Additionally, common statistical approaches for understanding animal movement are based on assumptions of independence, such that crucial features of movement such as spatio-temporal autocorrelation are handled by discarding valuable data or handling it in ad-hoc ways [38], [53], [54].
At the same time our findings predict independent encoding of the temporal features of movement sequences within the same areas as the spatial features.
The use of factor analysis allowed us to include the key features of movement patterns re-expressed as factor variables in the multivariable analyses.
These motor commands only parametrize the significant spatiotemporal features of movement trajectories (e.g. extent, shape, timing) as needed by our motor control engine ACE [ 16].
Overall, the independent encoding of the spatial and temporal features of movement sequences in premotor areas endows the nervous system with the ability for adjustments of individual movement parameters which would not be possible with a fixed integrated representation such as in M1 alone.
With spatial and temporal features of movement trajectories emerging from the same local circuits, an integrated representation as proposed in dynamical systems models (Shenoy et al., 2013) cannot explain the flexible transfer or independent adaptation of spatial and temporal features reported here and in previous studies (Ullén and Bengtsson, 2003; Ali et al., 2013; Kornysheva et al., 2013).
The concept was popularized by Jamie Beck and Kevin Burg, fashion photographers who created the dramatic moving images that feature areas of movement in otherwise static scenes.
In contrast with previous studies that were interested in the effect of kinematic/time features of movements on their visual perception, we focused on geometric/spatial features, and therefore used a static visual display.
However, some movements such as tracing, scribbling, or even reaching are also defined by their geometric features [23] [26], and it can be hypothesized that motor-perceptual interactions are evoked not only by the time-varying features of movements, but also by their geometrical features, that is the shape of the path they form.
This strategy can be adapted for encoding motion, where the first two components become the set of movements and the features of movements.
