In this study we tested the often suggested claim that people are able to recognize their dogs by their barks. Earlier studies in other species indicated that reliable discrimination between individuals cannot be made by listening to chaotically noisy vocalizations. As barking is typically such a chaotic noisy vocalization, we have hypothesized that reliable discrimination between individuals is not possible by listening to barks. In this study, playback experiments were conducted to explore (1) how accurately humans discriminate between dogs by hearing only their barks, (2) the impact of the eliciting context of calls on these discrimination performances, and (3) how much such discrimination depends on acoustic parameters (tonality and frequency of barks, and the intervals between the individual barks). Our findings were consistent with the previous studies: human performances did not pass the empirical threshold of reliable discrimination in most cases. But a significant effect of tonality was found: discrimination between individuals was more successful when listeners were listening to low harmonic-to-noise ratio (HNR) barks. The contexts in which barks were recorded affected significantly the listeners' performances: if the dog barked at a stranger, listeners were able to discriminate the vocalizations better than if they were listening to sounds recorded when the dog was separated from its owner. It is rendered probable that the bark might be a more efficient communication system between humans and dogs for communicating the motivational state of an animal than for discrimination among strange individuals.
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http://dx.doi.org/10.1016/j.beproc.2006.03.014 | DOI Listing |
BMC Psychol
January 2025
Department of Psychiatry, College of Medicine and Health Sciences, Government Hospitals, Arabian Gulf University, Manama, Bahrain.
Background: The concepts of masculinity and femininity have historically shaped gender roles, leading to inequality and gender-based discrimination. Women's autonomy, defined as the ability to make independent choices across various life domains, remains inadequately measured by existing scales. This study addresses this gap by developing and validating the Women Autonomy Scale (WAS).
View Article and Find Full Text PDFBMC Nurs
January 2025
Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha, 410011, China.
Background: The Transition Shock Scale for Undergraduate Nursing Students assesses transition to undergraduate nursing students. The TSS is a tool used in various countries and has been translated into Chinese; however, associate degree nurses dominate China's nursing workforce, it needs to be validated in associate degree nursing interns. This study aimed to analyze the TSS (Chinese version) validity and reliability in Chinese associate degree nursing interns.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jing Wu Road, No. 324, Jinan, 250021, Shandong, China.
To develop and validate non-contrast computed tomography (NCCT)-based radiomics method combines machine learning (ML) to investigate invisible microscopic acute ischaemic stroke (AIS) lesions. We retrospectively analyzed 1122 patients from August 2015 to July 2022, whose were later confirmed AIS by diffusion-weighted imaging (DWI). However, receiving a negative result was reported by radiologists according to the NCCT images.
View Article and Find Full Text PDFNeural Netw
January 2025
College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, 310027, China. Electronic address:
Unsupervised domain adaptation (UDA) aims to annotate unlabeled target domain samples using transferable knowledge learned from the labeled source domain. Optimal transport (OT) is a widely adopted probability metric in transfer learning for quantifying domain discrepancy. However, many existing OT-based UDA methods usually employ an entropic regularization term to solve the OT optimization problem, inevitably resulting in a biased estimation of domain discrepancy.
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