LoCoMoTe - a Framework for Classification of Natural Locomotion in VR by Task, Technique and Modality.

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.

A Methodological Framework to Study Change in Team Cognition Under the Dynamical Hypothesis.

The dynamical hypothesis claims that cognitive systems, such as teams, are dynamical systems (i.e., an interdependent collection of individuals and their technology that change together over time). Following this hypothesis, team researchers have adopted dynamical approaches to better understand the team cognitive processes and states that form team cognition, as well as how they emerge over time. One approach focuses on team coordination dynamics, which examines the coupling of signals between interacting individuals in various modalities, and has been shown to reflect aspects of team functioning including team cognition. However, how changes in team coordination relate to high-level team cognitive processes and states, as well as important events, are not yet fully understood. To this end, we advance a methodological framework for researching team cognition under the dynamical hypothesis. Subsequently, we provided an empirical case-study application of this framework. Thereby, this work contributes methodologically and empirically to a deeper understanding of team cognition, the dynamical hypothesis, and the synergy between them.

multiSyncPy: A Python package for assessing multivariate coordination dynamics.

In order to support the burgeoning field of research into intra- and interpersonal synchrony, we present an open-source software package: multiSyncPy. Multivariate synchrony goes beyond the bivariate case and can be useful for quantifying how groups, teams, and families coordinate their behaviors, or estimating the degree to which multiple modalities from an individual become synchronized. Our package includes state-of-the-art multivariate methods including symbolic entropy, multidimensional recurrence quantification analysis, coherence (with an additional sum-normalized modification), the cluster-phase 'Rho' metric, and a statistical test based on the Kuramoto order parameter. We also include functions for two surrogation techniques to compare the observed coordination dynamics with chance levels and a windowing function to examine time-varying coordination for most of the measures. Taken together, our collation and presentation of these methods make the study of interpersonal synchronization and coordination dynamics applicable to larger, more complex and often more ecologically valid study designs. In this work, we summarize the relevant theoretical background and present illustrative practical examples, lessons learned, as well as guidance for the usage of our package - using synthetic as well as empirical data. Furthermore, we provide a discussion of our work and software and outline interesting further directions and perspectives. multiSyncPy is freely available under the LGPL license at: https://github.com/cslab-hub/multiSyncPy , and also available at the Python package index.

Aviation and neurophysiology: A systematic review.

This paper systematically reviews 20 years of publications (N = 54) on aviation and neurophysiology. The main goal is to provide an account of neurophysiological changes associated with flight training with the aim of identifying neurometrics indicative of pilot's flight training level and task relevant mental states, as well as to capture the current state-of-art of (neuro)ergonomic design and practice in flight training. We identified multiple candidate neurometrics of training progress and workload, such as frontal theta power, the EEG Engagement Index and the Cognitive Stability Index. Furthermore, we discovered that several types of classifiers could be used to accurately detect mental states, such as the detection of drowsiness and mental fatigue. The paper advances practical guidelines on terminology usage, simulator fidelity, and multimodality, as well as future research ideas including the potential of Virtual Reality flight simulations for training, and a brain-computer interface for flight training.

A Typology for the Application of Team Coordination Dynamics Across Increasing Levels of Dynamic Complexity.

OBJECTIVE: This review and synthesis examines approaches for measuring and assessing team coordination dynamics (TCD). The authors advance a system typology for classifying TCD approaches and their applications for increasing levels of dynamic complexity. BACKGROUND: There is an increasing focus on how teams adapt their coordination in response to changing and uncertain operational conditions. Understanding coordination is significant because poor coordination is associated with maladaptive responses, whereas adaptive coordination is associated with effective responses. This issue has been met with TCD approaches that handle increasing complexity in the types of TCD teams exhibit. METHOD: A three-level system typology of TCD approaches for increasing dynamic complexity is provided, with examples of research at each level. For System I TCD, team states converge toward a stable, fixed-point attractor. For System II TCD, team states are periodic, which can appear complex, yet are regular and relatively stable. In System III TCD, teams can exhibit periodic patterns, but those patterns change continuously to maintain effectiveness. RESULTS: System I and System II are applicable to TCD with known or discoverable behavioral attractors that are stationary across mid-to long-range timescales. System III TCD is the most generalizable to dynamic environments with high requirements for adaptive coordination across a range of timescales. CONCLUSION: We outline current challenges for TCD and next steps in this burgeoning field of research. APPLICATION: System III approaches are becoming widespread, as they are generalizable to time- and/or scale-varying TCD and multimodal analyses. Recommendations for deploying TCD in team settings are provided.

Prospects for Augmenting Team Interactions with Real-Time Coordination-Based Measures in Human-Autonomy Teams.

Complex work in teams requires coordination across team members and their technology as well as the ability to change and adapt over time to achieve effective performance. To support such complex interactions, recent efforts have worked toward the design of adaptive human-autonomy teaming systems that can provide feedback in or near real time to achieve the desired individual or team results. However, while significant advancements have been made to better model and understand the dynamics of team interaction and its relationship with task performance, appropriate measures of team coordination and computational methods to detect changes in coordination have not yet been widely investigated. Having the capacity to measure coordination in real time is quite promising as it provides the opportunity to provide adaptive feedback that may influence and regulate teams' coordination patterns and, ultimately, drive effective team performance. A critical requirement to reach this potential is having the theoretical and empirical foundation from which to do so. Therefore, the first goal of the paper is to review approaches to coordination dynamics, identify current research gaps, and draw insights from other areas, such as social interaction, relationship science, and psychotherapy. The second goal is to collate extant work on feedback and advance ideas for adaptive feedback systems that have potential to influence coordination in a way that can enhance the effectiveness of team interactions. In addressing these two goals, this work lays the foundation as well as plans for the future of human-autonomy teams that augment team interactions using coordination-based measures.

Windowed multiscale synchrony: modeling time-varying and scale-localized interpersonal coordination dynamics.

Social interactions are pervasive in human life with varying forms of interpersonal coordination emerging and spanning different modalities (e.g. behaviors, speech/language, and neurophysiology). However, during social interactions, as in any dynamical system, patterns of coordination form and dissipate at different scales. Historically, researchers have used aggregate measures to capture coordination over time. While those measures (e.g. mean relative phase, cross-correlation, coherence) have provided a wealth of information about coordination in social settings, some evidence suggests that multiscale coordination may change over the time course of a typical empirical observation. To address this gap, we demonstrate an underutilized method, windowed multiscale synchrony, that moves beyond quantifying aggregate measures of coordination by focusing on how the relative strength of coordination changes over time and the scales that comprise social interaction. This method involves using a wavelet transform to decompose time series into component frequencies (i.e. scales), preserving temporal information and then quantifying phase synchronization at each of these scales. We apply this method to both simulated and empirical interpersonal physiological and neuromechanical data. We anticipate that demonstrating this method will stimulate new insights on the mechanisms and functions of synchrony in interpersonal contexts using neurophysiological and behavioral measures.

Investigating coregulation of emotional arousal during exposure-based CBT using vocal encoding and actor-partner interdependence models.

High patient emotional arousal during rationale development for in vivo exposure in CBT for panic disorder with agoraphobia might endanger comprehension of the exposure rationale. Since therapists are supposed to coregulate patients' emotions, this study investigated whether there was evidence of coregulation of vocally encoded emotional arousal, assessed by fundamental frequency (f0), during rationale development. Furthermore, the association of patient f0 stability and therapist coregulation with patients' perceived rationale plausibility was analyzed. N = 197 therapy videos-used to deduct f0-from a multicenter randomized controlled trial evaluating therapist-guided exposure on CBT outcome were analyzed post hoc. Plausibility of the exposure rationale was assessed by patients after its development. This trial-specific rating aggregates plausibility ratings for every manual component in the development of the exposure rationale and showed good internal consistency (Cronbach's alpha = .85). Stability in f0 and its coregulation were calculated using cross-lagged Actor-Partner Interdependence Models (APIMs), and APIM dyad estimates were associated with plausibility using linear regression analyses. Analyses indicated a relative stability in emotional arousal within both patients and therapists. Therapists' f0 had a significant effect on patients in that with therapist covariation, patients' f0 departed from their equilibrium level, while patients' f0 had no effect on therapists. Therapists' f0 covariation was positively associated with rationale plausibility. This study sheds light on interpersonal regulation mechanisms of patients' and therapists' emotional arousal during development of the exposure rationale. It suggests that coregulation of patients' emotional arousal supports patients' perceived rationale plausibility. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Multiscale movement coordination dynamics in collaborative team problem solving.

During collaborative problem solving (CPS), coordination occurs at different spatial and temporal scales. This multiscale coordination should play a functional role in facilitating effective collaboration. To evaluate this, we conducted a study of computer-based CPS with 42 dyadic teams. We used cross-wavelet coherence to examine movement coordination, extracted from videos, at several scales, and tested whether the observed coordination was greater than expected due to chance and due to task demands. We found that coordination at scales less than 2s was greater than chance and at most scales (except 16s, 1m, and 2m), was greater than expected due to task demands. Lastly, we observed that coherence at .25s and 1s scales was predictive of performance. However, when including relative phase, our results suggest that higher in-phase movement coordination at the 1s scale was the strongest predictor of CPS performance. Further, we used growth curve modeling to examine how movement coordination changes across the duration of the task and whether this is moderated by CPS performance. We found that coordination over the duration of the CPS task is quadratic (a U shape) and that better performing teams have higher coordination with a shallower curve. We discuss these findings and their relevance to understanding how low-level movement coordination facilitates CPS.

Problem-Solving Phase Transitions During Team Collaboration.

Multiple theories of problem-solving hypothesize that there are distinct qualitative phases exhibited during effective problem-solving. However, limited research has attempted to identify when transitions between phases occur. We integrate theory on collaborative problem-solving (CPS) with dynamical systems theory suggesting that when a system is undergoing a phase transition it should exhibit a peak in entropy and that entropy levels should also relate to team performance. Communications from 40 teams that collaborated on a complex problem were coded for occurrence of problem-solving processes. We applied a sliding window entropy technique to each team's communications and specified criteria for (a) identifying data points that qualify as peaks and (b) determining which peaks were robust. We used multilevel modeling, and provide a qualitative example, to evaluate whether phases exhibit distinct distributions of communication processes. We also tested whether there was a relationship between entropy values at transition points and CPS performance. We found that a proportion of entropy peaks was robust and that the relative occurrence of communication codes varied significantly across phases. Peaks in entropy thus corresponded to qualitative shifts in teams' CPS communications, providing empirical evidence that teams exhibit phase transitions during CPS. Also, lower average levels of entropy at the phase transition points predicted better CPS performance. We specify future directions to improve understanding of phase transitions during CPS, and collaborative cognition, more broadly.

A multivariate dynamic systems model for psychotherapy with more than one client.

The dynamics of the give and take between therapist and client(s) is frequently of interest to therapy process researchers. Characterizing the ways that therapists respond to clients and clients respond to therapists can be challenging in therapeutic encounters involving a single therapist and a single client. The complexity of this challenge increases as the number of people involved in a therapeutic encounter increases not only because there are more people responding to one another but also because the patterns of responding can become more complex. This manuscript demonstrates how dyadic cross-lagged panel models can be extended to psychotherapeutic encounters involving 3 people and used to test processes that exist between dyadic subsets of the larger group as well as the group as one cohesive unit. Three hundred seventy-nine talk turns of fundamental frequency from a couple therapy session were modeled using 3 dyadic cross-lagged panel models, and each individual's respiratory sinus arrhythmia (RSA) was treated as a moderator. Although the regression coefficients for each dyadic subset (e.g., therapist-husband) were nonsignificant, an eigenvalue/eigenvector decomposition of the regression coefficients from the 3 dyadic cross-lagged panel models suggests that interdependence exists at the level of the whole group (i.e., therapist-husband-wife) rather than between pairs of individuals within the group (e.g., husband-wife). Further, an interaction involving husband's RSA suggested that interdependence involving the husband ceased when the husband displayed greater regulatory effort. This combination of statistical methods allows for clearly distinguishing between dyadic therapeutic processes and group-level therapeutic processes. (PsycINFO Database Record

Changes in Dimensionality and Fractal Scaling Suggest Soft-Assembled Dynamics in Human EEG.

Humans are high-dimensional, complex systems consisting of many components that must coordinate in order to perform even the simplest of activities. Many behavioral studies, especially in the movement sciences, have advanced the notion of soft-assembly to describe how systems with many components coordinate to perform specific functions while also exhibiting the potential to re-structure and then perform other functions as task demands change. Consistent with this notion, within cognitive neuroscience it is increasingly accepted that the brain flexibly coordinates the networks needed to cope with changing task demands. However, evaluation of various indices of soft-assembly has so far been absent from neurophysiological research. To begin addressing this gap, we investigated task-related changes in two distinct indices of soft-assembly using the established phenomenon of EEG repetition suppression. In a repetition priming task, we assessed evidence for changes in the correlation dimension and fractal scaling exponents during stimulus-locked event-related potentials, as a function of stimulus onset and familiarity, and relative to spontaneous non-task-related activity. Consistent with predictions derived from soft-assembly, results indicated decreases in dimensionality and increases in fractal scaling exponents from resting to pre-stimulus states and following stimulus onset. However, contrary to predictions, familiarity tended to increase dimensionality estimates. Overall, the findings support the view from soft-assembly that neural dynamics should become increasingly ordered as external task demands increase, and support the broader application of soft-assembly logic in understanding human behavior and electrophysiology.

Evaluating Emotional and Biological Sensitivity to Maternal Behavior among Self-injuring and Depressed Adolescent Girls Using Nonlinear Dynamics.

High sensitivity and reactivity to behaviors of family members characterizes several forms of psychopathology, including self-inflicted injury (SII). We examined mother-daughter behavioral and psychophysiological reactivity during a conflict discussion using nonlinear dynamics to assess asymmetrical associations within time-series data. Depressed, SII, and control adolescents and their mothers participated (n=76 dyads). We expected that (1) mothers' evocative behaviors would affect behavioral and psychophysiological reactivity among depressed and, especially, SII adolescents, (2) adolescents' behaviors would not evoke mothers' behavioral or physiological reactivity, and (3) control teens and mothers would be less reactive, with no dynamic associations in either direction. Convergent cross-mapping with dewdrop regression, which identifies directional associations, indicated that mothers' behaviors evoked behavioral responses among depressed and SII participants, but psychophysiological reactivity for SII teens only. There were no effects of adolescents' behavior on mothers' reactivity. Results are interpreted based upon sensitivity theories and directions for further research are outlined.

Modeling Change in Project Duration and Completion: Scheduling Dynamics of NASA's Exploration Flight Test 1 (EFT-1) Activities.

In complex work domains and organizations, understanding schedule-ing dynamics can ensure objectives are reached and delays are mitigated. In the current paper, we examine the scheduling dynamics for NASA's Exploration Flight Test 1 (EFT-1) activities. For this examination, we specifically modeled simultaneous change in percent complete and estimated duration for a given project as they were included in monthly reports over time. In short, we utilized latent change score mixture modeling to extract the attractor dynamics within the scheduling data. We found three primarily patterns: an attractor at low duration, low percent complete; a saddle that was attractive toward full completion and repelled duration away from five months, and an attractor at full completion and high duration. We replicated these three patterns using multilevel modeling. Then, we examined how task dependencies, in terms of the number of predecessors and successors, affected the probability of exhibiting a given pattern over time. Thus, we offer a flexible method for understanding the patterns that can characterize scheduling dynamics as well as other dynamical systems. Several recommendations for future directions are discussed.

Modeling Multi-Agent Self-Organization through the Lens of Higher Order Attractor Dynamics.

Social interaction occurs across many time scales and varying numbers of agents; from one-on-one to large-scale coordination in organizations, crowds, cities, and colonies. These contexts, are characterized by emergent self-organization that implies higher order coordinated patterns occurring over time that are not due to the actions of any particular agents, but rather due to the collective ordering that occurs from the interactions of the agents. Extant research to understand these social coordination dynamics (SCD) has primarily examined dyadic contexts performing rhythmic tasks. To advance this area of study, we elaborate on attractor dynamics, our ability to depict them visually, and quantitatively model them. Primarily, we combine difference/differential equation modeling with mixture modeling as a way to infer the underlying topological features of the data, which can be described in terms of attractor dynamic patterns. The advantage of this approach is that we are able to quantify the self-organized dynamics that agents exhibit, link these dynamics back to activity from individual agents, and relate it to other variables central to understanding the coordinative functionality of a system's behavior. We present four examples that differ in the number of variables used to depict the attractor dynamics (1, 2, and 6) and range from simulated to non-simulated data sources. We demonstrate that this is a flexible method that advances scientific study of SCD in a variety of multi-agent systems.

Graphic methods for interpreting longitudinal dyadic patterns from repeated-measures actor-partner interdependence models.

Researchers commonly use repeated-measures actor-partner interdependence models (RM-APIM) to understand how romantic partners change in relation to one another over time. However, traditional interpretations of the results of these models do not fully or correctly capture the dyadic temporal patterns estimated in RM-APIM. Interpretation of results from these models largely focuses on the meaning of single-parameter estimates in isolation from all the others. However, considering individual coefficients separately impedes the understanding of how these associations combine to produce an interdependent pattern that emerges over time. Additionally, positive within-person, or actor, effects are commonly misinterpreted as indicating growth from one time point to the next when they actually represent decline. We suggest that change-as-outcome RM-APIMs and vector field diagrams (VFDs) can be used to improve the understanding and presentation of dyadic patterns of association described by standard RM-APIMs. The current article briefly reviews the conceptual foundations of RM-APIMs, demonstrates how change-as-outcome RM-APIMs and VFDs can aid interpretation of standard RM-APIMs, and provides a tutorial in making VFDs using multilevel modeling. (PsycINFO Database Record

Technology as Teammate: Examining the Role of External Cognition in Support of Team Cognitive Processes.

In this paper we advance team theory by describing how cognition occurs across the distribution of members and the artifacts and technology that support their efforts. We draw from complementary theorizing coming out of cognitive engineering and cognitive science that views forms of cognition as external and extended and integrate this with theorizing on macrocognition in teams. Two frameworks are described that provide the groundwork for advancing theory and aid in the development of more precise measures for understanding team cognition via focus on artifacts and the technologies supporting their development and use. This includes distinctions between teamwork and taskwork and the notion of general and specific competencies from the organizational sciences along with the concepts of offloading and scaffolding from the cognitive sciences. This paper contributes to the team cognition literature along multiple lines. First, it aids theory development by synthesizing a broad set of perspectives on the varied forms of cognition emerging in complex collaborative contexts. Second, it supports research by providing diagnostic guidelines to study how artifacts are related to team cognition. Finally, it supports information systems designers by more precisely describing how to conceptualize team-supporting technology and artifacts. As such, it provides a means to more richly understand process and performance as it occurs within sociotechnical systems. Our overarching objective is to show how team cognition can both be more clearly conceptualized and more precisely measured by integrating theory from cognitive engineering and the cognitive and organizational sciences.

Working memory performance inversely predicts spontaneous delta and theta-band scaling relations.

Electrophysiological studies have strongly implicated theta-band activity in human working memory processes. Concurrently, work on spontaneous, non-task-related oscillations has revealed the presence of long-range temporal correlations (LRTCs) within sub-bands of the ongoing EEG, and has begun to demonstrate their functional significance. However, few studies have yet assessed the relation of LRTCs (also called scaling relations) to individual differences in cognitive abilities. The present study addressed the intersection of these two literatures by investigating the relation of narrow-band EEG scaling relations to individual differences in working memory ability, with a particular focus on the theta band. Fifty-four healthy adults completed standardized assessments of working memory and separate recordings of their spontaneous, non-task-related EEG. Scaling relations were quantified in each of the five classical EEG frequency bands via the estimation of the Hurst exponent obtained from detrended fluctuation analysis. A multilevel modeling framework was used to characterize the relation of working memory performance to scaling relations as a function of general scalp location in Cartesian space. Overall, results indicated an inverse relationship between both delta and theta scaling relations and working memory ability, which was most prominent at posterior sensors, and was independent of either spatial or individual variability in band-specific power. These findings add to the growing literature demonstrating the relevance of neural LRTCs for understanding brain functioning, and support a construct- and state-dependent view of their functional implications.

Shifting the paradigm of music instruction: implications of embodiment stemming from an augmented reality guitar learning system.

Musical instruction often includes materials that can act as a barrier to learning. New technologies using augmented reality may aid in reducing the initial difficulties involved in learning music by lowering these barriers characteristic of traditional instructional materials. Therefore, this set of studies examined a novel augmented reality guitar learning system (i.e., the Fretlight® guitar) in regards to current theories of embodied music cognition. Specifically, we examined the effects of using this system in comparison to a standard instructional material (i.e., diagrams). First, we review major theories related to musical embodiment and specify a niche within this research space we call embodied music technology for learning. Following, we explicate two parallel experiments that were conducted to address the learning effects of this system. Experiment 1 examined short-term learning effects within one experimental session, while Experiment 2 examined both short-term and long-term effects across two sessions spaced at a 2-week interval. Analyses demonstrated that, for many of our dependent variables, all participants increased in performance across time. Further, the Fretlight® condition consistently led to significantly better outcomes via interactive effects, including significantly better long term retention for the learned information across a 2 week time interval. These results are discussed in the context of embodied cognition theory as it relates to music. Potential limitations and avenues for future research are described.

Complex collaborative problem-solving processes in mission control.

INTRODUCTION: NASA's Mission Control Center (MCC) is responsible for control of the International Space Station (ISS), which includes responding to problems that obstruct the functioning of the ISS and that may pose a threat to the health and well-being of the flight crew. These problems are often complex, requiring individuals, teams, and multiteam systems, to work collaboratively. Research is warranted to examine individual and collaborative problem-solving processes in this context. Specifically, focus is placed on how Mission Control personnel-each with their own skills and responsibilities-exchange information to gain a shared understanding of the problem. The Macrocognition in Teams Model describes the processes that individuals and teams undertake in order to solve problems and may be applicable to Mission Control teams. METHOD: Semistructured interviews centering on a recent complex problem were conducted with seven MCC professionals. In order to assess collaborative problem-solving processes in MCC with those predicted by the Macrocognition in Teams Model, a coding scheme was developed to analyze the interview transcriptions. RESULTS: Findings are supported with excerpts from participant transcriptions and suggest that team knowledge-building processes accounted for approximately 50% of all coded data and are essential for successful collaborative problem solving in mission control. Support for the internalized and externalized team knowledge was also found (19% and 20%, respectively). DISCUSSION: The Macrocognition in Teams Model was shown to be a useful depiction of collaborative problem solving in mission control and further research with this as a guiding framework is warranted.