Original Research: Human Reinforcement Learning in Static vs Dynamic Environments
In this series on Original Research, I will be sharing about my findings from some of the mini-projects that I have carried out on my own.
In life, we are accustomed to the idea that practicing on variations of the same task prepares us for scenarios that we have yet to encounter. For example, students learning algebra should solve different types of problems to understand the underlying mathematical concepts, and pilots learning to fly a plane should be exposed to different in-flight emergencies to know what to do when something out of the norm occurs.
These examples are the embodiment of function learning, which is essentially reinforcement learning based on specific functions (Schulz et al., 2016). However, what happens when there is an unexpected change in the function that has been learnt? Will people be able to adapt to the change? Do people really adapt better if they were learning in an environment with variations?
This study attempted to investigate these questions by examining how reinforcement learning works in a spatially correlated multi-armed bandit task that undergoes a function change. The function change is represented by a change in either the state function or reward function. Before the function change, participants were also subjected…
