| CODE | CGS5065 | ||||||||
| TITLE | Applied Methods in Cognitive Science | ||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||||
| MQF LEVEL | 7 | ||||||||
| ECTS CREDITS | 5 | ||||||||
| DEPARTMENT | Cognitive Science | ||||||||
| DESCRIPTION | The study-unit will guide students to design, implement, collect pilot data from, and report on an experiment. Students will have a choice of research areas reflecting their intended dissertation domain. Depending on the research area selected, the relevant programming skills and advanced data analysis techniques will be introduced during the study-unit. Study-unit Aims: The aim of this study-unit is to provide hands on experience of creating, conducting and reporting experimental research within a Cognitive Science Laboratory. Learning Outcomes: 1. Knowledge & Understanding By the end of the study-unit the student will be able to: - grasp and appreciate the importance of programming as a necessary tool in the implementation of cognitive science experiments; - identify the range of possible programming control structures and select the most appropriate for implementing a given experiment (e.g., use of for loops, and if-then decisions); - identify which data analysis techniques are applicable for different experimental designs and select the most appropriate for specific experiments and tasks. 2. Skills By the end of the study-unit the student will be able to: - write and execute simple programming control structures in an appropriate coding environment (e.g., MatLab, Python, R); - adapt existing programs to run a cognitive science experiment; - implement appropriate data analysis; - report the results of an experiment to the standard required of a Cognitive Science journal. Main Text/s and any supplementary readings: Main Texts - Dienes, Z. (2008). Understanding psychology as a science: An introduction to scientific and statistical inference. Palgrave Macmillan. - Borgo, M., Soranzo, A., & Grassi, M. (2012). MATLAB for Psychologists. Springer Science & Business Media. Supplementary Reading - Baayen, H. R. (2008). Analyzing linguistic data. A practical introduction to statistics using R. Cambridge, UK: Cambridge University Press. - Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3), 255–278. https://doi.org/10.1016/j.jml.2012.11.001 |
||||||||
| STUDY-UNIT TYPE | Practical and Tutorials | ||||||||
| METHOD OF ASSESSMENT |
|
||||||||
| LECTURER/S | Holger Mitterer Ian M Thornton |
||||||||
|
The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
|||||||||