Data for: Exploration-based learning of a stabilizing controller predicts locomotor adaptation

Humans can adapt to walking under widely different conditions. In the
manuscript 'Exploration-based learning of a stabilizing controller
predicts locomotor adaptation', we advance such a model of locomotor
learning and adaptation, consisting of a stabilizing feedback controller
at its core, which is modified gradually by an exploration-driven
reinforcement learner while using memory to store and use advantageous
strategies. We performed two prospective experimental studies to test some
predictions of the model and the dataset herein provides the relevant data
for these prospective experiments, all involving human participants
walking on a split-belt treadmill. A split-belt treadmill has two belts
side-by-side, with the participant walking with one foot on each belt and
the two belts being run at equal speeds (tied belt condition) or unequal
speeds (split-belt condition). The data corresponds to human participants
walking under the following three conditions: (1) Split belt adaptation
protocol with no belt noise: the two belts running at constant unequal
speeds, (2) Split belt adaptation protocol with noisy belt speeds: the two
belts running at unequal speeds, with the faster belt changing speed about
every second, and (3) Split belt adaptation protocol to test interference
due to a previously experienced opposite perturbation: the protocol is
TBA, where T stands for a tied belt condition, followed by a split-belt
condition B with constant unequal speeds, and followed by another
split-belt condition A, which has the same speeds of the two belts as B,
but switched between the left and right belts. Three data files are
provided, one for each of the three walking conditions. They are all
MATLAB .mat files. They can be opened and read in MATLAB (any version
after 2017) using the load command or with free software such as Octave or
Python. Each of these files has a single variable heelPos (referring to
the fore-aft heel position), stored as an array, one element for each
subject, with separate time series for leg1 and leg2 as fields of a MATLAB
struct. For instance, heelPos{2}.leg1 provides the motion data for leg1
and participant 2.