Reinforcement-based processes actively regulate motor exploration along redundant solution manifolds

From a baby’s babbling to a songbird practicing a new tune, exploration is critical to motor learning. A hallmark of exploration is the emergence of random walk behaviour along solution manifolds, where successive motor actions are not independent but rather become serially dependent. Such exploratory random walk behaviour is ubiquitous across species, neural firing, gait patterns, and reaching behaviour. Past work has suggested that exploratory random walk behaviour arises from an accumulation of movement variability and a lack of error-based corrections. Here we test a fundamentally different idea—that reinforcement-based processes regulate random walk behaviour to promote continual motor exploration to maximize success. Across three human-reaching experiments, we manipulated the size of both the visually displayed target and an unseen reward zone, as well as the probability of reinforcement feedback. Our empirical and modelling results parsimoniously support the notion that explorato..., Data was collected using a Kinarm and processed using Kinarm's Matlab scripts. The output of the Matlab scripts was then processed using Python (3.8.13) and stored in custom Python objects. , , # Reinforcement-Based Processes Actively Regulate Motor Exploration Along Redundant Solution Manifolds

[https://doi.org/10.5061/dryad.ngf1vhj10](https://doi.org/10.5061/dryad.ngf1vhj10)

All files are compressed using the Python package dill. Each file contains a custom Python object that has data attributes and analysis methods. For a complete list of methods and attributes, see Exploration\_Subject.py in the repository
https://github.com/CashabackLab/Exploration-Along-Solution-Manifolds-Data

Files can be read into a Python script via the class method \"from\_pickle\" inside the Exploration\_Subject class.