Affective cognition: Exploring lay theories of emotion

Abstract

Humans skillfully reason about others' emotions, a phenomenon we term affective cognition. Despite its importance, few formal, quantitative theories have described the mechanisms supporting this phenomenon. We propose that affective cognition involves applying domain-general reasoning processes to domain-specific content knowledge. Observers' knowledge about emotions is represented in rich and coherent lay theories, which comprise consistent relationships between situations, emotions, and behaviors. Observers utilize this knowledge in deciphering social agents' behavior and signals (e.g., facial expressions), in a manner similar to rational inference in other domains. We construct a computational model of a lay theory of emotion, drawing on tools from Bayesian statistics, and test this model across four experiments in which observers drew inferences about others' emotions in a simple gambling paradigm. This work makes two main contributions. First, the model accurately captures observers' flexible but consistent reasoning about the ways that events and others' emotional responses to those events relate to each other. Second, our work models the problem of emotional cue integration-reasoning about others' emotion from multiple emotional cues-as rational inference via Bayes' rule, and we show that this model tightly tracks human observers' empirical judgments. Our results reveal a deep structural relationship between affective cognition and other forms of inference, and suggest wide-ranging applications to basic psychological theory and psychiatry.

People learn other people’s preferences through inverse decision-making

“…This literature includes recent probabilistic accounts of emotion inference (Ong, Zaki, & Goodman, 2015) and attitude attribution (Walker, Smith, & Vul, 2015), and inverse decision-making accounts of belief and goal inference (Baker et al, 2009; Ullman et al, 2009; Tauber & Steyvers, 2011; Baker & Tenenbaum, 2014; Wu et al, 2014; Jern & Kemp, 2015; Jara-Ettinger et al, 2016; Baker et al, 2017). Although these accounts rely on different formal assumptions, they are all based on the idea that people interpret social behavior by inverting a model of the process that produced the behavior.…”

Formalizing emotion concepts within a Bayesian model of theory of mind

“…Capitalizing on this progress, the same formalizations can be used to model (some) human emotion predictions. For example, in a simple lottery context, two parameters of the target’s appraisal could be inferred directly from a description of the event — his overall reward, and his prediction error — and combined to capture in quantitative detail the emotions that observers predicted [18**]. Relatedly, Wu and colleagues showed participants simple moral scenarios, in which Grace puts white powder in another girl’s coffee [19].…”

“…[44]; similarly, vocal bursts are more informative for distinguishing among positive emotions [49]. As a result, depending on the context, the modality with the most reliable information will appear to dominate emotion attributions [18**,46]; when one cue is ambiguous, cues from other modalities can “sharpen” the inferred cause by shifting attributions among similar, or nearby, emotions [37]. On the other hand, event information is intuitively relevant to the cause of the emotion, rather than its consequences.…”

“…An observer can therefore predict emotions based on inferred appraisals (following the causal arrows) or from the observed expressions (inverse of the causal arrows). Compare similar models in [14,18**,19]. …”

Contributions of Neuroscience to a New Empathy Epistemology: Implications for Developmental Training

“…The social world has required a willingness and an ability to share cognitive experience within an affective state Ong, Zaki, & Goodman, 2015). The ability to understand the world rests in integrating and sharing in the other's emotional states (Decety, 2010).…”