Social psychophysiological compliance in a four-person research team

Henning, Robert A.; Armstead, Adam G.; Ferris, Joe

doi:10.1016/j.apergo.2009.04.009

Cited by 54 publications

(59 citation statements)

References 10 publications

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“…Coherence has been used by Henning and colleagues [86]. In these cases, it is important that appropriate corrections for autocorrelations within a signal are made [29,37].…”

Section: Emotional Convergencementioning

confidence: 99%

“…Another way is to calculate slopes of specific time windows and take the absolute value of differences between the slopes of two different users [85,86]. This can be expressed as…”

Section: Emotional Convergencementioning

confidence: 99%

“…Sometimes, weighted coherence is used by correcting for the total power within the spectrum. [37,86] ARMA models Model of individual time series using auto regressive and moving average components. Predictions can be made by regressing different people's ARMA models onto eachother.…”

Section: Emotional Convergencementioning

confidence: 99%

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A three-component framework for empathic technologies to augment human interaction

Janssen

2012

J Multimodal User Interfaces

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Empathy can be considered one of our most important social processes. In that light, empathic technologies are the class of technologies that can augment empathy between two or more individuals. To provide a basis for such technologies, a three component framework is presented based on psychology and neuroscience, consisting of cognitive empathy, emotional convergence, and empathic responding. These three components can be situated in affective computing and social signal processing and pose different opportunities for empathic technologies. To leverage these opportunities, automated measurement possibilities for each component are identified using (combinations of) facial expressions, speech, and physiological signals. Thereafter, methodological challenges are discussed, including ground truth measurements and empathy induction. Finally, a research agenda is presented for social signal processing. This framework can help to further research on empathic technologies and ultimately bring it to fruition in meaningful innovations. In turn, this could enhance empathic behavior, thereby increasing altruism, trust, cooperation, and bonding.

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“…Coherence has been used by Henning and colleagues [86]. In these cases, it is important that appropriate corrections for autocorrelations within a signal are made [29,37].…”

Section: Emotional Convergencementioning

confidence: 99%

“…Another way is to calculate slopes of specific time windows and take the absolute value of differences between the slopes of two different users [85,86]. This can be expressed as…”

Section: Emotional Convergencementioning

confidence: 99%

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A three-component framework for empathic technologies to augment human interaction

Janssen

2012

J Multimodal User Interfaces

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“…It is not immediately clear how measures of team members' individual physiological processes may be aggregated to form a meaningful collective measure of team workload. 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).…”

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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“…Applications to the learning area include, but are not limited to, interaction support [11], feedback [46], intelligent tutors [1], the current educational challenge of formative assessment [45], intervention [14], teacher training [22], emotion recognition in e-learning [47] and self-regulated learning [37]. A recent review on sensor-based platforms [46] did not identify prototypes purposely focused on learning applications.…”

Section: Rationalementioning

confidence: 99%

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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Social psychophysiological compliance in a four-person research team

Cited by 54 publications

References 10 publications

A three-component framework for empathic technologies to augment human interaction

A three-component framework for empathic technologies to augment human interaction

Conceptualization and Measurement of Team Workload

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

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