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

Reinforcement Learning

This part has the following subsections, it is best if you work through them in order:

1. Theory
   Here we will look at how we can define a model that can 1) learn about values of stimuli and 2) translate these values into choices.


2. Data plotting
   Before we delve into the modelling, let's have a good look at the data we acquired from the gambling task.


3. Simulating data
   Before we fit a model to data, we need to get to know our model. Here we will define a basic reinforcement learning model, and have a look at how this model behaves for different parameter values. In other words, we will let the model 'play' the task and assess what sort of behaviour it can and cannot capture. Next we will estimate the optimal parameter values for the model we specified, based on the data. This step allows us to quantify the 'identifiability' of the parameters.


4. Modelling real data
   Finally, we can look at our data! Just like in the simulated data we estimate the parameter values from the data that we collected from 'real' subjects. We will briefly touch upon looking at the parameter values, and think about the differences in behaviour from the simulated data and our real subjects.


5. Review
   We review the most important steps when modelling behavioural data.

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