• Use experimental approaches to understand real-life communication situations, including both language and visual communication.
• Have knowledge of a range of experimental approaches and typical findings from the field of cognitive science.
• Design simple behavioural experiments.
• Analyse experimental data using descriptive statistics and selected statistical tests.
• Communicate the results of experiments to both academic and professional audiences.

How do language users decode the communications they receive in private and professional contexts? And how do consumers make sense of the numerous images they encounter in both physical and online environments? Such questions are of central interest to the fields of communication and marketing, and they are increasingly investigated using experimental approaches.

In this course, we work with understanding users and consumers in cognitive terms, using a variety of experimental approaches and quantitative methods to do so. We introduce students to basic experimental approaches from the field of cognitive science which may be used to study language and visual communication in both academic and professional settings, and we cover the statistical methods necessary for analysing the data generated by such experiments.

We will start the course with an introduction to experimental approaches and research design, before moving on to the six core sessions of the course which will each cover a different experimental approach, including the Stroop task, priming, visual search, and eye tracking. Each of these core sessions will cover relevant theory and analytical methods, as well as a demo experiment which will be run in class. Student assignments and presentations will be based on the data collected in these experiments. Throughout the course, we will focus on the basic logic of the different experimental approaches, their main results, and how they may be applied in professional practice. We will cover basic statistical tools that will allow students to analyse and understand the data, but we do not assume any prior background in either mathematics or statistics.

The primary reading will be a textbook on research methods and statistics written for a social sciences/psychology audience. Chapters from the textbook will be supplemented by relevant journal articles.

Possible course text books:

Heiman, G. W. (2001). Understanding research methods and statistics: An integrated introduction for psychology (2nd ed.). Boston, MA: Houghton Mifflin.

Gliner, J. A., Morgan, G. A., & Leech, N. L. (2017). Research methods in applied settings: An integrated approach to design and analysis (3rd ed.). London, UK: Routledge.