# A Computational Framework for Motor Skill Acquisition

## TL;DR

This paper introduces a computational framework combining model-based and model-free reinforcement learning to explain motor skill acquisition, aligning with established theories and validated against human data.

## Contribution

It provides the first quantitative computational model that unifies Verwey's Dual Processor Model with reinforcement learning theories for motor skill learning.

## Key findings

- The framework aligns well with Verwey's DPM and Fitts' three phases of skill learning.
- Model fits human data on simple environment tasks like grid-world.
- The approach offers a quantitative basis for understanding motor skill acquisition.

## Abstract

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.