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Understanding human behaviour is a major challenge and a crucial step in solving many of the world’s most wicked problems. As access to computational power has grown and data sources have expanded computational modelling methods have become essential to progress in understanding cognitive and social processes. This course covers topics in perception, memory, language, reasoning, social structure and influence from a computational modelling perspective. For computer scientists, it illustrates how computational approaches can be used to capture and explain human behaviour. For behavioural scientists, it provides the skill set required to develop computational theories of cognition and social behaviour and to exploit new and emerging sources of digital data.
Intended learning outcomes
On completion of this subject, students should be able to:
- Recognise and identify key terms of cognitive processes including perception, memory, language and reasoning
- Explain and illustrate processes that give rise to social structure and influence
- Build, employ, and analyse computational models of cognitive and social processes
- Communicate and critique the outcomes of computational modelling exercises in written form.
- Think critically and coherently about complex problems in the behavioural sciences;
- Translate aims and objectives for investigating complex problems into measurable questions, hypotheses, findings, and answers;
- Evaluate both various kinds of evidence to support answers to, and inferences about, complex problems;
- Synthesise and present evidence in meaningful and interpretable ways for others to comprehend;
- Critically recognise and articulate various limitations when investigating complex problems.
Last updated: 31 January 2024