The evolution of complex behavior is driven by the interplay of morphological specializations and neuromuscular control mechanisms [1-3], and it is often difficult to tease apart their respective contributions. Avian vocal learning and associated neural adaptations are thought to have played a major role in bird diversification [4-8], whereas functional significance of substantial morphological diversity of the vocal organ remains largely unexplored. Within the most species-rich order, Passeriformes, "tracheophones" are a suboscine group that, unlike their oscine sister taxon, does not exhibit vocal learning [9] and is thought to phonate with tracheal membranes [10, 11] instead of the two independent sources found in other passerines [12-14]. Here we show tracheophones possess three sound sources, two oscine-like labial pairs and the unique tracheal membranes, which collectively represent the largest described number of sound sources for a vocal organ. Birds with experimentally disabled tracheal membranes were still able to phonate. Instead of the main sound source, the tracheal membranes constitute a morphological specialization, which, through interaction with bronchial labia, contributes to different acoustic features such as spectral complexity, amplitude modulation, and enhanced sound amplitude. In contrast, these same features arise in oscines from neuromuscular control of two labial sources [15-17]. These findings are supported by a modeling approach and provide a clear example for how a morphological adaptation of the tracheophone vocal organ can generate specific, complex sound features. Morphological specialization therefore constitutes an alternative path in the evolution of acoustic diversity to that of oscine vocal learning and complex neural control.
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http://dx.doi.org/10.1016/j.cub.2017.07.059 | DOI Listing |
Curr Opin Neurobiol
January 2025
Animal Physiology, Institute of Neurobiology, University of Tuebingen, Auf der Morgenstelle 28, 72076 Tuebingen, Germany. Electronic address:
Corvids, readily adaptable across social and ecological contexts, successfully inhabit almost the entire world. They are seen as highly intelligent birds, and current research examines their cognitive abilities. Despite being songbirds with a complete 'song system', corvids have historically received less attention in studies of song production, learning, and perception compared to non-corvid songbirds.
View Article and Find Full Text PDFLearn Behav
January 2025
Dolphin Research Center, 58901 Overseas Highway, Grassy Key, FL, 33050, USA.
A recent study demonstrated that marmoset "phee calls" include information specific to the intended receiver of the call, and that receivers respond more to calls that are specifically directed at them. The authors interpret this as showing that these calls are name-like vocal labels for individual marmosets, but there is at least one other possibility that would equally explain these data.
View Article and Find Full Text PDFMed J Armed Forces India
December 2024
Associate Professor, Dayanand Sagar Univerity, Bengaluru, India.
Background: Vital information about a person's physical and emotional health can be perceived in their voice. After sleep loss, altered voice quality is noticed. The circadian rhythm controls the sleep cycle, and when it is askew, it results in fatigue, which is manifested in speech.
View Article and Find Full Text PDFBMC Neurosci
December 2024
Department of Medicine, The University of Chicago, 5841 S Maryland Ave, Chicago, IL, 60637, USA.
Background: Understanding the neural basis of behavior requires insight into how different brain systems coordinate with each other. Existing connectomes for various species have highlighted brain systems essential to various aspects of behavior, yet their application to complex learned behaviors remains limited. Research on vocal learning in songbirds has extensively focused on the vocal control network, though recent work implicates a variety of circuits in contributing to important aspects of vocal behavior.
View Article and Find Full Text PDFAm J Otolaryngol
December 2024
Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin 300192, China; Institute of Otolaryngology of Tianjin, Tianjin, China; Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China; Key Clinical Discipline of Tianjin (Otolaryngology), Tianjin, China; Otolaryngology Clinical Quality Control Centre, Tianjin, China.
Purpose: To use deep learning technology to design and implement a model that can automatically classify laryngoscope images and assist doctors in diagnosing laryngeal diseases.
Materials And Methods: The experiment was based on 3057 images (normal, glottic cancer, granuloma, Reinke's Edema, vocal cord cyst, leukoplakia, nodules and polyps) from the dataset Laryngoscope8. A classification model based on deep neural networks was developed and tested.
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