Sensing Emotion in Voices: Negativity Bias and Gender Differences in a Validation Study of the Oxford Vocal (‘OxVoc’) Sounds Database

Emotional expressions are an essential element of human interactions. Recent work has increasingly recognized that emotional vocalizations can color and shape interactions between individuals. Here we present data on the psychometric properties of a recently developed database of authentic nonlinguistic emotional vocalizations from human adults and infants (the Oxford Vocal ‘OxVoc’ Sounds Database; Parsons, Young, Craske, Stein, & Kringelbach, 2014). In a large sample (n = 562), we demonstrate that adults can reliably categorize these sounds (as ‘positive,’ ‘negative,’ or ‘sounds with no emotion’), and rate valence in these sounds consistently over time. In an extended sample (n = 945, including the initial n = 562), we also investigated a number of individual difference factors in relation to valence ratings of these vocalizations. Results demonstrated small but significant effects of (a) symptoms of depression and anxiety with more negative ratings of adult neutral vocalizations (R(2) = .011 and R(2) = .008, respectively) and (b) gender differences in perceived valence such that female listeners rated adult neutral vocalizations more positively and infant cry vocalizations more negatively than male listeners (R(2) = .021, R(2) = .010, respectively). Of note, we did not find evidence of negativity bias among other affective vocalizations or gender differences in perceived valence of adult laughter, adult cries, infant laughter, or infant neutral vocalizations. Together, these findings largely converge with factors previously shown to impact processing of emotional facial expressions, suggesting a modality-independent impact of depression, anxiety, and listener gender, particularly among vocalizations with more ambiguous valence.