Humans are experts in cooperating with each other when trying to accomplish tasks they cannot achieve alone. Recent studies of joint action have shown that when performing tasks together people strongly rely on the neurocognitive mechanisms that they also use when performing actions individually, that is, they predict the consequences of their co‐actor’s behavior through internal action simulation. Context‐sensitive action monitoring and action selection processes, however, are relatively underrated but crucial ingredients of joint action. In the present paper, we try (...) to correct the somewhat simplified view on joint action by reviewing recent studies of joint action simulation, monitoring, and selection while emphasizing the intricate interrelationships between these processes. We complement our review by defining the contours of a neurologically plausible computational framework of joint action. (shrink)

The current study highlights individual differences in the joint articulation strategies used by novices practicing a hip-hop dance movement, the wave. Twelve young adults, all naive regarding hip-hop dance performance, practized the wave in 120 trials separated into four blocks with the order of internal or external attentional focus counterbalanced across subjects. Various kinematic analyses were analyzed to capture performance success while exploiting the observed individual differences in order to establish the reliability of the proposed performance indicators. An external focus (...) of attention marginally facilitated the smooth transfer of a wave motion across neighboring limb segments as characterized by a constant propagation speed combined with large wave amplitudes. Systematic correlations between the success indicators were found, exemplifying the various degrees of joint articulation that novices prove capable of during an initial practicing session to try and perform a novel complex motor task. (shrink)

Even if we recognize that the delta-lognormal model provides an excellent fit to a large variety of data, the question remains as to what we actually learn from such a model, which could be seen as merely another multiparameter account? Do we welcome such an encompassing account, or do we expect to learn more from the limitations that become apparent when applying dedicated models addressing specific classes of movements?