The Experts below are selected from a list of 3834 Experts worldwide ranked by ideXlab platform
Kristen A. Lindquist - One of the best experts on this subject based on the ideXlab platform.
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comment a role of language in infant Emotion concept acquisition
Emotion Review, 2020Co-Authors: Holly Shablack, Andrea G Stein, Kristen A. LindquistAbstract:Ruba and Repacholi (2020) review an important debate in the Emotion development literature: whether infants can perceive and understand facial configurations as instances of Discrete Emotion catego...
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the default mode network s role in Discrete Emotion
Trends in Cognitive Sciences, 2019Co-Authors: Ajay B Satpute, Kristen A. LindquistAbstract:Emotions are often assumed to manifest in subcortical limbic and brainstem structures. While these areas are clearly important for representing affect (e.g., valence and arousal), we propose that the default mode network (DMN) is additionally important for constructing Discrete Emotional experiences (of anger, fear, disgust, etc.). Findings from neuroimaging studies, invasive electrical stimulation studies, and lesion studies support this proposal. Importantly, our framework builds on a constructionist theory of Emotion to explain how instances involving diverse physiological and behavioral patterns can be conceptualized as belonging to the same Emotion category. We argue that this ability requires abstraction (from concrete features to broad mental categories), which the DMN is well positioned to support, and we make novel predictions from our proposed framework.
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a new look at Emotion perception concepts speed and shape facial Emotion recognition
Emotion, 2015Co-Authors: Erik C Nook, Kristen A. Lindquist, Jamil ZakiAbstract:Decades ago, the “New Look” movement challenged how scientists thought about vision by suggesting that conceptual processes shape visual perceptions. Currently, affective scientists are likewise debating the role of concepts in Emotion perception. Here, we utilized a repetition-priming paradigm in conjunction with signal detection and individual difference analyses to examine how providing Emotion labels—which correspond to Discrete Emotion concepts—affects Emotion recognition. In Study 1, pairing Emotional faces with Emotion labels (e.g., “sad”) increased individuals’ speed and sensitivity in recognizing Emotions. Additionally, individuals with alexithymia—who have difficulty labeling their own Emotions—struggled to recognize Emotions based on visual cues alone, but not when Emotion labels were provided. Study 2 replicated these findings and further demonstrated that Emotion concepts can shape perceptions of facial expressions. Together, these results suggest that Emotion perception involves conceptual processing. We discuss the implications of these findings for affective, social, and clinical psychology.
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Emotion perception but not affect perception is impaired with semantic memory loss
Emotion, 2014Co-Authors: Kristen A. Lindquist, Lisa Feldman Barrett, Maria Gendron, Bradford C DickersonAbstract:The ability to perceive Discrete Emotions such as anger, disgust, fear, sadness, etc. in other people is a fundamental part of social life. Without this ability, people lack empathy for loved ones, make poor social judgments in the boardroom and classroom, and have difficulty avoiding those who mean them harm. The dominant paradigm in Emotion research for the past 40 years, called the “basic Emotion” approach, assumes that humans express and detect in others Discrete Emotions such as anger (i.e., a scowl), sadness (i.e., a pout), fear (i.e., wide eyes), disgust (i.e., a wrinkled nose), or happiness (i.e., a smile) (Ekman et al., 1987; Izard, 1971; Matsumoto, 1992; Tracy & Robins, 2008). Scientists largely assume that this detection ability is inborn, universal across all cultures, and psychologically primitive (i.e., it cannot be broken down into more basic psychological processes). Concept knowledge about Discrete Emotion that is represented in language is assumed to be irrelevant to the ability to perceive Discrete Emotion in faces (Ekman & Cordaro, 2011). This “basic Emotion” view is a standard part of the psychology curriculum taught at universities in the Western world, and drives research in a range of disciplines including cognitive neuroscience (Sprengelmeyer et al., 1998), interpersonal communication and conflict negotiation (Kuppens et al., in press) and psychopathology (Fu et al., 2008; Kohler et al., 2010). The US government also relies on this framework to train security personnel to identify the covert intentions of people who pose a threat to its citizens (Burns, 2010; Weinberger, 2010).
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The brain basis of Emotion: A meta-analytic review
Behavioral and Brain Sciences, 2012Co-Authors: Kristen A. Lindquist, Eliza Bliss-moreau, Hedy Kober, Helmut Kober, Tor D. Wager, Lisa Feldman BarrettAbstract:Researchers have wondered how the brain creates Emotions since the early days of psychological science. With a surge of studies in affective neuroscience in recent decades, scientists are poised to answer this question. In this target article, we present a meta-analytic summary of the neuroimaging literature on human Emotion. We compare the locationist approach (i.e., the hypothesis that Discrete Emotion categories consistently and specifically correspond to distinct brain regions) with the psychological constructionist approach (i.e., the hypothesis that Discrete Emotion categories are constructed of more general brain networks not specific to those categories) to better understand the brain basis of Emotion. We review both locationist and psychological constructionist hypotheses of brain–Emotion correspondence and report meta-analytic findings bearing on these hypotheses. Overall, we found little evidence that Discrete Emotion categories can be consistently and specifically localized to distinct brain regions. Instead, we found evidence that is consistent with a psychological constructionist approach to the mind: A set of interacting brain regions commonly involved in basic psychological operations of both an Emotional and non-Emotional nature are active during Emotion experience and perception across a range of Discrete Emotion categories.
Lori M Hilt - One of the best experts on this subject based on the ideXlab platform.
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Emotion dysregulation as a mechanism linking peer victimization to internalizing symptoms in adolescents
Journal of Consulting and Clinical Psychology, 2009Co-Authors: Katie A Mclaughlin, Mark L Hatzenbuehler, Lori M HiltAbstract:Peer victimization experiences represent developmentally salient stressors among adolescents and are associated with the development of internalizing symptoms. However, the mechanisms linking peer victimization to adolescent psychopathology remain inadequately understood. This study examined Emotion dysregulation as a mechanism linking peer stress to changes in internalizing symptoms among adolescents in a longitudinal design. Peer victimization was assessed in a large (N = 1,065) racially diverse (86.6% non-White) sample of adolescents ages 11 to 14 using the Revised Peer Experiences Questionnaire. Emotion dysregulation and symptoms of depression and anxiety were also assessed. Structural equation modeling was used to create a latent construct of Emotion dysregulation from measures of Discrete Emotion processes and of peer victimization and internalizing symptoms. Peer victimization was associated with increased Emotion dysregulation over a 4-month period. Increases in Emotion dysregulation mediated the relationship between relational and reputational, but not overt, victimization and changes in internalizing symptoms over a 7-month period. Evidence for a reciprocal relationship between internalizing symptoms and relational victimization was found, but Emotion dysregulation did not mediate this relationship. The implications for preventive interventions are discussed.
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Emotion dysregulation as a mechanism linking peer victimization to internalizing symptoms in adolescents
Journal of Consulting and Clinical Psychology, 2009Co-Authors: Katie A Mclaughlin, Mark L Hatzenbuehler, Lori M HiltAbstract:Peer victimization experiences represent developmentally salient stressors among adolescents and are associated with the development of internalizing symptoms. However, the mechanisms linking peer victimization to adolescent psychopathology remain inadequately understood. This study examined Emotion dysregulation as a mechanism linking peer stress to changes in internalizing symptoms among adolescents in a longitudinal design. Peer victimization was assessed with the Revised Peer Experiences Questionnaire (M. J. Prinstein, J. Boergers, & E. M. Vernberg, 2001) in a large (N = 1,065), racially diverse (86.6% non-White) sample of adolescents 11-14 years of age. Emotion dysregulation and symptoms of depression and anxiety were also assessed. Structural equation modeling was used to create a latent construct of Emotion dysregulation from measures of Discrete Emotion processes and of peer victimization and internalizing symptoms. Peer victimization was associated with increased Emotion dysregulation over a 4-month period. Increases in Emotion dysregulation mediated the relationship between relational and reputational, but not overt, victimization and changes in internalizing symptoms over a 7-month period. Evidence for a reciprocal relationship between internalizing symptoms and relational victimization was found, but Emotion dysregulation did not mediate this relationship. The implications for preventive interventions are discussed.
Katie A Mclaughlin - One of the best experts on this subject based on the ideXlab platform.
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Emotion dysregulation as a mechanism linking peer victimization to internalizing symptoms in adolescents
Journal of Consulting and Clinical Psychology, 2009Co-Authors: Katie A Mclaughlin, Mark L Hatzenbuehler, Lori M HiltAbstract:Peer victimization experiences represent developmentally salient stressors among adolescents and are associated with the development of internalizing symptoms. However, the mechanisms linking peer victimization to adolescent psychopathology remain inadequately understood. This study examined Emotion dysregulation as a mechanism linking peer stress to changes in internalizing symptoms among adolescents in a longitudinal design. Peer victimization was assessed in a large (N = 1,065) racially diverse (86.6% non-White) sample of adolescents ages 11 to 14 using the Revised Peer Experiences Questionnaire. Emotion dysregulation and symptoms of depression and anxiety were also assessed. Structural equation modeling was used to create a latent construct of Emotion dysregulation from measures of Discrete Emotion processes and of peer victimization and internalizing symptoms. Peer victimization was associated with increased Emotion dysregulation over a 4-month period. Increases in Emotion dysregulation mediated the relationship between relational and reputational, but not overt, victimization and changes in internalizing symptoms over a 7-month period. Evidence for a reciprocal relationship between internalizing symptoms and relational victimization was found, but Emotion dysregulation did not mediate this relationship. The implications for preventive interventions are discussed.
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Emotion dysregulation as a mechanism linking peer victimization to internalizing symptoms in adolescents
Journal of Consulting and Clinical Psychology, 2009Co-Authors: Katie A Mclaughlin, Mark L Hatzenbuehler, Lori M HiltAbstract:Peer victimization experiences represent developmentally salient stressors among adolescents and are associated with the development of internalizing symptoms. However, the mechanisms linking peer victimization to adolescent psychopathology remain inadequately understood. This study examined Emotion dysregulation as a mechanism linking peer stress to changes in internalizing symptoms among adolescents in a longitudinal design. Peer victimization was assessed with the Revised Peer Experiences Questionnaire (M. J. Prinstein, J. Boergers, & E. M. Vernberg, 2001) in a large (N = 1,065), racially diverse (86.6% non-White) sample of adolescents 11-14 years of age. Emotion dysregulation and symptoms of depression and anxiety were also assessed. Structural equation modeling was used to create a latent construct of Emotion dysregulation from measures of Discrete Emotion processes and of peer victimization and internalizing symptoms. Peer victimization was associated with increased Emotion dysregulation over a 4-month period. Increases in Emotion dysregulation mediated the relationship between relational and reputational, but not overt, victimization and changes in internalizing symptoms over a 7-month period. Evidence for a reciprocal relationship between internalizing symptoms and relational victimization was found, but Emotion dysregulation did not mediate this relationship. The implications for preventive interventions are discussed.
Kurt Keutzer - One of the best experts on this subject based on the ideXlab platform.
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pdanet polarity consistent deep attention network for fine grained visual Emotion regression
ACM Multimedia, 2019Co-Authors: Sicheng Zhao, Zizhou Jia, Hui Chen, Guiguang Ding, Kurt KeutzerAbstract:Existing methods on visual Emotion analysis mainly focus on coarse-grained Emotion classification, i.e. assigning an image with a dominant Discrete Emotion category. However, these methods cannot well reflect the complexity and subtlety of Emotions. In this paper, we study the fine-grained regression problem of visual Emotions based on convolutional neural networks (CNNs). Specifically, we develop a Polarity-consistent Deep Attention Network (PDANet), a novel network architecture that integrates attention into a CNN with an Emotion polarity constraint. First, we propose to incorporate both spatial and channel-wise attentions into a CNN for visual Emotion regression, which jointly considers the local spatial connectivity patterns along each channel and the interdependency between different channels. Second, we design a novel regression loss, i.e. polarity-consistent regression (PCR) loss, based on the weakly supervised Emotion polarity to guide the attention generation. By optimizing the PCR loss, PDANet can generate a polarity preserved attention map and thus improve the Emotion regression performance. Extensive experiments are conducted on the IAPS, NAPS, and EMOTIC datasets, and the results demonstrate that the proposed PDANet outperforms the state-of-the-art approaches by a large margin for fine-grained visual Emotion regression. Our source code is released at: https://github.com/ZizhouJia/PDANet.
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pdanet polarity consistent deep attention network for fine grained visual Emotion regression
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Sicheng Zhao, Zizhou Jia, Hui Chen, Guiguang Ding, Kurt KeutzerAbstract:Existing methods on visual Emotion analysis mainly focus on coarse-grained Emotion classification, i.e. assigning an image with a dominant Discrete Emotion category. However, these methods cannot well reflect the complexity and subtlety of Emotions. In this paper, we study the fine-grained regression problem of visual Emotions based on convolutional neural networks (CNNs). Specifically, we develop a Polarity-consistent Deep Attention Network (PDANet), a novel network architecture that integrates attention into a CNN with an Emotion polarity constraint. First, we propose to incorporate both spatial and channel-wise attentions into a CNN for visual Emotion regression, which jointly considers the local spatial connectivity patterns along each channel and the interdependency between different channels. Second, we design a novel regression loss, i.e. polarity-consistent regression (PCR) loss, based on the weakly supervised Emotion polarity to guide the attention generation. By optimizing the PCR loss, PDANet can generate a polarity preserved attention map and thus improve the Emotion regression performance. Extensive experiments are conducted on the IAPS, NAPS, and EMOTIC datasets, and the results demonstrate that the proposed PDANet outperforms the state-of-the-art approaches by a large margin for fine-grained visual Emotion regression. Our source code is released at: this https URL.
Klaus R. R. Scherer - One of the best experts on this subject based on the ideXlab platform.
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automated recognition of Emotion appraisals
2015Co-Authors: Marcello Mortillaro, Ben Meuleman, Klaus R. R. SchererAbstract:Most computer models for the automatic recognition of Emotion from nonverbal signals (e.g., facial or vocal expression) have adopted a Discrete Emotion perspective, i.e., they output a categorical Emotion from a limited pool of candidate labels. The Discrete perspective suffers from practical and theoretical drawbacks that limit the generalizability of such systems. The authors of this chapter propose instead to adopt an appraisal perspective in modeling Emotion recognition, i.e., to infer the subjective cognitive evaluations that underlie both the nonverbal cues and the overall Emotion states. In a first step, expressive features would be used to infer appraisals; in a second step, the inferred appraisals would be used to predict an Emotion label. The first step is practically unexplored in Emotion literature. Such a system would allow to (a) link models of Emotion recognition and production, (b) add contextual information to the inference algorithm, and (c) allow detection of subtle Emotion states.
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Meta-analysis of the first facial expression recognition challenge
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2012Co-Authors: Michel Valstar, Bihan Jiang, Marc Mehu, Maja Pantic, Klaus R. R. SchererAbstract:Automatic facial expression recognition has been an active topic in computer science for over two decades, in particular facial action coding system action unit (AU) detection and classification of a number of Discrete Emotion states from facial expressive imagery. Standardization and comparability have received some attention; for instance, there exist a number of commonly used facial expression databases. However, lack of a commonly accepted evaluation protocol and, typically, lack of sufficient details needed to reproduce the reported individual results make it difficult to compare systems. This, in turn, hinders the progress of the field. A periodical challenge in facial expression recognition would allow such a comparison on a level playing field. It would provide an insight on how far the field has come and would allow researchers to identify new goals, challenges, and targets. This paper presents a meta-analysis of the first such challenge in automatic recognition of facial expressions, held during the IEEE conference on Face and Gesture Recognition 2011. It details the challenge data, evaluation protocol, and the results attained in two subchallenges: AU detection and classification of facial expression imagery in terms of a number of Discrete Emotion categories. We also summarize the lessons learned and reflect on the future of the field of facial expression recognition in general and on possible future challenges in particular.
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The first facial expression recognition and analysis challenge
Face and Gesture 2011, 2011Co-Authors: Michel Valstar, Bihan Jiang, Marc Mehu, Klaus R. R. Scherer, Maja Pantic, Klaus SchererAbstract:Automatic Facial Expression Recognition and Analysis, in particular FACS Action Unit (AU) detection and Discrete Emotion detection, has been an active topic in computer science for over two decades. Standardisation and comparability has come some way; for instance, there exist a number of commonly used facial expression databases. However, lack of a common evaluation protocol and lack of sufficient details to reproduce the reported individual results make it difficult to compare systems to each other. This in turn hinders the progress of the field. A periodical challenge in Facial Expression Recognition and Analysis would allow this comparison in a fair manner. It would clarify how far the field has come, and would allow us to identify new goals, challenges and targets. In this paper we present the first challenge in automatic recognition of facial expressions to be held during the IEEE conference on Face and Gesture Recognition 2011, in Santa Barbara, California. Two sub-challenges are defined: one on AU detection and another on Discrete Emotion detection. It outlines the evaluation protocol, the data used, and the results of a baseline method for the two sub-challenges.