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Abstract How do we learn who is good at what? Building on the idea that humans draw rich inferences from others’ emotional expressions, here we ask whether others’ surprised reactions to performance outcomes can elicit inferences about competence. Across three experiments, participants were asked to choose “who is better” in scenarios where two students performed identically on the same task but their teacher expressed surprise to only one of them. In Experiment 1 (n = 60, adults) and Experiment 2 (n = 90, 6- to 8-year-old children), participants’ responses were modulated by not only the students’ performance outcomes (success or failure) but also the teacher’s response to the outcomes (surprise or no surprise). Specifically, participants preferentially chose the student who did not elicit the teacher’s surprise as more competent when both students succeeded, but chose the student who elicited surprise when both failed. Experiment 3a (n = 150, 4- to 8-year-olds) replicated this pattern in 6- to 8-year-olds as a group—but not in 4- to 5-year-olds—with increasing robustness with age. Finally, this pattern was significantly reduced in Experiment 3b where the teacher’s surprise was directed at an irrelevant event rather than the student’s performance (n = 90, 6- to 8-year-olds). Taken together, these results suggest that even non-valenced emotional reactions to performance outcomes—being surprised at someone’s success or failure—can inform inferences about valenced qualities such as competence. More broadly, the current findings demonstrate that emotional expressions we observe in our daily lives can lead to nuanced yet consequential social judgments. 

Data-driven approach is promising for predicting impedance profile of grid-connected voltage source converters (VSCs) under a wide range of operating points (OPs). However, the conventional approaches rely on a one-to-one mapping between operating points and impedance profiles, which, as pointed out in this article, can be invalid for multiconverter systems. To tackle this challenge, this article proposes a stacked-autoencoder-based machine learning framework for the impedance profile predication of grid-connected VSCs, together with its detailed design guidelines. The proposed method uses features, instead of OPs, to characterize impedance profiles, and hence, it is scalable for multiconverter systems. Another benefit of the proposed method is the capability of predicting VSC impedance profiles at unstable OPs of the grid-VSC system. Such prediction can be realized solely based on data collected during stable operation, showcasing its potential for rapid online state estimation. Experiments on both single-VSC and multi-VSC systems validate the effectiveness of the proposed method. 

Abstract Human infants show systematic responses to events that violate their expectations. Can they also revise these expectations based on others’ expressions of surprise? Here we ask whether infants (N = 156, mean = 15.2 months, range: 12.0–18.0 months) can use an experimenter’s expression of surprise to revise their own expectations about statistically probable vs. improbable events. An experimenter sampled a ball from a box of red and white balls and briefly displayed either a surprised or an unsurprised expression at the outcome before revealing it to the infant. Following an unsurprised expression, the results were consistent with prior work; infants looked longer at a statistically improbable outcome than a probable outcome. Following a surprised expression, however, this standard pattern disappeared or was even reversed. These results suggest that even before infants can observe the unexpected events themselves, they can use others’ surprise to expect the unexpected. Starting early in life, human learners can leverage social information that signals others’ prediction error to update their own predictions. 

The majority of research on infants’ and children’s understanding of emotional expressions has focused on their abilities to use emotional expressions to infer how other people feel. However, an emerging body of work suggests that emotional expressions support rich, powerful inferences not just about emotional states but also about other unobserved states, such as hidden events in the physical world and mental states of other people (e.g., beliefs and desires). Here we argue that infants and children harness others’ emotional expressions as a source of information for learning about the physical and social world broadly. This “emotion as information” framework integrates affective, developmental, and computational cognitive sciences, extending the scope of signals that count as “information” in early learning. 

Emotional expressions are abundant in children’s lives. What role do they play in children’s causal inference and exploration? This study investigates whether preschool‐aged children use others’ emotional expressions to infer the presence of unknown causal functions and guide their exploration accordingly. Children (age: 3.0–4.9;N = 112, the United States) learned about one salient causal function of a novel toy and then saw an adult play with it. Children explored the toy more when the adult expressed surprise than when she expressed happiness (Experiment 1), but only when the adult already knew about the toy’s salient function (Experiment 2). These results suggest that children consider others’ knowledge and selectively interpret others’ surprise asvicarious predictionerrorto guide their own exploration.