Pre-requisite
Course Objective
On successful completion of this course, a student should be able to appreciate various research methods employed in cognitive science. The course will introduce the key concepts and techniques in qualitative, experimental and computational paradigms.
In the qualitative approach, focus will be on meaning making rather than information processing in the study of mind. Philosophical foundations of qualitative paradigm will be introduced. We will highlight how it complements rather than contradicts with experimental and/or computational approaches to research.
In the experimental approach, the student will be taught how to design his/her own experiment independently. The student will be made aware of the key concerns that must be kept in mind while conducting an experiment, and how one can translate a research hypothesis to testable experiments that can be relied upon.
Finally, in the module on the computational approach, we will expose students to computational data analysis and machine learning techniques deployed for data-driven research, and how the outputs of such techniques can be assessed and interpreted.
In all the modules, the students will learn how to frame a research question in order to address an important issue related to cognitive science.
Faculty
Course Content
In the module for qualitative methods, we will start by discussing the qualitative research paradigm, noting the ontological and epistemological assumptions and values of reflexivity and subjectivity. We then learn about planning and designing qualitative research and consider the sampling requirements and ethical concerns.
In the module for experimental methods, we will first introduce the assumptions behind experimentation and defining its nature and scope. We will then introduce topics such as control, randomization, design, factors, etc. that will equip the students to understand and test a scientific prediction using experimentation. The module will be a mix of lectures and practical sessions. Practical sessions will complement core concepts taught in the lectures by allowing the students to design their own experiment, collect data and do some preliminary analysis.
Finally, in the module for computational methods, we will introduce the students to some examples of data analytics and basic machine learning models.