An Investigation of Coding Schemes for Sample Entropy Analysis of Communications Data

Parker, J. M.; Best, Christopher; Funke, Gregory J.; Strang, Adam J.; Marion, Kaye

doi:10.1177/1541931213601025

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…Note that adopting this approach may require either the use of speaker-only codes (i.e., codes that only represent speaker and not semantic content; cf. Strang et al, 2012;Parker et al, 2016, when exploring alternative coding systems) or the use of methods from computational linguistics that can semantically categorize single utterances in near real time (e.g., Angus et al, 2012). Alternatively, one could use more fundamental linguistic aspects such as the categorization of vocal frequency (Black et al, 2013) or speech/pause dynamics (Fusaroli & Tyl en, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…This theory posits that complex systems exhibit effective performance when there is an optimal degree of order and disorder characterizing the system's behavior (Harrison & Stergiou, ; Stergiou & Decker, ). Thus, effective team performance can be achieved when the right amounts of both stability and adaptability are exhibited (Parker, Best, Funke, Strang, & Marion, ), optimally balancing task constraints and resources (cf. Bak, Tang, & Wiesenfeld, ; Likens et al., ).…”

Section: Discussionmentioning

confidence: 99%

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Problem‐Solving Phase Transitions During Team Collaboration

2017

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Problem‐Solving Phase Transitions During Team Collaboration

2017

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“…Semantic aspects of language that could be highly relevant to the treatment process in psychotherapy have also not yet received adequate attention. Related work, for example, has examined semantic coordination between interlocutors (Parker et al 2016;Russell et al 2012), particularly when there are points of change in the conversation (Wiltshire et al 2018), and how those semantic topics change over time (Angus et al 2012a, b;Angus et al 2012a, b;Atkins et al 2012).…”

Section: Terminological and Conceptual Shortcomingsmentioning

confidence: 99%

Interpersonal Coordination Dynamics in Psychotherapy: A Systematic Review

et al. 2020

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Background The aim of this study was to gain a better understanding of interpersonal coordination (i.e., covariation over time) in different modalities (e.g., physiology, language) during psychotherapy and their importance for understanding the dynamics of psychotherapeutic interaction and efficacy. Methods We conducted a systematic review of all studies examining some form of interpersonal coordination in a psychotherapeutic context. Results We first summarize details of the included studies such as gender composition, therapy types, and methods used. The collation of these studies provided evidence that, during psychotherapeutic contexts, interpersonal coordination occurs in physiology, movements, interpersonal displays, and language/vocalizations. Further, it also showed that movement coordination was most frequently associated with psychotherapy outcomes, physiological coordination was most frequently associated with empathy, and coordination in a variety of modalities including language/vocalizations were most frequently associated with therapeutic alliance. Conclusions We discuss these results, shortcomings with the current literature, and highlight three crucial questions for future research. Research on interpersonal coordination in psychotherapy has potential to advance the both the research and practice of psychotherapy.

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“…Autonomous agents are changing teamwork. Yet, at this point, teamwork is still traditionally grounded mainly in human-to-human interaction (Parker et al, 2016). Although human–human teamwork will long remain the standard, human–machine teamwork is becoming more prevalent and important in multiple contexts.…”

Section: Introductionmentioning

confidence: 99%

Team Situation Awareness and Conflict: A Study of Human–Machine Teaming

McNeese

Demir

Cooke

et al. 2021

Journal of Cognitive Engineering and Decision Making

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This article focuses on two fundamental human–human teamwork behaviors and seeks to understand them better in human–machine teams. Specifically, team situation awareness (TSA) and team conflict are examined in human–machine teams. There is a significant need to identify how TSA and team conflict occur during human–machine teaming, in addition to how they impact each other. In this work, we present an experiment aimed at understanding TSA and team conflict in the context of human–machine teaming in a remotely piloted aircraft system (RPAS). Three conditions were tested: (1) control: teams consisted of all humans; (2) synthetic: teams consisted of the pilot role being occupied by a computational agent based on ACT-R architecture that employed AI capabilities, with all other team roles being humans; and (3) experimenter: an experimenter playing the role of the pilot as a highly effective computational agent, with the other roles being humans. The results indicate that TSA improved over time in synthetic teams, improved and then stabilized over time in experimenter teams, and did not improve in control teams. In addition, results show that control teams had the most team conflict. Finally, in the control condition, team conflict negatively impacts TSA.

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An Investigation of Coding Schemes for Sample Entropy Analysis of Communications Data

Cited by 7 publications

References 11 publications

Problem‐Solving Phase Transitions During Team Collaboration

Problem‐Solving Phase Transitions During Team Collaboration

Interpersonal Coordination Dynamics in Psychotherapy: A Systematic Review

Team Situation Awareness and Conflict: A Study of Human–Machine Teaming

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