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:  CONCLUSION 
1. Abstract of Cited Paper (BIB001):  Abstract Introspectively, the awareness of actions includes the awareness of the intentions accompanying them. Therefore, the awareness of self-generated actions might be expected to differ from the awareness of other-generated actions to the extent that access to one’s own and to other’s intentions differs. However, we recently showed that the perceived onset times of self- vs. other-generated actions are similar, yet both are different from comparable events that are conceived as being generated by a machine. This similarity raises two interesting possibilities. First we could infer the intentions of others from their actions. Second and more radically, we could equally infer our own intentions from the actions we perform rather than sense them. We present two new experiments which investigate the role of action effects in the awareness of self- and other-generated actions by means of measuring the estimated onset time. The results show that the presence of action effects is necessary for the similarity of awareness of self- and other-generated actions. 
2. Abstract of Cited Paper (BIB002):  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. 
3. Abstract of Cited Paper (BIB003):  Abstract There is an increasing amount of empirical work investigating the sense of agency, i.e. the registration that we are the initiators of our own actions. Many studies try to relate the sense of agency to an internal feed-forward mechanism, called the “comparator model”. In this paper, we draw a sharp distinction between a non-conceptual level of feeling of agency and a conceptual level of judgement of agency. By analyzing recent empirical studies, we show that the comparator model is not able to explain either. Rather, we argue for a two-step account: a multifactorial weighting process of different agency indicators accounts for the feeling of agency, which is, in a second step, further processed by conceptual modules to form an attribution judgement. This new framework is then applied to disruptions of agency in schizophrenia, for which the comparator model also fails. Two further extensions are discussed: We show that the comparator model can neither be extended to account for the sense of ownership (which also has to be differentiated into a feeling and a judgement of ownership) nor for the sense of agency for thoughts. Our framework, however, is able to provide a unified account for the sense of agency for both actions and thoughts. 
4. Abstract of Cited Paper (BIB004):  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. 
5. Abstract of Cited Paper (BIB005):  Body movement provides a rich source of information about other people’s goals and intentions. In the present study, we examined a particular aspect concerned with the interpretation of bodily movement—how well people can distinguish between different social intentions by observing a reach-to-grasp movement. To ascertain to what extent intention-from-motion judgements rely on visual kinematics, we compared prediction performance on a typical temporal-occlusion video task with prediction performance on a temporal-occlusion point-light task. In the video task, participants observed videos showing a model reaching towards and grasping a wooden block with different intents: to cooperate with a partner in building a tower, compete with an opponent to be the first to put the object in the middle of the working surface, or perform an individual action. In the point-light task, participants observed point-light displays of the same movements. Although predictions were more accurate for the video task, prediction performance was not disrupted for the point-light task, suggesting that essential kinematic information available in point-light displays was indeed sufficient for intention-from-motion judgement. Importantly, the same kinematic landmarks were used to discriminate between social intentions for the video and the point-light task. This suggests that observers not only have the ability to use kinematic information when no other information is available, but they use kinematic information to discriminate between intentions when watching the motion of others under full light conditions. 
6. Abstract of Cited Paper (BIB006):  Intentional binding is the perceived shortening of the time between a voluntary action and its consequent effect and has been suggested as an implicit measure of agency. This shortening has been linked to processes underlying action preparation and is also affected by post-movement feedback. Intentional binding has been demonstrated in joint action tasks involving two humans, but it is unknown whether it occurs for tasks involving a human working alongside a non-human partner. This experiment investigated the influence of high-level feedback on the experience of agency and whether binding occurs in human–computer joint action settings. Participants were involved in two versions an action task involving another ‘agent’. In one version, two participants (a genuine participant and a confederate) sat side by side, separated by a curtain that prevented vision of the other person. In baseline conditions, both participants were instructed to make a self-paced action and judge the time of the action by reporting the position of a rotating clock-hand on a computer screen. In other baseline conditions, participants judged the time of an auditory tone. In operant conditions, participants made actions and the genuine participant’s action was followed 200 ms later by a tone on every trial. To examine the effect of post-movement information on binding and explicit agency judgments, a colour cue was presented on each trial informing participants about which person’s action caused the tone. In another version of the task, participants were paired with a computer instead of a human co-actor. Post-movement information affected the genuine participant’s explicit agency judgments but had no effect on intentional binding, which always occurred. In the human–computer version, participants never showed binding, even when they explicitly judged that their action had caused the tone. We suggest that human–human partnerships result in the formation of a new ‘we’ agentic identity, but that human–computer partnerships lead to inhibition of the processes that mediate the pre-reflective sense of agency. 
7. Abstract of Cited Paper (BIB007):  Sense of agency is the way in which we understand the causal relationships between our actions and sensory events. Agency is implicitly measured using intentional binding paradigms, where voluntary self-made actions and consequential sensory events are perceived as shifted closer together in time. However, a crucial question remains as to how we understand the relationship between others’ actions and sensory events. Do we use similar binding processes as for our own actions? Previous attempts to investigate this phenomenon in others’ have reached no clear consensus. Therefore, in an attempt to understand how we attribute the causal relationships between others’ actions and sensory events, we investigated intentional binding in others’ actions using an interval estimation paradigm. In a first experiment participants were required to make a button-press response to indicate the perceived interval between a self-made action and a tone, between a closely matched observed action and tone, and between two tones. For both self-made and observed actions, we found a significant perceived shortening of the interval between the actions and tones as compared with the interval between two tones, thus intentional binding was found for both self-made and observed actions. In a second experiment we validated the findings of the first by contrasting the perceived intervals between an observed action and tone with a matched visual–auditory stimulus and a tone. We again found a significant perceived shortening of the interval for observed action compared with the closely matched visual–auditory control stimulus. The occurrence of intentional binding when observing an action suggests we use similar processes to make causal attributions between our own actions, others’ actions, and sensory events. 
8. Abstract of Cited Paper (BIB008):  A key requirement for seamless human-robot collaboration is for the robot to make its intentions clear to its human collaborator. A collaborative robot's motion must be legible, or intent-expressive. Legibility is often described in the literature as and effect of predictable, unsurprising, or expected motion. Our central insight is that predictability and legibility are fundamentally different and often contradictory properties of motion. We develop a formalism to mathematically define and distinguish predictability and legibility of motion. We formalize the two based on inferences between trajectories and goals in opposing directions, drawing the analogy to action interpretation in psychology. We then propose mathematical models for these inferences based on optimizing cost, drawing the analogy to the principle of rational action. Our experiments validate our formalism's prediction that predictability and legibility can contradict, and provide support for our models. Our findings indicate that for robots to seamlessly collaborate with humans, they must change the way they plan their motion. 
9. Abstract of Cited Paper (BIB009):  Every day we make attributions about how our actions and the actions of others cause consequences in the world around us. It is unknown whether we use the same implicit process in attributing causality when observing others' actions as we do when making our own. The aim of this research was to investigate the neural processes involved in the implicit sense of agency we form between actions and effects, for both our own actions and when watching others' actions. Using an interval estimation paradigm to elicit intentional binding in self-made and observed actions, we measured the EEG responses indicative of anticipatory processes before an action and the ERPs in response to the sensory consequence. We replicated our previous findings that we form a sense of implicit agency over our own and others' actions. Crucially, EEG results showed that tones caused by either self-made or observed actions both resulted in suppression of the N1 component of the sensory ERP, with no difference in suppression between consequences caused by observed actions compared with self-made actions. Furthermore, this N1 suppression was greatest for tones caused by observed goal-directed actions rather than non-action or non-goal-related visual events. This suggests that top-down processes act upon the neural responses to sensory events caused by goal-directed actions in the same way for events caused by the self or those made by other agents. 
10. Abstract of Cited Paper (BIB010):  Many studies suggest that individuals are not able to build a sense of we-agency during joint actions with automated artificial systems. We sought to examine whether or not this lack of sense of control was linked with individuals' inability to represent automaton-generated action into their own cognitive system. Indeed, during human interactions, we automatically represent our partner's actions in our own sensorimotor system. This might sustain our capacity to build a sense of we-agency over our partner-generated actions. However, to our knowledge, no studies have investigated the potential relation between our ability to use our sensorimotor system to represent a partner's action and we-agency. Our approach consisted in performing a pilot study coupling together a Simon target detection task wherein RTs served as an index of action co-representation and an implicit measure of one's sense of agency as indicated by intentional binding phenomenon. The preliminary observations suggested that individuals could represent other-generated action and have a sense of agency over these actions provided that their partner was another human and not an artificial automated system.

Literature Review:
Predictive mechanisms are involved in action control and also in the sense of agency BIB001 BIB004 . Empirical data tend to show that predictions allow better coordination with human peers in joint actions and support action understanding of observed agents BIB006 . Predictive mechanisms are also involved in the sense of agency formation during the observation of actions BIB002 . However, humans experience difficulties when they have to collaborate with traditional machines  BIB010 BIB011 . IB does not occur when humans are observing a traditional automaton acting or when they act in partnership with this sort of automaton (Wohlschläger et al., 2003a,b; BIB007 BIB008 BIB011 . This has prompted some authors to attempt to give the machine a more human-like appearance in order to enhance human's sense of agency during interactions with robots BIB003 . Consistently, it appears that humanized artificial systems can help moderate participants' difficulties in coordinating their actions with them and experiencing a sense of agency when they interact with machines BIB003 BIB005 . Moreover, the implementation of biological motion laws in anthropomorphic robot allows a better implicit understanding of these actions thanks to observer's action/observation matching system, with the help of his or her own motor experience. Indeed, considering that operators interpret the intentions and the action outcomes of a system with their own "cognitive toolkit, " to implement human-like motions in robot can make it easier for humans to predict the machine actions. In addition to make automated systems more predictable (i.e., to optimize intention understanding from early action observation), maximizing action legibility (i.e., to facilitate action reading from kinematics) might be another requirement for machine-generated action better understanding BIB009 . Finally, even though research in social robotic keeps trying to understand how to optimize the interactions between social robots and humans, investigations about the sense of agency during these joint actions are still missing. Optimizing human robot interaction is a crucial issue for the design of future technological systems given that humans will be increasingly involved in tasks where they need to interact with highly automated environment. The science of agency can help a better comprehension about how individuals can have a sense of control over these automata.