We’re thrilled to announce a new series of DataViz talks for the start of the 2022/23 academic year! As ever, our events our open to everyone and you do not need to be a member of the University of Bristol.
We start with an in-person talk (remote option available) from Duncan Bradley @duncanbradley_ on the Cognitive Processing of Magnitude In Data Visualisations.
![A talk from the Bristol DataViz Interest Group, exploring data visualisation together Cognitive Processing of Magnitude In Data Visualisations by Duncan Bradley Tuesday 1st November, 3-4pm Royal Fort House, University of Bristol Free in-person talk (remote option available) Presenting research on: how axis limits inform judgements of how large or small plotted values are how specific design choices might help communicate messages effectively, or might mislead viewers.](https://dataviz.blogs.bristol.ac.uk/files/2022/10/image.png)
Tuesday 1st November, 3-4pm
Royal Fort House, University of Bristol
In-person talk (remote option available)
Data visualisations allow viewers to efficiently appraise many facets of a dataset. Systematically manipulating data visualisation designs helps us understand the processes involved in interpreting presented information. In this seminar, I will discuss a series of experiments revealing how axis limits inform judgements of how large or small plotted values are. These findings contribute to our understanding of the cognitive processing of magnitude in data visualisations, with potential consequences for design recommendations. This work provides insight into how specific design choices might help communicate messages effectively, or might mislead viewers.
![Duncan Bradley](https://dataviz.blogs.bristol.ac.uk/files/2022/10/DB-profile-1024x1024.jpg)
Duncan Bradley is a final-year PhD student in the Division of Neuroscience at the University of Manchester. He is interested in how data visualisations can leverage the human cognitive system to effectively convey messages. His research explores the cognitive processes involved in extracting meaning from data visualisations and the influence of design choices on the interpretation of presented information.