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DRPS : Course Catalogue : School of Philosophy, Psychology and Language Sciences : Psychology

Undergraduate Course: Induction: Analogy, Learning, and Generalisation in Humans and Machines (PSYL10176)

Course Outline
SchoolSchool of Philosophy, Psychology and Language Sciences CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThis course covers topics in human and machine inductive inference, with particular emphasis on uniquely human level induction (analogy and cross-domain generalisation).
Course description In this course we will cover topics in human and machine inductive inference. In the first half of the course, students will be exposed to the problem of induction and how the problem manifests in a range of domains such as object recognition, categorisation, and learning. The focus of the course will then turn to analogy and relational reasoning, areas were humans make generalisations across situations and domains with much more success and flexibility than non-human animals and conventional machine learning approaches. We will cover research in analogical reasoning as well as the development of analogical thinking and the representations that support analogy and generalisation.

The second half of the course will focus on computational theories of how humans and artificial (i.e., machine) systems perform induction and generalisation. We will cover broadly the main approaches to representing knowledge and modelling human cognition (symbolic and connectionist models). We will then cover how these approaches have been leveraged to explain human induction and learning with focus on traditional production system models, Bayesian models, neural network models, and symbolic-connectionist models.

Students will engage with lectures and readings every week and will be asked to actively engage with and discuss the topics covered. Students will complete a midterm consisting of MCQs and a short essay (500 words), a set of unassessed formative formal exercises including responses to readings and applications of simplified versions of existing computational models, and a final essay (2000 words).
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Psychology 2A (PSYL08011) AND Psychology 2B (PSYL08012) AND Data Analysis for Psychology in R 2 (PSYL08015)
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesVisiting students should be studying Psychology as their degree major, and have completed at least 3 Psychology courses at grade B or above. We will only consider University/College level courses. Applicants should note that, as with other popular courses, meeting the minimum does NOT guarantee admission.

**Please note that upper level Psychology courses are high-demand, meaning that they have a very high number of students wishing to enrol in a very limited number of spaces.** These enrolments are managed strictly by the Visiting Student Office, in line with the quotas allocated by the department, and all enquiries to enrol in these courses must be made through the CAHSS Visiting Student Office. It is not appropriate for students to contact the department directly to request additional spaces.
High Demand Course? Yes
Course Delivery Information
Academic year 2023/24, Available to all students (SV1) Quota:  0
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 20, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 176 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Midterm: Exam - MCQs and Short Answers (30%). This is a non centrally arranged exam organised by the School.

Final: Coursework - 2000 word essay (70%)
Feedback 1. In class feedback will be used to check understanding and to develop skills (e.g. discussion points with students, peer feedback on reading responses).
2. The mid-course assessment will also provide feedback as to whether students have mastered the foundational content of the course.
3. Structured optional assignments (reading responses and exercises with simplified computational models) will allow students to gain experience with computational scientific models and applying these models to answering questions about cognition.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Appreciate the problem of induction and how it manifests broadly across the problems that humans need to solve routinely.
  2. Understand the processes behind human analogy making and how relational generalization supports broad inference.
  3. Understand the basics of the symbolic and connectionist approaches to developing psychological models.
  4. Understand and explain historic and current theories and models of inductive inference and learning.
  5. Understand and explain theories and models of representation learning and speak to how (or if) these models develop representations that support human level inference and mirror human developmental trajectories.
Reading List
Students on the course will mainly engage with the topics through reading the primary research literature. Examples of the types of readings that may be used in the course are provided below.

Austerweil, J. L., Gershman, S. J., Tenenbaum, J. B., & Griffiths, T. L. (2015). Structure and flexibility in Bayesian models of cognition. Oxford handbook of computational and mathematical psychology, 187-208.

Doumas, L. A., & Hummel, J. E. (2005). Approaches to modeling human mental representations: What works, what doesn't and why. In KJ Holyoak & RG Morrison (eds.), The Cambridge handbook of thinking and reasoning, 73-94.

Doumas, L. A. A., Puebla, G., Martin, A. E., & Hummel, J. E. (2022). A theory of relation learning and cross-domain generalization. Psychological Review.

Halford, G. S., Wilson, W. H., & Phillips, S. (2010). Relational knowledge: The foundation of higher cognition. Trends in cognitive sciences, 14(11), 497-505.

Holyoak, K. J. (2012). Analogy and relational reasoning. In KJ Holyoak & RG Morrison (eds.), The oxford handbook of thinking and reasoning

Thibodeau, P. H., Blonder, A., & Flusberg, S. J. (2020). A connectionist account of the relational shift and context sensitivity in the development of generalisation. Connection Science, 32(4), 384-397.
Additional Information
Graduate Attributes and Skills The course will develop students' skills in mastering primary scientific literature and thinking critically about data and theories. Students will learn about the development of theories of inductive reasoning and how these theories are instantiated in computational models. In addition, students will have the opportunity to develop their statistical and formal analysis, primarily as they apply to developing theories of cognition.
KeywordsNot entered
Course organiserDr Alex Doumas
Tel: (0131 6)51 1328
Course secretaryMiss Georgiana Gherasim
Tel: (0131 6)50 3440
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