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Regular version of the site
2016/2017

Computational modelling

Type: Optional course (university)
Delivered by: School of Psychology
When: 1-3 module
Instructors: W.Joseph MacInnes
Language: English
ECTS credits: 2

Course Syllabus

Abstract

The goal of Psychology as a science is to develop and test accurate theories of the human mind/brain and has over the years established robust theories of cognition, memory, decision making, vision and attention, among others. Verbal theories, however, have two limitations: The language used to describe the theory can be imprecise, and the complexity of these theories continues to grow more difficult to capture as our understanding of the brain improves. The computational modelling approach to psychology is about providing a mathematical formalism of today’s best theories and forces a scientist to be precise in their description of psychological concepts. Models also allow psychologists to describe the brain as a complex system in ways similar to other fields like meteorology, physics economics. Finally, Models allow psychologists to connect to experiment data by statistically testing the predictions of the model against observed results in psychology and neuroscience. Models add a much needed component to the cycle of science: Theories describe our best understanding of the brain’s function; Models instantiate these theories and make predictions; experiments are designed which test these predictions; and results from these experiments are used to improve our theories and models. This class will be presented in three parts. The first section will focus on how models are used in psychology and how they differ from traditional statistical tests. The second section will present some of the most popular algorithms and architectures used in computational modeling today. The third section will focus on the computational modeling literature and examine the best models in various areas of psychology in recent years.
Learning Objectives

Learning Objectives

  • Understand the role of computational modelling in psychology and neuroscience
  • Read and understand the terminology used in published computational models
  • Receive hands on knowledge of a handful of algorithms used in the most popular models (Bayesian networks, and neural networks, for example)
  • To develop and test a model in an area of psychology of their choosing
Expected Learning Outcomes

Expected Learning Outcomes

  • Understand how computational models are created and tested, and how they contribute to the scientific process.
  • Know how models impact the current state of research in psychology.
  • Understand the basic workings of key algorithms
Course Contents

Course Contents

  • The role of models in psychology
  • Testing computational models
  • Methods and implementation Bayesian models
  • Current literature: models of consciousness
  • Current literature: models of attention and vision
  • Current literature: models of decision making
  • Current literature: models of memory
  • Methods and implementation of temporal models
  • Methods and implementation of neural net models
  • Methods and implementation of diffusion models
Assessment Elements

Assessment Elements

  • non-blocking assignments
  • non-blocking Final test
  • non-blocking Final project
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.3 * assignments + 0.4 * Final project + 0.3 * Final test
Bibliography

Bibliography

Recommended Core Bibliography

  • Gordon, B. M. (2011). Artificial Intelligence : Approaches, Tools, and Applications. New York: Nova Science Publishers, Inc. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=440805

Recommended Additional Bibliography

  • Busemeyer, J. R. (2015). The Oxford Handbook of Computational and Mathematical Psychology. Oxford: Oxford University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=958385