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:  Joint actions with humanized automated systems 
1. Abstract of Cited Paper (BIB001):  The current study investigated the properties of stimuli that lead to the activation of the human mirror neuron system, with an emphasis on those that are critically relevant for the perception of humanoid robots. Results suggest that robot actions, even those without objects, may activate the human mirror neuron system. Additionally, both volitional and nonvolitional human actions also appear to activate the mirror neuron system to relatively the same degree. Results from the current studies leave open the opportunity to use mirror neuron activation as a 'Turing test' for the development of truly humanoid robots. 
2. Abstract of Cited Paper (BIB002):  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. 
3. Abstract of Cited Paper (BIB003):  In human‐human interactions, corepresenting a partner’s actions is crucial to successfully adjust and coordinate actions with others. Current research suggests that action corepresentation is restricted to interactions between human agents facilitating social interaction with conspecifics. In this study, we investigated whether action corepresentation, as measured by the social Simon effect (SSE), is present when we share a task with a real humanoid robot. Further, we tested whether the believed humanness of the robot’s functional principle modulates the extent to which robotic actions are corepresented. We described the robot to participants either as functioning in a biologically inspired human-like way or in a purely deterministic machine-like manner. The SSE was present in the human-like but not in the machine-like robot condition. These findings suggest that humans corepresent the actions of nonbiological robotic agents when they start to attribute human-like cognitive processes to the robot. Our findings provide novel evidence for top-down modulation effects on action corepresentation in human‐robot interaction situations. 
4. Abstract of Cited Paper (BIB004):  Humans are very good at interacting with each other. This natural ability depends, among other factors, on an implicit communication mediated by motion observation. By simple action observation we can easily infer not only the goal of an agent, but often also some “hidden” properties of the object he is manipulating, as its weight or its temperature. This implicit understanding is developed early in childhood and is supposedly based on a common motor repertoire between the cooperators. In this paper, we have investigated whether and under which conditions it is possible for a humanoid robot to foster the same kind of automatic communication, focusing on the ability to provide cues about object weight with action execution. We have evaluated on which action properties weight estimation is based in humans and we have accordingly designed a set of simple robotic lifting behaviors. Our results show that subjects can reach a performance in weight recognition from robot observation comparable to that obtained during human observation, with no need of training. These findings suggest that it is possible to design robot behaviors that are implicitly understandable by nonexpert partners and that this approach could be a viable path to obtain more natural human-robot collaborations. 
5. Abstract of Cited Paper (BIB005):  When interacting with a robot, the level of mental workload, comfort and trust during the interaction are decisive factors for an effective interaction. Hence, current research focuses on the concept, whether ascribing gantry robot in assembly with anthropomorphic movements can lead to a better anticipation of its behavior by the human operator. Therefore, in an empirical study the effect of different degrees of anthropomorphism should be compared. This is based on the scientific research concerning the neural activity of the human brain when watching someone performing an action. Within the study videos of a virtual gantry robot and a digital human model during placing movements were designed to use in the functional magnetic resonance imaging (fMRI). The aim is to investigate the underlying brain mechanism during observing the movements of the two models.

Literature Review:
Additionally, BIB003 have shown that during hand-over interactions between a human being and a robotic arm, the predictability of the robotic arm motions for the human was strongly dependent on the automaton's motion laws and physical appearance BIB003 . Indeed, when the robotic arm was handing on a cube to the human seated in front of it, his or her reaction times to grasp the object were faster when the robot assumed human-like kinematics in comparison with a trapezoidal joint velocity, meaning that individuals were able to better predict the observed human-like movement endpoints. Interestingly, the effect of the kinematic profile on the reaction times was modulated by the external appearance of the robot: the humanoid robot led its human partner to have faster reaction times than the industrial robot system. Moreover, the reaction times tended to be faster when the robotic arm had a typical robotic motion profile but a humanoid appearance than when the robotic arm had a human-like kinematic but an industrial appearance BIB003 ). Thus, lowlevel sensorimotor information processing can be overweighted by high-level cognitive adjustment mechanisms that take into account the physical appearance of the partner. Alternatively, it can be possible that the participants were differently engaged in the interaction depending on whether they were acting with a human-like partner or a clearly non-human-like partner so BIB006 , (D) a human-like robotic arm in BIB002 , (E) a humanoid with torso, eyes-cameras, arms, and fingers in BIB004 and (F) a humanoid with torso, a human-like face, arms, and fingers in BIB005 . FIGURE 6 | Experimental setting of the Social Simon task in the study of BIB004 . that reaction times were faster when the robot had a humanoid appearance. Indeed,  mentioned that to recognize the other as similar to oneself and also as a potential agent is a prerequisite to engaging in a collaborative activity. In any case, this result emphasizes the role of predictive mechanisms in human-robot interaction. Research has also been focused on shared representation that occurs during action execution and observation. More specifically, BIB001 were interested in the motor interference phenomenon that occurs when an individual has to perform an action while perceiving a different one. In such a case, the execution of the action is hindered because of the strong interdependence of the brain areas involved in action perception and in action execution. In their study, participants had to perform a rhythmic arm movement while observing another human or DB, a humanoid robot (with a torso, a head and two arms with hands devoid of fingers, each arm having seven degrees of freedom). In the congruent condition, the two agents had to perform rhythmic movements, each in a similar direction (e.g., two vertical motions, or two horizontal motions). In the incongruent condition, the two agents had to perform rhythmic movements, each in perpendicular directions (e.g., one vertical motion and one horizontal motion). The authors made the kinematic of the robot vary so that the robot kinematics could adopt biological or robotic motion laws. Motor interference was measured as the variability of the participants' kinematics. The results showed that the interference effect (more deviations in the participants' kinematics when the two agents performs different actions) occurred only when the robot had human-like motion, suggesting that individuals are less sensitive to artificial motions. During a joint action with a humanoid robot, like a synchrony task for example, we implicitly include our partner action into our own motor plan, if this observed action obeys biological laws. Artificial motion does not induce such shared representations, even if the robot has an anthropomorphic appearance.