Physiological compliance and team performance

Elkins, Amanda N.; Muth, Eric R.; Hoover, Adam; Walker, Alexander D.; Carpenter, Thomas L.; Switzer, Fred S.

doi:10.1016/j.apergo.2009.02.002

Cited by 89 publications

(94 citation statements)

References 10 publications

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“…In training or team-based applications (e.g. Elkins et al, 2009;Espevik, Helge Johnsen, Eid, & Thayer, 2006;Stevens, Galloway, Berka & Sprang, 2009), it may be sufficient to provide additional state information via physiological workload monitoring, thus enabling either a human instructor or human teammates to more effectively adapt their own behavior, or adapt the team composition more appropriately to the task (e.g. Woolley et al, 2007).…”

Section: Discussionmentioning

confidence: 99%

Co-Adaptive Aiding and Automation Enhance Operator Performance

Christensen¹,

Estepp²

2013

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Section: Discussionmentioning

confidence: 99%

Co-Adaptive Aiding and Automation Enhance Operator Performance

Christensen¹,

Estepp²

2013

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“…However, recent research concerning neurophysiologic synchronization (e.g., Stevens, Galloway, Berka, & Sprang, 2009;Tognoli, Lagarde, DeGuzman, & Kelso, 2007; also referred to as social-psychophysiological compliance -Henning, Armstead, & Ferris, 2009-and physio-behavioral synchronicityStrang, Funke, Knott, & Warm, 2011, which occurs when team members exhibit synchronous changes in observed physio-behavioral states during performance of a team task, may provide additional, alternative methods for assessment of team workload. It has been suggested that synchronicity may index the degree of interpersonal coordination between teammates (e.g., Tognoli et al, 2007) and that greater synchronicity may enhance team performance (e.g., Elkins et al, 2009;Henning et al, 2001). As such, fluctuations in synchronicity may serve as useful indicators of changes in team workload.…”

Section: Physio-behavioral Measuresmentioning

confidence: 99%

Conceptualization and Measurement of Team Workload

et al. 2011

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Objective: The purpose of this article is to present and expand on current theories and measurement techniques for assessing team workload.Background: To date, little research has been conducted on the workload experienced by teams. A validated theory describing team workload, which includes an account of its relation to individual workload, has not been articulated.Method: The authors review several theoretical approaches to team workload. Within the team research literature, attempts to evaluate team workload have typically relied on measures of individual workload. This assumes that such measures retain their validity at the team level of measurement, but empirical research suggests that this method may lack sensitivity to the drivers of team workload.Results: On the basis of these reviews, the authors advance suggestions concerning a comprehensive theory of team workload and methods for assessing it in team settings. The approaches reviewed include subjective, performance, physiological, and strategy shift measures. Theoretical and statistical difficulties associated with aggregating individual-level workload responses to a team-level measure are discussed.Conclusion: Conception and measurement of team workload have not significantly matured alongside developments in individual workload.Application: Team workload remains a complex research area without simple measurement solutions, but as a research domain it remains open for contributions from interested and enterprising researchers.

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“…PCIs can be obtained on either an instantaneous or aggregated basis. Aggregated can be either for time windows, say for example 65s as in [15], or for the whole measurement session. They can all be aggregated but not all can be computed on an instantaneous basis.…”

Section: Pcis and Collaborationmentioning

confidence: 99%

“…Little or no agreement on how to call this physiological relationship has been reached. The diverse nomenclature found includes (chronologically sorted): physiological linkage [31], physiological compliance [50], physiological synchronization [24], physiological correlation [15], joint changes in the physiological signals [25], physiological coupling [11] and physiological markers of togetherness [39]. From now on, this paper uses physiological coupling (PC) for the idea of influence it suggests.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Investigating collaborative learning success with physiological coupling indices based on electrodermal activity

Pijeira‐Díaz

Drachsler²,

Järvelä

et al. 2016

Proceedings of the Sixth International Conference on Learning Analytics &Amp; Knowledge - LAK '16

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Collaborative learning is considered a critical 21 st century skill. Much is known about its contribution to learning, but still investigating a process of collaboration remains a challenge. This paper approaches the investigation on collaborative learning from a psychophysiological perspective. An experiment was set up to explore whether biosensors can play a role in analysing collaborative learning. On the one hand, we identified five physiological coupling indices (PCIs) found in the literature: 1) Signal Matching (SM), 2) Instantaneous Derivative Matching (IDM), 3) Directional Agreement (DA), 4) Pearson's correlation coefficient (PCC) and the 5) Fisher's z-transform (FZT) of the PCC. On the other hand, three collaborative learning measurements were used: 1) collaborative will (CW), 2) collaborative learning product (CLP) and 3) dual learning gain (DLG). Regression analyses showed that out of the five PCIs, IDM related the most to CW and was the best predictor of the CLP. Meanwhile, DA predicted DLG the best. These results play a role in determining informative collaboration measures for designing a learning analytics, biofeedback dashboard.

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Physiological compliance and team performance

Cited by 89 publications

References 10 publications

Co-Adaptive Aiding and Automation Enhance Operator Performance

Co-Adaptive Aiding and Automation Enhance Operator Performance

Conceptualization and Measurement of Team Workload

Investigating collaborative learning success with physiological coupling indices based on electrodermal activity

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