ORCID Profile
0000-0002-8087-2241
Current Organisation
UNSW Sydney
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2012
Publisher: ACM
Date: 09-12-2013
Publisher: IEEE
Date: 10-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: SPIE
Date: 05-2009
DOI: 10.1117/12.820786
Publisher: ACM
Date: 09-10-2023
Publisher: American Physical Society (APS)
Date: 22-06-2023
Publisher: Oxford University Press (OUP)
Date: 22-03-2023
Abstract: Despite a lack of scientific consensus on the definition of emotions, they are generally considered to involve several modifications in the mind, body, and behavior. Although psychology theories emphasized multi-componential characteristics of emotions, little is known about the nature and neural architecture of such components in the brain. We used a multivariate data-driven approach to decompose a wide range of emotions into functional core processes and identify their neural organization. Twenty participants watched 40 emotional clips and rated 119 emotional moments in terms of 32 component features defined by a previously validated componential model. Results show how different emotions emerge from coordinated activity across a set of brain networks coding for component processes associated with valuation appraisal, hedonic experience, novelty, goal-relevance, approach/avoidance tendencies, and social concerns. Our study goes beyond previous research that focused on categorical or dimensional emotions, by highlighting how novel methodology combined with theory-driven modeling may provide new foundations for emotion neuroscience and unveil the functional architecture of human affective experiences.
Publisher: ACM
Date: 29-10-2023
Publisher: IGI Global
Date: 2010
DOI: 10.4018/978-1-61692-892-6.CH007
Abstract: Nonverbal communication is the main channel through which we experience inner life of others, including their emotions, feelings, moods, social attitudes, etc. This attracts the interest of the computing community because nonverbal communication is based on cues like facial expressions, vocalizations, gestures, postures, etc. that we can perceive with our senses and can be (and often are) detected, analyzed and synthesized with automatic approaches. In other words, nonverbal communication can be used as a viable interface between computers and some of the most important aspects of human psychology such as emotions and social attitudes. As a result, a new computing domain seems to emerge that we can define “technology of nonverbal communication”. This chapter outlines some of the most salient aspects of such a potentially new domain and outlines some of its most important perspectives for the future.
Publisher: ACM
Date: 29-10-2012
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
Publisher: IEEE
Date: 09-2015
Publisher: IEEE
Date: 09-2019
Publisher: ACM
Date: 29-10-2023
Publisher: ACM
Date: 29-10-2023
Publisher: Cold Spring Harbor Laboratory
Date: 12-06-2020
DOI: 10.1101/2020.06.10.145201
Abstract: Emotions have powerful effects on the mind, body, and behavior. Although psychology theories emphasized multi-componential characteristics of emotions, little is known about the nature and neural architecture of such components in the brain. We used a multivariate data-driven approach to decompose a wide range of emotions into functional core processes and identify their neural organization. Twenty participants watched 40 emotional clips and rated 119 emotional moments in terms of 32 component features defined by a previously validated componential model. Results show how different emotions emerge from coordinated activity across a set of brain networks coding for component processes associated with valuation appraisal, hedonic experience, novelty, goal-relevance, approach/avoidance tendencies, and social concerns. Our study goes beyond previous research that focused on either categorical or dimensional emotions and highlighting how novel methodology combined with componential modelling may allow emotion neuroscience to move forward and unveil the functional architecture of human affective experiences.
Publisher: Cold Spring Harbor Laboratory
Date: 09-04-2021
DOI: 10.1101/2021.04.08.438559
Abstract: Emotions are rich and complex experiences involving various behavioral and physiological responses. While many empirical studies have focused on discrete and dimensional representations of emotions, these representations do not fully reconcile with recent neuroscience studies that increasingly suggest a multi-process mechanism underlying emotional experience. Moreover, the latter view accords with psychological theories that consider emotions as multicomponent phenomena, such as appraisal theories. Although there is no complete consensus on the specific components of emotions and fundamental principles defining their organization, the Component Process Model (CPM) is well established framework describing an emotion as a dynamic process with five major highly interrelated components: cognitive appraisal, expression, motivation, physiology and feeling. Yet, few studies have systematically investigated a range of discrete emotions through this full multi-componential view. In the present study, we therefore elicited various emotions during movie watching and measured their manifestation across these components. Our primary goal was to investigate the relationship between physiological measures and the theoretically defined components of emotions. In addition, we also investigated whether discrete emotions could be predicted from information provided by the multicomponent response patterns, as well as the specific contributions of each component in such predictions. Results suggest that physiological features are interrelated to all other components of emotion, but the least significant predictors for emotion classification. Overall, emotion prediction was significantly higher when classifiers were trained with all five components. The findings therefore support a description of emotion as a dynamic multicomponent process, in which the emergence of a conscious feeling state requires the integration of all the components.
Publisher: Springer International Publishing
Date: 2022
Publisher: ACM
Date: 29-10-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 2015
Publisher: ACM
Date: 25-10-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
No related grants have been discovered for Gelareh Mohammadi.