Auswahl der wissenschaftlichen Literatur zum Thema „Multidimensional Recurrence Quantification Analysis (MdRQA)“

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.

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.

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.

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.

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.

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.

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.