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

Probability and Likelihood

Say I toss a coin several times, and your job is to work out the probability that the coin comes up "heads" on any given toss.

Let's assume that you don't have any prior expectations about the probability of heads (i.e., you think the coin is just as likely to be weighted as fair).

I toss the coin 5 times and get the following outcomes:

H H T H T

What should I believe about the fairness, or otherwise, of the coin?

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