| Module Title |
Computational Psychiatry |
| Module Code |
PSY1102 |
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Faculty |
Psychology |
School |
Science & Health |
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NFQ level |
8 |
Credit Rating |
5 |
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Description
The aim of this module is to present the student with key theoretical and empirical sources to support their understanding of how computational methods combine with a variety of data types to identify and understand neural differences, predict behaviour and effectively treat clinical disorders.
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Learning Outcomes
1. Demonstrate an understanding of how differing computational methods combine with a variety of data types to identify and understand neural differences, predict behaviour and effectively treat clinical disorders
2. Demonstrate a critical appreciation of current cutting edge research in cognitive neuroscience and computational psychiatry
3. Demonstrate competency in theory-driven and data-driven approaches either separately or in combination to understand or predict behaviour
4. Demonstrate an understanding in how experimental design and environmental manipulation/control contribute to the field of computational neuroscience and psychiatry
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| Workload | Full time hours per semester | | Type | Hours | Description |
|---|
| Lecture | 24 | Lecture
Based on indicative content and learning outcomes | | Seminars | 11 | Post Lecture
Moderator and student-led tutorials | | Tutorial | 4 | Companion Tutorials
Moderator and student-led tutorials | | Independent Study | 86 | Self directed learning including exam preparation |
| Total Workload: 125 |
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| Section Breakdown | | CRN | 21170 | Part of Term | Semester 2 | | Coursework | 0% | Examination Weight | 0% | | Grade Scale | 40PASS | Pass Both Elements | Y | | Resit Category | RC1 | Best Mark | N | | Module Co-ordinator | Catherine Fassbender | Module Teacher | |
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| Assessment Breakdown |
| Type | Description | % of total | Assessment Date |
|---|
| Assignment | Critical Review/Journal Critique: This assessment will assess students' ability to critically evaluate state-of-the-art approaches in the field (theory-driven or data-driven methods) for elucidating clinical disorders. | 50% | n/a | | In Class Test | MCQs and short form answers addressing LOs | 50% | n/a |
| Reassessment Requirement Type |
Resit arrangements are explained by the following categories;
RC1: A resit is available for both* components of the module.
RC2: No resit is available for a 100% coursework module.
RC3: No resit is available for the coursework component where there is a coursework and summative examination element.
* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment
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Pre-requisite |
None
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Co-requisite |
None |
| Compatibles |
None |
| Incompatibles |
None |
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All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml
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Indicative Content and Learning Activities
Computational Psychiatry - Understanding, Predicting and Targeting Candidate Treatments
Developmental and Lifespan aspects; Candidate endophenotypes; Biomarkers. Theory driven versus data driven approaches to modelling behaviour.
Machine Learning: Computational Modelling
Typical and atypical decision-making
Theoretical Issues in Computational Psychiatry
Dimensions of functioning in human behaviour
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Indicative Reading List
Books:
- Alan Anticevic, John D Murray eds: 2018, Computational Psychiatry: Mathematical Modeling of Mental Illness., Academic Press,
- Anderson, Britt: 2014, Computational Neuroscience and Cognitive Modelling A Student's Introduction to Methods and Procedures, SAGE,
- Busemeyer, J. R., & Diederich, A: 2010, Cognitive Modelling, SAGE,
- Lee, M. D., & Wagenmakers, E. J.: 2014, Bayesian cognitive modeling: A practical course, Cambridge University Press.,
- Peggy Series: 2020, Computational Psychiatry: A Primer., MIT Press,
- Randall O’Reilly: 2020, Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain., MIT Press,
Articles:
None |
Other Resources
None |
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