An important challenge in developing a social robot is making the interaction between human and robot to be more pleasant and convenient. It could be obtained by making the robot to develop, i.e. change its behavior over the course of time. Here we study two aspects of development: behavioral adaptation and behavioral complexity to which we refer as growth. In this study, the adaptive behavior was implemented as a finite state machine with probability state transitions shaped by human feedback, while the behavioral growth was implemented as an unlocking behavior stages approach which was inspired by the development capability theory. The goal of this study is to examine if there is a significant effect of the adaptation and growth mechanism on human perceptions of aliveness, learning ability, and the behavior shaping control; and moreover how these perceptions influence interaction experience. We used a NAO robot for our studies. There were four conditions experimented from combination of adaptive and growing behavior. Twenty four (24) participants joined to interact with the robot in a within-subject experiment design where each participant interacted in two different conditions. As a result, we did not find a significant effect of the behavior manipulation in the experiment towards the measured perceptions. However, there is a significant positive correlation between the perception of learning ability and interaction experience.