  Understanding which features humans rely on -- in visually recognizing action
similarity is a crucial step towards a clearer picture of human action
perception from a learning and developmental perspective. In the present work,
we investigate to which extent a computational model based on kinematics can
determine action similarity and how its performance relates to human similarity
judgments of the same actions. To this aim, twelve participants perform an
action similarity task, and their performances are compared to that of a
computational model solving the same task. The chosen model has its roots in
developmental robotics and performs action classification based on learned
kinematic primitives. The comparative experiment results show that both the
model and human participants can reliably identify whether two actions are the
same or not. However, the model produces more false hits and has a greater
selection bias than human participants. A possible reason for this is the
particular sensitivity of the model towards kinematic primitives of the
presented actions. In a second experiment, human participants' performance on
an action identification task indicated that they relied solely on kinematic
information rather than on action semantics. The results show that both the
model and human performance are highly accurate in an action similarity task
based on kinematic-level features, which can provide an essential basis for
classifying human actions.
