A Computational Framework for Motor Skill Acquisition
  There have been numerous attempts in explaining the general learning
behaviours by various cognitive models. Multiple hypotheses have been put
further to qualitatively argue the best-fit model for motor skill acquisition
task and its variations. In this context, for a discrete sequence production
(DSP) task, one of the most insightful models is Verwey's Dual Processor Model
(DPM). It largely explains the learning and behavioural phenomenon of skilled
discrete key-press sequences without providing any concrete computational basis
of reinforcement. Therefore, we propose a quantitative explanation for Verwey's
DPM hypothesis by experimentally establishing a general computational framework
for motor skill learning. We attempt combining the qualitative and quantitative
theories based on a best-fit model of the experimental simulations of
variations of dual processor models. The fundamental premise of sequential
decision making for skill learning is based on interacting model-based (MB) and
model-free (MF) reinforcement learning (RL) processes. Our unifying framework
shows the proposed idea agrees well to Verwey's DPM and Fitts' three phases of
skill learning. The accuracy of our model can further be validated by its
statistical fit with the human-generated data on simple environment tasks like
the grid-world.
