By Julia Holzer and Rosanna Wegenstein

Who hasn’t experienced it? You attend a lecture, the topic sounds interesting, you are attentive and engaged – and yet, at the end, you are left with the feeling that you cannot quite say what you actually took away from it. Some pieces of information remain, but they feel like loose threads that never quite form a coherent whole. Without being connected, many of these fragments fade away by the next day.

This is why science communication is not about delivering as many facts as possible. The key question is whether information is presented in a way that allows the audience to understand it, integrate it, and connect it to what they already know. Only then, lasting knowledge is built – and often, genuine interest in exploring a topic further.

1. Genuine understanding increases motivation

Everyone likes the feeling of truly understanding something. Suddenly, things make sense, connections become clear, and uncertainty turns into clarity. These moments are not only pleasant – they are also motivating.

Established psychological research shows that this experience is central to human motivation (Deci & Ryan, 2000; Ryan et al., 2021)¹. Experiencing competence and success is one of the key psychological conditions that keeps us engaged with a topic. When we understand something, we are more likely to stay interested.

For science communication, this means that content should be linked to the audience’s existing knowledge and, ideally, to their everyday experiences. This enables meaningful processing and sustained attention (Craik & Lockhart, 1972; Weinstein et al., 2018). Conversely, when content is too complex or not sufficiently grounded, people quickly lose the thread and attention drops.

 2. We like stories and larger contexts

Stories move us. They structure information, make connections visible, and help us make sense of content both emotionally and cognitively. When individual facts are embedded in a larger framework, they become meaningful – and this sense of meaning is a central driver of human thinking and learning.

Psychological research shows that we process information more effectively when we can integrate it with existing knowledge, especially when we understand its personal relevance (Rogers et al., 1977; Martinez-Conde & Macknik, 2017).

For science communication, this means that content should be placed within a clear and understandable context. It is particularly effective when a talk begins by explaining how the topic fits into a broader framework – such as a lecture series or a larger research field – and why it matters in everyday life.

For example, in a lecture series on “One Health”², a talk on disease transmission between animals and humans might begin by explaining that a healthy ecological balance reduces the risk of such transmission, and that human behaviour affects both human and animal health. This immediately creates a meaningful connection. The audience can see a small story unfolding – one in which they themselves play a role – and may become curious about how it continues.

 3. Working memory sets the pace

Our brain is remarkably powerful. We can understand complex ideas, build knowledge over time, and store vast amounts of information. The real limitation is not long-term storage, but the number of new pieces of information we can actively process at once. This is often what determines whether a lecture is easy to follow or overwhelming.

Cognitive psychology shows that working memory can handle only a limited number of new information units at a time – on average around 4 ± 1 (Cowan, 2001; Mandler, 2013). What counts as a single “unit” strongly depends on prior knowledge. A sentence such as “Antibiotic resistance is relevant in the context of One Health” may represent one coherent unit for experts. For non-experts, however, it breaks down into several components – “antibiotics”, “resistance”, and “One Health” – each of which must first be understood before the overall meaning can be integrated.

For science communication, this requires an important shift in perspective: it is not the amount of content that determines quality, but how it is structured and presented. Complex topics should be broken down into manageable units that build on each other. Especially with non-expert audiences, it is often helpful to introduce key concepts individually before embedding them into broader explanations.

4. Visualizations may not speak for themselves

In scientific publications, figures are often expected to “speak for themselves.” For expert audiences, this usually works – a single glance at a graph can be enough to grasp the message.

In science communication for non-experts, however, this is not the case. Visual representations are not necessarily self-explanatory. Before they can serve as tools for understanding, their structure must be explicitly explained.

For example, consider a graph showing how frequently an influenza virus was detected in wild birds in Vienna between 2015 and 2025. To make the message accessible, the audience first needs orientation: what does the x-axis represent (years), and what does the y-axis show (number of detections)? Only once these basics are clear can the actual interpretation follow – for instance, whether infections increased or remained stable over time.

5. Explain abbreviations and use clear, accessible language

Language is the central tool of science communication – and also a common barrier. What is obvious within a discipline can be confusing for non-specialists. This is especially true for abbreviations: for researchers, they are efficient and unambiguous, but for others they can interrupt comprehension. Therefore, abbreviations should always be explained, especially when they are conceptually important. For example, if someone refers to “the CDC”, it may be unclear that this stands for the “U.S. Centers for Disease Control and Prevention”. Without clarification, important context is lost – such as the fact that this institution collects data to prevent disease outbreaks. Such background information helps integrate content into a coherent narrative.

Beyond abbreviations, clarity of language is essential. Clear and simple language is not a form of oversimplification, but a prerequisite for understanding. Short sentences and minimal jargon prevent the audience from losing the thread.

Language also carries a social dimension: it can create closeness or reinforce social distance. Unexplained technical language can unintentionally signal exclusion and reinforce a gap between science and society. This is problematic, as it risks making science appear distant and inaccessible.

For effective science communication, clarity is therefore not only about comprehension, but also about inclusion. Accessible language makes science approachable – and therefore truly communicable.

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SciCom resources

The LBI SOAP Science Outreach and Science Education team has prepared a flyer containing practical recommendations for presentations and talks in the context of One Health.
The flyer can be downloaded here

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Further explanations

¹ Experiencing success and competence plays a central role in Self-Determination Theory (SDT), one of the most influential motivation theories in psychology. It describes three basic psychological needs that shape intrinsic motivation: autonomy, relatedness, and competence. The latter is particularly strengthened through experiences of success and understanding.

² The term “One Health” refers to the close interconnection between the health of humans, animals, and the environment.

References

Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. The Behavioral and Brain Sciences, 24(1), 87–114. https://doi.org/10.1017/S0140525X01003922

Craik, F. I. M., & Lockhart, R. S. (1972). Levels of processing: A framework for memory research. Journal of Verbal Learning and Verbal Behavior, 11(6), 671–684. https://doi.org/10.1016/S0022-5371(72)80001-X

Mandler, G. (2013). The Limit of Mental Structures. The Journal of General Psychology, 140(4), 243–250. https://doi.org/10.1080/00221309.2013.807217

Martinez-Conde, S., & Macknik, S. L. (2017). Finding the plot in science storytelling in hopes of enhancing science communication. Proceedings of the National Academy of Sciences – PNAS, 114(31), 8127–8129. https://doi.org/10.1073/pnas.1711790114

Rogers, T. B., Kuiper, N. A., & Kirker, W. S. (1977). Self-reference and the encoding of personal information. Journal of Personality and Social Psychology, 35(9), 677–688. https://doi.org/10.1037/0022-3514.35.9.677

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037//0003-066x.55.1.68

Ryan, R. M., Deci, E. L., Vansteenkiste, M., & Soenens, B. (2021). Building a Science of Motivated Persons: Self-Determination Theory’s Empirical Approach to Human Experience and the Regulation of Behavior. Motivation Science, 7(2), 97–110. https://doi.org/10.1037/mot0000194

Weinstein, Y., Madan, C. R., & Sumeracki, M. A. (2018). Teaching the science of learning. Cognitive Research: Principles and Implications, 3(1), 2–17. https://doi.org/10.1186/s41235-017-0087-y