Is it possible to evaluate the moral cognition of artificial agents? In this work, we take inspiration from developmental and comparative psychology and develop a behavior-based analysis to evaluate one aspect of moral cognition---when an agent 'does the right thing for the right reasons.' We argue that, regardless of the nature of agent, morally-motivated behavior should persist despite mounting cost; by measuring an agent's sensitivity to this cost, we gain deeper insight into their underlying motivations. We apply this evaluation scheme to a particular set of deep reinforcement learning agents that can adapt to changes in cost. Our results shows that agents trained with a reward function including other-regarding preferences perform helping behavior in a way that is less sensitive to increasing cost than agents trained with more self-interested preferences. This project showcases how psychology can benefit the creation and evaluation of artificial moral cognition.