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Kauttonen J, Paekivi S, Kauramäki J, Tikka P. Unraveling dyadic psycho-physiology of social presence between strangers during an audio drama - a signal-analysis approach. Front Psychol 2023; 14:1153968. [PMID: 37928563 PMCID: PMC10622809 DOI: 10.3389/fpsyg.2023.1153968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 10/04/2023] [Indexed: 11/07/2023] Open
Abstract
A mere co-presence of an unfamiliar person may modulate an individual's attentive engagement with specific events or situations to a significant degree. To understand better how such social presence affects experiences, we recorded a set of parallel multimodal facial and psychophysiological data with subjects (N = 36) who listened to dramatic audio scenes alone or when facing an unfamiliar person. Both a selection of 6 s affective sound clips (IADS-2) followed by a 27 min soundtrack extracted from a Finnish episode film depicted familiar and often intense social situations familiar from the everyday world. Considering the systemic complexity of both the chosen naturalistic stimuli and expected variations in the experimental social situation, we applied a novel combination of signal analysis methods using inter-subject correlation (ISC) analysis, Representational Similarity Analysis (RSA) and Recurrence Quantification Analysis (RQA) followed by gradient boosting classification. We report our findings concerning three facial signals, gaze, eyebrow and smile that can be linked to socially motivated facial movements. We found that ISC values of pairs, whether calculated on true pairs or any two individuals who had a partner, were lower than the group with single individuals. Thus, audio stimuli induced more unique responses in those subjects who were listening to it in the presence of another person, while individual listeners tended to yield a more uniform response as it was driven by dramatized audio stimulus alone. Furthermore, our classifiers models trained using recurrence properties of gaze, eyebrows and smile signals demonstrated distinctive differences in the recurrence dynamics of signals from paired subjects and revealed the impact of individual differences on the latter. We showed that the presence of an unfamiliar co-listener that modifies social dynamics of dyadic listening tasks can be detected reliably from visible facial modalities. By applying our analysis framework to a broader range of psycho-physiological data, together with annotations of the content, and subjective reports of participants, we expected more detailed dyadic dependencies to be revealed. Our work contributes towards modeling and predicting human social behaviors to specific types of audio-visually mediated, virtual, and live social situations.
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Affiliation(s)
- Janne Kauttonen
- Competences, RDI and Digitalization, Haaga-Helia University of Applied Sciences, Helsinki, Finland
- School of Arts, Design and Architecture, Aalto University, Espoo, Finland
- Aalto NeuroImaging, Aalto University, Espoo, Finland
| | - Sander Paekivi
- Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
| | - Jaakko Kauramäki
- School of Arts, Design and Architecture, Aalto University, Espoo, Finland
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Cognitive Brain Research Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Pia Tikka
- School of Arts, Design and Architecture, Aalto University, Espoo, Finland
- Enactive Virtuality Lab, Baltic Film, Media and Arts School (BFM), Centre of Excellence in Media Innovation and Digital Culture (MEDIT), Tallinn University, Tallinn, Estonia
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Karageorgos P, Wallot S, Müller B, Schindler J, Richter T. Distinguishing between struggling and skilled readers based on their prosodic speech patterns in oral reading: An exploratory study in grades 2 and 4. Acta Psychol (Amst) 2023; 235:103892. [PMID: 36966640 DOI: 10.1016/j.actpsy.2023.103892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 02/06/2023] [Accepted: 03/13/2023] [Indexed: 04/03/2023] Open
Abstract
The purpose of this study was to examine if prosodic patterns in oral reading derived from Recurrence Quantification Analysis (RQA) could distinguish between struggling and skilled German readers in Grades 2 (n = 67) and 4 (n = 69). Furthermore, we investigated whether models estimated with RQA measures outperformed models estimated with prosodic features derived from prosodic transcription. According to the findings, struggling second graders appear to have a slower reading rate, longer intervals between pauses, and more repetitions of recurrent amplitudes and pauses, whereas struggling fourth graders appear to have less stable pause patterns over time, more pitch repetitions, more similar amplitude patterns over time, and more repetitions of pauses. Additionally, the models with prosodic patterns outperformed models with prosodic features. These findings suggest that the RQA approach provides additional information about prosody that complements an established approach.
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Moulder RG, Martynova E, Boker SM. Extracting Nonlinear Dynamics from Psychological and Behavioral Time Series Through HAVOK Analysis. MULTIVARIATE BEHAVIORAL RESEARCH 2023; 58:441-465. [PMID: 35001769 DOI: 10.1080/00273171.2021.1994848] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Analytical methods derived from nonlinear dynamical systems, complexity, and chaos theories offer researchers a framework for in-depth analysis of time series data. However, relatively few studies involving time series data obtained from psychological and behavioral research employ such methods. This paucity of application is due to a lack of general analysis frameworks for modeling time series data with strong nonlinear components. In this article, we describe the potential of Hankel alternative view of Koopman (HAVOK) analysis for solving this issue. HAVOK analysis is a unified framework for nonlinear dynamical systems analysis of time series data. By utilizing HAVOK analysis, researchers may model nonlinear time series data in a linear framework while simultaneously reconstructing attractor manifolds and obtaining a secondary time series representing the amount of nonlinear forcing occurring in a system at any given time. We begin by showing the mathematical underpinnings of HAVOK analysis and then show example applications of HAVOK analysis for modeling time series data derived from real psychological and behavioral studies.
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Brinberg M, Lydon-Staley DM. Conceptualizing and Examining Change in Communication Research. COMMUNICATION METHODS AND MEASURES 2023; 17:59-82. [PMID: 37122497 PMCID: PMC10139745 DOI: 10.1080/19312458.2023.2167197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Communication research often focuses on processes of communication, such as how messages impact individuals over time or how interpersonal relationships develop and change. Despite their importance, these change processes are often implicit in much theoretical and empirical work in communication. Intensive longitudinal data are becoming increasingly feasible to collect and, when coupled with appropriate analytic frameworks, enable researchers to better explore and articulate the types of change underlying communication processes. To facilitate the study of change processes, we (a) describe advances in data collection and analytic methods that allow researchers to articulate complex change processes of phenomena in communication research, (b) provide an overview of change processes and how they may be captured with intensive longitudinal methods, and (c) discuss considerations of capturing change when designing and implementing studies. We are excited about the future of studying processes of change in communication research, and we look forward to the iterations between empirical tests and theory revision that will occur as researchers delve into studying change within communication processes.
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Błażkiewicz M. Evaluation of Geometric Attractor Structure and Recurrence Analysis in Professional Dancers. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1310. [PMID: 36141196 PMCID: PMC9497806 DOI: 10.3390/e24091310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 09/08/2022] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Human motor systems contain nonlinear features. The purpose of this study was to evaluate the geometric structure of attractors and analyze recurrence in two different pirouettes (jazz and classic) performed by 15 professional dancers. METHODS The kinematics of the body's center of mass (CoM) and knee of the supporting leg (LKNE) during the pirouette were measured using the Vicon system. A time series of selected points were resampled, normalized, and randomly reordered. Then, every second time series was flipped to be combined with other time series and make a long time series out of the repetitions of a single task. The attractors were reconstructed, and the convex hull volumes (CHV) were counted for the CoM and LKNE for each pirouette in each direction. Recurrence quantification analysis (RQA) was used to extract additional information. RESULTS The CHVs calculated for the LKNE were significantly lower for the jazz pirouette. All RQA measures had the highest values for LKNE along the mediolateral axis for the jazz pirouette. This result underscores the high determinism, high motion recurrence, and complexity of this maneuver. CONCLUSIONS The findings offer new insight into the evaluation of the approximation of homogeneity in motion control. A high determinism indicates a highly stable and predictive motion trajectory.
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Affiliation(s)
- Michalina Błażkiewicz
- Faculty of Rehabilitation, Józef Piłsudski University of Physical Education in Warsaw, 00-809 Warszawa, Poland
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Yen SC, Qian S, Folmar E, Hasson CJ, Chou CA. Recurrence Quantification Analysis of Ankle Kinematics During Gait in Individuals With Chronic Ankle Instability. Front Sports Act Living 2022; 4:893745. [PMID: 35694321 PMCID: PMC9174592 DOI: 10.3389/fspor.2022.893745] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 04/25/2022] [Indexed: 12/05/2022] Open
Abstract
Purpose An investigation of the ankle dynamics in a motor task may generate insights into the etiology of chronic ankle instability (CAI). This study presents a novel application of recurrence quantification analysis (RQA) to examine the ankle dynamics during walking. We hypothesized that CAI is associated with changes in the ankle dynamics as assessed by measures of determinism and laminarity using RQA. Methods We recorded and analyzed the ankle position trajectories in the frontal and sagittal planes from 12 participants with CAI and 12 healthy controls during treadmill walking. We used time-delay embedding to reconstruct the position trajectories to a phase space that represents the states of the ankle dynamics. Based on the phase space trajectory, a recurrence plot was constructed and two RQA variables, the percent determinism (%DET) and the percent laminarity (%LAM), were derived from the recurrence plot to quantify the ankle dynamics. Results In the frontal plane, the %LAM in the CAI group was significantly lower than that in the control group (p < 0.05. effect size = 0.86). This indicated that the ankle dynamics in individuals with CAI is less likely to remain in the same state. No significant results were found in the %DET or in the sagittal plane. Conclusion A lower frontal-plane %LAM may reflect more frequent switching between different patterns of neuromuscular control states due to the instabilities associated with CAI. With further study and development, %LAM may have the potential to become a useful biomarker for CAI.
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Affiliation(s)
- Sheng-Che Yen
- Department of Physical Therapy, Movement and Rehabilitation Sciences, Bouvé College of Health Sciences, Northeastern University, Boston, MA, United States
- *Correspondence: Sheng-Che Yen
| | - Shaodi Qian
- Department of Mechanical and Industrial Engineering, College of Engineering, Northeastern University, Boston, MA, United States
| | - Eric Folmar
- Department of Physical Therapy, Movement and Rehabilitation Sciences, Bouvé College of Health Sciences, Northeastern University, Boston, MA, United States
| | - Christopher J. Hasson
- Department of Physical Therapy, Movement and Rehabilitation Sciences, Bouvé College of Health Sciences, Northeastern University, Boston, MA, United States
- Department of Bioengineering, Northeastern University, Boston, MA, United States
- Department of Biology, Northeastern University, Boston, MA, United States
| | - Chun-An Chou
- Department of Mechanical and Industrial Engineering, College of Engineering, Northeastern University, Boston, MA, United States
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Varley TF, Sporns O. Network Analysis of Time Series: Novel Approaches to Network Neuroscience. Front Neurosci 2022; 15:787068. [PMID: 35221887 PMCID: PMC8874015 DOI: 10.3389/fnins.2021.787068] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/20/2021] [Indexed: 12/12/2022] Open
Abstract
In the last two decades, there has been an explosion of interest in modeling the brain as a network, where nodes correspond variously to brain regions or neurons, and edges correspond to structural or statistical dependencies between them. This kind of network construction, which preserves spatial, or structural, information while collapsing across time, has become broadly known as "network neuroscience." In this work, we provide an alternative application of network science to neural data: network-based analysis of non-linear time series and review applications of these methods to neural data. Instead of preserving spatial information and collapsing across time, network analysis of time series does the reverse: it collapses spatial information, instead preserving temporally extended dynamics, typically corresponding to evolution through some kind of phase/state-space. This allows researchers to infer a, possibly low-dimensional, "intrinsic manifold" from empirical brain data. We will discuss three methods of constructing networks from nonlinear time series, and how to interpret them in the context of neural data: recurrence networks, visibility networks, and ordinal partition networks. By capturing typically continuous, non-linear dynamics in the form of discrete networks, we show how techniques from network science, non-linear dynamics, and information theory can extract meaningful information distinct from what is normally accessible in standard network neuroscience approaches.
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Affiliation(s)
- Thomas F. Varley
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States
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Osotsi A, Oravecz Z, Li Q, Smyth J, Brick TR. Individualized Modeling to Distinguish Between High and Low Arousal States Using Physiological Data. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2020; 4:91-109. [DOI: 10.1007/s41666-019-00064-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 09/30/2019] [Accepted: 11/27/2019] [Indexed: 10/25/2022]
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