Low Vocal Pitch Preference Drives First Impressions Irrespective of Context in Male Voices but Not in Female Voices.

Vocal pitch has been found to influence judgments of perceived trustworthiness and dominance from a novel voice. However, the majority of findings arise from using only male voices and in context-specific scenarios. In two experiments, we first explore the influence of average vocal pitch on first-impression judgments of perceived trustworthiness and dominance, before establishing the existence of an overall preference for high or low pitch across genders. In Experiment 1, pairs of high- and low-pitched temporally reversed recordings of male and female vocal utterances were presented in a two-alternative forced-choice task. Results revealed a tendency to select the low-pitched voice over the high-pitched voice as more trustworthy, for both genders, and more dominant, for male voices only. Experiment 2 tested an overall preference for low-pitched voices, and whether judgments were modulated by speech content, using forward and reversed speech to manipulate context. Results revealed an overall preference for low pitch, irrespective of direction of speech, in male voices only. No such overall preference was found for female voices. We propose that an overall preference for low pitch is a default prior in male voices irrespective of context, whereas pitch preferences in female voices are more context- and situation-dependent. The present study confirms the important role of vocal pitch in the formation of first-impression personality judgments and advances understanding of the impact of context on pitch preferences across genders.

A motion capture library for the study of identity, gender, and emotion perception from biological motion.

We present the methods that were used in capturing a library of human movements for use in computer-animated displays of human movement. The library is an attempt to systematically tap into and represent the wide range of personal properties, such as identity, gender, and emotion, that are available in a person's movements. The movements from a total of 30 nonprofessional actors (15 of them female) were captured while they performed walking, knocking, lifting, and throwing actions, as well as their combination in angry, happy, neutral, and sad affective styles. From the raw motion capture data, a library of 4,080 movements was obtained, using techniques based on Character Studio (plug-ins for 3D Studio MAX, AutoDesk, Inc.), MATLAB The MathWorks, Inc.), or a combination of these two. For the knocking, lifting, and throwing actions, 10 repetitions of the simple action unit were obtained for each affect, and for the other actions, two longer movement recordings were obtained for each affect. We discuss the potential use of the library for computational and behavioral analyses of movement variability, of human character animation, and of how gender, emotion, and identity are encoded and decoded from human movement.