# PSYC 6229. Statistical modelling of perception and cognition

**Department of Psychology**

York University

Winter term 2016

Tuesday and Thursday, 11:30-1:00

York University

Winter term 2016

Tuesday and Thursday, 11:30-1:00

**• Syllabus**

**General description:**This graduate course covers fundamental statistical concepts and their application to statistical modelling in psychology. Topics in statistical foundations include probability, random variables, common statistical distributions, and Bayes' theorem. To illustrate these concepts we cover classic statistical models of behaviour and physiology, such as signal detection theory, optimal cue combination, diffusion models of reaction times, probability summation, and ideal observers. We also discuss model fitting and testing, e.g., parameter estimation, bootstrapping, goodness of fit, and model selection. The course uses R, a statistical programming language, for illustrations and problems.

## Lecture notes

• Topic 1. Probability
| problems | solutions | ||

• Topic 2. Data structures in R
| code | problems | solutions | MATLAB-R MATLAB-Python-R |

• Topic 3. Calculus
| code | problems | solutions | calculus notes |

• Topic 4. More programming in R
| code | problems | solutions | |

• Topic 5. Random variables
| code | problems | solutions | |

• Topic 6. Signal detection theory
| code | problems | solutions | Wickens Gescheider |

• Topic 7. Cue combination
| code | problems | solutions | Alais |

• Topic 8. Maximum likelihood
| code | problems | solutions | |

• Topic 9. The general linear model
| code | problems | solutions | |

• Topic 10. Ideal observers
| code | problems | solutions | Geisler |

• Topic 11. Model testing
| code | problems | solutions | Wichmann |

## Tests and problem sets

**•**Test bank

**•**Problem set 1 solutions

**•**Problem set 2 solutions