Abstract
Reinforcement learning models are excellent models of learning in a variety of tasks. Many researches are interested in relating parameters of reinforcement learning models to psychological or neural variables of interest. However, these parameters are difficult to estimate reliably because the predictions of the model about choice change slowly with changes in the parameters. This identifiability problem has a large impact on power: we show that a researcher who wants to detect a medium sized correlation (r = .3) with 80% power between a psychological/neural variable and learning rate must collect 60% more subjects in order to account for the noise introduced by model fitting. We introduce a method that exploits the information contained in reaction times to constrain model fitting and show using simulation and empirical data that it improves the ability to recover learning rates.
Footnotes
Declarations of Interest: None.