You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Predictive Mechanisms Are Not Involved the Same Way during Human-Human vs. Human-Machine Interactions: A Review 
Chapter Title:  Predictive Systems for Shared Action with Machines 
1. Abstract of Cited Paper (BIB001):  Awareness of actions is partly based on the intentions accompanying them. Thus, the awareness of self- and other-generated actions should differ to the extent that access to own and other's intentions differs. Recent studies have found a brain circuit (the mirrorneuron system) that represents self- and other-generated actions in an integrated fashion. This system does not respond to actions made by nonagents, such as machines. We measured the estimated onset time of actions that subjects either executed themselves or observed being executed by someone else or by a machine. In three experiments, the estimates of the machine actions always differed from those of self- and other-generated actions, whereas the latter two were indistinguishable. Our results are consistent with the view that intentions are attributed to others but not to machines. They also raise the interesting possibility that people attribute intentions to themselves in the same way as they do to others. 
2. Abstract of Cited Paper (BIB002):  A long-standing question within the robotics community is about the degree of human-likeness robots ought to have when interacting with humans. We explore an unexamined aspect of this problem: how people empathize with robots along the anthropomorphic spectrum. We conducted an experiment that measured how people empathized with robots shown to be experiencing mistreatment by humans. Our results indicate that people empathize more strongly with more human-looking robots and less with mechanical-looking robots. 
3. Abstract of Cited Paper (BIB003):  When we have to physically interact with a robot, the benchmark for natural and efficient performance is our experience of daily interactions with other humans. This goal is still far despite significant advances in human-robot interaction. While considerable progress is made in various areas ranging from improving the hardware over safety measures to better sensor systems, the research on basic mechanisms of interaction and its technical implementation is still in its infancy. In the following, we give an overview of our own work aiming at improving human-robot interaction and joint action. When humans jointly collaborate to achieve a common goal, the actions of each partner need to be properly coordinated to assure a smooth and efficient workflow. This includes timing of the actions, but also, in the case of physical interaction, the spatial coordination. We thus first investigated how a simple physical interaction, a hand-over task between two humans without verbal communication, is achieved. Our results with a human as receiver and both humans and robots as delivering agent show that both the position and the kinematics of the partner's movement are used to increase the confidence in predicting hand-over in time and space. These results underline that for successful joint action the robot must act predictably for the human partner. However, in a more realistic scenario, robot and human constitute a dynamic system with each agent predicting and reacting to the actions and intentions of the other. We therefore investigated and implemented an assembly task where the robot acts as assistant for the human. Using a Bayesian estimation framework, the robot predicts assembly duration in order to deliver the next part just in time.

Literature Review:
Robotic research has been deeply interested in optimizing human-machine interactions in order to enhance efficiency and safety. So far, studies had involved a large variety of different automated systems with varying complexity, from computers to robotic arms, from levers to full humanoids (see Figure 5 for an overview). Yet, there has been a trend toward machine humanization during these recent years. While some researchers took an interest in humanizing the external appearance of the machine BIB002 BIB003 , others have focused on its motor characteristics BIB001 BIB004 . Interestingly, current research tends to show that human-like machine-generated actions can engage the same predictive mechanisms that are brought into play during human interactions whereas this ability remain impaired during interactions with traditional automata.