Emotional States from Affective Dynamics

Cunningham, William A.; Dunfield, Kristen A.; Stillman, Paul E.

doi:10.1177/1754073913489749

Cited by 127 publications

(124 citation statements)

References 87 publications

(98 reference statements)

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“…14). Nonetheless, research has demonstrated that individual emotions ebb and flow over time with some regularity (15,16), and that temporal information facilitates social functioning (17). These findings hint that certain emotions may flow into others with some regularity.…”

mentioning

confidence: 94%

Mental models accurately predict emotion transitions

Thornton

Tamir

2017

Proc. Natl. Acad. Sci. U.S.A.

110

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Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.emotion | experience-sampling | social cognition | theory of mind H umans must navigate a wide variety of stimuli in everyday life, ranging from apples and oranges to automobiles and computer operating systems. However, other humans are perhaps the most consequential stimuli of all, potentially driving the very evolution of the human brain (1). Despite the dazzling array of actions and internal states of which humans are capable, people are remarkably good at understanding each other (2-4). Indeed, the social mind appears particularly attuned to the problem of predicting other people (5). Perceivers make use of a wide variety of perceptible cues-including social context, facial expression, and tone of voiceto infer what emotions others' are feeling (6-8), likely because emotions predict behavior (9, 10). However, these perceptual mechanisms only get us so far: we cannot see what expression our friend will wear next week, nor hear tomorrow's tone of voice. How might we make social predictions beyond the immediate future? Such foresight could convey significant strategic advantages: in the social domain, as in the game of chess (11), success may depend on the depth and breadth of a player's search through others' possible future moves. Here we propose that people use a powerful mechanism for gain...

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mentioning

confidence: 94%

Mental models accurately predict emotion transitions

Thornton

Tamir

2017

Proc. Natl. Acad. Sci. U.S.A.

110

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“…that feelings of unpleasantness and arousal are an indication that a stimulus is disgusting v s fearful) (Cunningham et al , 2013; Lindquist, 2013; Barrett, 2014). This type of prediction is referred to as ‘conceptualization’ and is supported by regions that are thought to represent prior experiences and semantic knowledge such as the dorsal and ventral medial prefrontal cortex, posterior cingulate cortex, hippocampus, lateral temporal cortex, anterior temporal lobe and the ventrolateral prefrontal cortex (i.e.…”

Section: Introductionmentioning

confidence: 99%

The role of language in the experience and perception of emotion: a neuroimaging meta-analysis

Brooks

Shablack

Gendron

et al. 2016

Soc Cogn Affect Neurosci

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Recent behavioral and neuroimaging studies demonstrate that labeling one’s emotional experiences and perceptions alters those states. Here, we used a comprehensive meta-analysis of the neuroimaging literature to systematically explore whether the presence of emotion words in experimental tasks has an impact on the neural representation of emotional experiences and perceptions across studies. Using a database of 386 studies, we assessed brain activity when emotion words (e.g. ‘anger’, ‘disgust’) and more general affect words (e.g. ‘pleasant’, ‘unpleasant’) were present in experimental tasks vs not present. As predicted, when emotion words were present, we observed more frequent activations in regions related to semantic processing. When emotion words were not present, we observed more frequent activations in the amygdala and parahippocampal gyrus, bilaterally. The presence of affect words did not have the same effect on the neural representation of emotional experiences and perceptions, suggesting that our observed effects are specific to emotion words. These findings are consistent with the psychological constructionist prediction that in the absence of accessible emotion concepts, the meaning of affective experiences and perceptions are ambiguous. Findings are also consistent with the regulatory role of ‘affect labeling’. Implications of the role of language in emotion construction and regulation are discussed.

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“…The researcher is bound to using emotions easily separable in AV space, otherwise the affect recognition part of their system might underperform. Moreover, the nature and number of the basic structural elements of emotion is still under investigation [18], and as a result, the same applies to the dimensional representation of emotion in AC systems.…”

Section: Classifying Emotionmentioning

confidence: 99%