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Effective team performance often requires that individuals engage in team training exercises. However, organizing team-training scenarios presents economic and logistical challenges and can be prone to trainer bias and fatigue. Accordingly, a growing body of research is investigating the effectiveness of employing artificial agents (AAs) as synthetic teammates in team training simulations, and, relatedly, how to best develop AAs capable of robust, human-like behavioral interaction. Motivated by these challenges, the current study examined whether task dynamical models of expert human herding behavior could be embedded in the control architecture of AAs to train novice actors to perform a complex multiagent herding task. Training outcomes were compared to human-expert trainers, novice baseline performance, and AAs developed using deep reinforcement learning (DRL). Participants’ subjective preferences for the AAs developed using DRL or dynamical models of human performance were also investigated. The results revealed that AAs controlled by dynamical models of human expert performance could train novice actors at levels equivalent to expert human trainers and were also preferred over AAs developed using DRL. The implications for the development of AAs for robust human-AA interaction and training are discussed, including the potential benefits of employing hybrid Dynamical-DRL techniques for AA development.

Objective The objective of the current study was to examine the driving performance of young drivers with a history of moderate to severe traumatic brain injury (TBI) compared with an uninjured control group. The impact of cell phone related distraction (conversation and texting) and executive functioning (EF) were also explored. Method Individuals aged 16-25 years with (n = 19) and without (n = 19) a history of TBI engaged in a simulated drive under 3 distraction conditions (no distraction, cell phone conversation, and texting). Mean speed, maximum speed, standard deviation of speed, standard deviation of lane position, and crash rates were used as outcomes. The Global Executive Composite (GEC) from the Behavior Rating Inventory of Executive Functioning (BRIEF) was used to measure EF. Results Significant Injury × Distraction × GEC interaction effects were noted on max speed and speed variability, with a trending Distraction × GEC interaction noted for lane position variability. The effect of distraction was most notable among individuals with greater GEC scores, across both injury groups. Conclusions A history of pediatric TBI did not specifically impact driving performance independent of EF, with EF playing a central role in functioning across domains of driving performance. Consistent effect of EF suggests that deficits in driving performance may be associated with EF specifically, with individuals with EF difficulties following TBI at greater risk for poor driving performance. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Virtual environments are increasingly being used to support team training. However, despite their growing ubiquity, less is known on how task difficulty in such environments mitigates verbal communication patterns. We report on a study investigating this research question, finding that although manipulations of task difficulty did appear to impact broad team level communication measures (e.g. how much they talked), it largely had minimal impact on team communication dynamics (e.g. the flow of communication between various roles in the teams). The implications of this study for the design of future team training environments along with the possibility of artificial agents to support team training as a novel use case are then discussed in turn.

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.

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.

Recent innovations in Deep Reinforcement Learning (DRL) and Artificial Intelligence (AI) techniques have allowed for the development of artificial agents that can outperform human counterparts. But when it comes to multiagent task contexts, the behavioral patterning of AI agents is just as important as their performance. Indeed, successful multi-agent interaction requires that co-actors behave reciprocally, anticipate each other’s behaviors, and readily perceive each other’s behavioral intentions. Thus, developing AI agents that can produce behaviors compatible with human co-actors is of vital importance. Of particular relevance here, research exploring the dynamics of human behavior has demonstrated that many human behaviors and actions can be modeled using a small set of dynamical perceptual-motor primitives (DPMPs) and, moreover, that these primitives can also capture the complex behavior of humans in multiagent scenarios. Motived by this understanding, the current paper proposes methodologies which use DPMPs to augment the training and action dynamics of DRL agents to ensure that the agents inherit the essential pattering of human behavior while still allowing for optimal exploration of the task solution space during training. The feasibility of these methodologies is demonstrated by creating hybrid DPMP-DRL agents for a multiagent herding task. Overall, this approach leads to faster training of DRL agents while also exhibiting behavior characteristics of expert human actors.

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.

Most human actions are composed of two fundamental movement types, discrete and rhythmic movements. These movement types, or primitives, are analogous to the two elemental behaviors of nonlinear dynamical systems, namely, fixed-point and limit cycle behavior, respectively. Furthermore, there is now a growing body of research demonstrating how various human actions and behaviors can be effectively modeled and understood using a small set of low-dimensional, fixed-point and limit cycle dynamical systems (differential equations). Here, we provide an overview of these dynamical motorprimitives and detail recent research demonstrating how these dynamical primitives can be used to model the task dynamics of complex multiagent behavior. More specifically, we review how a task-dynamic model of multiagent shepherding behavior, composed of rudimentary fixed-point and limit cycle dynamical primitives, can not only effectively model the behavior of cooperating human co-actors, but also reveals how the discovery and intentional use of optimal behavioral coordination during task learning is marked by a spontaneous, self-organized transition between fixed-point and limit cycle dynamics (i.e., via a Hopf bifurcation).

Introduction Eye-tracking research has been widely used in radiology applications. Prior studies exclusively analysed either temporal or spatial eye-tracking features, both of which alone do not completely characterise the spatiotemporal dynamics of radiologists’ gaze features. Purpose Our research aims to quantify human visual search dynamics in both domains during brain stimuli screening to explore the relationship between reader characteristics and stimuli complexity. The methodology can be used to discover strategies to aid trainee radiologists in identifying pathology, and to select regions of interest for machine vision applications. Method The study was performed using eye-tracking data 5 seconds in duration from 57 readers (15 Brain-experts, 11 Other-experts, 5 Registrars and 26 Naïves) for 40 neuroradiological images as stimuli (i.e., 20 normal and 20 pathological brain MRIs). The visual scanning patterns were analysed by calculating the fractal dimension (FD) and Hurst exponent (HE) using re-scaled range (R/S) and detrended fluctuation analysis (DFA) methods. The FD was used to measure the spatial geometrical complexity of the gaze patterns, and the HE analysis was used to measure participants’ focusing skill. The focusing skill is referred to persistence/anti-persistence of the participants’ gaze on the stimulus over time. Pathological and normal stimuli were analysed separately both at the “First Second” and full “Five Seconds” viewing duration. Results All experts were more focused and a had higher visual search complexity compared to Registrars and Naïves. This was seen in both the pathological and normal stimuli in the first and five second analyses. The Brain-experts subgroup was shown to achieve better focusing skill than Other-experts due to their domain specific expertise. Indeed, the FDs found when viewing pathological stimuli were higher than those in normal ones. Viewing normal stimuli resulted in an increase of FD found in five second data, unlike pathological stimuli, which did not change. In contrast to the FDs, the scanpath HEs of pathological and normal stimuli were similar. However, participants’ gaze was more focused for “Five Seconds” than “First Second” data. Conclusions The HE analysis of the scanpaths belonging to all experts showed that they have greater focus than Registrars and Naïves. This may be related to their higher visual search complexity than non-experts due to their training and expertise.

Runeson (Scandanavian Journal of Psychology 18:172-179, 1977) suggested that the polar planimeter might serve as an informative model system of perceptual mechanism. The key aspect of the polar planimeter is that it registers a higher order property of the environment without computational mediation on the basis of lower order properties, detecting task-specific information only. This aspect was posited as a hypothesis for the perception of jumping and reaching affordances for the self and another person. The findings supported this hypothesis. The perception of reaching while jumping significantly differed from an additive combination of jump-without-reaching and reach-without-jumping perception. The results are consistent with Gibson's (The senses considered as perceptual systems, Houghton Mifflin, Boston, MA; Gibson, The senses considered as perceptual systems, Houghton Mifflin, Boston, MA, 1966; The ecological approach to visual perception, Houghton Mifflin, Boston, MA; Gibson, The ecological approach to visual perception, Houghton Mifflin, Boston, MA, 1979) theory of information-that aspects of the environment are specified by patterns in energetic media.

Interactive or collaborative pick-and-place tasks occur during all kinds of daily activities, for example, when two or more individuals pass plates, glasses, and utensils back and forth between each other when setting a dinner table or loading a dishwasher together. In the near future, participation in these collaborative pick-and-place tasks could also include robotic assistants. However, for human-machine and human-robot interactions, interactive pick-and-place tasks present a unique set of challenges. A key challenge is that high-level task-representational algorithms and preplanned action or motor programs quickly become intractable, even for simple interaction scenarios. Here we address this challenge by introducing a bioinspired behavioral dynamic model of free-flowing cooperative pick-and-place behaviors based on low-dimensional dynamical movement primitives and nonlinear action selection functions. Further, we demonstrate that this model can be successfully implemented as an artificial agent control architecture to produce effective and robust human-like behavior during human-agent interactions. Participants were unable to explicitly detect whether they were working with an artificial (model controlled) agent or another human-coactor, further illustrating the potential effectiveness of the proposed modeling approach for developing systems of robust real/embodied human-robot interaction more generally.

The shepherding problem is interesting for multiagent systems research as it requires multiple actors (e.g., dogs, humans) to exert indirect control over autonomous agents (e.g., sheep, cattle) for containment or transportation. Accordingly, plenty of research has focused on designing algorithms for robotic agents to solve such tasks. Almost no research, however, has utilized this task to investigate human-human or human-machine interactions, even though the shepherding problem encapsulates desirable qualities for an experimental paradigm to investigate the dynamics of human group and mixed-group coordination in complex tasks. This paper summarizes our recent research that has employed the shepherding problem to study complex multiagent human-human and human-machine interaction. The paper concludes with a discussion of practical applications for using the shepherding problem for the design of assistive agents that can be incorporated into human groups or enhance training and human learning.

Working with others not only improves behavioral efficiency, but also facilitates learning. Such multiagent activity is fundamental to everyday life and, increasingly, virtual and robotic agents are finding a place in these contexts. The effectiveness of human-machine interaction (HMI), however, relies on artificial systems being able to anticipate their partner and select actions that not only lead to achieving the shared goal, but does so efficiently. Here, a multiagent "shepherding" task was used to study coordination and behavior-switching during HMI. The task required the coordinated control of a complex environment, where a non-obvious solution leads to near-optimal task performance. Previous research has demonstrated that a virtual agent, with knowledge of the optimal solution, can effectively steer novices to discover the optimal task behavior [3]. Conversely, results here demonstrate that when completing the task with a virtual avatar incapable of producing this behavior, a subset of novices still discovered and enforced this optimal behavior in the virtual avatar by modulating the sheep-herd's dynamics. These results provide evidence that learning efficient solutions may result from interaction patterns early in the interaction, which may be exploited by adaptive artificial-agents in HMI contexts to facilitate skill acquisition.

A recent study (Grover et al. Exp Brain Res 236(10):2531–2544, 2018) found that the grip force applied to maintain grasp of a hand-held object exhibited intermittent coupling to the changing load forces exerted by the object as it was oscillated. In particular, the strength and consistency of grip force response to load force oscillations was tied to overall load force levels and the prominence of load force oscillations. This contrasts with previous reports of grip force-load force coupling as generally continuous and stable and, therefore, has implications for theoretical accounts of grip force control that are predicated on these prior understandings of the coupling. The finding of intermittency additionally raises questions about the consistency of the temporal relation (i.e., lead/lag) between grip force and load force over time. The objective of the current study was, therefore, to investigate how the time-varying pattern (i.e., the regularity vs. complexity) of load force variations contribute to shifts between more intermittent and more continuous grip force control, and to determine the temporal consistency of the coupling. It was found that grip force became more tightly and continuously responsive to load force as load force changes became less predictable. Additionally, we report strong evidence that the temporal (i.e., lead/lag) relation between grip force and load force and the strength of their coupling vary substantially over time.

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.

Objectives To evaluate associations between pre-season oculomotor performance on visual tracking tasks and in-season head impact incidence during high school boys ice hockey. Design Prospective observational study design. Methods Fifteen healthy high school aged male hockey athletes (M = 16.50 ± 1.17 years) performed two 30 s blocks each of a prosaccade and self-paced saccade task, and two trials each of a slow, medium, and fast smooth pursuit task (90° s−1; 180° s−1; 360° s−1) during the pre-season. Regular season in-game collision data were collected via helmet-mounted accelerometers. Simple linear regressions were used to examine relations between oculomotor performance measures and collision incidence at various impact thresholds. Results The variability of prosaccade latency was positively related to total collisions for the 20 g force cutoff (p = 0.046, adjusted R2 = 0.28). The average self-paced saccade velocity (p = 0.020, adjusted R2 = 0.37) and variability of smooth pursuit gaze velocity (p = 0.012, adjusted R2 = 0.47) were also positively associated with total collisions for the 50 g force cutoff. Conclusions These results provide preliminary evidence that less efficient oculomotor performance on three different oculomotor tasks is associated with increased incidence of head impacts during a competitive ice hockey season. The variability of prosaccade latency, the average self-paced saccade velocity and the variability of gaze velocity during predictable smooth pursuit all related to increased head impacts. Future work is needed to further understand player initiated collisions, but this is an important first step toward understanding strategies to reduce incidence of injury risk in ice hockey, and potentially contact sports more generally.