The conflicting findings from the few studies conducted with regard to gender differences in the recognition of vocal expressions of emotion have left the exact nature of these differences unclear. Several investigators have argued that a comprehensive understanding of gender differences in vocal emotion recognition can only be achieved by replicating these studies while accounting for influential factors such as stimulus type, gender-balanced samples, number of encoders, decoders and emotional categories. This study aimed to account for these factors by investigating (...) whether emotion recognition from vocal expressions differs as a function of both listeners’ and speakers’ gender. A total of N = 290 participants were randomly and equally allocated to two groups. One group listened to words and pseudo-words, while the other group listened to sentences and affect bursts. Participants were asked to categorize the stimuli with respect to the expressed emotions in a fixed-choice response format. Overall, females were more accurate than males when decoding vocal emotions, however, when testing for specific emotions these differences were small in magnitude. Speakers’ gender had a significant impact on how listeners’ judged emotions from the voice. The group listening to words and pseudo-words had higher identification rates for emotions spoken by male than by female actors, whereas in the group listening to sentences and affect bursts the identification rates were higher when emotions were uttered by female than male actors. The mixed pattern for emotion-specific effects, however, indicates that, in the vocal channel, the reliability of emotion judgments is not systematically influenced by speakers’ gender and the related stereotypes of emotional expressivity. Together, these results extend previous findings by showing effects of listeners’ and speakers’ gender on the recognition of vocal emotions. They stress the importance of distinguishing these factors to explain recognition ability in the processing of emotional prosody. (shrink)

When we hear another person laugh or scream, can we tell the kind of situation they are in – for example, whether they are playing or fighting? Nonverbal expressions are theorised to vary systematically across behavioural contexts. Perceivers might be sensitive to these putative systematic mappings and thereby correctly infer contexts from others’ vocalisations. Here, in two pre-registered experiments, we test the prediction that listeners can accurately deduce production contexts (e.g. being tickled, discovering threat) from spontaneous nonverbal vocalisations, like sighs (...) and grunts. In Experiment 1, listeners (total n = 3120) matched 200 nonverbal vocalisations to one of 10 contexts using yes/no response options. Using signal detection analysis, we show that listeners were accurate at matching vocalisations to nine of the contexts. In Experiment 2, listeners (n = 337) categorised the production contexts by selecting from 10 response options in a forced-choice task. By analysing unbiased hit rates, we show that participants categorised all 10 contexts at better-than-chance levels. Together, these results demonstrate that perceivers can infer contexts from nonverbal vocalisations at rates that exceed that of random selection, suggesting that listeners are sensitive to systematic mappings between acoustic structures in vocalisations and behavioural contexts. (shrink)

Can we read emotions in faces? Many studies suggest that we can, yet skeptics contend that these studies employ methods that unwittingly help subjects in matching faces with emotions. Some studies present subjects with posed faces, which may be more exaggerated than spontaneous ones. And some studies provide subjects with a list of emotion words to choose from, which forces them to interpret faces in specific emotion terms. I argue that the skeptics’ challenge rests on a false assumption: that once (...) subjects leave the lab, they no longer receive help in matching faces with emotions. I contend that people receive as much help in the wild as they do in the lab. People unconsciously amplify their spontaneous expressions in the presence of others, thereby making them easier to read. And people teach children to interpret faces in the same specific emotion terms found in the experimenters’ word lists. I argue that we are good at readings emotions in faces because we can normally count on a little help from our friends. (shrink)

Different styles of social interaction are one of the core characteristics of autism spectrum disorder. Social differences among individuals with ASD often include difficulty in discerning the emotions of neurotypical people based on their facial expressions. This review first covers the rich body of literature studying differences in facial emotion recognition in those with ASD, including behavioral studies and neurological findings. In particular, we highlight subtle emotion recognition and various factors related to inconsistent findings in behavioral studies of FER in (...) ASD. Then, we discuss the dual problem of FER – namely facial emotion expression or the production of facial expressions of emotion. Despite being less studied, social interaction involves both the ability to recognize emotions and to produce appropriate facial expressions. How others perceive facial expressions of emotion in those with ASD has remained an under-researched area. Finally, we propose a method for teaching FER [FER teaching hierarchy ] based on recent research investigating FER in ASD, considering the use of posed vs. genuine emotions and static vs. dynamic stimuli. We also propose two possible teaching approaches: a standard method of teaching progressively from simple drawings and cartoon characters to more complex audio-visual video clips of genuine human expressions of emotion with context clues or teaching in a field of images that includes posed and genuine emotions to improve generalizability before progressing to more complex audio-visual stimuli. Lastly, we advocate for autism interventionists to use FER stimuli developed primarily for research purposes to facilitate the incorporation of well-controlled stimuli to teach FER and bridge the gap between intervention and research in this area. (shrink)

People dedicate significant attention to others’ facial expressions and to deciphering their meaning. Hence, knowing whether such expressions are genuine or deliberate is important. Early research proposed that authenticity could be discerned based on reliable facial muscle activations unique to genuine emotional experiences that are impossible to produce voluntarily. With an increasing body of research, such claims may no longer hold up to empirical scrutiny. In this article, expression authenticity is considered within the context of senders’ ability to produce convincing (...) facial displays that resemble genuine affect and human decoders’ judgments of expression authenticity. This includes a discussion of spontaneous vs. posed expressions, as well as appearance- vs. elicitation-based approaches for defining emotion recognition accuracy. We further expand on the functional role of facial displays as neurophysiological states and communicative signals, thereby drawing upon the encoding-decoding and affect-induction perspectives of emotion expressions. Theoretical and methodological issues are addressed with the aim to instigate greater conceptual and operational clarity in future investigations of expression authenticity. (shrink)