Amplification in the Evaluation of Emotional Expressions Over Time

Goldenberg, Ana; J, Schöne; Huang, Zhenzhen; Sweeny, Timothy D.; Dc, Ong; Tf, Brady; Mm, Robinson; Levari, David; Zaki, Jamil; Gross, Jerome

doi:10.31234/osf.io/rfgy3

Cited by 4 publications

(3 citation statements)

References 46 publications

(77 reference statements)

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“…Although Markovian transition dynamics offer a useful first-order approximation of mental state dynamics, we doubt that real-world dynamics are memory-less in this way. Indeed, there is considerable evidence that emotions demonstrate hysteresis (history dependence) in a way that would violate a Markovian assumption (Goldenberg et al, 2022;Hao, 2017;Sacharin et al, 2012). As we alluded to earlier, confidence followed by sadness seems to tell a very different story from confidence followed by joy, and these different sequences likely make different predictions about people's future states.…”

Section: Limitationsmentioning

confidence: 98%

Transition dynamics shape mental state concepts.

Thornton¹,

Rmus²,

Vyas³

et al. 2023

Journal of Experimental Psychology: General

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People have a unique ability to represent other people's internal thoughts and feelings-their mental states. Mental state knowledge has a rich conceptual structure, organized along key dimensions, such as valence. People use this conceptual structure to guide social interactions. How do people acquire their understanding of this structure? Here we investigate an underexplored contributor to this process: observation of mental state dynamics. Mental states-including both emotions and cognitive states-are not static. Rather, the transitions from one state to another are systematic and predictable. Drawing on prior cognitive science, we hypothesize that these transition dynamics may shape the conceptual structure that people learn to apply to mental states. Across nine behavioral experiments (N = 1,439), we tested whether the transition probabilities between mental states causally shape people's conceptual judgments of those states. In each study, we found that observing frequent transitions between mental states caused people to judge them to be conceptually similar. Computational modeling indicated that people translated mental state dynamics into concepts by embedding the states as points within a geometric space. The closer two states are within this space, the greater the likelihood of transitions between them. In three neural network experiments, we trained artificial neural networks to predict real human mental state dynamics. The networks spontaneously learned the same conceptual dimensions that people use to understand mental states. Together these results indicate that mental state dynamicsand the goal of predicting them-shape the structure of mental state concepts. Public Significance StatementWhere does our understanding of others' minds come from? Using behavioral experiments and neural network modeling, the studies in this article investigated the role of mental state dynamics in shaping mental state concepts. The results indicate that mental states that tend to precede or follow one another become viewed as conceptually similar as a result.

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Section: Limitationsmentioning

confidence: 98%

Transition dynamics shape mental state concepts.

Thornton¹,

Rmus²,

Vyas³

et al. 2023

Journal of Experimental Psychology: General

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“…This amplification in the evaluation of collective emotion seems to occur due to the fact that perceivers tend to spend more time attending to more emotional (compared to more neutral) expressions (Goldenberg et al, 2021). Amplification in evaluating collective emotions seems to be happening not only when people are exposed to emotions concurrently, but also sequentially (Goldenberg, Schöne, et al, 2022). This is driven mainly by the tendency to remember stronger emotional expressions when integrating sequential information.…”

Section: Cognitionmentioning

confidence: 99%

What makes groups emotional?

Goldenberg¹

2022

Preprint

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When people experience emotions in a group, their emotions tend to have stronger intensity and to last longer, why is that? This question has occupied thinkers throughout history and with the increased use of digital media it is even more pressing than ever. Historically, attention has focused on the amplification driven by the way emotions are communicated and shared between people via emotional interactions. While interactions are a major driver of group emotionality, I argue that understanding group emotionality requires a broader view which integrates two additional processes: how emotions unfold within the social infrastructure in which they are communicated, and how these processes are affected by people’s cognition about others’ emotions. I propose an infrastructure, cognition, interaction framework that contributes to a broader understanding of group emotionality, which should improve our ability to explain and predict group emotionality and to change these emotions when they are undesired.

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“…Our first three hypotheses were derived from existing evidence of the crowd-emotionamplification effect (Goldenberg et al, 2021(Goldenberg et al, , 2022. First, we predicted that people would be more likely to evaluate crowds with more emotional faces as emotional than non-emotional.…”

Section: The Present Researchmentioning

confidence: 99%

Judgment of Crowds as Emotional Increases with the Proportion of Black Faces

Goldenberg¹,

LaFollette

Huang³

et al. 2022

Preprint

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Does the racial composition of a crowd affect judgments about the group’s emotionality? In Experiments 1-2, we showed participants crowds of faces expressing different degrees of happiness or anger, manipulating the ratio of lighter-skinned faces racialized as White and darker-skinned faces racialized as Black. Greater proportion of Black faces increased the likelihood that the crowd was perceived as emotional. In Experiment 3 we confirmed that this effect was not merely driven by the contrast between White and Black faces within a crowd; crowds composed of solely Black faces were more likely to be evaluated as emotional than crowds of White and Black faces. We used a hierarchical drift diffusion model to compare potential mechanisms, and found that Black faces expressing emotions weighed more heavily than White faces in pushing participants to judge a crowd as emotional. These results shed light on an important way in which racialization impacts social cognition.

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Amplification in the Evaluation of Emotional Expressions Over Time

Cited by 4 publications

References 46 publications

Transition dynamics shape mental state concepts.

Transition dynamics shape mental state concepts.

What makes groups emotional?

Judgment of Crowds as Emotional Increases with the Proportion of Black Faces

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