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:  Passive observation of 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):  Abstract Changes in power in the mu frequency band (8–13 Hz) of the electroencephalogram (EEG) is thought to indirectly reflect the activity of mirror neurons in premotor cortex. Activation of these neurons by self-performed, observed or imagined motor actions is assumed to produce asynchronous firing and a reduction in mu rhythm oscillation (referred to as mu suppression) in sensorimotor cortex. A recent fMRI study by Saygin et al. [Saygin AP, Wilson SM, Hagler Jr DJ, Bates E, Sereno MI. Point-light biological motion perception activates human premotor cortex. J Neurosci 2004;24:6181–8] revealed that the premotor brain regions containing mirror-neurons are also activated in response to point-light human motion. The perceived movement of these light cues are integrated into one percept of a complete human action (e.g. jumping jacks), rather than seen as individual moving lights. The present study examined whether recruitment of the mirror neuron system, as reflected in mu rhythm suppression, mediates recognition of point-light biological motion. Changes in mu power were recorded while subjects viewed point-light biological motion videos, matched scrambled versions of these animations, and visual white-noise (baseline). The results revealed that point-light biological animations produced mu suppression relative to baseline, while scrambled versions of these animations did not. This supports the hypothesis that the mirror neuron system is involved in inferring human actions by recovering object information from sparse input. 
3. Abstract of Cited Paper (BIB003):  In humans and monkeys the mirror neuron system transforms seen actions into our inner representation of these actions. Here we asked if this system responds also if we see an industrial robot perform similar actions. We localised the motor areas involved in the execution of hand actions, presented the same subjects blocks of movies of humans or robots perform a variety of actions. The mirror system was activated strongly by the sight of both human and robotic actions, with no significant differences between these two agents. Finally we observed that seeing a robot perform a single action repeatedly within a block failed to activate the mirror system. This latter finding suggests that previous studies may have failed to find mirror activations to robotic actions because of the repetitiveness of the presented actions. Our findings suggest that the mirror neuron system could contribute to the understanding of a wider range of actions than previously assumed, and that the goal of an action might be more important for mirror activations than the way in which the action is performed. 
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:
Behavioral studies indicate that individual's predictive abilities increase when they observe automata that are implemented with human-like motion laws BIB005 . For example, BIB005 investigated individual's ability to infer an object weight from an observed robotic-generated action directed toward the object. In their study, participants had to watch movies where either a human or a humanoid robot iCub (with a torso, an anthropomorphic face and two arms with five fingers, each hand having nine degrees of freedom) was manipulating an object. The object was identical in all movies but its weight could vary from 100 to 400 g. The authors manipulated the kinematics of the robot, replicating a human-like biological correlation between action kinematics and object weigh (see "proportional condition" in the paper). After each movie, the participants had to judge the perceived weight of the object, with a rating scale from 1 to 9 corresponding to weights from 50 to 450 g. The authors showed that participants could infer the mass of the object from the robot's action as accurately as from the observation of the human lifting. This supports that observers are sensitive to biological motion, and by simulating the observed action that is part of their own motor repertoire (through the forward model), they can infer an object property that is not accessible through vision, with the help of their motor expertise (through the inverse model). This is minimized when the robot motion is not human-like, even if it has a humanoid physical appearance. At the cerebral level, the issue of whether or not biological motion can activate the observer's predictive mechanisms remains controversial. Using fMRI, BIB006 asked participants to watch videos showing the arm of an agent displacing a cylinder. The physical appearance of the arm was either human or industrial robotic and the motion kinematic was either human-like or robotic-like. The results revealed that brain areas involved in predictive mechanisms were activated during the observation of the human-like and the robotic-like actions, with a stronger activation for the former BIB006 . Likewise, using electroencephalographic recording on volunteers who were watching a video showing a humanoid robotic arm grasping a ball with human-like motion properties or performing the very same action in the absence of the object, it has been shown that the observer's predictive system was elicited in both cases BIB002 . Both studies involved actions performed by a robot with biological features, which could explain why the predictive system was strongly engaged. Indeed, BIB003 showed that the action biological kinematic content per se was enough to solicit the observer's predictive system BIB003 . On the contrary, it has been shown that the observation of a non-biological reach-to-grasp action executed by a humanoid robotic arm did not elicit the predictive system BIB001 . However, BIB004 have tried to understand why BIB001 failed to find predictive system activation during robotic action observation. To do so, they scanned participants who were watching a robot perform either 5 different actions within a block (as in their previous described experiment) or 5 times the same action within a block (as in Tai et al. study) . In the first case, the author found a significant activation of predictive system whereas no such effect occurred in the latter case. Hence, the authors proposed that robot-generated actions could activate the observer's predictive system as soon as different actions are shown in the same block in order to avoid habituation effect BIB004 . To sum up, even if there is no clear consensus, most of the neuroimaging studies seem to report the activation of predictive mechanisms during the observation of actions that have been generated both by humanized automated systems and by traditional automated systems (see Table 1 for a summary of the neuroimaging results). Nevertheless, behavioral data emphasizes the point that the more the machine is humanized (through its physical appearance or its kinematics), the more the human-machine interactions are improved.