Machine Analysis of Facial Expressions

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Self-Deception as Affective Coping. An Empirical Perspective on Philosophical Issues

In the philosophical literature, self-deception is mainly approached through the analysis of paradoxes. Yet, it is agreed that self-deception is motivated by protection from distress. In this paper, we argue, with the help of findings from cognitive neuroscience and psychology, that self-deception is a type of affective coping. First, we criticize the main solutions to the paradoxes of self-deception. We then present a new approach to self-deception. Self-deception, we argue, involves three appraisals of the distressing evidence: (a) appraisal of the strength of evidence as uncertain, (b) low coping potential and (c) negative anticipation along the lines of Damasio’s somatic marker hypothesis. At the same time, desire impacts the treatment of flattering evidence via dopamine. Our main proposal is that self-deception involves emotional mechanisms provoking a preference for immediate reward despite possible long-term negative repercussions. In the last part, we use this emotional model to revisit the philosophical paradoxes

Earning the Right to Lead in Defining Moments: The Act of Taking Leadership

True leaders emerge and earn their right to lead — not as a result of an organization’s day-to-day activities — but through courageous acts exhibited during the organization’s defining moments, whether such moments be characterized as stable or disastrous. Remaining strong and steadfast in testy situations while demonstrating the ability to bond to earn the trust of others are the predominant emotional and interpersonal characteristics on display when a leader earns his or her right to lead

Integrating Emotion Recognition Tools for Developing Emotionally Intelligent Agents

Emotionally responsive agents that can simulate emotional intelligence increase the acceptance of users towards them, as the feeling of empathy reduces negative perceptual feedback. This has fostered research on emotional intelligence during last decades, and nowadays numerous cloud and local tools for automatic emotional recognition are available, even for inexperienced users. These tools however usually focus on the recognition of discrete emotions sensed from one communication channel, even though multimodal approaches have been shown to have advantages over unimodal approaches. Therefore, the objective of this paper is to show our approach for multimodal emotion recognition using Kalman filters for the fusion of available discrete emotion recognition tools. The proposed system has been modularly developed based on an evolutionary approach so to be integrated in our digital ecosystems, and new emotional recognition sources can be easily integrated. Obtained results show improvements over unimodal tools when recognizing naturally displayed emotions