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Valence is an inferred criterion from instinctively generated emotions; it is the property specifying whether feelings/affects are positive, negative or neutral. [2] The existence of at least temporarily unspecified valence is an issue for psychological researchers who reject the existence of neutral emotions (e.g. surprise , sublimation). [ 2 ]
Emotional granularity is an individual's ability to differentiate between the specificity of their emotions. Similar to how an interior decorator is aware of fine gradations in shades of blue, where others might see a single color, [1] an individual with high emotional granularity would be able to discriminate between their emotions that all fall within the same level of valence and arousal ...
The feelings of the others diverge from and in some way complement the feelings of the original agent. People may not only react emotionally, but may also draw inferences about emotive agents such as the social status or power of an emotive agent, their competence and their credibility. [ 53 ]
The pressure to achieve is leaving some teens burned out, according to a new survey. While parents are part of the problem, they can also be part of the solution. Teens are feeling pressured and ...
Historians, like other social scientists, assume that emotions, feelings and their expressions are regulated in different ways by both different cultures and different historical times, and the constructivist school of history claims even that some sentiments and meta-emotions, for example schadenfreude, are learnt and not only regulated by ...
For example, a positive valence would shift the emotion up the top vector and a negative valence would shift the emotion down the bottom vector. [11] In this model, high arousal states are differentiated by their valence, whereas low arousal states are more neutral and are represented near the meeting point of the vectors.
Nearly three-quarters of U.S. teens say they feel happy or peaceful when they don't have their phones with them, according to a new report from the Pew Research Center. In a survey published ...
Decades of scientific research have been conducted developing and evaluating methods for automated emotion recognition. There is now an extensive literature proposing and evaluating hundreds of different kinds of methods, leveraging techniques from multiple areas, such as signal processing, machine learning, computer vision, and speech processing.
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