Emotion Download Php
Elena Piersanti
What are the cognitive processes that allow flexible responses to the changing demands of varying emotional contexts? Previous research suggests that higher levels of cognitive control are linked to successful emotion regulation. In particular, the implementation of emotion regulation strategies has been associated with individual differences in cognitive control, including (a) inhibiting prepotent responses, (b) updating information in working memory, and (c) shifting mental sets. Although most of this work has focused on the relationship between cognitive control and the short-term implementation of regulatory strategies, cognitive control may be even more important for understanding the dynamic adaptation to varying emotional contexts, that is, emotion regulation flexibility. However, cognitive control and emotion regulation flexibility have not been investigated in conjunction, resulting in a lack of a coherent understanding. In this article, we describe a framework outlining the importance of cognitive control for understanding three key components of emotion regulation flexibility: (a) strategy stopping or switching, (b) strategy maintenance, and (c) monitoring. We highlight the relevance of studying each of these components through the lens of cognitive control processes, particularly focusing on the tradeoff between shielding versus shifting goals and goal-directed behavior in various emotional contexts. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
Emotion regulation abilities, measured on a test of emotional intelligence, were related to several indicators of the quality of individuals' social interactions with peers. In a sample of 76 college students, emotion regulation abilities were associated with both self-reports and peer nominations of interpersonal sensitivity and prosocial tendencies, the proportion of positive vs. negative peer nominations, and reciprocal friendship nominations. These relationships remained statistically significant after controlling for the Big Five personality traits as well as verbal and fluid intelligence.
It is commonly assumed that a person's emotional state can be readily inferred from his or her facial movements, typically called emotional expressions or facial expressions. This assumption influences legal judgments, policy decisions, national security protocols, and educational practices; guides the diagnosis and treatment of psychiatric illness, as well as the development of commercial applications; and pervades everyday social interactions as well as research in other scientific fields such as artificial intelligence, neuroscience, and computer vision. In this article, we survey examples of this widespread assumption, which we refer to as the common view, and we then examine the scientific evidence that tests this view, focusing on the six most popular emotion categories used by consumers of emotion research: anger, disgust, fear, happiness, sadness, and surprise. The available scientific evidence suggests that people do sometimes smile when happy, frown when sad, scowl when angry, and so on, as proposed by the common view, more than what would be expected by chance. Yet how people communicate anger, disgust, fear, happiness, sadness, and surprise varies substantially across cultures, situations, and even across people within a single situation. Furthermore, similar configurations of facial movements variably express instances of more than one emotion category. In fact, a given configuration of facial movements, such as a scowl, often communicates something other than an emotional state. Scientists agree that facial movements convey a range of information and are important for social communication, emotional or otherwise. But our review suggests an urgent need for research that examines how people actually move their faces to express emotions and other social information in the variety of contexts that make up everyday life, as well as careful study of the mechanisms by which people perceive instances of emotion in one another. We make specific research recommendations that will yield a more valid picture of how people move their faces to express emotions and how they infer emotional meaning from facial movements in situations of everyday life. This research is crucial to provide consumers of emotion research with the translational information they require.
What did you think of the last commercial you watched? Was it funny? Confusing? Would you buy the product? You might not remember or know for certain how you felt, but increasingly, machines do. New artificial intelligence technologies are learning and recognizing human emotions, and using that knowledge to improve everything from marketing campaigns to health care.
El Kaliouby said she sees potential in expanding the technology to new use cases, for example, using the call center technology to understand the emotional well-being of employees, or for other mental health uses. But concern over coming off as Big Brother is a legitimate worry, and one that will have to be continuously addressed within the scope of privacy and this technology. To that point, el Kaliouby said that Affectiva requires opt-in and consent for all use cases of its technology.
These prior assessments of the relationship between specific emotions and forming accuracy judgments are potentially also compatible with the classical reasoning account of why people fall for fake news. For instance, sad individuals may engage in analytic thinking more often and thus are more skeptical of fake news, while the opposite may be true for happy individuals (see Forgas 2019).
We aim to add to the current state of knowledge regarding belief in fake news in three main ways. First, little previous work has looked at the effects of experiencing specific emotions on belief in fake news. Looking at these effects will help us determine whether the potential effect(s) of emotion on fake news belief is isolated to a few specific emotions (presumably for a few idiosyncratic reasons) or whether a broader dual-process framework where emotion and reason are differentially responsible for the broad phenomenon of falling for fake news is more appropriate.
Second, much prior work on fake news has focused almost exclusively on reasoning, rather than investigating the role of emotional processing per se. In other words, prior research has treated the extent of reason and emotion as unidimensional, such that any increase in use of reason necessarily implies a decrease in use of emotion and vice-versa. In contrast, both emotion and reason may complimentarily aid in the formation of beliefs (Mercer 2010). The current study addresses this issue by separately modulating the use of reason and use of emotion. This approach, as well as the inclusion of a baseline condition in our experimental design, allows us to ask whether belief in fake news is more likely to be the result of merely failing to engage in reasoning rather than being specifically promoted by reliance on emotion. Furthermore, it allows for differentiable assessments regarding use of reason and use of emotion, rather than treating reason and emotion simply as two directions on the same continuum.
Study 1 investigates the association between state-based emotionality and accuracy judgments of real and fake news. In particular, we assess whether increased experience of emotion prior to viewing news headlines is associated with heightened belief in fake news headlines and decreased ability to discern between fake and real news.
We not only find statistically significant associations between experiencing emotion and believing fake news but also observe rather substantial effect sizes. Our mixed-effects model indicates that belief in fake news (relative to the scale minimum value of 1) is nearly twice as high for participants with the highest aggregated positive and negative emotion scores (accuracy ratings of 0.96 and 1.45 above scale minimum, respectively) compared to participants with the lowest aggregated positive and negative emotion scores (accuracy ratings of 0.34 and 0.50 above scale minimum, respectively). Therefore, although even participants who experience high emotion are still, on average, able to discern between fake and true news, we observe notable increases in belief in fake news as emotionality increases.
As shown by most of our 20 previous linear mixed-effects models, both positive and negative emotion are associated with higher accuracy ratings for fake headlines (Fig. 2), and this relationship does not exist as clearly for real headlines.
Another potential concern with Study 1 is that participants with higher PANAS scores are simply less attentive, and these inattentive participants are those performing worse on discriminating between real and fake news. However, this alternative explanation does not account for our findings that certain emotions (e.g., interested, alert, attentive) are not associated with decreased discernment between real and fake news, which demonstrate that our correlational findings are specific to a distinct set of emotions assessed by the PANAS, thus alleviating some concerns of floor effects driving our results.
Study 2 expands on the findings of Study 1 in several ways. First, Study 1 found that experienced emotion, regardless of the specific type of emotion, was associated with increased belief in fake news, as well as decreased ability to differentiate between real and fake news. To explain this association, we hypothesized that individuals who experienced greater emotionality also relied on emotion to a greater extent when making accuracy judgments of news headlines (otherwise, why increased emotionality should impact decision-making is not clear). Therefore, in Study 2, we directly manipulate the way that individuals engage in emotional processing while evaluating the veracity of news headlines. We manipulate the extent to which individuals rely on emotion (in generalFootnote 4) or reason when judging the accuracy of news headlines. We investigate whether reliance on emotion versus reason causally affects judgments of fake news, as well as the ability to discern between real and fake news.
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