentalist account of creation violate both principles.

If you can’t agree on these two things, it makes very little sense to discuss details of evolution that derive from them, because the differences in the very basic assumptions held by people can’t be resolved – you’ve got to agree on things like standards of evidence and causality. If you don’t do that you can’t even agree on the meaning of words. That’s what makes these fundamental principles ghosts in “debates” on evolution, and they are the things you need to clarify before getting involved in one. And, of course, you have to insist that the participants act in a way that is intellectually fair and honest, with integrity.

There are a lot of other debates in science – such as controversies over animal experimentation – in which this doesn’t happen. Reputable organizations make inflammatory remarks and hold untenable positions on points of fact, and refuse to back down when you refute their points. Then you get barroom brawls rather than civil discussions about important topics.


You came up with this concept of “ghosts” while working on texts by students and other scientists. Why are they a particular problem for students?

An active researcher is so deeply engaged with his models that they have become a fully natural, shorthand style of thought. Most projects in research take place in a fairly exact dialog with specific models you are either trying to elaborate on by adding details, or extend to new systems, or refute through new evidence. This makes models very dynamic, and there’s no single reference on the Internet or wherever where you can go and find them. In biology virtually every topic gets reviewed every year or two, which is an expert’s attempt to summarize the most recent findings in a field to keep people in a field more or less on the same page. That’s the group that a lot of papers and talks are addressed to, at least most scientists think that way – and they assume the readers will have more or less the same concepts, models and frameworks in mind. Anything that is widely shared, people often fail to say – they think they don’t need to. And it’s impossible to lay out all the assumptions and frameworks that underlie a paper within it – you can’t define every single term, for example. So these become ghosts that aren’t explicitly mentioned but lie behind the meaning of every paper. The two really huge basic principles I mentioned above are rarely, rarely described in papers.

And even the details of the models more directly addressed by a piece of work – the physical structure of the components of signaling pathways, or all the events within a developmental process – aren’t mentioned very often. Those models are embedded in higher-level models, and the relationships in this hierarchy are not only hard to see – there’s no single way of explaining them. Scientists sometimes work these things out fairly intuitively as they extend the meaning of a specific set of results to other situations and higher levels of organization.

Now imagine a science student who is absorbing tons of information from papers like these. As he reads he’s grappling with understanding a lot of new material, but he’s also actively building a cognitive structure in his head – I call it the “inner laboratory, or cognitive laboratory.” It consists of a huge architecture in which concepts are linked together in a certain structure. The degree to which he understands a new piece of science depends on how that structure is put together, and where he plugs in new information. If the text he’s reading doesn’t explicitly tell him how to do this, there will be a lot of misinterpretations.

How can his professor or the head of his lab tell whether a scientist under his supervision is assembling this architecture in a reasonable way? You catch glimpses of part of it in the way someone designs an experiment, but I think the only method that gives you a very thorough view of it is to have the young scientist write. That process forces him to make the way he links ideas explicit and put them down in a way you can analyse each step. In writing – or other forms of representation, such as drawing images or making concept maps – you articulate a train of thought that someone else can follow, providing a means of interrogating each step. Most texts are pretty revealing about that architecture; if you read them closely you can see gaps, wrong turns, logical errors, and all kinds of links between ideas that a reader can examine very carefully.

The problem is that in most education systems in continental Europe, in which most of the scientists I deal with were educated, writing is not part of the curriculum. Whatever training they have is done in all sorts of ways, and the teaching is usually not content-based. Instructors use all kinds of exercises on general topics, but that learning doesn’t transfer well to real practice. Why not? Because when you write about a general theme, your knowledge is usually arranged very similarly to that of the teacher’s and any general audience. In your specialized field, on the other hand, your knowledge is likely to be very differently arranged, and that’s where the ghosts start to wreak real havoc on communication.


So ghosts aren’t just things that scientists leave out of texts – they’re also phenomena that arise from the reader or audience…?

Absolutely – they arise from differences in the way a speaker and listener or a writer and reader have their knowledge organized. That can happen in any kind of communication, but in science it’s actually possible to pin ghosts down fairly precisely. In political discussions or other types of debates there aren’t really formal rules about the types of arguments that are allowed… But if you know how meaning in science is established, you can point to a specific connection in a text or image and say, “To understand what the scientist means, you have to know this or this other thing.” Again, since neither of you can directly see what’s in the other’s head, a reader may not guess that some of the meaning comes from very high levels of assumptions, or a way of organizing information that you’re not being told. And some have been digested so thoroughly by scientists that they’re no longer really aware that they are there.

Some of the most interesting ghosts appear when you try use someone’s description of a structure or process to draw a scheme or diagram. I recently had to draw an image of how a few molecules bind to DNA because we needed an illustration for a paper. I thought I had it clear in my mind, but I ended up drawing it five times – each version incorporating some new piece of information the scientist told me – before I got it the way she wanted it. You learn an incredible amount that way.

A scientific text is often based on an image of a component or process that a scientist has in his mind. He’s trying to get a point across, and to understand what he means you have to see it the way he sees it – but if he leaves anything out, it’s easy to completely miss the logic. It’s like trying to follow someone’s directions… That works best if the person who’s giving the instructions can “see the route” the way it will appear to you, maybe driving it one time to look for the least ambiguous landmarks, or taking public transportation and watching exactly what signs are the most visible. And thinking it through with the idea, “Now where could this go wrong?”


Another thing you refer to is concept maps – you include several examples in the article. How do they fit in?

Concept mapping is a system invented by a great educator named Joe Novak; it gives you a visual method to describe very complex architectures of information. It’s extremely useful in communication, teaching, and analyzing communication problems. One reason it’s so important is that our minds deal with incredibly complex concepts that are linked together in many ways. Think of trying to play a game of chess without a board – that’s incredibly difficult, but a chess set is a fairly simple system compared to most of those that science deals with. There’s really no way to keep whole systems in your head at the same time. Making a map gives you a chance to see the whole and manipulate it in ways that would be impossible just by thinking about it.

But the real genius of this system appears in communication and its most precise form – education – where a teacher ought to understand what he is really trying to communicate, and how it’s likely to be understood by the students or audience. In most cases you’re hoping to do more than just “transmit” a list of single facts; you’re trying to get across a coherent little network of related ideas, linked in specific ways. If you do that successfully, the audience will leave with a pattern they can reproduce later. It might be a story, a sequence of events, or a metaphor – the main thing is, they have seen how the pieces are related to each other.

A great way to do this is to make a map of the story you’re trying to tell, and then make your best guess about how this information is arranged in the heads of your target audience. What can you realistically expect them to know, and what information and links are likely to be new? If you see the pattern you’re trying to communicate very clearly, and make a reasonable guess about how some type of knowledge you can relate it to is arranged in your audience’s head, you know what you have to change to get them to see things the way you’re hoping. In schools they’re teaching kids to make concept maps early on. Then before a lesson about something like the solar system, the teacher has the kids draw a map of what they think about the sun, moon, planets, and so on. After the lesson the kids make a new map – comparing the two tells you what they’ve really learned.


In your article you point out ghosts that come from schemes like sequences of events or tables…

A lot of scientific models consist of sequences of interactions between the components of a system. Those start somewhere and involve steps arranged in a particular order, and it’s important for the reader to have a view of the steps and that order in his mind. You’d be surprised how often scientists describe these processes in some bizarre order that doesn’t go from A to K, but starts at G, goes to H and I, then goes back to G and works backward to F, E, and D… Again, if you are already familiar with the sequence or pathway this is no problem. But if you don’t, you’re probably expecting the reader to try to assemble the process in some reasonable order. That may be possible through a careful reading of the text, but it takes far more “processing time” than a reader would need if the whole sequence were simply laid out in order in the first place.

Tables are interesting because a lot of experiments are designed with a structure that’s pretty much inherently that of a table. Say you have two experimental systems plus a control, and you apply two procedures to all of them. To make a claim about the results, you have to march through all these cases – basically a table that’s 3×2 or 2×3. Here again, you’d be surprised how many scientists’ descriptions skip over some of the cells of the table, mostly because the results aren’t very informative. Or they tell you, “Procedure A caused a 5-fold increase over Procedure B,” without telling you what happened in the control.

Both of these effects are due to a scientist’s failure to recognize the structure of the information he has in his head and is trying to present… Then he fails to present that structure in the text in a way that’s easy for the reader to rebuild in his own head.


You’ve said that ghosts are one component of a larger model you’re working on that reformulates the relationship between science and communication… What else is there?

A lot of the other points can be captured through an exploration of what I call this “inner” or “cognitive” laboratory of science. The really good scientists I know have a very clear understanding of their own thinking. They know the assumptions that have gone into the models they are using, and are aware of the limitations, where there are gaps and so on. That type of clarity usually translates into good communication, no matter what the audience.

One thing I found during this project that was very surprising was the extent to which writing and communication for all kinds of audiences was connected, and how addressing very diverse audiences could clarify thinking in a way that improved a scientist’s research. When you find a scientist struggling with clarity in a text, it usually means one of two things. Either a topic is not clear in his head at that moment, or it’s not clear in anybody’s head at this moment in science… That second case is very interesting because it means you can find interesting questions just through a very careful reading of a text, realizing that it’s asking you to build a certain structure of ideas. If you have difficulty, that means something. One of the basic strategies I used in working these things out was that problems are meaningful – they’re trying to tell you something about how good science communication works, or how scientific thinking works… usually both.

Speaking to a general public with really no specialized knowledge of a field can be a truly profound exercise for a scientist. It makes him interrogate his own knowledge in alternative ways. He has to come to a much more basic understanding of the patterns in his inner laboratory and apply different metaphors, trying to map that knowledge onto someone else’s patterns. Well, the cognitive laboratory is already metaphorical, based on concepts rather than real objects, and applying new patterns or metaphors to what’s in there is extremely interesting. It can suggest questions you’ve never thought of before. This means that tools that have been developed by linguists and communicators can be used as tools to crack open scientific models.

I’ve actually done this – used those tools to expose an assumption about evolution that everyone was making but wasn’t usually aware of. The assumption had never been tested, so my friend Miguel Andrade decided to take it on as a project, and put a postdoc on it. The results were really interesting, showing that there were a lot of cases where the assumption didn’t hold – and we got a published, peer-reviewed paper out of it. That was three years ago, and in the meantime I’ve been involved in a number of similar projects that have had a similar outcome. A communicator who pursues questions about meaning and language has a different set of tools to understand how ideas are linked in scientific models. You’re freer to apply slightly different metaphors and patterns to ideas; you may be more rigorous in perceiving assumptions; metaphors and other tropes help you see cases in which people are reasoning by analogy rather than strictly adhering to the system at hand.

So these ideas aren’t just a way to help people plan and communication better – although they certainly help in those tasks. In fact they are much more fundamental in scientific thinking. Understanding these relationships between communication and science is a pathway to doing better research, through a better understanding of its cognitive side. I’ve noticed recently, for example, a lot of cases where the way people are thinking of complicated processes is drifting away from the language they use to describe them. The language is conservative and it may be hard to adjust. But that will be essential as the models these fields are using move forward and become so complex that our minds – and our language – may not be truly able to capture them.




Ghosts, models and meaning in science

Rethinking the role of communication in science

by Russ Hodge, copyright 2018

Read the article here

This article is intended for all the stakeholders in the broad field of science communication: from practicing scientists at all stages of their careers to science students and teachers, journalists, communicators, and educators. It could also be of interest to linguists, cognitive psychologists, and others interested in the connection between thinking and language. I hope it will be read by those responsible for university programs across Europe, because it provides several arguments for making communications training a standard part of their curricula.

Here I bring together ideas that have been dealt with superficially in other pieces (1, 2, 3, 4) on the blog.

This is a rough draft, one of at least three more major parts to come. In it I aim to demonstrate that the relationship between science and communication is far more profound and interesting than we usually consider. The process that most of us go through when we want to communicate well is crucial to clarifying thinking, and it offers tools that could be used much more strategically in posing new scientific questions and interpreting data. To say this as boldly and plainly as possible: learning to communicate well can improve your scientific work – not only because your papers have better grammar, but because it requires a type of thinking that is extremely useful for science.

I do not say this lightly; I know how skeptically most scientists will greet it. That’s fine; I have waited a long time to write this piece because I needed to collect powerful examples to support it and put them together in a convincing way. If you are a scientist, I hope you will recognize aspects of your own thinking in this piece, and feel that it puts words to things that have become your daily habits. It may even surprise you by revealing “mechanisms” of thinking that you have never considered, yet use all the time.

It has been a long road to get here: 20 years of interacting with scientists at all levels of their careers on a daily basis, working together to find didactic approaches to a wide range of problems, and over 30 years as a teacher overall. Yet it wasn’t until a few years ago that I finally decided to confront some frustrating, content-related problems that constantly arise while helping my students and colleagues write, speak, or communicate in other ways about their work . I realized that we didn’t have a very good model to describe and hopefully understand a lot of the problems they encountered. That motivated four years of systematically analyzing these problems. I came to several conclusions:

  1. Science and communication are profoundly linked at a deeper level than we usually appreciate, which has significant implications for science education programs and the ways individuals, institutes and organisations communicate their work.
  2. The process of writing or preparing a talk is usually essential in clarifying and organising one’s own scientific thinking.
  3. This process requires a thoughtful reconsideration of the scientific models related to a project and can expose weaknesses or hidden assumptions that need to be reexamined.
  4. Every experiment represents a dialogue with models of many types and levels and the results may say something about all of them.
  5. Becoming aware of hidden connections in the structure of scientific thinking can powerfully affect our interpretation of results and generate important new questions.
  6. Communication offers an extensive set of tools which can be systematically applied to scientific problems and improve the quality of research.
  7. Scientific models are highly complex cognitive architectures that individuals construct in their minds and integrate into an “inner laboratory” where the “real science” takes place.
  8. The only way to examine these architectures is by externalizing them in writing, talks, images, or other modes of representation
  9. Effectively speaking to the public or non-specialist audiences usually requires seeing familiar systems through new patterns. Doing it well requires a process that can clean up sloppy thinking, help us approach an old theme in a new way, generate new scientific questions and suggest alternative interpretations of experimental results.

I know, the last one’s the big one.

The text starts with a short theoretical introduction. After that I apply the principles it introduces to nine case studies taken from real students’ texts, papers, images and other examples of science communication.

This model is just a beginning, but it has some powerful implications for the way we train scientists and teach them to communicate. It strongly suggests that effective training in these skills should be an integral part of a scientific education early on and continue through a student’s career. But before people start changing their curricula, scientists need to have a convincing model that shows them why it is important, and the method of teaching must be effective. I think this is a start, but it will need to be tested in many formats and teaching environments to be validated and improved.

The model I propose is not comprehensive; I will add another major section on metaphors and patterns in scientific models and a third that specifically explores how these ideas can be practically translated into teaching. I am hoping to work with teachers who are interested in learning the theory and methodology, applying it to other types of science, and becoming multipliers. I think this is the only way to achieve the long-term goal of institutionalising this type of training and ensuring that it becomes a staple of university science curricula throughout Europe.

I need and would greatly appreciate feedback from all stakeholders in this process. Please be as critical as you like; the model has to be tough enough to take it. I will consider all of your comments very carefully, report on them here, and use them to develop better versions of this text, the model it presents, and the teaching that results from it.


Thanks in advance,

Russ Hodge

Please contact me at  hodge@mdc-berlin.de if you would like to discuss this personally. Also if you are interested in teaching or training in these fields, in learning the methodology yourself, or would like to discuss setting up workshops or a program to implement its ideas.

Russ Hodge, March 2018

Read the article

I would like to thank all the scientists who have been such great teachers and given so generously of their time helping me over the past 20 years, the students who continue to inspire this project, the teachers who have been a continual inspiration, and my family, friends, and colleagues present and past for their support. 

I would like to particularly thank Prof. James Hartman of the University of Kansas, an extraordinary teacher, lifelong mentor and friend, for setting me on this path so many years ago and stimulating my ideas at exactly the right moments over the years;

Joseph Novak, father of Concept Mapping and one of the most brilliant educators I have ever met, who in a single week at Cold Spring Harbor completely changed my views of the goals of teaching and the methods needed to achieve them;

Jochen Wittbrodt and the COS department at the University of Heidelberg, Gareth Griffiths at the University of Oslo, and Thoralf Niendorf at the MDC for being constantly supportive and serving as the guinea pigs in this crazy endeavour.



A new model of the profound relationship between science and communication

One reason the term “science communication” has broadened to include so many activities is that research is leaping across the boundaries of disciplines and into our daily lives more quickly and profoundly than ever before. Without a basic understanding of scientific results and the methods by which they are obtained, people can’t be expected to digest complex information about their health or the global impact of their lifestyles and respond in reasonable ways. This has stimulated diverse efforts by many types of communicators to broaden and raise the level of scientific literacy in society as a whole. The pace of science has also created challenges for scientists as they confront massive amounts of data that can only be understood by teaching a computer how to cope with them, excruciatingly detailed models, and problems that can only be solved by transcending the boundaries of classical disciplines whose practitioners come from different backgrounds and speak different languages – both literally and figuratively.

Many well-meaning efforts aimed at explaining the significance of a piece of research – or the aims of science as a whole – somehow fail. That’s true at the interface of science and wider sectors of society, but forms of the general problem are also  common within research communities, where communication is fundamental to daily practice. Good communication skills boost careers and the progress of a field. Failing to help scientists develop them, I will argue, has effects not only on their careers but also on the quality of their research. This comes from working in the field a long time and witnessing countless examples demonstrating that excellent scientists are often superb at explaining their work to very diverse audiences. Is there a connection? You don’t truly understand something until you can explain it to someone else; does this old adage hold true for the highest levels of research and communication? If so, can you make people better scientists by making them better communicators? A few years ago I decided to try to find out.

A meaningful approach to answering these questions would have to encompass both theory and practice; it would require a thorough understanding and analysis of not only the strategies people were using to communicate, but the content they were trying to get across. I had access to plenty of examples through the scientists I encountered every day, the difficulties I encounter myself in writing about their work, and from hundreds of students over the years whom I had tried to help write and present their science to many types of audiences. As a general approach I stole a page from the handbook of the early fly geneticists, who uncovered the functions of hundreds of genes by studying how mutations disrupted biological systems. Maybe problems in communication could be used the same way: maybe they could show how things ought to work.

Over several years I followed this strategy in studying communication problems and funneling much of what I learned back into the courses I was teaching. The result was a steady but dramatic change in my understanding of the relationship between communication and science. I believe that these two fields of effort are connected at a profound level that is incompletely understood and rarely explicitly discussed or taught.

This project offers a new model of that relationship which attempts to connect how scientists communicate their work – effectively or not – to deeper underlying aspects of the way they think. It shows how many communication problems stem from chaos in the laboratory: not the physical benches where scientists spend their days, but the mental laboratory they are constantly constructing and rebuilding as they learn science.

It’s in this inner laboratory that real science happens, and understanding this gives communication a fundamental role: it is a means of exposing, exploring, and manipulating the cognitive models that give every scientific question and every piece of data its meaning. Disorder in the mental laboratory almost always leads to chaos in communication, and the act of communicating science offers ways not only to detect it, but also to straighten things out. In fact, it’s often the only way to even notice that the disorder is there. Our minds make assumptions and carry out logical jumps we aren’t aware of; until they are articulated aloud, our innermost beliefs and convictions are prey to influences that lie outside of science. A scientist’s examination of any system – even before a first encounter with it – is already styled by experiences of other systems, expectations, and models built using other systems long in the past; the recognition that this generates bias and can even reach into data in ways that reconfigure it is the reason why double-blind studies are so important. By putting something on paper, scientists can carry out a more careful, analytical scrutiny of their assumptions and models – to ask the questions, “Is this conclusion founded,” or “Are other interpretations possible?” one must first see the whole train of a thought. Then it can be broken down and mercilessly queried, step by step, and weak points can be discerned.

The process of communicating science thus externalises thought to permit a self-critical scrutiny that may otherwise be impossible or at least extremely difficult. Inevitably one becomes aware of gaps that have been invisible. It allows a person, at least to some extent, to look at his or her own ideas more the way another reader would. This skill can be trained, and it is the first step toward developing distance toward a set of ideas – and even to apply the perspective of a potential audience. That process not only improves the quality of a researcher’s communication – it can also affect the work. Sometimes the only thing necessary to discover fascinating new questions and develop better models is to notice the structure of a system in a text or diagram.

Most of the models in today’s science are so complex that they can’t even be thought about clearly without some form of representation – in language, images, or mathematical formulae. Papers and talks and other communicative acts open this complexity to inspection, analysis, discussion, criticism, and correction from the community. Trying to do science without communicating it is like trying to play chess – or teach someone else to play – without a board. For those who aren’t geniuses, a physical board offers a playing field to try things out, move the components around, and probe new strategies. To become a good scientist a person needs to look at many, many games, recorded in the literature, and extract the patterns and rules that lead to success.

Today’s students are constantly flooded with massive amounts of information which they are expected to arrange in their mental laboratories in a certain way. The hypotheses they frame, the experiments they design, and the way they interpret results are manifestations – symptoms – of the architecture they have built in their heads. But the only way to catch a real glimpse of this architecture, and measure their success at assembling it, is by watching how they put their work into the larger, logical framework of a text or talk. Explaining their science to non-specialists requires stepping farther back, seeing the more basic and generic patterns that underlie models, and trying to capture those patterns using tools such as metaphors.

That’s an important process because the inner mental laboratory of science is a metaphorical one as well. When a scientist frames a hypothesis regarding a specific problem – say, the behaviour or structure of a molecule – the form of the question is determined by the concept of a molecule, and what we think others think about it, rather than the molecule itself. The simplest things we think about are highly complex models and they are intermingled in a messy knot of other concepts, abstractions, and many types of knowledge that all come to bear on how clearly we are thinking.

So I am convinced that it is no accident at all that the best scientists I know have a very good understanding of their own thought processes, as applied to science. Often they have arrived at this point intuitively, devining rules and models through an intense study of the games going on around them. There are many parallels to learning a language: small children construct models that allow them to produce grammatical sentences by taking in and imitating the sounds around them, and attaching those sounds to things in contexts that have meaning for them, and testing them against the practices of others. What ultimately comes out is a compromise between the things they want to talk about, social contracts about the meaning of words and sentence structures, genre-like expectations about what is likely to be said when and where, and fundamental aspects of the biology of our brains – the extent of short-term memory determines, to a great extent, how many things we can think about and process at once. That influences how complex a grammar can be, and it also determines how much of a model can be processed without an external reference such as a text or diagram.

The rules for how an adult learns a new language are different than those for a child, and this means that teaching must do more than delivering single facts or pieces of evidence that we expect non-native speakers to assemble properly. People come to science after a long process of intellectual development in which so many concepts and expectations are already fixed, which means that moving into an artificial system of scientific models is more like the second type of language learning. Teachers usually take advantage of their students’ intelligence by presenting them with models of sentences and methods of producing new ones for the real communicative contexts that give them meaning. The same is true for research, and looking at it this way has profound implications for how we teach science and how we teach people to communicate it. I think that these efforts are most likely to succeed with a better understanding of the complex rules by which models give scientific ideas their meaning, and an understanding of the cognitive nature of the models themselves, and an search for methods aimed at resolving these parts of the “communication problem.”

* * * * *

Some of my colleagues and other professionals in the field of science communication might be surprised that this enterprise doesn’t start with a discussion of issues we usually confront and talk about the most, such as the fact that people who have something to say about science and their audiences often have very different agendas in coming together. Their knowledge and interests often diverge very widely. Dialogues that are started as a way of generating mutual understanding sometimes lead to even greater misunderstandings, and in the worst case achieve exactly the opposite response. Audiences sometimes leave “popular science talks” thinking, “Science is so hard I’ll never understand any of it,” “Why can’t scientists ever give me a straight answer to a question?” and even, “They’re trying to hide something from me.”

Miscommunication is often the result of getting off on the wrong foot from the very beginning: a failure to consider exactly what you hope to communicate, which has to be a function of a rational decision about what it’s possible and desirable to achieve with a specific audience, and what you expect them to do with the message. The usual result of this failure is a mis-match: a message doesn’t resonate because it hasn’t taken into account an audience’s interests, needs, or their motivation in entering into a dialogue in the first place.

These situations and less severe symptoms of poor communication are deeply connected to the cognitive models by which we navigate science and nearly everything else in our lives. They constantly arise in teaching because most of the students I deal with have never been introduced to very basic principles of functional communication, where success depends on a good understanding of the message one wishes to share, the expectations and knowledge of the target audience, and the modes and genres that are available to deliver it. The quickest path to a communicative breakdown is a mis-match between any of these things.

My experience is a meaning-based approach to teaching communication – which in science requires thinking about the connection between specific questions, results, and models – is extremely effective at solving these more fundamental problems. In following entries regarding this project I will use examples to explore the details of this model of science communication and how it can be translated into a didactic approach. In science, the first step toward solving a problem is usually to articulate a question very clearly. The same thing is true in teaching: to help a student acquire skills, we should first grasp what they need to learn. Communication begins with the construction of meaning, and the better we understand that process, the better we will be able to teach researchers to explain what they mean – no matter whom they need to address.

Russ Hodge, Sept. 2017

Tips for reducing talk anxiety (2b, more responses to reader comments)

This is a follow-up to the original piece.


Dr. Krishna Kumari Challa wrote: Well, if you are an introvert, your brain goes haywire with the great stimulation given by larger audiences (an introvert’s mind needs less stimulation to have the same level of understanding about a situation). Controlling it is more important, according to psychologists, before thinking about the points given in the blog. Only when the stimulation is controlled you can control other things. That is why introverts try to hide behind something, look at their papers or at the screen instead of the audiences in the initial stages. They try to reduce the stimulation by doing so.
I would be grateful to you if you could give tips on how to reduce this ‘too much stimulation’ issue.


Hi Krishna – an excellent point! My experience with students suggests that there are surely “types”, such as those you call introverts, who dislike being the focus of attention and whose brains experience an exaggerated response that powerfully influences their bodies and behavior in public presentations and similar situations. Usually giving them help requires close observation, then developing an individual plan of practice that addresses specific behavioral symptoms – like those mentioned in the earlier post. This will take time and patience. Here, too, it can be useful to help them focus on the content of their presentation, and this is a theme to be covered in a future post. That said, I’m not a psychologist, and some types of anxiety clearly have very deep roots that need to be addressed therapeutically before any satisfying “cure” is really achieved.

But still there are things that can help: It’s crucial to try to define the parameters of the problem as precisely as possible. Are there any situations in which the person manages to handle their anxiety? Are they equally affected if they give the presentation to two or three close friends, or their lab? Is one of the issues trust: with people they know, does a fear of disapproval or a negative response disappear? If they can handle small groups, then this can become a cornerstone of practice. They need to give the talk as often as possible in such settings in a way that helps them internalize the positive effects and extend the experience to situations with larger audiences.

Personally I learned something about this through music. My first violin teacher, Lewis Hoyt, said that one of his teachers had always told him to imagine the heads in the audience as cabbages! Later he began studying through a new method which took exactly the opposite approach – enjoying the presence of an audience and fully engaging them as human beings in your personal music, work, or story. Over the long term, if one can manage it, this tends to work better than pretending like they aren’t there.

Your point about “control” reminds me of the student I discussed in point 9 of the original article – where the issue was managing all of the ideas bombarding his brain and the flow of the content. A few simple strategies to ensure that you can stay on track (point 8) and won’t get lost go a great way toward reinforcing confidence. The trick is to extend them to behaviors that are really hard to control: blushing, stammering, shaking, etc.

Each speaker needs to build on the strengths she/he has and use them to support areas of weakness. Can you tell a joke? Can you tell a funny little personal story about something that happened during the project? If some weird little accident happens, can you improvise and get people to laugh? There are lots of potential methods to change the atmosphere – disasrming a stressful situation – which can almost instantly relieve a lot of the tension. As a teacher or speaker you may have to dig deep into the rhetorical repertoire to find something that works, but there’s often something there to draw on.

A lot more needs to be said about this; I’ll keep thinking about it, and the comments on the piece have been extremely helpful in pulling out essential points for consideration. I’ll be teaching several courses over the next couple of months and will be able to report more specific examples from actual practice.


If you like these pieces, you might be interested in the article:

“The Dinner Party: Learning to explain your work to a general audience can make you a better scientist.”


If you’d rather enter the bizarre, twilight world where science collides with humor, check out the Devil’s Dictionary of Scientific Words and Phrases, or the text of a talk I gave in Oslo in 2015, plus everything else in the categories “Hilarious moments in science communication” or satire.


And if you haven’t yet seen the most popular post so far from this blog, check out the “God” article:  “Even God’s first paper got rejected.

Tips for reducing talk anxiety (part 2a, first feedback from readers)

Wow! The article on performance anxiety is getting a lot of traction; thanks very much for the feedback and I’m hoping for a lot more. (See the full article here or just scroll down if you’ve landed at the blog main page. Click here for a list of other pieces devoted to teaching and training.)

Two readers have provided tips that I include here with a couple of comments:

From Jennifer Kirwan, the head of the Metabolomics Unit at the Berlin Institute of Health, come two pointers:

Two tips I was given years ago when public speaking:

1)      Never ever use a laser pointer or wooden stick. Instead, use powerpoint animations to circle or otherwise highlight the point of interest. Not only does this eliminate the issue of shaky hand syndrome, but it also serves to engage your audience more as frequently people have to struggle to see the laser dot on the screen, especially when it’s moving.

2)      Many people tend to find they blush when faced with public speaking. We were advised if we have this problem to wear clothes that cover our shoulders and avoid low cut clothes. It makes the blushing less obvious and, if you are less worried about people seeing you blush, you’re less likely to start.

Great points. A couple of remarks:
To 1: It’s absolutely true that the spot from a laser pointer can be hard to see – especially under certain lighting conditions and on some slide backgrounds. And a pointer can be terribly distracting in the hands of speakers with that awful habit of drawing really fast circles around the thing they want you to look at. (…which may be an unconscious strategy they’ve adopted to hide trembling – or the effect of a major caffeine overdose). And the pointer is often a lazy person’s way of compensating for a slide that’s crammed with too much information, or whose design is unclear and hard to scan. (And, of course, if the audience is looking at the laser dot, they won’t be looking at you.)
Caveats: Some people (including me) aren’t very fond of PowerPoint. I’m not as fanatical about this as other people (including the peerless Edward Tufte), but they make very good points. It’s crucial to map out content and your message before you choose the template for each slide and the talk in general. When you do, you’ll often find that none of the templates really fits. Most people do it the other way around. They pick some template, or simply start with the default that some other user set up long ago, and try to fit their information into it. This can impose a structure on the message that doesn’t fit it at all, and you may not even be aware of it.
But for anyone who does use PowerPoint, or another system with similar animation features, Jennifer’s advice has some clever added benefits. Picking spots to animate or highlight will force you to plan a rhetorical path through the information on the slide – those points represent the key landmarks in this chapter of your story. Defining that path can help you distinguish important information from unnecessary details, showing you things that can be left out. (General rule: leave out as much as possible, and then a little bit more.) Animations can also help during your presentation. If, God forbid, you do have a blackout, the next highlight will point you back into the story.
Even so, I would always have a pointer on hand: you may need it for other reasons. Someone may pose a question that requires you to return to a slide and focus on something you haven’t anticipated; you may need to point it out for the rest of the audience. Secondly, something can happen that makes you abandon your original plan. If time gets short you may need to skip things
Suppose, for example, the topic of the last speaker overlaps with yours. You  may want to build a bridge between the talks that you hadn’t anticipated: “The previous speaker addressed this question at the level of the transcriptome. At the level of the proteome, however, we didn’t see any upregulation of pathway components – as you can see here, and here.” (Since the focus of your talk is slightly different, you hadn’t highlighted those particular molecules.)
To 2); I really like Jennifer’s second point about using your wardrobe to cover blushing. That makes great sense.
(Although in my personal case, I’d need a different strategy, being the kind of person who rarely bares his shoulders or exposes much cleavage during a talk. Maybe I could wear a bright scarlet suit that made my face look pale, or go to the Solarium and get a mild sunburn beforehand, or blind the audience with my laser pointer, etc. etc. … Sorry, Jennifer, I couldn’t resist.)

The second comment came from my friend and former colleague Alan (aka Rex) Sawyer, and is interesting on several levels: cultural, pharmacological, and rhetorical:

I needed this advice 25 years ago while preparing for my first paid gig as a counter-tenor soloist (Friedenskirche, Handschusheim, Johann Sebastian Bach, BWV 4, “Christ Lag in Todesbanden”). But what broke the ice as I went on stage was that the stagehand had failed to provide a seat for me. The audience laughed good-humouredly, which totally banished my case of nerves as it got the audience on my side. Later I got a top tip to eat three bananas about an hour before going on stage. Bananas contain trace quantities of a natural beta blocker. The effect is subtle, but it really works.
To that I can only say: if you’re already taking beta blockers, consult your physician before eating bananas; otherwise you may be comatose when it comes time to give your talk. Wait several hours before operating any heavy equipment. A laser pointer is probably safe.
And don’t get confused and eat three watermelons by mistake. The effects might resemble those of another pharmaceutical product: reports claim that a substance called citrulline in watermelon acts as a sort of natural Viagra. Although you’d probably have to eat an awful lot of it to experience the effects. And at that point, you might not want to walk onstage to give a talk.
If you like these pieces, you might be interested in the article:
If you’d rather enter the bizarre, twilight world where science collides with humor, check out the Devil’s Dictionary of Scientific Words and Phrases, or the text of a talk I gave in Oslo in 2015, plus everything else in the categories “Hilarious moments in science communication” or satire.

Tips for reducing talk anxiety (part 1)

This is part of a series of articles on the blog (a few already published, more in the works) devoted to didactics and the communication of science (and other things). I am currently working on a handbook that includes ideas such as these and explores in depth the myriad problems of presenting content. More pieces to come on that.

The tips given here are related to performance anxiety and represent just a sample of things I’ve learned from my own excellent teachers, from my experience in training lots of scientists and other types of speakers, from my own experiences in public speaking, and from the process by which I completely eliminated my own stage fright when performing as a musician (yes, it’s possible – and that’s when the fun and the real music begin!). In the courses I give we always find a way to adapt these principles to individuals and their problems.

Please help me by contributing your own experiences and tips, so we can build a useful, very practical resource that will help as many students and teachers as possible! I will add your points to the list and mention their sources!

The first step in learning is to identify any barriers that exist – to define the problem as clearly as possible. So it’s crucial to carry out some self-exploration: you need to carefully study your own body in situations of fear, anxiety and stress.

These mental and physical techniques require practice, and they work best if you imagine yourself as concretely as possible in the environment you will face when giving a talk. Visualise the room – ideally, visit it ahead of time, and maybe go to another talk there. Sit toward the back and listen. If you can’t visit the room, then imagine various scenarios: a large classroom, an intimate seminar room, a packed auditorium, an almost empty auditorium.

Next close your eyes and imagine the moment before you are invited to speak. Imagine someone getting up and introducing you; you’re sitting there and will be headed onstage in 30 seconds. Find out if possible whether you will be standing or sitting down; imagine the size of the audience you will be facing, mentally prepare for a moment where the beamer doesn’t work and needs to be fiddled with, if the microphone suddenly doesn’t work, etc. Have some strategy for “vamping” the time, with a joke or some other device that engages the audience. (“While we’re waiting, I’d like to conduct an informal survey about a question of tremendous scientific relevance: Where does that stuff in your belly button come from, anyway?” There’s actually a very interesting study out about this… )

  1. Nervousness is usually accompanied by various physiological and mental symptoms, and here the goal is to deal with common and specific symptoms such as stress and tension, a nervous voice, a wavy pointer, and blackouts. By removing these symptoms you can trick your body into thinking it’s comfortable, and the cognitive issues often fade along with them. But there are clear strategies for dealing with blackouts, too.
  2. The first step is to try to replicate the condition of your body when you’re nervous, by imagining you’re in the situation, or remembering the feelings you had the last time.
  3. Anxiety is usually marked by muscle tension in very specific parts of your body. The first goal is to be aware of their positions and consciously relax them. My own technique is very simple: I totally relax my ankles, letting go of all tension in my ankles and then my feet. When I do this – and it’s true for most other people as well – it is very hard to maintain tension anywhere else – in my back, my vocal chords, etc. Try it – totally relax your ankles, and while doing so try to make a muscle tense in your back, or your arms. If it’s difficult, that means you can use this approach as well. If not, you need to find some other part of your body that you can deliberately relax and thus force yourself to relax the stressed muscles as well. Stand up and relax your ankles. This should be the first thing you do after you’re standing at the lecturn or whatever, and you’ll have to practice remembering to do it.
  4. Remember that the first 30 seconds or so of a talk are less about the content than about the audience learning to listen to your voice and style. If you realize that, then you realize that it’s also a time that you can use to get comfortable. First of all, BREATHE. Then speak SLOWLY and CLEARLY and have a clear strategy prepared to invite your listeners to engage with you right from the beginning. This is something you have to practice as well – people are usually most nervous at the beginning of a talk, and that’s when they usually talk the fastest. Additionally, for predictable reasons, they tend to say the highly technical terms they are most familiar with the fastest – and these are just the words that need to be spoken the most clearly and distinctly. Practice the beginning of your talk with a metronome or by slowly pacing around in a way that forces you to slow the rate of syllables as you speak. You’ll have to practice this a lot of times until you instinctively start slowly rather than with the rush of nervousness.
  5. Engagement #1: try to engage the listeners at the very beginning. Before you speak, look around at some of their faces and smile. If you’re not fixed to a podium or a position at the front, move toward them, as if you’re in a more informal setting.
  6. Engagement #2: if possible, start off with a real question that interests you and has motivated the work, if you can find one that’s general enough to be grasped by the entire audience. Why? If you’re lucky, they’ll actually try to come up with an answer in their own minds, or focus on the question. This immediately draws the audience into the content, rather than a focus on you and your behavior. At that point you’ve engaged them in the subject matter. If they really try to answer the question, they’ll think something like, “Oh, that’s interesting; I would have tried to do it this way…” and you’ll immediately have set up a dialogue that will continue throughout the talk and will provide plenty of good feedback at the end.
  7. Engagement #3: Rhetorically speaking, most data slides are also shown to answer specific questions. (“Does protein A interact with protein B?” Well, to find out, here’s what we did. You see the results here, which provides the following answer…) Unfortunately, most speakers don’t realize that this is what’s happening. They use the ANSWER to the question as the title of the slide, and often start trying to explain the answer before clearly presenting either the question, the methodology, and the results. This confuses the rather simple story-line inherent in the slide. It can also disrupt the talk as a whole because an answer (end of slide) usually stimulates the next question (beginning of next slide). You don’t have to make all the titles of your slides questions, but you should realize this is what is going on (and actually, why not do it?). It has the benefit of gluing separate slides together in a smooth story. And it also can stop a big problem that occurs if the order of information on a slide is different from the order you are using while speaking. When that happens, people are trying to read and listen at the same time, are getting different information from those two channels, and probably won’t remember anything.
  8. Boiling a talk down into a big question and many sub-questions can have a huge effect on anxiety when you’re worried about content blackouts. All you need to remember (or have on tiny cards in your hand) are the questions. You know the answers – that’s what you’ve been doing for the past 100 years. The question-answer method serves to create a real dialogue that engages the public and also an outline of your talk.
  9. Practice other specific performance problems that you are aware of. The first step in finding a cure is to identify what has been disrupted at the right level (it’s just like practicing music that way). A while back I had a student who was having what looked like blackouts during a talk. Later he explained that they weren’t blackouts – instead, every idea was bombarding his brain at once, and he couldn’t figure out where to start. I suggested a method by which he put up a slide and practiced fixing his eyes precisely on the thing he would talk about first, then moving them to the next thing, and so on. The very next day he gave a talk in front of 400 people without a single glitch or “brain freeze.”
  10. Shaky voice. If your voice quavers or trembles while you speak, the problem may be tension in some part of your body (see number 3 above). Often there is another problem, especially (but not only) if you are speaking in a foreign language. You may be pitching your voice too high or too low, which puts tension on your vocal cords and that will extend into your face and throat and shoulders and then the rest of your body – and then you’re doomed! This often happens in a foreign language, where people sometimes choose a “base pitch” (the tone – in a musical sense – at which you would speak if you were talking in a monotone) that is at the wrong place of the spectrum. This is really likely to happen if you subjectively consider your voice too high or too low (to be “sexy”) and try to place it differently. How do you know the right base pitch that your voice should have? A friend who has become a well-known speech pathologist gave me this tip. Go to a piano, and find the highest and lowest keys that you can comfortably The appropriate ground tone for your voice should be between the half-way mark and a third of the way from the bottom of this range. If you try to speak at a pitch that’s too low, you’ll experience the “creaky voice” phenomenon. If your voice is too high, in general, you’ll strain your vocal chords and eventually get hoarse or lose your voice. If either of these things happens to you anyway on a regular basis, you may be pitching your everyday voice too high or low. Also try different volumes of voice. You may arrive in a big room with no microphone, and you’ll have to project. Aim your voice at the person in the back, without shouting at the people in the front row. Your diaphragm and vocal cords have the potential to cause all the air in the room to vibrate and communicate your message. Singing teachers know the secrets of projection. I don’t, but it has a lot to do with breathing deeply and comfortably, and not tightening your throat or larynx.
  11. Shaky pointer syndrome. The reason a pointer shakes is because of tension in the muscles that control your arm and hand. The solution is to let your shoulder hang, without any muscular activity from the back or upper arm, and imagine that all the weight is on your elbow, and that it’s sitting on a table. Now use only the muscles you need to raise your forearm (preserving this feeling of all the weight in your elbow) and aim the pointer at a spot on the wall. Let it remain on the same point for a while. If it shakes, there’s probably some tension still in your upper arm (it’s really hard to make the forearm tense if your upper arm and shoulder are relaxed). Once you can hold the point relatively still, try moving it back and forth in a horizontal line. Here, too, you should imagine that your elbow is resting on the table, taking all the weight from your shoulder, and you’re just sliding your forearm back and forth.
  12. Those nerdy, highly technical slides… Although most scientists tell me that nowadays, most of the talks they give are to mixed, non-specialist audiences, you’re bound to have a few slides that are complex or obscure and you won’t have time to teach people “how to read them.” Example: I’m working with scientists who are developing mathematical models of biological processes, and at some point in their talks they want to show the real deal – math and formulas. They know a lot of people will be intimidated by this, but they still need to show the real work. On the other hand, they don’t want people to “tune out” and give up on understanding the rest of the presentation. At this point what I recommend is to say something like, “Now my next slide is specially made for you math nerds out there; the rest of you can take a short mental vacation and I’ll pick you up in just a minute on the other side.”
  13. Imagine the “personality” you’ll project when you become the leading expert in your field. Pretend like you’ve given the talk a hundred times to enormous success, and now you’re on the lecture circuit, giving it to audiences that think you’re the Greatest and are eager to provide input and their own ideas. How will you look up there? What kind of voice will you have? What types of rhetorical devices can you use to project “modest authority”? When a musician has practiced and practiced a piece for months, and gets stuck, sometimes all you have to do to make the next big step is to imagine what it will sound like when you play it a year from now. If you can imagine that, as concretely as possible, usually the next time you play it will be much closer to that vision. The same thing goes for giving talks.
  14. Criteria for success… If I give someone directions to a party, there’s a simple test that reveals whether I’ve done a good job or not – whether they arrive on time, on the right day… What’s the equivalent for a talk? (Pause while you think about it a minute…) The best answer I’ve heard is this: Imagine you leave the room and there’s somebody waiting outside who says, “Damn! I really wanted to hear that talk; what did he/she say?” At that point a member of the audience should be able to give the person a short summary, and it should fit two criteria: 1) the speaker would agree with it, and 2) most members of the audience should give very similar answers. As a speaker, how do you ensure that this happens? Well, the most obvious way – which few people really ever consider – is to close your talk by saying this: “Now imagine when you leave the room, there’s somebody standing outside who tells you, ‘Damn, I really wanted to hear that talk; what did he/she say?’ Well, here’s what you should tell them…” And then sum it up in a nice little package that’s tight enough to be remembered, with a clean, predictable story line. Remember you’re not trying to simply communicate single facts! You’re trying to answer a question – which you have to be able to articulate very precisely – and you need to explain the meaning of that question in terms of models and concepts that you share with the audience. You need to put information into a structure that can be grasped and remembered, in a way that holds the attention of the audience and engages their intelligence. This means you have to provide information in a relational, coherent structure – and if they don’t share your background and models, you’ll have to provide it. If you do that, you’ll get the kind of smart questions and feedback you’d like, the kind that will help you improve your thinking and your research.

The last points relate to content, which will be the subject of more articles very soon.

ALL of these points require practice – numerous repetitions while mentally imagining the real-life situation as it will feel, as closely as possible. You may always feel anxious before or during a talk; it may never go away. But most people can deal with the symptoms, using strategies like these, and that makes all the difference.

Two final points: First, remember what it’s like to be in the audience when a speaker is really nervous. Everybody is rooting for him or her – they’re on your side! Take comfort in that and try to engage people in the sense that “you’re all in this together”: you’re inviting them to think about an interesting question with you, rather than waiting for them to throw rocks (or shoes) at you.

Secondly, you’ve got to be engaged in the content. Even when you think your story isn’t that great or sexy, or leaves lots of questions up in the air – well, that’s what most science is like, folks! Remember that you’re presenting something that has an inherent interest to a lot of scientists. And negative results are useful as well because they can save your colleagues a lot of time; it will prevent them from following the same old leads, time and time again, without realizing that other labs have tried and failed and been unable to publish their results. Closing off blind alleys is a great service to scientists everywhere – it’s a key step toward progress by forcing people to rethink and revise the basic models they are using.

These are some of the very basics I’ve learned through experience in many performance situations of my own as well as working with a lot of students with different problems over the years. I have learned a lot from the fantastic teachers I have had the privilege of studying with (and continue to do so in the life-long process of learning). I also absorbed a lot from a fantastic book about performance anxiety, whose focus is music but every bit of it is applicable to public speaking, which I highly recommend here:

The Inner Game of Music
Overcome obstacles, improve concentration and reduce nervousness to reach a new level of musical performance
Barry Green with Timothy Gallwey (co-author of the Inner Game of Tennis)
London: Pan Books, 2015.
ISBN: 978-1-4472-9172-5

At Amazon, also available on Kindle

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