  We investigate interaction patterns for humans interacting with explainable
and non-explainable robots. Non-explainable robots are here robots that do not
explain their actions or non-actions, neither do they give any other feedback
during interaction, in contrast to explainable robots. We video recorded and
analyzed human behavior during a board game, where 20 humans verbally
instructed either an explainable or non-explainable Pepper robot to move
objects on the board. The transcriptions and annotations of the videos were
transformed into transactions for association rule mining. Association rules
discovered communication patterns in the interaction between the robots and the
humans, and the most interesting rules were also tested with regular chi-square
tests. Some statistically significant results are that there is a strong
correlation between men and non-explainable robots and women and explainable
robots, and that humans mirror some of the robot's modality. Our results also
show that it is important to contextualize human interaction patterns, and that
this can be easily done using association rules as an investigative tool. The
presented results are important when designing robots that should adapt their
behavior to become understandable for the interacting humans.
