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The purpose of this paper is to present a model of emotion in negotiation, which reflects the active role emotions play in
decision taking as modifiers of theory-of-mind models, goals and strategies. The model is based on empirical studies of human
interaction in different activities such as plea bargains, simulated negotiations, doctor patient con...
Citations
... This, in turn, can facilitate the group's ability to effectively understand problems and make rational decisions through collaborative elements. On the other hand, emotions have been found to also contribute to competitive relationships, particularly in conflict and negotiating tasks [31], [32]. Chang et al. [33] found that individuals tend to behave more competitively towards emotional robots in a group setting, resulting in the limited influence of robots on decisionmaking. ...
This study investigates how different virtual agent (VA) behaviors
influence subjects’ perceptions and group decision-making. Participants carried out experimental group discussions with a VA
exhibiting varying levels of engagement and affective behavior.
Engagement refers to the VA’s focus on the group task, whereas
affective behavior reflects the VA’s emotional state.
The findings revealed that VA’s engagements effectively captured participants’ attention even in the group setting and enhanced group synergy, thereby facilitating more in-depth discussion and producing better consensus.
On the other hand, VA’s affective behavior negatively affected the perceived social presence and trustworthiness.
Consequently, in the context of group discussion, participants preferred the engaged and non-affective VA to the non-engaged and affective VA.
The study provides valuable insights for improving the VA’s behavioral design as a team member for collaborative tasks.
... A key capacity in any negotiation context is empathy, or the capacity to see the world from another party's point of view (Martinovski et al. 2007;Galinsky et al. 2008;Martinovski and Mao 2009).10 Empathy not only sensitizes actors to previously unseen potential contract zones but also helps cultivate trustwhich in turn enhances negotiation payoffs (Olekalns et al. 2007;Grasso 2011;. ...
People often disagree about what counts as “just” in a particular case. Such disagreement is natural and understandable once we realize that people commonly bring to the concept of justice different understandings of what makes something just or unjust, interpret general principles differently in specific circumstances, and/or fail to see eye to eye on appropriate ways of resolving justice disputes. But in all cases, disagreement about what is just logically requires that the parties share an understanding of what it is that they are disagreeing about. Similarly, any analysis of the role justice might play in a particular domain – here, negotiation – requires a shared understanding of what it is that is playing the role in question. The purpose of this article is to articulate and justify a shared understanding of the concept of justice that facilitates both the understanding and resolution of justice disputes.
... Emotion is the basis of daily human life and plays an essential role in human cognitive functions, rational decisions, and interpersonal communications (Waldron, 1994;Picard et al., 2001;Martinovski and Mao, 2009). It is extremely important to identify emotions accurately especially in the field of brain-computer interaction (Cowie et al., 2002;Jin et al., 2020Jin et al., , 2021. ...
Objectve
Emotional brain-computer interface can recognize or regulate human emotions for workload detection and auxiliary diagnosis of mental illness. However, the existing EEG emotion recognition is carried out step by step in feature engineering and classification, resulting in high engineering complexity and limiting practical applications in traditional EEG emotion recognition tasks. We propose an end-to-end neural network, i.e., E2ENNet.
Methods
Baseline removal and sliding window slice used for preprocessing of the raw EEG signal, convolution blocks extracted features, LSTM network obtained the correlations of features, and the softmax function classified emotions.
Results
Extensive experiments in subject-dependent experimental protocol are conducted to evaluate the performance of the proposed E2ENNet, achieves state-of-the-art accuracy on three public datasets, i.e., 96.28% of 2-category experiment on DEAP dataset, 98.1% of 2-category experiment on DREAMER dataset, and 41.73% of 7-category experiment on MPED dataset.
Conclusion
Experimental results show that E2ENNet can directly extract more discriminative features from raw EEG signals.
Significance
This study provides a methodology for implementing a plug-and-play emotional brain-computer interface system.
... During argumentation, the physical and emotional responses that an individual perceives are associated with his or her argumentation competence (Chichekian & Shore, 2016). During argumentation, individuals' interaction with both the members of their group and the physical materials they use to form an argument affect the quality of the argument, while this effect may cause the individual to create a feeling that supports their self-efficacy regardingtheir intellectual skills (Martinovski & Mao, 2009). When the quality of group interaction during argumentation is high, a preservice teacher may offer proposals that they regard as weak without hesitation, or when their proposal needs to be changed, they may carry out this change without feeling apprehensive. ...
The aim of this study is to examine the relationships between preservice teachers’ critical thinking skills and their self-efficacy for argumentation. The participants of the research consisted of 858 preservice teachers (447 female, 411 male) studying in education faculties at five different state universities in Turkey. In this study, the “Self-Efficacy for Argumentation Scale” (SEAS) and the “Critical Thinking Standards Scale” (CTSS) were used to gather the data. SEAS include “effort for argumentation” “confidence for argumentation,” while CTSS include “depth, breadth and sufficiency,” “precision and accuracy” and “significance, relevance and clarity.” The findings indicated that both the “effort for argumentation” and “confidence for argumentation” were significant, positive predictors of “depth-breadth-sufficiency” and “significance-relevance-clarity.” Although, the “effort for argumentation” predicted the “precision and accuracy,” it was found that the “confidence for argumentation” did not predict this dimension.
... C'est aussi ce qu'avaient mis de l'avant Isohätälä et al. (2018) dans une rare recherche sur la régulation partagée qui traite de l'argumentation et de processus socioémotifs. Comme ils l'ont constaté, l'argumentation englobe des processus cognitifs (Asterhan et Schwarz, 2016;Baker, M., 2009;Osborne, 2010), en plus d'inclure des éléments de nature émotionnelle (Polo et al., 2016), étant souvent accompagnée d'irritation, d'anxiété, de joie, d'empathie ou d'autres sentiments affectifs (Gilbert, 2004;Martinovski et Mao, 2009). L'analyse de Ucan et Webb (2015) avait aussi montré que, en présence de divergences, le recours aux processus de régulation métacognitifs pouvait contribuer au succès de l'apprentissage. ...
... Besides, high-level cognitive interactions that are emotional (e.g. argumentation; Polo et al., 2016) are sometimes accompanied by anxiety, impulsivity, nervousness or other affective reactions (Martinovski & Mao, 2009). ...
Students’ social knowledge construction and socio-emotional interactions in computer-supported collaborative learning (CSCL) are shaped by one another and work together to affect the group’s learning performance. However, few studies have combined both social knowledge construction and socio-emotional interactions and examined how they contribute to improved learning performance. This study examines the dynamics of students’ social knowledge construction and socio-emotional interactions in the context of computer-supported collaborative writing and compares six high- and six low-performing groups. Quantitative content analysis and sequential analysis were used to reveal the characteristics of groups’ behaviour frequencies and patterns. The high-performing groups demonstrated more systematic and meaningful social knowledge construction and socio-emotional interaction patterns, while the low-performing groups only engaged in single repeated behaviours. It is worth noting that memes played different roles in the two groups.
... If the arguments are in opposition, the emotions reflected from them tend to be negative related; similarly, the emotion tends to be positive if arguments support to each other (Benlamine et al., 2015;Villata et al., 2017). As an engine of argumentation, emotions play an active role in decision taking as modifiers of theory-ofmind models, goals and strategies (Martinovski & Mao, 2009). Unlike prior studies, which are less finegrained, we probe how emotion differs in terms of several aspects of argumentation, such as agreement/disagreement, approach (fact-based or appealing to emotion), and the use of nasty or attacking language or sarcasm. ...
It is increasingly common for people to debate over various topics through online debate forums. While it has been shown that participants' emotional states affect debate processes and outcomes, it is unknown how different types of emotions are represented in online debates and what correlations exist between the emotions and other aspects of the debates such as their debate topic. We conduct a large‐scale analysis of the emotions in two online debate forums, namely, 4Forums and ConvinceMe. Specifically, we first develop an emotion recognition algorithm that uses multiple channels BLSTM with a feedforward attention mechanism, which outperforms the state‐of‐the‐art emotion recognition algorithm. Next, we label the emotions of each comment in the selected 4Forums and ConvinceMe discussions and analyze various aspects of the emotion's influence in the online debates. We observe that certain types of emotions are more likely dependent on the debate topic, and the prevalence of different emotions is independent of the individual discussions. We also observe emotion contagion between a comment and the immediately previous comment. We investigate the emotions of different types of respondents are less likely to express joy when they disagree and more likely to express disgust when they attack or disrespect to others.
... The definition of argumentation in collaborative learning as a critical debate over divergent perspectives emphasizes the cognitive nature of argumentation, encompassing reasoning, co-elaboration, and negotiation (Asterhan & Schwarz, 2016;Baker, 2009;Osborne, 2010). However, argumentation is also emotional (Polo et al., 2016), as it can involve irritation, anxiety, joy, empathy, or other affective feelings (Gilbert, 2004;Martinovski & Mao, 2009;Plantin, 2004). ...
Collaborative learning in small groups is a societally relevant but challenging way of learning. It requires a rich understanding of how people think and co-elaborate knowledge together (cognitive processes) and how they feel and relate to each other (socio-emotional processes). The objective of this dissertation is to explore the interplay of cognitive and socio-emotional processes as it manifests in face-to-face social interaction during collaborative learning. The results were derived from qualitative, process-oriented analyses of video-recorded social interactions in two datasets pertaining to small groups of Finnish teacher education students (N=43) who collaborated on mathematics and environmental science tasks. The results are reported in four empirical articles.
The results show that the cognitive and socio-emotional processes fluctuated in the social interactions over the course of collaborative learning. The socio-emotional processes became especially overt and thematic in the social interactions when groups regulated their learning. During such regulation, groups’ metacognitive planning, monitoring, and evaluating could intertwine expressions of emotion, talking about emotions, or giving socio-emotional support. These moments activated group members’ joint participation and allowed them to establish agreement, respond to challenges, and recognize strengths or weaknesses, which were important functions for collaborative learning. At times, the social interaction was more directed toward cognitive processes when group members concentrated on performing task activities. However, the socio-emotional processes were still intertwined with cognitive processes. This dissertation illustrates how a case episode of argumentation proceeded through a series of counterarguments, reformulations, and elaborations, but also involved subtle ways of expressing claims tentatively, showing consideration of divergent claims, and relaxing tension.
This dissertation highlights that cognitive and socio-emotional processes of collaborative learning are continuously intertwined but fluctuate in social interaction. The intertwining gives rise to meaningful functions for collaborative learning. Attempts to support collaborative learning in education or work must acknowledge the interplay of cognitive and socio-emotional processes in social interaction.
... Model building was done by integrating and elaborating on Davis' (1996) model and that of Martinovski and Mao (2009), the result of which is presented as Figure 1 below. ...
... In future work, we intend to develop the treatment of emotion in the framework as the topic has received little attention in the computational argumentation literature. Exceptions are [39] which provide rules for specifying scenarios where empathy is given or received, and [40] which investigates relationships between emotions that participants feel during a debate (measured physiologically) and arguments. In contrast, affective computing has put emotion at the centre of the relationship between users and computing systems [41]. ...
Participants in dialogical argumentation often make strategic choices of move, for example to maximize the probability that they will persuade the other opponents. Multiple dimensions of information about the other agents (e.g., the belief and likely emotional response that the other agents might have in the arguments) might be used to make this strategic choice. To support this, we present a framework with implementation for multi-criteria decision making for strategic argumentation. We provide methods to improve the computational viability of the framework, and analyze these methods theoretically and empirically. We finally present decision rules supported by the psychology literature and evidence using human experiments.
