The collective performance displayed by groups or teams, whether in solving complex problems or excelling in esports competitions, hinges on their coordination dynamics. While explicit coordination (e.g., verbal commands) and its affects on collective outcomes have been studied extensively, implicit coordination, especially in dynamic, fast-paced environments have been under investigated. In this study, we examine the competitive esportDota 2(D2) as a setting to explore within-team implicit coordination, which we model as player avatar movement synchrony. Utilizing the cluster phase method, we analyze spatio-temporal patterns of player movements to quantify implicit movement coordination. We observe a negative linear relationship between team movement synchrony and team performance in rank for D2 competitions across two tournaments. While some research suggests stronger coordination leads to favorable outcomes, we leverage our findings to discuss the complexity of team coordination, showcasing a delicate balance between specialization of individual team members and collective action. This study not only extends complex systems techniques used in physical sports to the rapidly evolving esports arena, but also invites further exploration into the multidimensional nature of coordination in team-based activities.

Virtual reality (VR) research has provided overviews of locomotion techniques, how they work, their strengths and overall user experience. Considerable research has investigated new methodologies, particularly machine learning to develop redirection algorithms. To best support the development of redirection algorithms through machine learning, we must understand how best to replicate human navigation and behaviour in VR, which can be supported by the accumulation of results produced through live-user experiments. However, it can be difficult to identify, select and compare relevant research without a pre-existing framework in an ever-growing research field. Therefore, this work aimed to facilitate the ongoing structuring and comparison of the VR-based natural walking literature by providing a standardised framework for researchers to utilise. We applied thematic analysis to study methodology descriptions from 140 VR-based papers that contained live-user experiments. From this analysis, we developed the LoCoMoTe framework with three themes: navigational decisions, technique implementation, and modalities. The LoCoMoTe framework provides a standardised approach to structuring and comparing experimental conditions. The framework should be continually updated to categorise and systematise knowledge and aid in identifying research gaps and discussions.

Abstract This study discusses the effect of semi-supervised learning in combination with pretrained language models for data-to-text generation. It is not known whether semi-supervised learning is still helpful when a large-scale language model is also supplemented. This study aims to answer this question by comparing a data-to-text system only supplemented with a language model, to two data-to-text systems that are additionally enriched by a data augmentation or a pseudo-labeling semi-supervised learning approach. Results show that semi-supervised learning results in higher scores on diversity metrics. In terms of output quality, extending the training set of a data-to-text system with a language model using the pseudo-labeling approach did increase text quality scores, but the data augmentation approach yielded similar scores to the system without training set extension. These results indicate that semi-supervised learning approaches can bolster output quality and diversity, even when a language model is also present.

Physiological data have shown to be useful in tracking and differentiating cognitive processes in a variety of experimental tasks, such as numerical skills and arithmetic tasks. Numerical skills are critical because they are strong predictors of levels of ability in cognitive domains such as literacy, attention, and understanding contexts of risk and uncertainty. In this work, we examined frontal and parietal electroencephalogram signals recorded from 36 healthy participants performing a mental arithmetic task. From each signal, six RQA-based features (Recurrence Rate, Determinism, Laminarity, Entropy, Maximum Diagonal Line Length and, Average Diagonal Line Length) were extracted and used for classification purposes to discriminate between participants performing proficiently and participants performing poorly. The results showed that the three classifiers implemented provided an accuracy above 0.85 on 5-fold cross-validation, suggesting that such features are effective in detecting performance independently from the specific classifiers used. Compared to other successful methods, RQA-based features have the potential to provide insights into the nature of the physiological dynamics and the patterns that differentiate levels of proficiency in cognitive tasks.

This paper augments recent advances in social cognitive and affective neuroscience (SCAN) and illustrates their relevance to the development of novel human-machine systems. Advances in this area are crucial for understanding and exploring the social, cognitive, and neural processes that arise during human interactions with complex sociotechnological systems. Overviews of the major areas of SCAN research, including emotion, theory of mind, and joint action, are provided as the basis for describing three applications of SCAN to human-machine systems research and development. Specifically, this paper provides three examples to demonstrate the broad interdisciplinary applicability of SCAN and the ways it can contribute to improving a number of human-machine systems with the pursuit of further research in this vein. These include applying SCAN to learning and training, informing the field of human-robot interaction (HRI), and, finally, for enhancing team performance. The goal is to draw attention to the insights that can be gained by integrating SCAN with ongoing human-machine system research and to provide guidance to foster collaborations of this nature. Toward this end, we provide a systematic set of notional research questions for each detailed application within the context of the three major emphases of SCAN research. In turn, this study serves as a roadmap for preliminary investigations that integrate SCAN and human-machine system research.