Emotion concepts are embodied

Niedenthal, Paula M.; Ric, François; Krauth‐Gruber, Silvia

doi:10.1037/e633912013-171

Cited by 26 publications

(30 citation statements)

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“…For example, if the process results in the appraisal of an event as obstructive to personal goals, then an action tendency will result from high sympathetic nervous system arousal (Scherer et al, 2003); in contrast, if the appraisal is that it will be easy to cope with a situation, then the event does not control the emotion system and the individual can establish a new equilibrium (Ellsworth and Scherer, 2003). A first automatic appraisal, related to the assessment of whether a situation could be potentially dangerous for wellbeing, happens through a fast subcortical route similar to a reflex (Niedenthal et al, 2006). During fast appraisal the sensory valence and arousal of sound stimuli are processed, whereas a slow, cognitive appraisal relies on cortical processes for the evaluation of the individual’s capacity to cope with a situation (Niedenthal et al, 2006).…”

Section: Stages Of Processing In Temporal Ordermentioning

confidence: 99%

“…A first automatic appraisal, related to the assessment of whether a situation could be potentially dangerous for wellbeing, happens through a fast subcortical route similar to a reflex (Niedenthal et al, 2006). During fast appraisal the sensory valence and arousal of sound stimuli are processed, whereas a slow, cognitive appraisal relies on cortical processes for the evaluation of the individual’s capacity to cope with a situation (Niedenthal et al, 2006). According to Silvia (2005), a positive aesthetic emotion and judgment derives from the subjective appraisal of events according to the fulfillment of personal goals.…”

Section: Stages Of Processing In Temporal Ordermentioning

confidence: 99%

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Toward a Neural Chronometry for the Aesthetic Experience of Music

Brattico¹,

Bogert²,

Jacobsen³

2013

Front. Psychol.

164

200

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Music is often studied as a cognitive domain alongside language. The emotional aspects of music have also been shown to be important, but views on their nature diverge. For instance, the specific emotions that music induces and how they relate to emotional expression are still under debate. Here we propose a mental and neural chronometry of the aesthetic experience of music initiated and mediated by external and internal contexts such as intentionality, background mood, attention, and expertise. The initial stages necessary for an aesthetic experience of music are feature analysis, integration across modalities, and cognitive processing on the basis of long-term knowledge. These stages are common to individuals belonging to the same musical culture. The initial emotional reactions to music include the startle reflex, core “liking,” and arousal. Subsequently, discrete emotions are perceived and induced. Presumably somatomotor processes synchronizing the body with the music also come into play here. The subsequent stages, in which cognitive, affective, and decisional processes intermingle, require controlled cross-modal neural processes to result in aesthetic emotions, aesthetic judgments, and conscious liking. These latter aesthetic stages often require attention, intentionality, and expertise for their full actualization.

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Section: Stages Of Processing In Temporal Ordermentioning

confidence: 99%

Section: Stages Of Processing In Temporal Ordermentioning

confidence: 99%

Toward a Neural Chronometry for the Aesthetic Experience of Music

Brattico¹,

Bogert²,

Jacobsen³

2013

Front. Psychol.

164

200

View full text Add to dashboard Cite

show abstract

“…In cognitive science, we wish to relate behavior with input variables, e.g., responses to observed facial expressions of emotion [14] or facial action coding [22]. In psychiatry, we wish to understand how psychopathologies affect behavior [36]. In biology, one may be interested in studying animal behavior [13, 17].…”

Section: Introductionmentioning

confidence: 99%

Labeled Graph Kernel for Behavior Analysis

Zhao

Martı́nez

2016

IEEE Trans. Pattern Anal. Mach. Intell.

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Automatic behavior analysis from video is a major topic in many areas of research, including computer vision, multimedia, robotics, biology, cognitive science, social psychology, psychiatry, and linguistics. Two major problems are of interest when analyzing behavior. First, we wish to automatically categorize observed behaviors into a discrete set of classes (i.e., classification). For example, to determine word production from video sequences in sign language. Second, we wish to understand the relevance of each behavioral feature in achieving this classification (i.e., decoding). For instance, to know which behavior variables are used to discriminate between the words apple and onion in American Sign Language (ASL). The present paper proposes to model behavior using a labeled graph, where the nodes define behavioral features and the edges are labels specifying their order (e.g., before, overlaps, start). In this approach, classification reduces to a simple labeled graph matching. Unfortunately, the complexity of labeled graph matching grows exponentially with the number of categories we wish to represent. Here, we derive a graph kernel to quickly and accurately compute this graph similarity. This approach is very general and can be plugged into any kernel-based classifier. Specifically, we derive a Labeled Graph Support Vector Machine (LGSVM) and a Labeled Graph Logistic Regressor (LGLR) that can be readily employed to discriminate between many actions (e.g., sign language concepts). The derived approach can be readily used for decoding too, yielding invaluable information for the understanding of a problem (e.g., to know how to teach a sign language). The derived algorithms allow us to achieve higher accuracy results than those of state-of-the-art algorithms in a fraction of the time. We show experimental results on a variety of problems and datasets, including multimodal data.

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“…Participants are asked to rate the likelihood of these features for the various emotions. This methodology is comprehensive in its scope as it allows the multicultural comparison of emotion conceptualisations on all six of the emotion categories recognised by emotion theorists [ 7 , 21 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Affective Robotics: Modelling and Testing Cultural Prototypes

Wilson

Lewandowska‐Tomaszczyk

2014

Cogn Comput

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If robots are to successfully interact with humans, they need to measure, quantify and respond to the emotions we produce. Similar to humans, the perceptual cue inputs to any modelling that allows this will be based on behavioural expression and body activity features that are prototypical of each emotion. However, the likely employment of such robots in different cultures necessitates the tuning of the emotion feature recognition system to the specific feature profiles present in these cultures. The amount of tuning depends on the relative convergence of the cross-cultural mappings between the emotion feature profiles of the cultures where the robots will be used. The GRID instrument and the cognitive corpus linguistics methodology were used in a contrastive study analysing a selection of behavioural expression and body activity features to compare the feature profiles of joy, sadness, fear and anger within and between Polish and British English. The intra-linguistic differences that were found in the profile of emotion features suggest that weightings based on this profile can be used in robotic modelling to create emotion-sensitive socially interacting robots. Our cross-cultural results further indicate that this profile of features needs to be tuned in robots to make them emotionally competent in different cultures.

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Emotion concepts are embodied

Cited by 26 publications

References 0 publications

Toward a Neural Chronometry for the Aesthetic Experience of Music

Toward a Neural Chronometry for the Aesthetic Experience of Music

Labeled Graph Kernel for Behavior Analysis

Affective Robotics: Modelling and Testing Cultural Prototypes

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