Data from: Automatic learning mechanisms for flexible human locomotion

Movement flexibility and automaticity are necessary to successfully
navigate different environments. When encountering difficult terrains such
as a muddy trail, we can change how we step almost immediately so that we
can continue walking. This flexibility comes at a cost since we initially
must pay deliberate attention to how we are moving. Gradually, after a few
minutes on the trail, stepping becomes automatic so that we do not need to
think about our movements. Canonical theory indicates that different
adaptive motor learning mechanisms confer these essential properties to
movement: explicit control confers rapid flexibility, while forward model
recalibration confers automaticity. Here, we uncover a distinct mechanism
of treadmill walking adaptation – an automatic stimulus-response mapping –
that confers both properties to movement. The mechanism is flexible as it
learns stepping patterns that can be rapidly changed to suit a range of
treadmill configurations. It is also automatic as it can operate without
deliberate control or explicit awareness by the participants. Our findings
reveal a tandem architecture of forward model recalibration and automatic
stimulus-response mapping mechanisms for walking, reconciling different
findings of motor adaptation and perceptual realignment.