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Hall, Caitrín, Ji Chul Kim, and Alexandra Paxton. "Multidimensional recurrence quantification analysis of human-metronome phasing." PLOS ONE 18, no. 2 (February 23, 2023): e0279987. http://dx.doi.org/10.1371/journal.pone.0279987.
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Perception-action coordination (also known as sensorimotor synchronization, SMS) is often studied by analyzing motor coordination with auditory rhythms. The current study assesses phasing—a compositional technique in which two people tap the same rhythm at varying phases by adjusting tempi—to explore how SMS is impacted by individual and situational factors. After practice trials, participants engaged in the experimental phasing task with a metronome at tempi ranging from 80–140 beats per minute (bpm). Multidimensional recurrence quantification analysis (MdRQA) was used to compare nonlinear dynamics of phasing performance. Varying coupling patterns emerged and were significantly predicted by tempo and linguistic experience. Participants who successfully phased replicated findings from an original case study, demonstrating stable tapping patterns near in-phase and antiphase, while those unsuccessful at phasing showed weaker attraction to in-phase and antiphase.
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Laudańska, Zuzanna, David López Pérez, Alicja Radkowska, Karolina Babis, Anna Malinowska-Korczak, Sebastian Wallot, and Przemysław Tomalski. "Changes in the Complexity of Limb Movements during the First Year of Life across Different Tasks." Entropy 24, no. 4 (April 15, 2022): 552. http://dx.doi.org/10.3390/e24040552.
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Infants’ limb movements evolve from disorganized to more selectively coordinated during the first year of life as they learn to navigate and interact with an ever-changing environment more efficiently. However, how these coordination patterns change during the first year of life and across different contexts is unknown. Here, we used wearable motion trackers to study the developmental changes in the complexity of limb movements (arms and legs) at 4, 6, 9 and 12 months of age in two different tasks: rhythmic rattle-shaking and free play. We applied Multidimensional Recurrence Quantification Analysis (MdRQA) to capture the nonlinear changes in infants’ limb complexity. We show that the MdRQA parameters (entropy, recurrence rate and mean line) are task-dependent only at 9 and 12 months of age, with higher values in rattle-shaking than free play. Since rattle-shaking elicits more stable and repetitive limb movements than the free exploration of multiple objects, we interpret our data as reflecting an increase in infants’ motor control that allows for stable body positioning and easier execution of limb movements. Infants’ motor system becomes more stable and flexible with age, allowing for flexible adaptation of behaviors to task demands.
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Høffding, Simon, Wenbo Yi, Eigil Lippert, Victor Gonzales Sanchez, Laura Bishop, Bruno Laeng, Anne Danielsen, Alexander Refsum Jensenius, and Sebastian Wallot. "Into the Hive-Mind: Shared Absorption and Cardiac Interrelations in Expert and Student String Quartets." Music & Science 6 (January 2023): 205920432311685. http://dx.doi.org/10.1177/20592043231168597.
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Expert musicians portray awe-inspiring precision, timing, and phrasing and may be thought to partake in a “hive-mind.” Such a shared musical absorption is characterized by a heightened empathic relation, mutual trust, and a sense that the music “takes over,” thus uniting the performers’ musical intentions. Previous studies have found correlations between empathic concern or shared experience and cardiac synchrony (CS). We aimed to investigate shared musical absorption in terms of CS by analyzing CS in two quartets: a student quartet, the Borealis String Quartet (BSQ), and an expert quartet, the Danish String Quartet (DSQ), world-renowned for their interpretations and cohesion. These two quartets performed the same Haydn excerpt in seven conditions, some of which were designed to disrupt their absorption. Using multidimensional recurrence quantification analysis (MdRQA), we found that: (1) performing resulted in significantly increased CS in both quartets compared with resting; (2) across all conditions, the DSQ had a significantly higher CS than the BSQ; (3) the BSQ's CS was inversely correlated with the degree of disruption; 4) for the DSQ, the CS remained constant across all levels of disruption, besides one added extreme disruption—a sight-reading condition. These findings tentatively support the claim that a sense of shared musical absorption, as well as group expertise, is correlated with CS.
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Wallot, Sebastian. "Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA) – A Method for Quantifying Correlation between Multivariate Time-Series." Multivariate Behavioral Research 54, no. 2 (December 20, 2018): 173–91. http://dx.doi.org/10.1080/00273171.2018.1512846.
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Tolston, Michael T., Gregory J. Funke, Gene M. Alarcon, Brent Miller, Margaret A. Bowers, Christina Gruenwald, and August Capiola. "Have a Heart: Predictability of Trust in an Autonomous Agent Teammate through Team-Level Measures of Heart Rate Synchrony and Arousal." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 714–15. http://dx.doi.org/10.1177/1541931218621162.
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Progression toward sophisticated machines with the capacity to act as partners in tactical and strategic situations means that human operators will increasingly rely on collaborative input from agent teammates (e.g., Masiello, 2013). However, plans to team autonomous agents with humans raise new questions regarding the effects that such teammates might have on important team psychological processes, such as team cognition and trust. Specifically, it is not known how modifications in team structure, such as changes in team size, influence team dynamics and psychological processes when the team includes an artificial agent, nor how trust established in such teams transfers to new environments, nor how measures that have been used to predict trust in humans generalize to agent teammates. Our current research examined these effects through the detection and analysis of an objective team phenomenon known as physio-behavioral coupling (PBC) using Multidimensional Recurrence Quantification Analysis (MdRQA; Wallot, Roepstorff, and Mønster, 2016) of shared physiological arousal during initial team formation and training. In particular, as shared physiological arousal measured in changing heart rhythms within human teams has been shown to be associated with measures of trust (Mitkidis, McGraw, Roepstorff, & Wallot, 2015) and concern for others (Konvalinka et al., 2011), we investigated how shared physiological arousal predicts willingness to trust an agent teammate in a novel task environment. We conducted an experiment consisting of collecting physio-behavioral data (i.e., heart rate) from teams of different sizes as they performed a series of collaborative, consensus building tasks. The independent variable was team size (teams of 2 or 3 human players, with an artificial agent teammate always present), and there were two separate team-oriented tasks: A first-round consensus-building wagering task, and a second-round task in which teams were able to make wagers on the expected performance of the agent teammate in a subsequent maze running task called Checkmate (Alarcon et al., 2017). We predicted that complimentary combinations of PBC (e.g., measures of overall similarity and stability in heart rate dynamics) obtained from MdRQA, along with self-reported measures of team and agent trust, would be positively related to future trusting behaviors in the agent teammate, and that increasing the number of teammates would result in higher order, more complex structure in the physio- behavioral data that would not be reducible to simpler patterns (e.g., Wallot et al., 2016). To this end, we predicted that measures of self-reported trust and multivariate PBC would be reducible to meaningful lower dimensional structures using principal components analysis (PCA), and that PBC calculated from the first task from the full team, but not from averages aggregated from subsets of the team, would significantly predict trusting behavior in the second task. Ninety-two participants (31 men and 61 women) recruited from the campus of a midwestern university in the U.S. took part in this study (19 dyads and 18 triads). Ages ranged from 18 to 42 ( M = 22, SD = 5.48). The experiment was a univariate (team size; two or three human teammates with an agent teammate always present) between-subjects design. Self-reported measures were collected from each team member before each of the two tasks and included items that measured: Team ability, team benevolence, team integrity, and team trust (adapted from Mayer & Davis, 1999); trust in human teammates (adapted from Naquin & Paulson, 2003); agent competence, cognitive trust in the agent, emotional trust in the agent, intention to delegate to the agent, and intention to adopt the agent as an aid (adapted from Komiak & Benbasat, 2006); and collective efficacy (adapted from Riggs & Knight, 1994). Factor analysis of the composite scales from aggregated survey data indicated the data loaded well onto factors that corresponded to trust in the team and trust in the agent teammate. Factor analysis of MdRQA from the full team and from the averaged lower order analyses showed that each had one component with an eigenvalue greater than what would be expected by chance. Results from analyses using logistic regression to predict Checkmate betting showed that self-reported measures of trust in the agent and MdRQA of full team PBC in the initial task significantly predicted subsequent trusting behavior in an agent teammate in Checkmate, but lower-order PBC estimated from averages of team subgroups did not. These results suggest that multivariate team-level coupling has predictive power in subsequent team outcomes that cannot be fully captured using data aggregated from subgroup averages, and that measures of PBC measured from human teammates is related to trust in an agent teammate. We note two important contributions of the present study. First, that PBC and subjective measures of trust were significant predictors of observed trusting behavior regardless of team size suggests that important team processes and outcomes are at least partially invariant to changes in team size, a promising outcome for the prospect of meaningfully scaling measures of PBC beyond the typical dyadic context. Second, we have shown that shared team- level arousal is a significant predictor of subsequent trusting behavior in an agent teammate in a novel task, demonstrating that these objective measures are extensible to trust in non-human partners.
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He, Qian, and Jingjing Huang. "Multiwavelet scale multidimensional recurrence quantification analysis." Chaos: An Interdisciplinary Journal of Nonlinear Science 30, no. 12 (December 2020): 123109. http://dx.doi.org/10.1063/5.0025882.
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Coco, Moreno,I, Dan Mønster, Giuseppe Leonardi, Rick Dale, and Sebastian Wallot. "Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa." R Journal 13, no. 1 (2021): 145. http://dx.doi.org/10.32614/rj-2021-062.
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Shekhar, Himanshu Kumar, Chetan Kamble, Ashish Thakur, and Sanjeet Kumar Bhagat. "Application of Recurrence Quantification Analysis for Non-Linear Dynamical Systems." International Journal of Engineering and Advanced Technology 9, no. 1s5 (December 30, 2019): 92–94. http://dx.doi.org/10.35940/ijeat.a1010.1291s519.
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The Recurrence plots (RPs) have been introduced in several different scientific and medical disciplines. The main purpose of recurrence plot is used to of identify the higher dimensional phase space trajectories. RPs are purely graphically representation which have been designed for the detection of hidden dynamical patterns and non-linearity present in the data, the evaluation of error which is caused by observational noise can be done by Recurrence Quantification Analysis (RQA). RQA method is initially used to minimize the error present in the given signals. RQA method is a basically a technique for the analysis of nonlinear data to quantify the number and duration of a dynamical systems. The recurrence plot is used for time series domain for multidimensional signal also. Recurrence is the property of non-stationary and dynamical system to characteristics the time series analysis in phase space trajectories. Recurrence Quantification Analysis is used to derive from recurrence plots, which are based upon distances matrices of time series.
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Zervou, Michaela Areti, Effrosyni Doutsi, Pavlos Pavlidis, and Panagiotis Tsakalides. "Structural classification of proteins based on the computationally efficient recurrence quantification analysis and horizontal visibility graphs." Bioinformatics 37, no. 13 (May 28, 2021): 1796–804. http://dx.doi.org/10.1093/bioinformatics/btab407.
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Abstract Motivation Protein structural class prediction is one of the most significant problems in bioinformatics, as it has a prominent role in understanding the function and evolution of proteins. Designing a computationally efficient but at the same time accurate prediction method remains a pressing issue, especially for sequences that we cannot obtain a sufficient amount of homologous information from existing protein sequence databases. Several studies demonstrate the potential of utilizing chaos game representation along with time series analysis tools such as recurrence quantification analysis, complex networks, horizontal visibility graphs (HVG) and others. However, the majority of existing works involve a large amount of features and they require an exhaustive, time consuming search of the optimal parameters. To address the aforementioned problems, this work adopts the generalized multidimensional recurrence quantification analysis (GmdRQA) as an efficient tool that enables to process concurrently a multidimensional time series and reduce the number of features. In addition, two data-driven algorithms, namely average mutual information and false nearest neighbors, are utilized to define in a fast yet precise manner the optimal GmdRQA parameters. Results The classification accuracy is improved by the combination of GmdRQA with the HVG. Experimental evaluation on a real benchmark dataset demonstrates that our methods achieve similar performance with the state-of-the-art but with a smaller computational cost. Availability and implementation The code to reproduce all the results is available at https://github.com/aretiz/protein_structure_classification/tree/main. Supplementary information Supplementary data are available at Bioinformatics online.
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Algumaei, Mohammed, Imali Hettiarachchi, Rakesh Veerabhadrappa, and Asim Bhatti. "Physiological Synchrony Predict Task Performance and Negative Emotional State during a Three-Member Collaborative Task." Sensors 23, no. 4 (February 17, 2023): 2268. http://dx.doi.org/10.3390/s23042268.
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Evaluation of team performance in naturalistic contexts has gained popularity during the last two decades. Among other human factors, physiological synchrony has been adopted to investigate team performance and emotional state when engaged in collaborative team tasks. A variety of methods have been reported to quantify physiological synchrony with a varying degree of correlation with the collaborative team task performance and emotional state, reflected in the inconclusive nature of findings. Little is known about the effect of the choice of synchrony calculation methods and the level of analysis on these findings. In this research work, we investigate the relationship between outcomes of different methods to quantify physiological synchrony, emotional state, and team performance of three-member teams performing a collaborative team task. The proposed research work employs dyadic-level linear (cross-correlation) and team-level non-linear (multidimensional recurrence quantification analysis) synchrony calculation measures to quantify task performance and the emotional state of the team. Our investigation indicates that the physiological synchrony estimated using multidimensional recurrence quantification analysis revealed a significant negative relationship between the subjectively reported frustration levels and overall task performance. However, no relationship was found between cross-correlation-based physiological synchrony and task performance. The proposed research highlights that the method of choice for physiological synchrony calculation has direct impact on the derived relationship of team task performance and emotional states.
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Wang, Mukai, Tianfeng Wang, Duhui Lu, and Shuhui Cui. "Intelligent Diagnosis of Bearing Failures Based on Recurrence Quantification and Energy Difference." Applied Sciences 14, no. 21 (October 22, 2024): 9643. http://dx.doi.org/10.3390/app14219643.
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Bearing health is key for maintaining good performance and safety in rotating machinery. As the diagnosis of mechanical faults develops toward intelligence and automation, accurate and systematic fault diagnosis algorithms are imperative. Focusing on the diagnosis of rolling bearing failures, this study utilizes a sliding time window to extract essential data segments. A series of signal processing techniques, including filtering, amplitude–frequency analysis, Hilbert envelope analysis, and energy analysis, is applied to establish a comprehensive dataset. For extraction of the hidden properties of the data, the recurrence quantity spectrum is defined for the input of the neural network. The goal is to obtain a cleaner dataset with enhanced features. A convolution neural network is constructed. Different activation functions in the activation layer are compared for better fault diagnosis algorithms. The established feature matrices are specifically defined to accurately identify the subtlest defects of bearings, thereby facilitating early detection. The proposed procedure distinguishes various fault modes. As for the multidimensional complexities of fault signals, this study carries out a comprehensive comparison of energies, recurrence quantification, and amplitude–frequency characteristics of bearing fault detection to assess the accuracy, computational efficiency, and robustness of bearing fault diagnosis. The proposed method and bearing fault detection procedures have potential in practical applications.
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Eloy, Lucca, Cara Spencer, Emily Doherty, and Leanne Hirshfield. "Capturing the Dynamics of Trust and Team Processes in Human-Human-Agent Teams via Multidimensional Neural Recurrence Analyses." Proceedings of the ACM on Human-Computer Interaction 7, CSCW1 (April 14, 2023): 1–23. http://dx.doi.org/10.1145/3579598.
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As collaborative technologies evolve from supportive tools to interactive teammates, there is a growing need to understand how trust and team processes develop in human-agent teams. To contribute effectively, these systems must be able to support human teammates in a task without disrupting the delicate interpersonal states and team processes that govern successful collaboration. In order to break down the complexity of monitoring multiple actors in human-agent collaborations, there is a need to identify interpretable, generalizable measures that can monitor the emergence of interpersonal and team-level processes that underlie effective teaming. We address this gap by using functional Near-Infrared Spectroscopy to concurrently measure brain activity of two individuals in a human-human-agent team during a complex, ecologically valid collaborative task, with a goal of identifying quantitative markers of cognition- and affect-based trust alongside team processes of coordination, strategy formulation, and affect management. Two multidimensional extensions of recurrence quantification analysis, a nonlinear method based in dynamical systems theory, are presented to quantify interpersonal coupling and team-level regularity as reflected in the hemodynamics of three cortical regions across multiple time-scales. Mixed-effects regressions reveal that neural recurrence between individuals uniquely reflects changes in self-reported trust, while team-level neural regularity inversely predicts self-reported team processes. Additionally, we show that recurrence metrics capture temporal dynamics of affect-based trust consistent with existing theory, showcasing the interpretability and specificity of these metrics for disentangling complex team states and processes. This paper presents a novel, interpretable, and computationally efficient model-free method capable of differentiating between latent trust and team processes a complex, naturalistic task setting. We discuss the potential applications of this technique for continuous monitoring of team states, providing clear targets for the future development of adaptive human-agent teaming systems.
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Borrie, Stephanie A., Tyson S. Barrett, Julie M. Liss, and Visar Berisha. "Sync Pending: Characterizing Conversational Entrainment in Dysarthria Using a Multidimensional, Clinically Informed Approach." Journal of Speech, Language, and Hearing Research 63, no. 1 (January 22, 2020): 83–94. http://dx.doi.org/10.1044/2019_jslhr-19-00194.
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Purpose Despite the import of conversational entrainment to successful spoken dialogue, the systematic characterization of this behavioral syncing phenomenon represents a critical gap in the field of speech pathology. The goal of this study was to acoustically characterize conversational entrainment in the context of dysarthria using a multidimensional approach previously validated in healthy populations (healthy conversations; Borrie, Barrett, Willi, & Berisha, 2019 ). Method A large corpus of goal-oriented conversations between participants with dysarthria and healthy participants (disordered conversations) was elicited using a “spot the difference” task. Expert clinical assessment of entrainment and a measure of conversational success (communicative efficiency) was obtained for each of the audio-recorded conversations. Conversational entrainment of acoustic features representing rhythmic, articulatory, and phonatory dimensions of speech was identified using cross-recurrence quantification analysis with clinically informed model parameters and validated with a sham condition involving conversational participants who did not converse with one another. The relationship between conversational entrainment and communicative efficiency was examined. Results Acoustic evidence of entrainment was observed in phonatory, but not rhythmic and articulatory, behavior, a finding that differs from healthy conversations in which entrainment was observed in all speech signal dimensions. This result, that disordered conversations showed less acoustic entrainment than healthy conversations, is corroborated by clinical assessment of entrainment in which the disordered conversations were rated, overall, as being less in sync than healthy conversations. Furthermore, acoustic entrainment was predictive of communicative efficiency, corroborated by a relationship between clinical assessment and the same outcome measure. Conclusions The findings confirm our hypothesis that the pathological speech production parameters of dysarthria disrupt the seemingly ubiquitous phenomenon of conversational entrainment, thus advancing entrainment deficits as an important variable in dysarthria, one that may have causative effects on the success of everyday communication. Results further reveal that while this approach provides a broad overview, methodologies for characterizing conversational entrainment in dysarthria must continue to be developed and refined, with a focus on clinical utility. Supplemental Material https://osf.io/ktg5q
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Demir, Mustafa, Nancy J. Cooke, Christopher S. Lieber, and Sarah Ligda. "Understanding Controller-Pilot Interaction Dynamics in The Context of Air Traffic Control." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 1225–26. http://dx.doi.org/10.1177/1071181319631493.
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Literature New capabilities to modernize the U.S. National Airspace System (NAS) include support of real-time information streams derived from many data sources across the NAS. As an emergent property, safety of the NAS arises from interactions between many elements at different levels, ranging from those attributable to humans, technology, and the environment. Each component in the NAS needs to interact with other components, exchange resources and information, and operate under broad regulations to achieve overall system objectives (Harris & Stanton, 2010). Sometimes, incidents and accidents result from insufficient interaction (communication and coordination) between humans (e.g., pilot-controller). The content of communication provides value and supports understanding with a multitude of individual, group, team, and data sets within air traffic research. In addition, another dimension to communication with a potentially rich source of understanding is everything outside of its explicit meaning. Cooke and Gorman (2009) describe methods of communication flow between teams (considered to be a system) that have proven insightful. The first is a ratio of team members speech quantity, which can indicate the degree of influence one member has over others. Another is the communication required and passed score, or how much variation there is in actual team communication from expectations. Flow quantity represents how much speech each member of the team produces. Gorman et al.’s (2012) study applied discrete Recurrence Quantification Analysis (RQA) to team communication flow data in order to visualize and measure coordination dynamics of Unnamed Aircraft Vehicle (UAV) teams, both mixed teams (i.e., team members changed) and intact teams (i.e., team members stayed the same over successive experimental sessions). Interestingly, mixed teams were better able to adjust to unexpected perturbations; this ability was linked to team level coordination dynamics. That is, mixed teams adopted a globally stable pattern of communication while exhibiting strong temporal dependence (Gorman, Cooke, Amazeen, & Fouse, 2012). Similarly, Demir, Cooke, & Amazeen (2018) applied discrete RQA on human-robot interaction in an Urban Search and Rescue task and multivariate extension of RQA on human-synthetic team in a UAV task. They underline that metastable team coordination (not too stable nor too flexible) between team members is associated with the ability to successfully overcome novel events (i.e., team situation awareness) in a dynamic task environment. The current project addresses the question of how human factors related to air traffic control (ATC), specifically situation awareness and cognitive load, interact with other factors in the NAS to affect ATC performance and a result in a safe and effective NAS? One way to answer this question is focusing on ATC-pilot communication as a chief performance indicator. In the current study, we investigate the potential of dynamical systems perspectives to capture the differential dynamics of three cases between controller-pilot communication flow during incidents and accidents. Method One of the approaches for investigating interaction patterns between system components (in the controller-pilot case) and their change over time involves looking at communication flow using discrete Recurrence Plot (RP) and corresponding Recurrence Quantification Analysis (RQA), which quantifies how many recurrences with a certain length are present by multidimensional space (phase space) trajectory in a dynamical system (Marwan, Carmen Romano, Thiel, & Kurths, 2007). RP is the basis of discrete RQA (Eckmann, Kamphorst, & Ruelle, 1987), which is a visual tool for demonstrating a system’s recurrent structure in the phase space when a system revisits specific states or sequences of states within a region of phase space over a period of time. In the case of two or more systems, discrete RP displays the times when two or more separate dynamical systems show a recurrence simultaneously (Marwan et al., 2007). Three cases of controller-pilot audio transmissions with their communication time stamps were obtained from “Cockpit Voice Recorder Transcripts” (2019), visualized using RP, and analyzed via discrete RQA. The cases represent situations of particular interest, communication, and coordination. Discrete RQA quantifies not only the effect of interventions (such as unexpected events) on instability, but also the dyad interaction processes and the dynamics that contribute to that process. The RQA was used to produce several measures, including percent recurrence rate, percent determinism (DET), longest diagonal line, longest vertical line, entropy, and laminarity. Of these, the focal variable was determinism (Marwan et al., 2007), which indicates the amount of organization in the communication of a system. DET is derived from the recurrence plot by examining how the recurrent points are distributed. Dyads with high determinism tend to repeat sequences of states many times, while a controller-pilot with low determinism rarely repeats a sequence of states, producing few diagonal lines. Results and discussion One of the objectives of this study is to monitor human performance indicators in real-time in the NAS to make predictions about risk. The current exploratory paper presents an idea about how to model human interaction between two or more roles with the larger purpose of developing NAS risk prognostics. We have presented three controller-pilot communication flows via discrete RP and RQA methods that differentiate three real cases based on discrete interaction sequences. The measures extracted from the RQA and visualizations of the interaction patterns show that effective communication and coordination is needed for effective situation awareness, i.e., overcoming the failures. Based on previous studies (Demir et al., 2018), we expected that the rigidity of the coordination dynamics between controller and pilot in one of the cases would associated with a fatal accident as well as lack of communication (confusion during the landing), resulting in a lack of situation awareness. On the other hand, two other incidents demonstrated more flexible behavior across the roles (controller-pilot) to adapt to the dynamic environment. In this case, the key lies in the dynamic transition between interaction and the environment. The controller and pilot are compelled to adjust their interaction patterns (flexibility) to adapt to changes in the environment and maintain a stable trajectory toward meeting their goals, such as landing safely. Thus, there are three crucial states for effective interaction in both temporal and spatial states: what needs to be communicated, when it needs to be coordinated, and how it needs to be communicated and coordinated”.
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Wallot, Sebastian, and Giuseppe Leonardi. "Analyzing Multivariate Dynamics Using Cross-Recurrence Quantification Analysis (CRQA), Diagonal-Cross-Recurrence Profiles (DCRP), and Multidimensional Recurrence Quantification Analysis (MdRQA) – A Tutorial in R." Frontiers in Psychology 9 (December 4, 2018). http://dx.doi.org/10.3389/fpsyg.2018.02232.
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Naher, Nurun, Mary Jean Amon, and Stephen M. Fiore. "Interpretable Models for Near-real-time Prediction of Team Cognitive Workload in Complex Sociotechnical Environments Using Behavioral and Physiological Data." Proceedings of the Human Factors and Ergonomics Society Annual Meeting, August 29, 2024. http://dx.doi.org/10.1177/10711813241275911.
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This work develops interpretable models to predict near-real-time cognitive workload (CWL) in teams operating in complex environments. Existing approaches using neurological sensors are impractical for field use. Our approach integrates multimodal data from non-invasive behavioral and physiological sensors to robustly detect CWL changes. We apply multidimensional recurrence quantification analysis (MdRQA) with a novel pattern analysis extension to identify recurring multimodal signatures indicative of different CWL states. A multiparty dataset with fNIRS, behavioral, and physiological measures from teams performing a gamified search and rescue mission and individual resting state tasks were used. The findings indicate that the multimodal patterns derived from non-invasive measures were significantly associated with a neurological measure of CWL within 10s time slices. Moreover, the multimodal patterns were predictive of individual and team performance over-and-above the neurological measure of CWL. This can enable timely interventions by intelligent systems to optimally manage team CWL and enhance human-machine teaming in demanding environments.
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Wallot, Sebastian, Andreas Roepstorff, and Dan Mønster. "Multidimensional Recurrence Quantification Analysis (MdRQA) for the Analysis of Multidimensional Time-Series: A Software Implementation in MATLAB and Its Application to Group-Level Data in Joint Action." Frontiers in Psychology 7 (November 22, 2016). http://dx.doi.org/10.3389/fpsyg.2016.01835.
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Sharika, K. M., Swarag Thaikkandi, Nivedita, and Michael L. Platt. "Interpersonal heart rate synchrony predicts effective information processing in a naturalistic group decision-making task." Proceedings of the National Academy of Sciences 121, no. 21 (May 16, 2024). http://dx.doi.org/10.1073/pnas.2313801121.
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Groups often outperform individuals in problem-solving. Nevertheless, failure to critically evaluate ideas risks suboptimal outcomes through so-called groupthink. Prior studies have shown that people who hold shared goals, perspectives, or understanding of the environment show similar patterns of brain activity, which itself can be enhanced by consensus-building discussions. Whether shared arousal alone can predict collective decision-making outcomes, however, remains unknown. To address this gap, we computed interpersonal heart rate synchrony, a peripheral index of shared arousal associated with joint attention, empathic accuracy, and group cohesion, in 44 groups (n = 204) performing a collective decision-making task. The task required critical examination of all available information to override inferior, default options and make the right choice. Using multidimensional recurrence quantification analysis (MdRQA) and machine learning, we found that heart rate synchrony predicted the probability of groups reaching the correct consensus decision with >70% cross-validation accuracy–significantly higher than that predicted by the duration of discussions, subjective assessment of team function or baseline heart rates alone. We propose that heart rate synchrony during group discussion provides a biomarker of interpersonal engagement that facilitates adaptive learning and effective information sharing during collective decision-making.
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Whitehead, Ridwan, Andy Nguyen, and Sanna Järvelä. "Exploring the role of gaze behaviour in socially shared regulation of collaborative learning in a group task." Journal of Computer Assisted Learning, June 2, 2024. http://dx.doi.org/10.1111/jcal.13022.
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AbstractBackgroundThe necessity of supporting socially shared regulation of learning (SSRL) for learners is widely acknowledged as a crucial element for successful collaborative learning (CL). However, inherent challenges are presented for the observation, study, and support of SSRL. Non‐verbal behaviours have been identified as a potentially rich resource for understanding learner interactions within CL. Yet, the exploration of its impact on SSRL remains largely unexplored.ObjectiveThe present study aims to explore how gaze behaviour, as a non‐verbal behaviour, relates to verbal interactions that characterise SSRL in CL.MethodsTwenty‐seven Finnish secondary school students were given a 30‐min group task of creating a breakfast smoothie, in which trigger events were integrated to examine possible interactions that characterise SSRL. In this experimental setting, data from multiple modalities were collected. For analysis, process mining was used to explore patterns of interaction, and multidimensional recurrence quantification analysis (MdRQA) was used, alongside with statistical analysis.Results and ConclusionsOur study revealed that gaze behaviour is intricately linked with SSRL interactions, suggesting a complex interplay in collaborative learning contexts. When temporally investigated, SSRL interactions varied in frequency while gaze behaviour remained constant. Moreover, the recurrence of gaze and SSRL interactions did not vary due to trigger events, confirming an established collaborative pattern.ImplicationsThis study advances the field by extending research on SSRL by introducing a ‘non‐verbal perspective’. This perspective enriches our understanding of collaborative learning environments by revealing hidden interaction patterns and guiding the development of more responsive and adaptive digital learning tools.
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Tomashin, Alon, Ilanit Gordon, Giuseppe Leonardi, Yair Berson, Nir Milstein, Matthias Ziegler, Ursula Hess, and Sebastian Wallot. "Lagged multidimensional recurrence quantification analysis for determining leader–follower relationships within multidimensional time series." Psychological Methods, October 10, 2024. http://dx.doi.org/10.1037/met0000691.
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"Application of Recurrence Quantification Analysis for Non-Linear Dynamical Systems." International Journal of Engineering and Advanced Technology 9, no. 1S5 (December 30, 2019): 92–94. http://dx.doi.org/10.35940/ijeat.a1010.1291s52019.
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The Recurrence plots (RPs) have been introduced in several different scientific and medical disciplines. The main purpose of recurrence plot is used to of identify the higher dimensional phase space trajectories. RPs are purely graphically representation which have been designed for the detection of hidden dynamical patterns and non-linearity present in the data, the evaluation of error which is caused by observational noise can be done by Recurrence Quantification Analysis (RQA). RQA method is initially used to minimize the error present in the given signals. RQA method is a basically a technique for the analysis of nonlinear data to quantify the number and duration of a dynamical systems. The recurrence plot is used for time series domain for multidimensional signal also. Recurrence is the property of non-stationary and dynamical system to characteristics the time series analysis in phase space trajectories. Recurrence Quantification Analysis is used to derive from recurrence plots, which are based upon distances matrices of time series.
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Amon, Mary Jean, Stephen Mattingly, Aaron Necaise, Gloria Mark, Nitesh Chawla, Anind Dey, and Sidney D'Mello. "Flexibility versus routineness in multimodal health indicators: A sensor-based longitudinal in situ study of information workers." ACM Transactions on Computing for Healthcare, March 15, 2022. http://dx.doi.org/10.1145/3514259.
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Although some research highlights the benefits of behavioral routines for individual functioning, other research indicates that routines can reflect an individual's inflexibility and lower well-being. Given conflicting accounts on the benefits of routine, research is needed to examine how routineness versus flexibility in health-related behaviors correspond to personality traits, health, and occupational outcomes. We adopt a nonlinear dynamical systems approach to understanding routine using automatically sensed health-related behaviors collected from 483 information workers over a roughly two-month period. We utilized multidimensional recurrence quantification analysis to derive a measure of health regularity (routineness) from measures of daily step count, sleep duration, and heart rate variability (which relates to stress). Participants also completed measures of personality, health, and job performance at the start of the study and for two months via Ecological Momentary Assessments. Greater regularity was associated with higher neuroticism, lower agreeableness, and greater interpersonal and organizational deviance. Importantly, these results were independent of overall levels of each health indicator in addition to demographics. It is often believed that routine is desirable, but the results suggest that associations with routineness are more nuanced, and wearable sensors can provide insights into beneficial health behaviors.
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Reuscher, Tom Frank, Peyman Toreini, and Alexander Maedche. "The state of the art of diagnostic multiparty eye tracking in synchronous computer-mediated collaboration." Journal of Eye Movement Research 16, no. 2 (June 19, 2023). http://dx.doi.org/10.16910/jemr.16.2.4.
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In recent years, innovative multiparty eye tracking setups have been introduced to synchronously capture eye movements of multiple individuals engaged in computer-mediated collaboration. Despite its great potential for studying cognitive processes within groups, the method was primarily used as an interactive tool to enable and evaluate shared gaze visualizations in remote interaction. We conducted a systematic literature review to provide a comprehensive overview of what to consider when using multiparty eye tracking as a diagnostic method in experiments and how to process the collected data to compute and analyze group-level metrics. By synthesizing our findings in an integrative conceptual framework, we identified fundamental requirements for a meaningful implementation. In addition, we derived several implications for future research, as multiparty eye tracking was mainly used to study the correlation between joint attention and task performance in dyadic interaction. We found multidimensional recurrence quantification analysis, a novel method to quantify group-level dynamics in physiological data, to be a promising procedure for addressing some of the highlighted research gaps. In particular, the computation method enables scholars to investigate more complex cognitive processes within larger groups, as it scales up to multiple data streams.
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van Eijndhoven, Kyana H. J., Travis J. Wiltshire, Elwira A. Hałgas, and Josette M. P. Gevers. "A Computational Approach to Examining Team Coordination Breakdowns During Crisis Situations." Journal of Cognitive Engineering and Decision Making, February 20, 2023, 155534342311564. http://dx.doi.org/10.1177/15553434231156417.
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During crisis situations, teams are more prone to coordination breakdowns that are characterized by a temporary, diminished ability to function effectively as a team. However, team research currently lacks robust approaches for identifying transitions from effective team functioning to coordination breakdowns. With the current study, we aimed to develop such robust approaches, and to deepen our understanding of how team coordination dynamics across various physiological signals reflect coordination breakdowns. Consequently, we used audiovisual data from four-person teams involved in a stressful collaborative game task to manually identify coordination breakdowns. Next, we set out to computationally identify coordination breakdowns by applying continuous measures of team coordination (windowed synchronization coefficient and multidimensional recurrence quantification analysis) to photoplethysmogram and electrodermal activity data obtained during the task, and identifying transitions therein with change point and nonlinear prediction algorithms. We found that our computational coordination breakdown identification approaches can identify up to 96% of the manually identified coordination breakdowns although our results also show that the precision of our approaches falls far behind. Our findings contribute theoretically and methodologically to the systematic investigation of coordination breakdowns, which may ultimately facilitate the support of teams in responding to and mitigating negative consequences of crisis situations.
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Hudson, Dan, Travis J. Wiltshire, and Martin Atzmueller. "multiSyncPy: A Python package for assessing multivariate coordination dynamics." Behavior Research Methods, May 5, 2022. http://dx.doi.org/10.3758/s13428-022-01855-y.
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AbstractIn 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.
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Tomashin, Alon, Ilanit Gordon, and Sebastian Wallot. "Interpersonal Physiological Synchrony Predicts Group Cohesion." Frontiers in Human Neuroscience 16 (July 12, 2022). http://dx.doi.org/10.3389/fnhum.2022.903407.
Full textAbstract:
A key emergent property of group social dynamic is synchrony–the coordination of actions, emotions, or physiological processes between group members. Despite this fact and the inherent nested structure of groups, little research has assessed physiological synchronization between group members from a multi-level perspective, thus limiting a full understanding of the dynamics between members. To address this gap of knowledge we re-analyzed a large dataset (N = 261) comprising physiological and psychological data that were collected in two laboratory studies that involved two different social group tasks. In both studies, following the group task, members reported their experience of group cohesion via questionnaires. We utilized a non-linear analysis method-multidimensional recurrence quantification analysis that allowed us to represent physiological synchronization in cardiological interbeat intervals between group members at the individual-level and at the group-level. We found that across studies and their conditions, the change in physiological synchrony from baseline to group interaction predicted a psychological sense of group cohesion. This result was evident both at the individual and the group levels and was not modified by the context of the interaction. The individual- and group-level effects were highly correlated. These results indicate that the relationship between synchrony and cohesion is a multilayered construct. We re-affirm the role of physiological synchrony for cohesion in groups. Future studies are needed to crystallize our understanding of the differences and similarities between synchrony at the individual-level and synchrony at the group level to illuminate under which conditions one of these levels has primacy, or how they interact.