Postgraduate Course: Seminar in Cognitive Modelling (INFR11210)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This 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. For specific topics, see the course web page or contact the instructor directly.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
It is RECOMMENDED that students have passed
Computational Cognitive Science (INFR10054)
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Co-requisites | |
| Prohibited Combinations | Students MUST NOT also be taking
Informatics Research Review (INFR11136)
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Other requirements | 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 as would be acquired in Computational Cognitive Science.
Data visualization and programming experience will be useful but there is no required programming. |
Information for Visiting Students
| Pre-requisites | 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 as would be acquired in Computational Cognitive Science.
Data visualization and programming experience will be useful but there is no required programming. |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2022/23, Available to all students (SV1)
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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 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
100% coursework.
Coursework will include a portfolio of weekly brief («200 words), engagement responses to readings and in-class discussions (30%), an essay in first semester (40%) and an oral presentation in the second semester (30%). Students will also be required to make a presentation in the first semester and will be provided feedback. |
| Feedback |
Written feedback on essays and portfolio.
Verbal feedback on presentations.
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- demonstrate understanding of a range of classic and current articles in cognitive science/modelling by summarizing and critiquing their central ideas and/or results.
- demonstrate understanding of the relationship between computational models and cognitive theories, by being able to critically assess the theoretical adequacy of a given model.
- compare and contrast the strengths and weaknesses of different models of the same behaviour.
- search the literature and synthesize information from several papers on the same topic and create a coherent oral presentation on that topic.
- communicate (written and oral) key findings in cognitive science/modelling to inter-disciplinary audiences.
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Reading List
| None provided. |
Additional Information
| Graduate Attributes and Skills |
Critical/analytical thinking, knowledge integration and application, independent learning, creativity, interpersonal skills, verbal, written and cross-disciplinary commmunication |
| Keywords | SCM,Cognitive science,cognitive modelling,science communication |
Contacts
| Course organiser | Dr Francis Mollica
Tel: (0131 6)50 4224
Email: |
Course secretary | Ms Lindsay Seal
Tel: (0131 6)50 2701
Email: |
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