A primitive-based representation of dance: modulations by experience and perceptual validity.

The generation of complex movements such as dance might be possible due to the utilization of movement building blocks, i.e., movement primitives. However, it is largely unexplored how the temporal structure of a movement sequence and the recruitment of these primitives change with experience. Therefore, we obtained a representation of primitives with the temporal movement primitive model from the motion capture data of dancers with varying experiences, both for improvised and choreographed movements (elements from contemporary/modern/jazz) with different qualitative expressions. We analyzed differences between movement conditions regarding the number of temporal segments and the number of primitives, as well as their association with dance experience. Especially for the choreography with a neutral expression, the results indicate a negative association between experience and the number of segments and a positive association between experience and the number of primitives. The variation in the recruitment of these primitives suggests an increased consistency of modular control with experience, particularly for improvised dance. A prerequisite for the meaningful interpretation of these results regarding human movement production is that the model can generate perceptually valid dance movements. This was confirmed in a subsequent experiment, although the validity was slightly impaired for improvised movements. Overall, the results of the choreographed movement sequences suggest that experience is associated with an increase in motor repertoire that might facilitate fewer and longer temporal segments.NEW & NOTEWORTHY This study demonstrates that a temporal movement primitive model, trained with movements performed by dancers with different levels of experience, is able to generate natural-looking dance movements. The results suggest that motor experience in dance is associated not only with fewer temporal segments but also with an increase in the number of underlying movement building blocks. The recruitment of these primitives, which might be used to simplify movement production, additionally seems to become more consistent with experience.

Muscle activity in explicit and implicit sequence learning: Exploring additional measures of learning and certainty via tensor decomposition.

Sequence learning in serial reaction time tasks (SRTTs) is usually inferred through the reaction time measured by a keyboard. However, this chronometric parameter offers no information beyond the time point of the button-press. We therefore examined whether sequence learning can be measured by muscle activations via electromyography (EMG) in a dual-task paradigm. The primary task was a SRTT, in which the stimuli followed a fixed sequence in some blocks, whereas the sequence was random in the control condition. The secondary task stimulus was always random. One group was informed about the fixed sequence, and the other not. We assessed three dependent variables. The chronometric parameter premotor time represents the duration between stimulus onset and the onset of EMG activity, which indicates the start of the response. The other variables describe the response itself considering the EMG activity after response start. The EMG integral was analyzed, and additionally, tensor decomposition was implemented to assess sequence dependent changes in the contribution of the obtained subcomponents. The results show explicit sequence learning in this dual-task setting. Specifically, the informed group show shorter premotor times in fixed than random sequences as well as larger EMG integral and tensor contributions. Further, increased activity seems to represent response certainty, since a decrease is found for both groups in trials following erroneous responses. Interestingly, the sensitivity to sequence and post-error effects varies between the subcomponents. The results indicate that muscle activity can be a useful indicator of response behavior in addition to chronometric parameters.