Exploring how healthcare teams balance the neurodynamics of autonomous and collaborative behaviors: a proof of concept

Team members co-regulate their activities and move together at the collective level of behavior while coordinating their actions toward shared goals. In parallel with team processes, team members need to resolve uncertainties arising from the changing task and environment. In this exploratory study we have measured the differential neurodynamics of seven two-person healthcare teams across time and brain regions during autonomous (taskwork) and collaborative (teamwork) segments of simulation training. The questions posed were: (1) whether these abstract and mostly integrated constructs could be separated neurodynamically; and, (2) what could be learned about taskwork and teamwork by trying to do so? The taskwork and teamwork frameworks used were Neurodynamic Information (NI), an electroencephalography (EEG) derived measure shown to be a neurodynamic proxy for the pauses and hesitations associated with individual uncertainty, and inter-brain EEG coherence (IBC) which is a required component of social interactions. No interdependency was observed between NI and IBC, and second-by-second dynamic comparisons suggested mutual exclusivity. These studies show that proxies for fundamental properties of teamwork and taskwork can be separated neurodynamically during team performances of ecologically valid tasks. The persistent expression of NI and IBC were not simultaneous suggesting that it may be difficult for team members to maintain inter-brain coherence while simultaneously reducing their individual uncertainties. Lastly, these separate dynamics occur over time frames of 15–30 s providing time for real-time detection and mitigation of individual and collaborative complications during training or live patient encounters

Optimizing Hypervideo Navigation Using a Markov Decision Process Approach

Interaction with hypermedia documents is a required feature for new sophisticated yet flexible multimedia applications. This paper presents an innovative adaptive technique to stream hypervideo that takes into account user behaviour. The objective is to optimize hypervideo prefetching in order to reduce the latency caused by the network. This technique is based on a model provided by a Markov Decision Process approach. The problem is solved using two methods: classical stochastic dynamic programming algorithms and reinforcement learning. Experimental results under stochastic network conditions are very promising

Spiking Neural Network that Maps from Generalized Coordinates to Cartesian Coordinates

In this thesis, I look to understand how insects compute task-level quantities by integrating range-fractionated sensory signals to create a sparse-spatial coding of Cartesian positions. I created biologically plausible 2-D and 3-D models of one species of the stick insect (Carausius morosus) leg and encoded the foot position through a spiking neural network. This model used spiking afferents from three angles of an insect leg which are integrated by one non-spiking interneuron. This model contains many dendritic compartments and one somatic compartment that encode the foot’s position relative to the body. The Functional Subnetwork Approach (FSA) was used to tune the conductances between the compartments (Szczecinski et al., 2017). Also, the Product of Exponentials (POE) was used to calculate the spatial kinematic chain of the stick insect leg (Murray et al., 1994). The system accurately encodes the foot position and depends on the width of the sensory encoding curves, or the “bell curves”. Discussion of limitations and other studies that relate to this work, as well as motivation for future work are included

New paradigmatic orientations and research agenda of human factors science in the intelligence era

Our recent research shows that the design philosophy of human factors science in the intelligence age is expanding from "user-centered design" to "human-centered AI". The human-machine relationship presents a trans-era evolution from "human-machine interaction" to "human-machine/AI teaming". These changes have raised new questions and challenges for human factors science. The interdisciplinary field of human factors science includes any work that adopts a human-centered approach, such as human factors, ergonomics, engineering psychology, and human-computer interaction. These changes compel us to re-examine current human factors science's paradigms and research agenda. Existing research paradigms are primarily based on non-intelligent technologies. In this context, this paper reviews the evolution of the paradigms of human factors science. It summarizes the new conceptual models and frameworks we recently proposed to enrich the research paradigms for human factors science, including a human-AI teaming model, a human-AI joint cognitive ecosystem framework, and an intelligent sociotechnical systems framework. This paper further enhances these concepts and looks forward to the application of these concepts. This paper also looks forward to the future research agenda of human factors science in the areas of "human-AI interaction", "intelligent human-machine interface", and "human-AI teaming". It analyzes the role of the research paradigms on the future research agenda. We believe that the research paradigms and agenda of human factors science influence and promote each other. Human factors science in the intelligence age needs diversified and innovative research paradigms, thereby further promoting the research and application of human factors science.Comment: 26 pages, in Chinese languag

The Lived Experiences of Adult Male Trauma Survivors with Dance Movement Therapy

In the United States, approximately 7.7 million individuals are affected by posttraumatic stress disorder (PTSD) at any given time. Though women are likelier to develop PTSD symptoms, men are exposed to more traumatic events in their lifetimes. Empirically- supported PTSD options exist, however clinical application of these treatments may not consistently culminate in beneficial outcomes. Dance Movement Therapy (DMT) has demonstrated positive treatment outcomes for a variety of mental and physical disorders. Nonetheless, there is a lack of robust research related to the treatment experiences of men who have participated in DMT for trauma-related symptoms. The purpose of this phenomenological study was to explore this research gap. Focusing on adult male trauma survivors, the research question addressed the lived experiences of participating in DMT and the meaning ascribed to this involvement. Eleven adult male participants were interviewed via audio-recorded telephone interviews consisting of semistructured interview questions. Through a constructivist lens, the modified Van Kaam method of analysis was implemented revealing 4 emergent themes. The findings of this explorative study suggested positive PTSD symptom outcomes for all 11 participants including improvements in social belongingness, social acceptance, quality of life, and a reduction in symptoms of anxiety and depression. Accordingly, the findings of this research may help to advance social change through broadening clinical awareness of the beneficial neurogenic treatment advantages of somatic and creative interventions such as DMT for PTSD. Moreover, these findings may augment existing research related to movement- based treatment options for individuals coping with PTSD and trauma-related symptoms

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201