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Multiagent activity is commonplace in everyday life and can improve the behavioral efficiency of task performance and learning. Thus, augmenting social contexts with the use of interactive virtual and robotic agents is of great interest across health, sport, and industry domains. However, the effectiveness of human–machine interaction (HMI) to effectively train humans for future social encounters depends on the ability of artificial agents to respond to human coactors in a natural, human-like manner. One way to achieve effective HMI is by developing dynamical models utilizing dynamical motor primitives (DMPs) of human multiagent coordination that not only capture the behavioral dynamics of successful human performance but also, provide a tractable control architecture for computerized agents. Previous research has demonstrated how DMPs can successfully capture human-like dynamics of simple nonsocial, single-actor movements. However, it is unclear whether DMPs can be used to model more complex multiagent task scenarios. This study tested this human-centered approach to HMI using a complex dyadic shepherding task, in which pairs of coacting agents had to work together to corral and contain small herds of virtual sheep. Human–human and human–artificial agent dyads were tested across two different task contexts. The results revealed (i) that the performance of human–human dyads was equivalent to those composed of a human and the artificial agent and (ii) that, using a “Turing-like” methodology, most participants in the HMI condition were unaware that they were working alongside an artificial agent, further validating the isomorphism of human and artificial agent behavior.

Social animals have the remarkable ability to organize into collectives to achieve goals unobtainable to individual members. Equally striking is the observation that despite differences in perceptual-motor capabilities, different animals often exhibit qualitatively similar collective states of organization and coordination. Such qualitative similarities can be seen in corralling behaviors involving the encirclement of prey that are observed, for example, during collaborative hunting amongst several apex predator species living in disparate environments. Similar encirclement behaviors are also displayed by human participants in a collaborative problem-solving task involving the herding and containment of evasive artificial agents. Inspired by the functional similarities in this behavior across humans and non-human systems, this paper investigated whether the containment strategies displayed by humans emerge as a function of the task’s underlying dynamics, which shape patterns of goal-directed corralling more generally. This hypothesis was tested by comparing the strategies naïve human dyads adopt during the containment of a set of evasive artificial agents across two disparate task contexts. Despite the different movement types (manual manipulation or locomotion) required in the different task contexts, the behaviors that humans display can be predicted as emergent properties of the same underlying task-dynamic model.

Research investigating the dynamics of coupled physical systems has demonstrated that small feedback delays can allow a dynamic response system to anticipate chaotic behavior. This counterintuitive phenomenon, termed anticipatory synchronization, has been observed in coupled electrical circuits, laser semi-conductors, and artificial neurons. Recent research indicates that the same process might also support the ability of humans to anticipate the occurrence of chaotic behavior in other individuals. Motivated by this latter work, the current study examined whether the process of feedback delay induced anticipatory synchronization could be employed to develop an interactive artificial agent capable of anticipating chaotic human movement. Results revealed that incorporating such delays within the movement-control dynamics of an artificial agent not only enhances an artificial agent's ability to anticipate chaotic human behavior, but to synchronize with such behavior in a manner similar to natural human-human anticipatory synchronization. The implication of these findings for the development of human-machine interaction systems is discussed.

Static posturographic recordings were obtained from six Parkinson's patients and six age-matched, healthy control participants. The availability of vision and visuo-spatial cognitive load were manipulated. Postural sway patterns were analyzed using recurrence quantification analysis (RQA), which revealed differences in center of pressure (COP) dynamics between Parkinson's and control participants. AP COP trajectories for the Parkinson's group were not only significantly more variable than for the control group, but also exhibited distinct patterns of temporal dynamics. The visual manipulation did not differentially affect the two groups. No cognitive load effects were found. The results are generally consistent with the hypothesis that pathological physiological systems exhibit a tendency for less flexible, more deterministic dynamic patterns.

Upper-limb prostheses are subject to high rates of abandonment. Prosthesis abandonment is related to a reduced sense of embodiment, the sense of self-location, agency, and ownership that humans feel in relation to their bodies and body parts. If a prosthesis does not evoke a sense of embodiment, users are less likely to view them as useful and integrated with their bodies. Currently, visual feedback is the only option for most prosthesis users to account for their augmented activities. However, for activities of daily living, such as grasping actions, haptic feedback is critically important and may improve sense of embodiment. Therefore, we are investigating how converting natural haptic feedback from the prosthetic fingertips into vibrotactile feedback administered to another location on the body may allow participants to experience haptic feedback and if and how this experience affects embodiment. While we found no differences between our experimental manipulations of feedback type, we found evidence that embodiment was not negatively impacted when switching from natural feedback to proximal vibrotactile feedback. Proximal vibrotactile feedback should be further studied and considered when designing prostheses.

The authors determined the effects of changes in task demands on interpersonal and intrapersonal coordination. Participants performed a joint task in which one participant held a stick to which a circle was attached at the top (holding role), while the other held a pointer through the circle without touching its borders (pointing role). Experiment 1 investigated whether interpersonal and intrapersonal coordination varied depending on task difficulty. Results showed that interpersonal and intrapersonal coordination increased in degree and stability with increments in task difficulty. Experiment 2 explored the effects of individual constraints by increasing the balance demands of the task (one or both members of the pair stood in a less stable tandem stance). Results showed that interpersonal coordination increased in degree and stability as joint task demands increased and that coupling strength varied depending on joint and individual task constraints. In all, results suggest that interpersonal and intrapersonal coordination are affected by the nature of the task performed and the constraints it places on joint and individual performance.

Coordinating with another person requires that one can perceive what the other is capable of doing. This ability often benefits from opportunities to practice and learn. Two experiments were conducted in which we investigated perceptual learning in the context of perceiving the maximum height to which an actor could jump to reach an object. Those estimates were compared with estimates that perceivers made for themselves. In Experiment 1, participants initially underestimated the maximum jumping-reach height both for themselves and for the actor. Over time, without explicit feedback, the participants were able to improve estimates of their own maximum jumping-reach height, but estimates for the actor did not improve. In Experiment 2, participants observed the actor perform either an action related but nonidentical to jumping (lifting a weight by squatting) or a nonrelated activity (rotating the torso). The participants who observed the actor perform the related action were able to improve the accuracy of their perceptual reports for the actor’s maximum jumping-reach height, but the participants who watched the actor perform the nonrelated task were unable to do so. The results indicate some degree of independence between perceived affordances for the self and others, suggesting that affordance judgments are not entirely dependent on or determined by characteristics of the perceiver.

Perceived heaviness has been shown to be specific to an object's rotational inertia (I), its resistance to rotational acceleration. According to the kinematic specification of dynamics (KSD) principle, we hypothesized that I is optically specified by rotational kinematics. Using virtual depictions of wielded objects, we investigated whether the visually detected rotational kinematics of wielded objects would influence perceived heaviness in a manner consistent with the inertial model of heaviness perception. We scaled the virtual object's movement so that it rotated more or less than its wielded counterpart, specifying lower and higher I, respectively. Perceived heaviness was inversely related to the rotational scaling factor, consistent with a KSD interpretation of the inertial model.

Previous research has demonstrated that perceived self-motion can be manipulated by the relation between optic flow rate and walking rate. Other studies have revealed that verbal reports of perceived distance are influenced by the energy that would be expended to traverse the distance in question. In an effort to integrate these findings, we investigated how action-based distance judgments are influenced by multimodally specified energy expenditure (MSEE)—the metabolic cost associated with traversing an optically specified distance—using a virtual-reality treadmill environment. The energy expenditure associated with walking, measured as the volume of oxygen consumed, was manipulated by changing treadmill speed or grade. Optically specified distance was manipulated by changing the virtual optic flow rate. All three manipulations of MSEE (walking rate, grade, and optic flow rate) influenced distance reports in the predicted directions and to equivalent degrees.