| Unit name | Neuropsychological Analysis Tools |
|---|---|
| Unit code | PSYCM0044 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Baddeley |
| Open unit status | Not open |
| Pre-requisites |
n/a |
| Co-requisites |
n/a |
| School/department | School of Psychological Science |
| Faculty | Faculty of Life Sciences |
The central aim of this unit is to extend students knowledge and understanding of the important role the choice and execution of data analysis plays in psychological research, and in particular, in neuropsychological research. To achieve this, students will have to learn the basic programming skills (here Matlab) needed to handle more complex neuropsychological data sets. After learning the basics of Matlab, and the tool set associated with exploratory data analysis, the skills learned will be applied in a number of domains (e.g. eye movements, motion capture , EEG).
Two enrichment periods further reinforce this knowledge by providing hands-on experience with the range of neuropsychological research tools used within the research environment of the School of Experimental Psychology for the assessment of cerebral function.
Intended learning outcomes are a thorough preparation of the students for understanding analysis methodologies employed in the neuropsychological research literature.
Students are expected to understand the basics of data handling such as being able to extract raw data from complex data structures, using Matlab or similar programming languages.
The teaching consists of 10 x 2 hour lectures and 10 x 2 hour practicals. Lectures are given by research active staff members, with a strong interactive component. Conceptual coverage of neuropsychological theory, data collection and analysis choice, will be combined with analytical approaches so that statistical theory and practice are blended.
For the application domains, the underlying logic of neuropsychological data analysis is explained with hands-on examples and is delivered in interactive sessions where the component skills and the operation of the software are integrated.
One piece of coursework involving the writing of a program to perform a bootstrap analysis of the mean and standard deviation of a given data set together with documentation of this program (50%).
A 2-hour unseen examination assessing the material covered in the sections on specific application domains (50%).
Borgo, M., Soranzo, A., & Grassi, M. (2012). MATLAB for psychologists. Springer-Verlag: New York Kolb, B., & Wishaw, I.Q. (2009). Fundamentals of human neuropsychology. 6th Edition. Worth Publishers: New York Luck, S.J. (2014). An introduction to the event-related potential technique. 2nd Ed. The MIT Press: Cambridge, Massachusetts,
