Which emotions are associated with universally recognized non-verbal signals?We address this issue by examining how reliably non-linguistic vocalizations (affect bursts) can convey emotions across cultures. Actors from India, Kenya, Singapore, and USA were instructed to produce vocalizations that would convey nine positive and nine negative emotions to listeners. The vocalizations were judged by Swedish listeners using a within-valence forced-choice procedure, where positive and negative emotions were judged in separate experiments. Results showed that listeners could recognize a wide range of positive and negative emotions with accuracy above chance. For positive emotions, we observed the highest recognition rates for relief, followed by lust, interest, serenity and positive surprise, with affection and pride receiving the lowest recognition rates. Anger, disgust, fear, sadness, and negative surprise received the highest recognition rates for negative emotions, with the lowest rates observed for guilt and shame. By way of summary, results showed that the voice can reveal both basic emotions and several positive emotions other than happiness across cultures, but self-conscious emotions such as guilt, pride, and shame seem not to be well recognized from non-linguistic vocalizations.

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BackgroundIn research on event-related potentials (ERP) to emotional pictures, greater attention to emotional than neutral stimuli (i.e., motivated attention) is commonly indexed by two difference waves between emotional and neutral stimuli: the early posterior negativity (EPN) and the late positive potential (LPP). Evidence suggests that if attention is directed away from the pictures, then the emotional effects on EPN and LPP are eliminated. However, a few studies have found residual, emotional effects on EPN and LPP. In these studies, pictures were shown at fixation, and picture composition was that of simple figures rather than that of complex scenes. Because figures elicit larger LPP than do scenes, figures might capture and hold attention more strongly than do scenes. Here, we showed negative and neutral pictures of figures and scenes and tested first, whether emotional effects are larger to figures than scenes for both EPN and LPP, and second, whether emotional effects on EPN and LPP are reduced less for unattended figures than scenes.ResultsEmotional effects on EPN and LPP were larger for figures than scenes. When pictures were unattended, emotional effects on EPN increased for scenes but tended to decrease for figures, whereas emotional effects on LPP decreased similarly for figures and scenes.ConclusionsEmotional effects on EPN and LPP were larger for figures than scenes, but these effects did not resist manipulations of attention more strongly for figures than scenes. These findings imply that the emotional content captures attention more strongly for figures than scenes, but that the emotional content does not hold attention more strongly for figures than scenes.

The auditory gating paradigm was adopted to study how much acoustic information is needed to recognize emotions from speech prosody and music performances. In Study 1, brief utterances conveying ten emotions were segmented into temporally fine-grained gates and presented to listeners, whereas Study 2 instead used musically expressed emotions. Emotion recognition accuracy increased with increasing gate duration and generally stabilized after a certain duration, with different trajectories for different emotions. Above-chance accuracy was observed for ≤100 ms stimuli for anger, happiness, neutral, and sadness, and for ≤250 ms stimuli for most other emotions, for both speech and music. This suggests that emotion recognition is a fast process that allows discrimination of several emotions based on low-level physical characteristics. The emotion identification points, which reflect the amount of information required for stable recognition, were shortest for anger and happiness for both speech and music, but recognition took longer to stabilize for music vs speech. This, in turn, suggests that acoustic cues that develop over time also play a role for emotion inferences (especially for music). Finally, acoustic cue patterns were positively correlated between speech and music, suggesting a shared acoustic code for expressing emotions.

This study explored the perception of emotion appraisal dimensions on the basis of speech prosody in a cross-cultural setting. Professional actors from Australia and India vocally portrayed different emotions (anger, fear, happiness, pride, relief, sadness, serenity and shame) by enacting emotion-eliciting situations. In a balanced design, participants from Australia and India then inferred aspects of the emotion-eliciting situation from the vocal expressions, described in terms of appraisal dimensions (novelty, intrinsic pleasantness, goal conduciveness, urgency, power and norm compatibility). Bayesian analyses showed that the perceived appraisal profiles for the vocally expressed emotions were generally consistent with predictions based on appraisal theories. Few group differences emerged, which suggests that the perceived appraisal profiles are largely universal. However, some differences between Australian and Indian participants were also evident, mainly for ratings of norm compatibility. The appraisal ratings were further correlated with a variety of acoustic measures in exploratory analyses, and inspection of the acoustic profiles suggested similarity across groups. In summary, results showed that listeners may infer several aspects of emotion-eliciting situations from the non-verbal aspects of a speaker's voice. These appraisal inferences also seem to be relatively independent of the cultural background of the listener and the speaker.