And in a precision movement task, requiring precise, speeded movements through an arc-shaped channel (Shmuelof, Krakauer & Mazzoni, 2012):

In a cursor-control task with a highly non-intuitive mapping (often described as “de novo” learning, since it requires learning a brand new controller rather than adapting an existing one; Haith, Yang, Pakpoor & Kita, 2002):

Across three quite different tasks, policy-gradient RL models of learning account very well for the patterns of improvement in the mean and variance of people’s actions across a range of tasks.

In Müller and Sternad’s skittles task (Sternad, Huber, Kuznetzov, 2014):

Policy-gradient RL is a simple, model-free RL method that is a pillar of impressive recent advances in robotics. Here, I show that a trial-by-trial learning rule based on policy-gradient RL accounts remarkably well for the way people improve at a skill through practice.

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