Tutorial 1: Learning to Predict
Tutorial 1: Learning to Predict
This tutorial presents how to estimate state-value functions in a classical conditioning paradigm using Temporal Difference (TD) learning and examine TD-errors at the presentation of the conditioned and unconditioned stimulus (CS and US) under different CS-US contingencies. These exercises will provide you with an understanding of both how reward prediction errors (RPEs) behave in classical conditioning and what we should expect to see if dopamine represents a "canonical" model-free RPE.
Topics covered in this lesson
- Using the standard tapped delay line conditioning model
- How reward prediction errors (RPEs) move to conditional stimulus (CS)
- How variability in reward size affects RPEs
- How differences in conditioned and unconditioned stimulus (US-CS) timing affect RPEs
External Links
Prerequisites
Experience with Python Programming Language
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