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 Machines Joint actions with traditional automated systems 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  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:
Co-represention of computer-generated actions in the human motor system during joint actions has been addressed by BIB003 . In their study, there was a human-computer joint go-nogo condition wherein participants had to act jointly with a computer in order to realize a target detection task. The participants had to detect one type of target (e.g., a green circle) that could appear on the left or the right of a screen with a right key while the computer was to detect an alternative target (e.g., a red circle). The author found no compatibility effect (i.e., no faster reaction times for the compatible trials compared to the incompatible trials). This absence of motor interference on the incongruent trials suggests that participants were not able to represent the computer-generated actions into their cognitive system. It has also been shown that motor expertise in tennis played a crucial role during human interactions but had no influence during human-machine interactions. Indeed, expert tennis players have faster reaction times than novice tennis players when they are playing with another human but they do not benefit from their experience anymore when they are playing against a cloaked ball machine  . Such results confirm the role of our own motor planning in our representation of the partner's action, and the difficulties that could arise in cases of non-human co-agent. Indeed, the comparator model implies that individuals make predictions about the ongoing other-generated action based on their own motor repertoire. BIB002 also used a variant of the social Simon task BIB001 to investigate the human ability to represent actions that have been realized by a humanoid robot. In the study of BIB002 , the participants were sitting next to Tombatossals, a humanoid robot (with a torso, an anthropomorphic head, eyes-cameras, two arms, and a threefinger four-degrees-of-freedom left hand) described either as an intelligent and active agent or a passive machine acting in a deterministic way. The participants had to detect one type of target (e.g., a white square) that could appear on the left or the right side of a screen. The task of the robot was to detect another type of target (e.g., a white diamond) on the same screen. The experimental setting of the Social Simon experiment with a robot is presented in Figure 6 . The authors analyzed the reaction times of the participants. Interestingly, the authors found a compatibility effect when the robot was introduced as a human-like robot who can actively act but not when the robot was introduced as a deterministic machine. Hence, the corepresentation of other-generated actions can also occur during joint actions with automated artificial agents (and not only with human peers) provided that the robot is considered as a humanlike partner. Probably, to envisage the others as similar to us is needed in order to map their actions into our own cognitive system and to compute a forward model.