Neurophysiological changes in visuomotor sequence learning provide insight in general learning processes: Measures of brain activity, skin conductance, heart rate and respiration

Abstract

Prior research has shown neurophysiological measures of learning yield large effect sizes, suggesting that these measures have high potential in providing insight into learning. Yet, most literature on learning and neurophysiological measures focused on a single outcome measure, neglecting the interplay between different types of measures. Additionally, it is not yet clear which measures change robustly in a way specific to the learning process. The current study assessed implicit visuomotor sequence learning through multiple neurophysiological outcome measures. In two experiments participants were presented with an arm-movement version of the Serial Reaction Time Task with blocks in which targets were selected in a repeating sequence and blocks in which targets were selected randomly. While participants were executing this task, measures of EEG, skin conductance, heart rate (variability) and respiration, in addition to measures of behavioral performance, were collected. Although behavioral performance was sensitive to sequence learning, as demonstrated by faster responses in sequence than in random blocks, neurophysiology was not sensitive to sequence learning. However, in both experiments, skin conductance level and parietal EEG alpha and gamma power were sensitive to task induction and changed during sequence blocks in the direction of a pre-task baseline and were related to behavioral performance. In general, models including only EEG parietal gamma power were just as powerful in explaining behavioral measures during learning as models including a combination of neurophysiological outcome measures. The findings of the current study demonstrate that neurophysiology is not sensitive to implicit sequence learning specifically, but that general learning effects on a visuomotor learning task are reflected in measures of neurophysiology. Additionally, the findings highlight that a combination of neurophysiological outcome measures is not necessarily better in explaining task learning than a single measure.