24
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Crowd vocal learning induces vocal dialects in bats: Playback of conspecifics shapes fundamental frequency usage by pups

      research-article
      1 , 1 , 1 , 1 , 2 , *
      PLoS Biology
      Public Library of Science

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Vocal learning, the substrate of human language acquisition, has rarely been described in other mammals. Often, group-specific vocal dialects in wild populations provide the main evidence for vocal learning. While social learning is often the most plausible explanation for these intergroup differences, it is usually impossible to exclude other driving factors, such as genetic or ecological backgrounds. Here, we show the formation of dialects through social vocal learning in fruit bats under controlled conditions. We raised 3 groups of pups in conditions mimicking their natural roosts. Namely, pups could hear their mothers' vocalizations but were also exposed to a manipulation playback. The vocalizations in the 3 playbacks mainly differed in their fundamental frequency. From the age of approximately 6 months and onwards, the pups demonstrated distinct dialects, where each group was biased towards its playback. We demonstrate the emergence of dialects through social learning in a mammalian model in a tightly controlled environment. Unlike in the extensively studied case of songbirds where specific tutors are imitated, we demonstrate that bats do not only learn their vocalizations directly from their mothers, but that they are actually influenced by the sounds of the entire crowd. This process, which we term “crowd vocal learning,” might be relevant to many other social animals such as cetaceans and pinnipeds.

          Author summary

          The spontaneous acquisition of speech by human infants is considered a keystone of human language, but the ability to reproduce vocalizations acquired by hearing is not commonly described in other mammals. This skill, termed vocal learning, is challenging to study in nonhuman animals since such investigation requires the detection and exclusion of innate developmental effects. The recognition of vocal dialects among different populations can open a window on the vocal learning abilities of animals, but such findings in the wild may reflect genetic or ecological differences between groups rather than the learning of group-specific vocal behavior. In this study, we used a playback-based lab experiment to induce vocal dialects in fruit bat pups. By exposing groups of pups to different playbacks of conspecific calls, we could establish separate dialects, demonstrating the vocal learning skill of these bats. Furthermore, while songbirds, for instance, learn their songs directly from a specific tutor, our bats showed the ability to pick up vocal variations from the surrounding crowd, without direct interaction with any given tutor.

          Related collections

          Most cited references31

          • Record: found
          • Abstract: found
          • Article: not found

          The different roles of social learning in vocal communication.

          While vocal learning has been studied extensively in birds and mammals, little effort has been made to define what exactly constitutes vocal learning and to classify the forms that it may take. We present such a theoretical framework for the study of social learning in vocal communication. We define different forms of social learning that affect communication and discuss the required methodology to show each one. We distinguish between contextual and production learning in animal communication. Contextual learning affects the behavioural context or serial position of a signal. It can affect both usage and comprehension. Production learning refers to instances where the signals themselves are modified in form as a result of experience with those of other individuals. Vocal learning is defined as production learning in the vocal domain. It can affect one or more of three systems: the respiratory, phonatory and filter systems. Each involves a different level of control over the sound production apparatus. We hypothesize that contextual learning and respiratory production learning preceded the evolution of phonatory and filter production learning. Each form of learning potentially increases the complexity of a communication system. We also found that unexpected genetic or environmental factors can have considerable effects on vocal behaviour in birds and mammals and are often more likely to cause changes or differences in vocalizations than investigators may assume. Finally, we discuss how production learning is used in innovation and invention, and present important future research questions. Copyright 2000 The Association for the Study of Animal Behaviour.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            YIN, a fundamental frequency estimator for speech and music.

            An algorithm is presented for the estimation of the fundamental frequency (F0) of speech or musical sounds. It is based on the well-known autocorrelation method with a number of modifications that combine to prevent errors. The algorithm has several desirable features. Error rates are about three times lower than the best competing methods, as evaluated over a database of speech recorded together with a laryngograph signal. There is no upper limit on the frequency search range, so the algorithm is suited for high-pitched voices and music. The algorithm is relatively simple and may be implemented efficiently and with low latency, and it involves few parameters that must be tuned. It is based on a signal model (periodic signal) that may be extended in several ways to handle various forms of aperiodicity that occur in particular applications. Finally, interesting parallels may be drawn with models of auditory processing.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              An estimator for pairwise relatedness using molecular markers.

              I propose a new estimator for jointly estimating two-gene and four-gene coefficients of relatedness between individuals from an outbreeding population with data on codominant genetic markers and compare it, by Monte Carlo simulations, to previous ones in precision and accuracy for different distributions of population allele frequencies, numbers of alleles per locus, actual relationships, sample sizes, and proportions of relatives included in samples. In contrast to several previous estimators, the new estimator is well behaved and applies to any number of alleles per locus and any allele frequency distribution. The estimates for two- and four-gene coefficients of relatedness from the new estimator are unbiased irrespective of the sample size and have sampling variances decreasing consistently with an increasing number of alleles per locus to the minimum asymptotic values determined by the variation in identity-by-descent among loci per se, regardless of the actual relationship. The new estimator is also robust for small sample sizes and for unknown relatives being included in samples for estimating allele frequencies. Compared to previous estimators, the new one is generally advantageous, especially for highly polymorphic loci and/or small sample sizes.
                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: ValidationRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                31 October 2017
                October 2017
                31 October 2017
                : 15
                : 10
                : e2002556
                Affiliations
                [1 ] School of Zoology, Faculty of Life sciences, Tel Aviv University, Tel Aviv, Israel
                [2 ] Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
                Princeton University, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-2939-9858
                Article
                pbio.2002556
                10.1371/journal.pbio.2002556
                5663327
                29088225
                92c6805c-7856-4761-9098-92bd8f398ec8
                © 2017 Prat et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 14 March 2017
                : 28 September 2017
                Page count
                Figures: 3, Tables: 0, Pages: 14
                Funding
                European Research Council (ERC – GPSBAT) https://erc.europa.eu (grant number ERC-2015-StG - 679186_GPS-Bat) (to YY). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Short Reports
                Biology and Life Sciences
                Behavior
                Animal Behavior
                Animal Signaling and Communication
                Vocalization
                Biology and Life Sciences
                Zoology
                Animal Behavior
                Animal Signaling and Communication
                Vocalization
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
                Bats
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
                Bats
                Fruit Bats
                Social Sciences
                Linguistics
                Language Acquisition
                Physical Sciences
                Physics
                Acoustics
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Psychology
                Learning
                Biology and Life Sciences
                Psychology
                Cognitive Psychology
                Learning
                Social Sciences
                Psychology
                Cognitive Psychology
                Learning
                Biology and Life Sciences
                Neuroscience
                Learning and Memory
                Learning
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Linear Discriminant Analysis
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Linear Discriminant Analysis
                Physical Sciences
                Mathematics
                Discrete Mathematics
                Combinatorics
                Permutation
                Custom metadata
                All relevant data are within the paper and its Supporting Information files

                Life sciences
                Life sciences

                Comments

                Comment on this article