Models of Learning

• Introduction

  1. Task description

• Reinforcement Learning

  1. Theory
     • Rescorla-Wagner
     • SoftMax
  2. Data plotting
  3. Simulation
     • Example participants
     • More participants
     • Explore Parameters
     • Grid Search
     • Marginal distributions
     • Summary values
  4. Real data
     • Fit the data
     • Statistical inference
  5. Review
     • Modelling how-to

• Bayesian Learning

  1. Coin tossing
     • Probability
     • Likelihood
     • Uncertainty
     • Learning
     • Review
  2. Bandit task
     • Updating
     • Transition function
     • Learning rate

Developed by
Jill O'Reilly
Hanneke den Ouden
-August 2015

Hi!

Welcome to the wonderful world of behavioural modelling.

In this tutorial, you will get a taste for how the use of computational models may help us understand behaviour. We aim to give you a flavour of the mechanics and different steps involved. We chose to focus on very basic learning models from two classes of models: one reinforcement learning model, and one normative Bayesian learning model, and show you how you can start to use these models understand your subjects’ behaviour. Rather than purporting to be complete, we will hand you the tools to start thinking and reading for yourself.

This tutorial consists of 2 sections that can be completed independently. Section A is on standard reinforcement learning models (mainly written by Hanneke), and section B on Bayesian learning models (mainly written by Jill). If you do both, we recommend that you complete them in this order.

Enjoy!

Jill & Hanneke

To complete this tutorial, you will need

• Some basic matlab knowledge
• Matlab license
• Ideally the matlab toolbox Psychtoolbox
• Tutorial task code and data, which you can download here.
  This downloads the matlab code to run the task and tutorial, as well as 43 datasets. These datasets were collected from students of the 2015 Computational Approaches to Cognitive Neuroscience Summerschool at the Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen.

Getting started

To get the most out of this tutorial we strongly recommend you start by playing the brief gambling game provided (this is what you need Psychtoolbox for).

• Start up Matlab and change directories (cd) to the RLtutorial folder
• To start the game, in the command window type:

  >> cd taskReversalLearn
  >> revlRun(XX)

  Where XXX is a subject number that you can choose yourself, between 100 and 999.
• Read the instructions carefully and try to do your best in playing the game.
• When you are done, your data will be saved to the data folder, which also contains the 43 other datasets.

Click here for a debrief on the gambling task - No peeking until you have done the task!