Pushing Interdisciplinary in the Study of Groups and Teams

Keyton, Joann; Heylen, Dirk

doi:10.1177/1046496417732528

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Although group-level emotion recognition has received increasing attention in the computer vision community, as well, our proposed approaches achieved the considerable results in group-level emotion recognition, it still exists some improvements need to be discussed in future. Recently, Keyton and Heylen in [56] stated that the interaction of computer science and social science "will benefit when interdisciplinary collaborations make important contributions to both". It is recommended that we can collaborate social scientists on group-level emotion recognition in three following issues:…”

Section: Resultsmentioning

confidence: 99%

Analyzing Group-Level Emotion with Global Alignment Kernel based Approach

Huang

Dhall

Goecke

et al. 2022

IEEE Trans. Affective Comput.

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From the perspective of social science, understanding group emotion has become increasingly important for teams to considerably accomplish organizational work. Currently, automatically analyzing the perceived affect of a group of people has been received increasingly interest in affective computing community. The variability in group size makes difficulty for group-level emotion recognition to straightforwardly measure the feature distance of two group-level images. Recent works attempted to resolve the preceding problem by using feature encoding. However, the early works lack of efficiency. To alleviate this problem, this paper aims to design a new method to effectively analyze the group behavior from a group-level image. Motivated by time-series kernel approaches explored in dynamic facial expression classification, this paper mainly concentrates on global alignment kernel and design support vector machine with the combined global alignment kernels (SVM-CGAK) to better recognize group-level emotion. Specifically, we first propose to use global alignment kernel to explicitly measure the distance of two group-level images. For improving the performance of global alignment kernel, we use the global weight sort scheme based on their spatial relation information to sort the faces from group-level image, making an efficient data structure to the global alignment kernel. With this new global alignment kernel, we construct the backbone of SVM-CGAK, namely, support vector machine with global alignment kernel. Furthermore, considering the challenging environment, we construct two global alignment kernels based on Reisz-based Volume Local Binary Pattern and deep convolutional neural network features, respectively. Lastly, to make the robustness of group-level emotion recognition, we propose SVM-CGAK combining both global alignment kernels with multiple kernel learning approach. It can enhance the discriminative ability of each global alignment kernel. Intensive experiments are conducted on three challenging group-level emotion databases. The experimental results demonstrate that the proposed approach achieves promising performance for group-level emotion recognition compared with the recent state-of-the-art methods.

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Section: Resultsmentioning

confidence: 99%

Analyzing Group-Level Emotion with Global Alignment Kernel based Approach

Huang

Dhall

Goecke

et al. 2022

IEEE Trans. Affective Comput.

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“…The Imagining Autism project was in effect testing and evaluating a programme of practical workshops. The Imagining Autism team was a group according to Keyton and Heylen's (2017) definition, however, some members, particularly those who were not principal (PI) or co-investigators (Co-Is), may have felt more disconnected. Whilst the team had a 'shared vision' (Hare and O'Neill 2000), they were not strictly an academic peer group, in part because of the hierarchy and nature of the sub-teams that comprised the project.…”

Section: Context and Backgroundmentioning

confidence: 99%

Researcher experiences in practice-based interdisciplinary research

Leigh

Brown

2021

Research Evaluation

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This article reports on a study that followed up on an initial interdisciplinary project and focused specifically on the experiences of researchers involved in practice-based interdisciplinary research. We share an approach to research evaluation that focuses on the experiences of those conducting the research rather than the outputs. The study allowed those involved in the initial successful project to reflect post hoc on their experiences. We show that neglecting fundamental conceptions about how the research is conceptualized can lead to challenges with the research itself. In addition to alternative understandings of research and concepts, practical and logistical issues, whilst seeming trivial, feed into communication issues such as misunderstanding of terms and language. We argue that tensions and confusions around the very nature of the research—what was being researched, and what was valued as research, epistemological differences between the disciplinary perspectives—need to be explored and interrogated in order to maximize the benefits of interdisciplinary research. We conclude with considerations of the relationship between interdisciplinary research in a team and identity work of team members, and the implications this may have for research design, an area of research evaluation that certainly needs further exploration.

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“…Despite their creative potential, research suggests interdisciplinary teams may struggle to integrate their expertise into a unified whole, and thus not reach their innovative potential (Mesmer-Magnus & DeChurch, 2009; Salazar et al, 2012; Xiao et al, 2016). As such, research on team innovation and the question of why some teams are more innovative than others remains a central focus of teams research (Hoever et al, 2018; Keyton & Heylen, 2017; van Knippenberg, 2017).…”

mentioning

confidence: 99%

Interdisciplinarity and Team Innovation: The Role of Team Experiential and Relational Resources

Vestal

Mesmer‐Magnus

2020

Small Group Research

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Interdisciplinary teams composed of members with different expertise possess a variety of perspectives, which increases their potential for innovation. In reality, team members often fail to integrate their expertise, resulting in the team not reaching its innovative potential. It is argued the unsharedness of expertise within interdisciplinary teams has an inverted-U relationship with innovation. To explore the conditions under which the unsharedness of expertise enhances or impairs innovation, the resource-based view of organizational productivity is applied to teams. It is argued the amount and configuration of team relational and experiential resources facilitate teams’ ability to integrate members’ expertise for innovation.

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Pushing Interdisciplinary in the Study of Groups and Teams

Cited by 5 publications

References 11 publications

Analyzing Group-Level Emotion with Global Alignment Kernel based Approach

Analyzing Group-Level Emotion with Global Alignment Kernel based Approach

Researcher experiences in practice-based interdisciplinary research

Interdisciplinarity and Team Innovation: The Role of Team Experiential and Relational Resources

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