Can a Machine Represent the Anxiety? : The Development of Emotion Representation Technology and its Phenomenological Implication

2018 ◽  
Vol 79 ◽  
pp. 105-125
Author(s):  
Seung Ug Park
Keyword(s):  
Emotion Representation ◽  
Phenomenological Implication

scholarly journals Emotional Motion Generation Using Emotion Representation Rules Modeled for Human Affection

2010 ◽  
Vol 22 (1) ◽  
pp. 39-51 ◽  
Author(s):  
Yoshikazu YANO ◽  
Atsushi YAMAGUCHI ◽  
Shinji DOKI ◽  
Shigeru OKUMA
Keyword(s):  
Motion Generation ◽  
Emotion Representation

scholarly journals A DYNAMIC EMOTION REPRESENTATION MODEL WITHIN A FACIAL ANIMATION SYSTEM

2006 ◽  
Vol 03 (03) ◽  
pp. 293-300 ◽  
Author(s):  
EMMANUEL TANGUY ◽  
PHILIP J. WILLIS ◽  
JOANNA J. BRYSON
Keyword(s):  
Time Scales ◽  
Emotional State ◽  
Facial Animation ◽  
Emotional Expressions ◽  
Communicative Functions ◽  
Representation Model ◽  
Emotion Representation ◽  
Animation System ◽  
Different Time Scales

This paper presents the Dynamic Emotion Representation (DER), and demonstrates how an instance of this model can be integrated into a facial animation system. The DER model has been implemented to enable users to create their own emotion representation. Developers can select which emotions they include and how these interact. The instance of the DER model described in this paper is composed of three layers, each representing states changing over different time scales: behavior activations, emotions and moods. The design of this DER is discussed with reference to emotion theories and to the needs of a facial animation system. The DER is used in our Emotionally Expressive Facial Animation System (EE-FAS) to produce emotional expressions, to select facial signals corresponding to communicative functions in relation to the emotional state of the agent and also in relation to the comparison between the emotional state and the intended meanings expressed through communicative functions.

The adolescent emotional maelstrom

2008 ◽  
Vol 3 (1) ◽  
pp. 9-28
Author(s):  
Renata F.I. Meuter ◽  
Leigh Buckley
Keyword(s):  
Simon Task ◽  
Lexical Representation ◽  
Concomitant Increase ◽  
Attention Task ◽  
Response Latencies ◽  
Emotion Words ◽  
Inhibitory Ability ◽  
Age Related ◽  
Emotion Representation ◽  
Related Increase

To determine how differences in emotion representation and/or inhibitory ability affect adolescents’ responses to emotion words, 13-yr and 16-yr olds, as well as adults, were compared on the processing of emotion-laden and neutral words. Word ratings revealed that 16-yr olds tended towards perceiving all words as more arousing than did adults, irrespective of valence. Also, they rated words more negatively than 13-yr olds. Performance on an Affective Simon task revealed a marked incongruency effect only for 13-yr olds (and then only for negative words) but not for 16-yr olds (who responded fastest) or adults. Performance on a sustained attention task confirmed the expected age-related increase in inhibitory ability and a concomitant increase in response latencies. Our conclusions are two-fold. First, there are age-related differences in lexical representation which appear more marked for 16-yr olds. Second, 16-yr olds are more reactive, irrespective of the emotional content they are processing, yet appear to control its impact as efficiently as adults.

scholarly journals Using genetic algorithms to uncover individual differences in how humans represent facial emotion

2021 ◽  
Vol 8 (10) ◽  
Author(s):  
Christina O. Carlisi ◽  
Kyle Reed ◽  
Fleur G. L. Helmink ◽  
Robert Lachlan ◽  
Darren P. Cosker ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Individual Differences ◽  
Population Level ◽  
Future Research ◽  
Emotional Expressions ◽  
Emotional Facial Expressions ◽  
Emotion Representation ◽  
Test Retest Reliability ◽  
High Degree ◽  
Subjective Representation

Emotional facial expressions critically impact social interactions and cognition. However, emotion research to date has generally relied on the assumption that people represent categorical emotions in the same way, using standardized stimulus sets and overlooking important individual differences. To resolve this problem, we developed and tested a task using genetic algorithms to derive assumption-free, participant-generated emotional expressions. One hundred and five participants generated a subjective representation of happy, angry, fearful and sad faces. Population-level consistency was observed for happy faces, but fearful and sad faces showed a high degree of variability. High test–retest reliability was observed across all emotions. A separate group of 108 individuals accurately identified happy and angry faces from the first study, while fearful and sad faces were commonly misidentified. These findings are an important first step towards understanding individual differences in emotion representation, with the potential to reconceptualize the way we study atypical emotion processing in future research.

Simplifying the Design of Human-Like Behaviour

2010 ◽  
Vol 1 (1) ◽  
pp. 30-50 ◽  
Author(s):  
Joanna J. Bryson ◽  
Emmanuel Tanguy
Keyword(s):  
Emotional Expression ◽  
Autonomous Agents ◽  
Emotional State ◽  
Emotional Responses ◽  
Full Time ◽  
Emotional States ◽  
Virtual Character ◽  
Agent Behavior ◽  
Emotion Representation ◽  
Representational Framework

Human intelligence requires decades of full-time training before it can be reliably utilized in modern economies. In contrast, AI agents must be made reliable but interesting in relatively short order. Realistic emotion representations are one way to ensure that even relatively simple specifications of agent behavior will be expressed with engaging variation, and those social and temporal contexts can be tracked and responded to appropriately. We describe a representation system for maintaining an interacting set of durative states to replicate emotional control. Our model, the Dynamic Emotion Representation (DER), integrates emotional responses and keeps track of emotion intensities changing over time. The developer can specify an interacting network of emotional states with appropriate onsets, sustains, and decays. The levels of these states can be used as input for action selection, including emotional expression. We present both a general representational framework and a specific instance of a DER network constructed for a virtual character. The character’s DER uses three types of emotional state as classified by duration timescales, keeping with current emotional theory. We demonstrate the system with a virtual actor. We also demonstrate how even a simplified version of this representation can improve goal arbitration in autonomous agents.

Simplifying the Design of Human-Like Behaviour

2012 ◽  
pp. 32-53
Author(s):  
Joanna J. Bryson ◽  
Emmanuel Tanguy
Keyword(s):  
Autonomous Agents ◽  
Emotional Responses ◽  
Human Intelligence ◽  
Full Time ◽  
Emotional States ◽  
Virtual Character ◽  
Specific Instance ◽  
Emotion Representation ◽  
Representational Framework ◽  
Over Time

Human intelligence requires decades of full-time training before it can be reliably utilised in modern economies. In contrast, AI agents must be made reliable but interesting in relatively short order. Realistic emotion representations are one way to ensure that even relatively simple specifications of agent behaviour will be expressed with engaging variation, and those social and temporal contexts can be tracked and responded to appropriately. We describe a representation system for maintaining an interacting set of durative states to replicate emotional control. Our model, the Dynamic Emotion Representation (DER), integrates emotional responses and keeps track of emotion intensities changing over time. The developer can specify an interacting network of emotional states with appropriate onsets, sustains and decays. The levels of these states can be used as input for action selection, including emotional expression. We present both a general representational framework and a specific instance of a DER network constructed for a virtual character. The character’s DER uses three types of emotional state as classified by duration timescales, keeping with current emotional theory. We demonstrate the system with a virtual actor. We also demonstrate how even a simplified version of this representation can improve goal arbitration in autonomous agents.

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