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How is coordination achieved in asymmetric joint actions where co-actors have unequal access to task information? Pairs of participants performed a non-verbal tapping task with the goal of synchronizing taps to different targets. We tested whether ‘Leaders’ knowing the target locations would support ‘Followers’ without this information. Experiment 1 showed that Leaders tapped with higher amplitude that also scaled with specific target distance, thereby emphasizing differences between correct targets and possible alternatives. This strategic communication only occurred when Leaders’ movements were fully visible, but not when they were partially occluded. Full visual information between co-actors also resulted in higher and more stable behavioral coordination than partial vision. Experiment 2 showed that Leaders’ amplitude adaptation facilitated target prediction by independent Observers. We conclude that fully understanding joint action coordination requires both representational (i.e., strategic adaptation) and dynamical systems (i.e., behavioral coupling) accounts.

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.

Effectively coordinating one's behaviors with those of others is essential for successful multiagent activity. In recent years, increased attention has been given to understanding the dynamical principles that underlie such coordination because of a growing interest in behavioral synchrony and complex-systems phenomena. Here, we examined the behavioral dynamics of a novel, multiagent shepherding task, in which pairs of individuals had to corral small herds of virtual sheep in the center of a virtual game field. Initially, all pairs adopted a complementary, search-and-recover mode of behavioral coordination, in which both members corralled sheep predominantly on their own sides of the field. Over the course of game play, however, a significant number of pairs spontaneously discovered a more effective mode of behavior: coupled oscillatory containment, in which both members synchronously oscillated around the sheep. Analysis and modeling revealed that both modes were defined by the task's underlying dynamics and, moreover, reflected context-specific realizations of the lawful dynamics that define functional shepherding behavior more generally.

This study investigated the utility of supervised machine learning (SML) and explainable artificial intelligence (AI) techniques for modeling and understanding human decision-making during multiagent task performance. Long short-term memory (LSTM) networks were trained to predict the target selection decisions of expert and novice players completing a multiagent herding task. The results revealed that the trained LSTM models could not only accurately predict the target selection decisions of expert and novice players but that these predictions could be made at timescales that preceded a player’s conscious intent. Importantly, the models were also expertise specific, in that models trained to predict the target selection decisions of experts could not accurately predict the target selection decisions of novices (and vice versa). To understand what differentiated expert and novice target selection decisions, we employed the explainable-AI technique, SHapley Additive explanation (SHAP), to identify what informational features (variables) most influenced modelpredictions. The SHAP analysis revealed that experts were more reliant on information about target direction of heading and the location of coherders (i.e., other players) compared to novices. The implications and assumptions underlying the use of SML and explainable-AI techniques for investigating and understanding human decision-making are discussed.

Deep learning-based approaches to markerless 3D pose estimation are being adopted by researchers in psychology and neuroscience at an unprecedented rate. Yet many of these tools remain unvalidated. Here, we report on the validation of one increasingly popular tool (DeepLabCut) against simultaneous measurements obtained from a reference measurement system (Fastrak) with well-known performance characteristics. Our results confirm close (mm range) agreement between the two, indicating that under specific circumstances deep learning-based approaches can match more traditional motion tracking methods. Although more work needs to be done to determine their specific performance characteristics and limitations, this study should help build confidence within the research community using these new tools.

During conversations people coordinate simultaneous channels of verbal and nonverbal information to hear and be heard. But the presence of background noise levels such as those found in cafes and restaurants can be a barrier to conversational success. Here, we used speech and motion-tracking to reveal the reciprocal processes people use to communicate in noisy environments. Conversations between twenty-two pairs of typical-hearing adults were elicited under different conditions of background noise, while standing or sitting around a table. With the onset of background noise, pairs rapidly adjusted their interpersonal distance and speech level, with the degree of initial change dependent on noise level and talker configuration. Following this transient phase , pairs settled into a sustaining phase in which reciprocal speech and movement-based coordination processes synergistically maintained effective communication, again with the magnitude of stability of these coordination processes covarying with noise level and talker configuration. Finally, as communication breakdowns increased at high noise levels, pairs exhibited resetting behaviors to help restore communication—decreasing interpersonal distance and/or increasing speech levels in response to communication breakdowns. Approximately 78 dB SPL defined a threshold where behavioral processes were no longer sufficient for maintaining effective conversation and communication breakdowns rapidly increased.

The coordination of attention between individuals is a fundamental part of everyday human social interaction. Previous work has focused on the role of gaze information for guiding responses during joint attention episodes. However, in many contexts, hand gestures such as pointing provide another valuable source of information about the locus of attention. The current study developed a novel virtual reality paradigm to investigate the extent to which initiator gaze information is used by responders to guide joint attention responses in the presence of more visually salient and spatially precise pointing gestures. Dyads were instructed to use pointing gestures to complete a cooperative joint attention task in a virtual environment. Eye and hand tracking enabled real-time interaction and provided objective measures of gaze and pointing behaviours. Initiators displayed gaze behaviours that were spatially congruent with the subsequent pointing gestures. Responders overtly attended to the initiator’s gaze during the joint attention episode. However, both these initiator and responder behaviours were highly variable across individuals. Critically, when responders did overtly attend to their partner’s face, their saccadic reaction times were faster when the initiator’s gaze was also congruent with the pointing gesture, and thus predictive of the joint attention location. These results indicate that humans attend to and process gaze information to facilitate joint attention responsivity, even in contexts where gaze information is implicit to the task and joint attention is explicitly cued by more spatially precise and visually salient pointing gestures.

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.

Even high functioning children with Autism Spectrum Disorder (ASD) exhibit impairments that affect their ability to carry out and maintain effective social interactions in multiple contexts. One aspect of subtle nonverbal communication that might play a role in this impairment is the whole-body motor coordination that naturally arises between people during conversation. The current study aimed to measure the time-dependent, coordinated whole-body movements between children with ASD and a clinician during a conversational exchange using tools of nonlinear dynamics. Given the influence that subtle interpersonal coordination has on social interaction feelings, we expected there to be important associations between the dynamic motor movement measures introduced in the current study and the measures used traditionally to categorize ASD impairment (ADOS-2, joint attention and theory of mind). The study found that children with ASD coordinated their bodily movements with a clinician, that these movements were complex and that the complexity of the children's movements matched that of the clinician's movements. Importantly, the degree of this bodily coordination was related to higher social cognitive ability. This suggests children with ASD are embodying some degree of social competence during conversations. This study demonstrates the importance of further investigating the subtle but important bodily movement coordination that occurs during social interaction in children with ASD.