Budgerigars were trained to produce specific vocalizations (calls) using operant conditioning and food reinforcement. The bird's call was compared to a digital representation of the call stored in a computer to determine a match. Once birds were responding at a high level of precision, we measured the effect of several manipulations upon the accuracy and the intensity of call production. Also, by differentially reinforcing other aspects of vocal behavior, budgerigars were trained to produce a call that matched another bird's contact call and to alter the latency of their vocal response. Both the accuracy of vocal matching and the intensity level of vocal production increased significantly when the bird could hear the template immediately before each trial. Moreover, manipulating the delay between the presentation of an acoustic reference and the onset of vocal production did not significantly affect either vocal intensity or matching accuracy. Interestingly, the vocalizations learned and reinforced in these operant experiments were only occasionally used in more natural communicative situations, such as when birds called back and forth to one another in their home cages.
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http://dx.doi.org/10.1121/1.2835440 | DOI Listing |
Arch Sex Behav
January 2025
Department of Applied Social Sciences, Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.
Jealousy responses to potential mating rivals are stronger when those rivals display cues indicating higher mate quality. One such cue is vocal femininity in women's voices, with higher-pitched voices eliciting greater jealousy responses. However, cues to mate quality are not evaluated in isolation.
View Article and Find Full Text PDFAnn Fam Med
January 2025
Departments of Psychiatry and Emergency Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas.
Purpose: Mental health screening is recommended by the US Preventive Services Task Force for all patients in areas where treatment options are available. Still, it is estimated that only 4% of primary care patients are screened for depression. The goal of this study was to evaluate the efficacy of machine learning technology (Kintsugi Voice, v1, Kintsugi Mindful Wellness, Inc) to detect and analyze voice biomarkers consistent with moderate to severe depression, potentially allowing for greater compliance with this critical primary care public health need.
View Article and Find Full Text PDFJ Speech Lang Hear Res
January 2025
Center for Laryngeal Surgery and Voice Rehabilitation, Massachusetts General Hospital, Boston.
Purpose: The Daily Phonotrauma Index (DPI) can quantify pathophysiological mechanisms associated with daily voice use in individuals with phonotraumatic vocal hyperfunction (PVH). Since DPI was developed based on weeklong ambulatory voice monitoring, this study investigated if DPI can achieve comparable performance using (a) short laboratory speech tasks and (b) fewer than 7 days of ambulatory data.
Method: An ambulatory voice monitoring system recorded the vocal function/behavior of 134 females with PVH and vocally healthy matched controls in two different conditions.
Emotion
January 2025
Department of Psychology, Cognitive and Affective Neuroscience Unit, University of Zurich.
Affective voice signaling has significant biological and social relevance across various species, and different affective signaling types have emerged through the evolution of voice communication. These types range from basic affective voice bursts and nonverbal affective up to affective intonations superimposed on speech utterances in humans in the form of paraverbal prosodic patterns. These different types of affective signaling should have evolved to be acoustically and perceptually distinctive, allowing accurate and nuanced affective communication.
View Article and Find Full Text PDFSong acquisition behavior observed in the songbird system provides a notable example of learning through trial- and-error which parallels human speech acquisition. Studying songbird vocal learning can offer insights into mechanisms underlying human language. We present a computational model of song learning that integrates reinforcement learning (RL) and Hebbian learning and agrees with known songbird circuitry.
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