Transition dynamics shape mental state concepts

Thornton, Mark Allen; Rmus, Milena; Tamir, Diana

doi:10.31234/osf.io/kbcsj

Cited by 10 publications

(15 citation statements)

References 46 publications

(83 reference statements)

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“…Adults predict emotion transitions with remarkable accuracy, and increasingly so over the first few months of a relationship (Zhao & Tamir, 2020). Adults can also learn sequences of novel emotion states (Thornton et al, 2020). Collectively, these findings suggest that children may develop an adult-like pattern of emotion durations and transitions by tracking patterns in their social environment.…”

Section: Discussionmentioning

confidence: 79%

“…Statistical learning may allow children to extract information about the patterns of emotion dynamics in their environment. Although there is no work directly testing children's ability to learn emotion dynamics in this way, support for this possibility comes from three lines of work: (1) children track statistics in many other cognitive and social domains (e.g., events, speech, and actions; Addyman et al, 2014;Lew-Williams et al, 2011;Monroy et al, 2017Monroy et al, , 2019Saffran & Kirkham, 2018), (2) they do so for other features of emotion, such as emotion categories (e.g., which facial configurations are categorized as neutral or angry depends on their frequency; Plate et al, 2019;Woodard et al, 2021), and (3) adults learn transition probabilities between emotions (Thornton et al, 2020). These findings collectively suggest that the adult-like pattern of emotion dynamics may be learned.…”

mentioning

confidence: 99%

“…Indeed, children learn emotion categories more easily when presented with verbal labels (Ogren & Sandhofer, 2022;, verbal abilities relate to the emergence of emotion understanding from childhood to adulthood (Nook et al, 2017), and children begin to correctly match labels and facial expressions at around the same age that they produce emotion labels (i.e., 2 years of age; Frank et al, 2017;Wu et al, 2022). Research has also shown a tight connection between how people think about the similarities of emotion words and how emotion transitions typically unfold (Barrett, 2006;Thornton et al, 2020;Thornton & Tamir, 2017). For example, both typical emotion transitions and words tend to be organized along the valence dimension (i.e., positive vs. negative; Thornton et al, 2022;Thornton & Tamir, 2017).…”

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confidence: 99%

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The co-emergence of emotion vocabulary and organized emotion dynamics in childhood

Nencheva¹,

Nook²,

Thornton³

et al. 2023

Preprint

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Emotions change from one moment to the next. They have a duration from seconds to hours, and then transition to other emotions. Here we describe the early ontology of these key aspects of emotion dynamics. In five cross-sectional studies that combine parent surveys and ecological momentary assessment, we characterize how emotion duration and transitions change over the first five years of life, and how they relate to children’s language development. Over this developmental period, the duration of children’s emotions increased, and emotion transitions became increasingly organized by valence, such that children were more likely to transition between similarly valenced emotions. Children with these more mature emotion profiles also had larger vocabularies and produced labels for the tested emotions. These findings advance our understanding of emotion and communication by highlighting their intertwined nature in development and by charting how dynamic features of emotional experiences change over the first years of life.

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Section: Discussionmentioning

confidence: 79%

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confidence: 99%

mentioning

confidence: 99%

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The co-emergence of emotion vocabulary and organized emotion dynamics in childhood

Nencheva¹,

Nook²,

Thornton³

et al. 2023

Preprint

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“…If people are unable to perceive others' emotions because they cannot infer emotions from facial expressions, vocal tone, or emotional language, then this disrupts the process of learning emotion dynamics. Without an understanding of emotion dynamics, a person cannot build a model for emotions transitioning from one to the next (40), leading to inaccurate emotion predictions. Our findings support this idea, showing that individuals with difficulty perceiving emotions from others' facial expressions make more inaccurate emotion predictions.…”

Section: Discussionmentioning

confidence: 99%

Individual differences in emotion prediction and implications for social success

Barrick

Thornton²,

Zhao

et al. 2023

Preprint

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The social world requires people to predict others’ thoughts, feelings, and actions. People who successfully predict others’ emotions experience significant social advantages. What makes a person good at predicting emotions? To predict others’ future emotional states, a person must know how one emotion transitions to the next. People learn how emotions transition from at least two sources: (i) internal information, or one’s own emotion experiences, and (ii) external information, such as the social cues detected in a person’s face. Across five studies, we find evidence that both sources of information are related to accurate emotion prediction: individuals with atypical personal emotion transitions, difficulty understanding their own emotional experiences, and impaired emotion perception displayed impaired emotion prediction. This ability to predict others’ emotions has real-world social implications. Individuals who make accurate emotion predictions have better relationships with their friends and communities and experience less loneliness. In contrast, disruptions in both internal and external information sources explain prediction inaccuracy in individuals with social difficulties, specifically with social communication difficulties common in autism spectrum disorder. These findings provide evidence that successful emotion prediction, which relies on the perception of accurate internal and external data about how emotions transition, may be key to social success.

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“…For instance, deep nets trained to predict people's emotional responses to images have started to reveal the perceptual processing underlying these responses (Kragel et al, 2019). Other deep nets trained to predict emotion transitions have spontaneously learned to apply a human-like conceptual structure to those emotions, suggesting that dynamics play a key role in conceptual construction (Thornton et al, 2020).…”

Section: Deep Learning In Existing Researchmentioning

confidence: 99%

Advancing naturalistic affective science with deep learning

Lin¹,

Bulls²,

Tepfer³

et al. 2023

Preprint

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People express emotions via a variety of behaviors, including facial muscle movements, body poses and gestures, vocal prosody, and speech. To understand how people experience and perceive emotion, it is crucial to quantify and model these behaviors. However, existing methods are insufficient to address this need. Manually annotating behavior is very time-consuming, making it infeasible to do at scale. Moreover, common linear models cannot fully capture the complex, nonlinear, and interactive affective processes embodied by these behaviors. In this methodology review, we describe how deep learning addresses these challenges and thereby promises to advance naturalistic affective science. First, deep learning provides accessible and efficient tools to annotate dynamic, complex, multi-modal behaviors. These automated annotation tools can scale up behavioral quantification to a degree impossible with human coders, enabling many new, more naturalistic approaches to affective science. Second, deep learning offers innovative paradigms for optimizing and manipulating naturalistic stimuli. This application makes it possible to generate experiment designs with greater generalizability, statistical power, and external validity. Third, deep learning can support flexible, powerful cognitive models of naturalistic affective processing. These novel cognitive models make it possible to explain how the mind and brain engage in the emotional world in ways that are both broader and more precise. However, deep learning is not without its limitations, so we also explore important failure cases, practical issues, and ethical concerns. By detailing the promise and the peril of deep learning, this review paves the way for a more naturalistic and generalizable affective science.

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Transition dynamics shape mental state concepts

Cited by 10 publications

References 46 publications

The co-emergence of emotion vocabulary and organized emotion dynamics in childhood

The co-emergence of emotion vocabulary and organized emotion dynamics in childhood

Individual differences in emotion prediction and implications for social success

Advancing naturalistic affective science with deep learning

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