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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Seminar in Cognitive Modelling (INFR11210)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThis course provides students an opportunity to explore their choice of topic in cognitive science in depth while honing their science communication skills and broadly surveying the foundations of cognitive science. The course aims to expose students to a variety of cognitive models (e.g., connectionist, Bayesian, quantum models) and to discuss and evaluate competing models for similar problems.

Students will be expected to present and critique classic and recent research articles from the cognitive modelling literature, chosen from a list provided by the instructor.
Course description The first semester will focus on developing research skills (finding / reading / reviewing literature and science communication) while surveying foundational topics in cognitive science. The second semester will focus specifically on evaluating and presenting cognitive models. Each semester is split into two parts. In the first part, the instructor will provide introductory information and background material, as well as information on how to develop skills in reading scientific papers and presenting them. In the second part, students will present papers, chosen from a list provided by the instructor (or approved by the instructor).

Topics covered by the instructor will include:
- How to read, analyse, and present research papers in cognitive modelling
- Example presentation(s) of papers
- Introduction and overview of modelling approaches/philosophies
- Model comparison and evaluation methods

Topics available for students to present will vary depending on the instructor. Topics may include: analogical reasoning, animal cognition, attention, biological motion, categorization, causality, communication, concepts, development, ecological considerations of modelling, event cognition, inductive reasoning, judgment & decision making, language, learning, memory, meta-cognition, number cognition, object cognition, physical reasoning, perception, problem solving, rationality, social reasoning, spatial cognition, specialization, theory of mind, temporal cognition etc.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites It is RECOMMENDED that students have passed Computational Cognitive Science (INFR10054) OR Computational Cognitive Neuroscience (UG) (INFR11233)
Prohibited Combinations Students MUST NOT also be taking Informatics Research Review (INFR11136) OR Seminar in Cognitive Modelling (UG) (INFR11237)
Other requirements MSc students must register for this course, while Undergraduate students must register for INFR11237 instead.

This course is only open to students in Informatics and PPLS whose DPT lists this course. PPLS students and PTs should take note of the "other requirements" if they cannot take the recommended co-requisite.

The course assumes knowledge of cognitive science and, by the second semester, knowledge of linear algebra (vectors / matrix multiplication, orthogonality, eigenvectors), probability theory (discrete and continuous univariate random variables, expectations, Bayes rule), statistics (linear / logistic regression) and model evaluation.

Data visualization and programming experience will be useful but there is no required programming.
Information for Visiting Students
Pre-requisitesAs above.
High Demand Course? Yes
Course Delivery Information
Academic year 2023/24, Available to all students (SV1) Quota:  40
Course Start Full Year
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 6, Seminar/Tutorial Hours 27, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 163 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Weekly brief («200 words) engagement responses to readings and in-class discussions (30%)
Essay in first semester (40%)
Oral presentation in the second semester (30%)
Feedback Written feedback on essays and portfolio.
Verbal feedback on presentations.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. demonstrate understanding of a range of classic and current articles in cognitive science / modelling by summarizing and critiquing their central ideas and/or results
  2. demonstrate understanding of the relationship between computational models and cognitive theories, by being able to critically assess the theoretical adequacy of a given model
  3. compare and contrast the strengths and weaknesses of different models of the same behaviour
  4. search the literature and synthesize information from several papers on the same topic and create a coherent oral presentation on that topic
  5. communicate (written and oral) key findings in cognitive science/modelling to inter-disciplinary audiences
Reading List
Additional Information
Graduate Attributes and Skills Critical / analytical thinking, knowledge integration and application, independent learning, creativity, interpersonal skills, verbal, written and cross-disciplinary communication
KeywordsSCM,Cognitive science,cognitive modelling,science communication
Course organiserDr Neil Bramley
Tel: (0131 6)50 4643
Course secretaryMs Lindsay Seal
Tel: (0131 6)50 2701
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