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Wirthlin ME, Schmid TA, Elie JE, Zhang X, Kowalczyk A, Redlich R, Shvareva VA, Rakuljic A, Ji MB, Bhat NS, Kaplow IM, Schäffer DE, Lawler AJ, Wang AZ, Phan BN, Annaldasula S, Brown AR, Lu T, Lim BK, Azim E, Clark NL, Meyer WK, Pond SLK, Chikina M, Yartsev MM, Pfenning AR, Andrews G, Armstrong JC, Bianchi M, Birren BW, Bredemeyer KR, Breit AM, Christmas MJ, Clawson H, Damas J, Di Palma F, Diekhans M, Dong MX, Eizirik E, Fan K, Fanter C, Foley NM, Forsberg-Nilsson K, Garcia CJ, Gatesy J, Gazal S, Genereux DP, Goodman L, Grimshaw J, Halsey MK, Harris AJ, Hickey G, Hiller M, Hindle AG, Hubley RM, Hughes GM, Johnson J, Juan D, Kaplow IM, Karlsson EK, Keough KC, Kirilenko B, Koepfli KP, Korstian JM, Kowalczyk A, Kozyrev SV, Lawler AJ, Lawless C, Lehmann T, Levesque DL, Lewin HA, Li X, Lind A, Lindblad-Toh K, Mackay-Smith A, Marinescu VD, Marques-Bonet T, Mason VC, Meadows JRS, Meyer WK, Moore JE, Moreira LR, Moreno-Santillan DD, Morrill KM, Muntané G, Murphy WJ, Navarro A, Nweeia M, Ortmann S, Osmanski A, Paten B, Paulat NS, Pfenning AR, Phan BN, Pollard KS, Pratt HE, Ray DA, Reilly SK, Rosen JR, Ruf I, Ryan L, Ryder OA, Sabeti PC, Schäffer DE, Serres A, Shapiro B, Smit AFA, Springer M, Srinivasan C, Steiner C, Storer JM, Sullivan KAM, Sullivan PF, Sundström E, Supple MA, Swofford R, Talbot JE, Teeling E, Turner-Maier J, Valenzuela A, Wagner F, Wallerman O, Wang C, Wang J, Weng Z, Wilder AP, Wirthlin ME, Xue JR, Zhang X. Vocal learning-associated convergent evolution in mammalian proteins and regulatory elements. Science 2024; 383:eabn3263. [PMID: 38422184 DOI: 10.1126/science.abn3263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 02/20/2024] [Indexed: 03/02/2024]
Abstract
Vocal production learning ("vocal learning") is a convergently evolved trait in vertebrates. To identify brain genomic elements associated with mammalian vocal learning, we integrated genomic, anatomical, and neurophysiological data from the Egyptian fruit bat (Rousettus aegyptiacus) with analyses of the genomes of 215 placental mammals. First, we identified a set of proteins evolving more slowly in vocal learners. Then, we discovered a vocal motor cortical region in the Egyptian fruit bat, an emergent vocal learner, and leveraged that knowledge to identify active cis-regulatory elements in the motor cortex of vocal learners. Machine learning methods applied to motor cortex open chromatin revealed 50 enhancers robustly associated with vocal learning whose activity tended to be lower in vocal learners. Our research implicates convergent losses of motor cortex regulatory elements in mammalian vocal learning evolution.
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Affiliation(s)
- Morgan E Wirthlin
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Tobias A Schmid
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94708, USA
| | - Julie E Elie
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94708, USA
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94708, USA
| | - Xiaomeng Zhang
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Amanda Kowalczyk
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Ruby Redlich
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Varvara A Shvareva
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94708, USA
| | - Ashley Rakuljic
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94708, USA
| | - Maria B Ji
- Department of Psychology, University of California, Berkeley, Berkeley, CA 94708, USA
| | - Ninad S Bhat
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94708, USA
| | - Irene M Kaplow
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Daniel E Schäffer
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Alyssa J Lawler
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Andrew Z Wang
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - BaDoi N Phan
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Siddharth Annaldasula
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Ashley R Brown
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Tianyu Lu
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Byung Kook Lim
- Neurobiology section, Division of Biological Science, University of California, San Diego, La Jolla, CA 92093, USA
| | - Eiman Azim
- Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Nathan L Clark
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Wynn K Meyer
- Department of Biological Sciences, Lehigh University, Bethlehem, PA 18015, USA
| | | | - Maria Chikina
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Michael M Yartsev
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94708, USA
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94708, USA
| | - Andreas R Pfenning
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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Henley L, Jones O, Mathews F, Woolley TE. Bat Motion can be Described by Leap Frogging. Bull Math Biol 2024; 86:16. [PMID: 38197980 PMCID: PMC10781826 DOI: 10.1007/s11538-023-01233-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 11/01/2023] [Indexed: 01/11/2024]
Abstract
We present models of bat motion derived from radio-tracking data collected over 14 nights. The data presents an initial dispersal period and a return to roost period. Although a simple diffusion model fits the initial dispersal motion we show that simple convection cannot provide a description of the bats returning to their roost. By extending our model to include non-autonomous parameters, or a leap frogging form of motion, where bats on the exterior move back first, we find we are able to accurately capture the bat's motion. We discuss ways of distinguishing between the two movement descriptions and, finally, consider how the different motion descriptions would impact a bat's hunting strategy.
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Affiliation(s)
- Lucy Henley
- Cardiff School of Mathematics Cardiff University, Senghennydd Road, Cardiff, CF24 4AG, UK
| | - Owen Jones
- Cardiff School of Mathematics Cardiff University, Senghennydd Road, Cardiff, CF24 4AG, UK
| | - Fiona Mathews
- University of Sussex, John Maynard Smith Building, Falmer, Brighton, BN1 9RH, UK
| | - Thomas E Woolley
- Cardiff School of Mathematics Cardiff University, Senghennydd Road, Cardiff, CF24 4AG, UK.
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Elie JE, Muroy SE, Genzel D, Na T, Beyer LA, Swiderski DL, Raphael Y, Yartsev MM. Effects of deafening on vocal production learning in the Egyptian fruit-bat. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.21.568126. [PMID: 38045408 PMCID: PMC10690156 DOI: 10.1101/2023.11.21.568126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Some species have evolved the ability to use the sense of hearing to modify existing vocalizations, or even create new ones. This ability corresponds to various forms of vocal production learning that are all possessed by humans, and independently displayed by distantly related species. Among mammals, a few species, including the Egyptian fruit-bat, would possess such vocal production learning abilities. Yet the necessity of an intact auditory system for the development of the Egyptian fruit-bat typical vocal repertoire has not been tested. Here we addressed this gap by eliminating pups' sense of hearing at birth and assessing its effects on vocal production in adulthood. The deafening treatment enabled us to both causally test these bats vocal learning ability and discern learned from innate aspects of their vocalizations. Leveraging wireless individual audio recordings from freely interacting adults, we show that a subset of the Egyptian fruit-bat vocal repertoire necessitates auditory feedback. Intriguingly, these affected vocalizations belong to different acoustic groups in the vocal repertoire of males and females. These findings open the possibilities for targeted studies of the mammalian neural circuits that enable sexually dimorphic forms of vocal learning. Significance Vocal production learning is the rare capacity amongst animals where hearing is used to modify or create new vocalizations. The Egyptian fruit-bat is believed to possess this capacity, yet whether they need audition to achieve a mature vocal repertoire is unknown. Furthermore, a systematic causal examination of learned and innate aspects of the entire repertoire has never been performed in a vocal learner. Here, we addressed these major gaps directly by abolishing hearing in Egyptian fruit-bats at birth and investigating its effects on the vocal production in adulthood. Leveraging simultaneous individual wireless audio-recordings from freely interacting adult bats, we identify the subset of learned vocalizations and provide evidence that vocal learning is sexually dimorphic in that species.
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Kiai A, Clemens J, Kössl M, Poeppel D, Hechavarría J. Flexible control of vocal timing in Carollia perspicillata bats enables escape from acoustic interference. Commun Biol 2023; 6:1153. [PMID: 37957351 PMCID: PMC10643407 DOI: 10.1038/s42003-023-05507-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
In natural environments, background noise can degrade the integrity of acoustic signals, posing a problem for animals that rely on their vocalizations for communication and navigation. A simple behavioral strategy to combat acoustic interference would be to restrict call emissions to periods of low-amplitude or no noise. Using audio playback and computational tools for the automated detection of over 2.5 million vocalizations from groups of freely vocalizing bats, we show that bats (Carollia perspicillata) can dynamically adapt the timing of their calls to avoid acoustic jamming in both predictably and unpredictably patterned noise. This study demonstrates that bats spontaneously seek out temporal windows of opportunity for vocalizing in acoustically crowded environments, providing a mechanism for efficient echolocation and communication in cluttered acoustic landscapes.
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Affiliation(s)
- Ava Kiai
- Institute for Cell Biology and Neuroscience, Goethe University, Frankfurt am Main, Germany.
| | - Jan Clemens
- European Neuroscience Center, Göttingen, Germany
| | - Manfred Kössl
- Institute for Cell Biology and Neuroscience, Goethe University, Frankfurt am Main, Germany
| | - David Poeppel
- Ernst Strüngmann Institute, Frankfurt am Main, Germany
| | - Julio Hechavarría
- Institute for Cell Biology and Neuroscience, Goethe University, Frankfurt am Main, Germany.
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Perceptual hearing sensitivity during vocal production. iScience 2022; 25:105435. [PMID: 36388966 PMCID: PMC9650033 DOI: 10.1016/j.isci.2022.105435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 09/18/2022] [Accepted: 10/20/2022] [Indexed: 11/09/2022] Open
Abstract
Vocalization, such as speaking, inevitably generates sensory feedback that can cause self-generated masking. However, perceptual hearing sensitivity during vocal production is poorly understood. Using an adaptive psychophysical method, we measured the perceptual hearing sensitivity of an echolocating bat, Hipposideros pratti, in a passive listening (PL) task to detect pure tones, an active listening (AL) task to detect pure tones triggered by its vocalization, and a phantom echo task. We found that hanging H. pratti had the best hearing sensitivity of approximately 0 dB sound pressure level (SPL) in the PL task but much lower hearing sensitivity (nearly 40 dB worse) in the echo task. In the AL task, all bats gradually increased call frequency by 0.8–1.1 kHz, which improved their hearing sensitivity by 25–29 dB. This study underscores the need for studying the sensory capability of subjects engaged in active behaviors. Vocal production strongly affects the perceptual hearing sensitivity of bats Forward masking explains the reduced hearing sensitivity during vocalization Long-term vocal plasticity enables bats to overcome self-generated auditory masking
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6
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Selection levels on vocal individuality: strategic use or byproduct. Curr Opin Behav Sci 2022. [DOI: 10.1016/j.cobeha.2022.101140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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7
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Torres Borda L, Jadoul Y, Rasilo H, Salazar Casals A, Ravignani A. Vocal plasticity in harbour seal pups. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200456. [PMID: 34719248 PMCID: PMC8558775 DOI: 10.1098/rstb.2020.0456] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/28/2021] [Indexed: 12/22/2022] Open
Abstract
Vocal plasticity can occur in response to environmental and biological factors, including conspecifics' vocalizations and noise. Pinnipeds are one of the few mammalian groups capable of vocal learning, and are therefore relevant to understanding the evolution of vocal plasticity in humans and other animals. Here, we investigate the vocal plasticity of harbour seals (Phoca vitulina), a species with vocal learning abilities observed in adulthood but not puppyhood. To evaluate early mammalian vocal development, we tested 1-3 weeks-old seal pups. We tailored noise playbacks to this species and age to induce seal pups to shift their fundamental frequency (f0), rather than adapt call amplitude or temporal characteristics. We exposed individual pups to low- and high-intensity bandpass-filtered noise, which spanned-and masked-their typical range of f0; simultaneously, we recorded pups' spontaneous calls. Unlike most mammals, pups modified their vocalizations by lowering their f0 in response to increased noise. This modulation was precise and adapted to the particular experimental manipulation of the noise condition. In addition, higher levels of noise induced less dispersion around the mean f0, suggesting that pups may have actively focused their phonatory efforts to target lower frequencies. Noise did not seem to affect call amplitude. However, one seal showed two characteristics of the Lombard effect known for human speech in noise: significant increase in call amplitude and flattening of spectral tilt. Our relatively low noise levels may have favoured f0 modulation while inhibiting amplitude adjustments. This lowering of f0 is unusual, as most animals commonly display no such f0 shift. Our data represent a relatively rare case in mammalian neonates, and have implications for the evolution of vocal plasticity and vocal learning across species, including humans. This article is part of the theme issue 'Voice modulation: from origin and mechanism to social impact (Part I)'.
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Affiliation(s)
- Laura Torres Borda
- Comparative Bioacoustics Group, Max Planck Institute for Psycholinguistics, Wundtlaan 1, 6525 XD Nijmegen, The Netherlands
- Research Department, Sealcentre Pieterburen, Hoofdstraat 94-A, 9968 AG Pieterburen, The Netherlands
| | - Yannick Jadoul
- Comparative Bioacoustics Group, Max Planck Institute for Psycholinguistics, Wundtlaan 1, 6525 XD Nijmegen, The Netherlands
- Artificial Intelligence Lab, Vrije Universiteit Brussel, 1050 Elsene/Ixelles, Belgium
| | - Heikki Rasilo
- Artificial Intelligence Lab, Vrije Universiteit Brussel, 1050 Elsene/Ixelles, Belgium
| | - Anna Salazar Casals
- Research Department, Sealcentre Pieterburen, Hoofdstraat 94-A, 9968 AG Pieterburen, The Netherlands
| | - Andrea Ravignani
- Comparative Bioacoustics Group, Max Planck Institute for Psycholinguistics, Wundtlaan 1, 6525 XD Nijmegen, The Netherlands
- Research Department, Sealcentre Pieterburen, Hoofdstraat 94-A, 9968 AG Pieterburen, The Netherlands
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8
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Abstract
Vocal production learning, the ability to modify the structure of vocalizations as a result of hearing those of others, has been studied extensively in birds but less attention has been given to its occurrence in mammals. We summarize the available evidence for vocal learning in mammals from the last 25 years, updating earlier reviews on the subject. The clearest evidence comes from cetaceans, pinnipeds, elephants and bats where species have been found to copy artificial or human language sounds, or match acoustic models of different sound types. Vocal convergence, in which parameter adjustments within one sound type result in similarities between individuals, occurs in a wider range of mammalian orders with additional evidence from primates, mole-rats, goats and mice. Currently, the underlying mechanisms for convergence are unclear with vocal production learning but also usage learning or matching physiological states being possible explanations. For experimental studies, we highlight the importance of quantitative comparisons of seemingly learned sounds with vocal repertoires before learning started or with species repertoires to confirm novelty. Further studies on the mammalian orders presented here as well as others are needed to explore learning skills and limitations in greater detail. This article is part of the theme issue 'Vocal learning in animals and humans'.
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Affiliation(s)
- Vincent M Janik
- Scottish Oceans Institute, School of Biology, University of St Andrews, St Andrews KY16 8LB, UK
| | - Mirjam Knörnschild
- Museum für Naturkunde, Leibniz-Institute for Evolution and Biodiversity Science, Berlin, Germany.,Animal Behavior Lab, Freie Universität, Berlin, Germany.,Smithsonian Tropical Research Institute, Balboa, Ancón, Panama
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9
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Lattenkamp EZ, Hörpel SG, Mengede J, Firzlaff U. A researcher's guide to the comparative assessment of vocal production learning. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200237. [PMID: 34482725 DOI: 10.1098/rstb.2020.0237] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Vocal production learning (VPL) is the capacity to learn to produce new vocalizations, which is a rare ability in the animal kingdom and thus far has only been identified in a handful of mammalian taxa and three groups of birds. Over the last few decades, approaches to the demonstration of VPL have varied among taxa, sound production systems and functions. These discrepancies strongly impede direct comparisons between studies. In the light of the growing number of experimental studies reporting VPL, the need for comparability is becoming more and more pressing. The comparative evaluation of VPL across studies would be facilitated by unified and generalized reporting standards, which would allow a better positioning of species on any proposed VPL continuum. In this paper, we specifically highlight five factors influencing the comparability of VPL assessments: (i) comparison to an acoustic baseline, (ii) comprehensive reporting of acoustic parameters, (iii) extended reporting of training conditions and durations, (iv) investigating VPL function via behavioural, perception-based experiments and (v) validation of findings on a neuronal level. These guidelines emphasize the importance of comparability between studies in order to unify the field of vocal learning. This article is part of the theme issue 'Vocal learning in animals and humans'.
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Affiliation(s)
- Ella Z Lattenkamp
- Division of Neurobiology, Department of Biology II, LMU Munich, Germany.,Neurogenetics of Vocal Communication Group, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Stephen G Hörpel
- Neurogenetics of Vocal Communication Group, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.,Department of Animal Sciences, Chair of Zoology, TU Munich, Germany
| | - Janine Mengede
- Neurogenetics of Vocal Communication Group, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Uwe Firzlaff
- Department of Animal Sciences, Chair of Zoology, TU Munich, Germany
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10
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Rose MC, Styr B, Schmid TA, Elie JE, Yartsev MM. Cortical representation of group social communication in bats. Science 2021; 374:eaba9584. [PMID: 34672724 PMCID: PMC8775406 DOI: 10.1126/science.aba9584] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Social interactions occur in group settings and are mediated by communication signals that are exchanged between individuals, often using vocalizations. The neural representation of group social communication remains largely unexplored. We conducted simultaneous wireless electrophysiological recordings from the frontal cortices of groups of Egyptian fruit bats engaged in both spontaneous and task-induced vocal interactions. We found that the activity of single neurons distinguished between vocalizations produced by self and by others, as well as among specific individuals. Coordinated neural activity among group members exhibited stable bidirectional interbrain correlation patterns specific to spontaneous communicative interactions. Tracking social and spatial arrangements within a group revealed a relationship between social preferences and intra- and interbrain activity patterns. Combined, these findings reveal a dedicated neural repertoire for group social communication within and across the brains of freely communicating groups of bats.
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Affiliation(s)
- Maimon C. Rose
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA
| | - Boaz Styr
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA
| | - Tobias A. Schmid
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA
| | - Julie E. Elie
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA
| | - Michael M. Yartsev
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA
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11
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Vocal learning and flexible rhythm pattern perception are linked: Evidence from songbirds. Proc Natl Acad Sci U S A 2021; 118:2026130118. [PMID: 34272278 PMCID: PMC8307534 DOI: 10.1073/pnas.2026130118] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
We can recognize the cadence of a friend’s voice or the rhythm of a familiar song across a wide range of tempi. This shows that our perception of temporal patterns relies strongly on the relative timing of events rather than on specific absolute durations. This tendency is foundational to speech and music perception, but to what extent is it shared by other species? We hypothesize that animals that learn their vocalizations are more likely to share this tendency. Here, we show that a vocal learning songbird robustly recognizes a basic rhythmic pattern independent of rate. Our findings pave the way for neurobiological studies to identify how the brain represents and perceives the temporal structure of auditory sequences. Rhythm perception is fundamental to speech and music. Humans readily recognize a rhythmic pattern, such as that of a familiar song, independently of the tempo at which it occurs. This shows that our perception of auditory rhythms is flexible, relying on global relational patterns more than on the absolute durations of specific time intervals. Given that auditory rhythm perception in humans engages a complex auditory–motor cortical network even in the absence of movement and that the evolution of vocal learning is accompanied by strengthening of forebrain auditory–motor pathways, we hypothesize that vocal learning species share our perceptual facility for relational rhythm processing. We test this by asking whether the best-studied animal model for vocal learning, the zebra finch, can recognize a fundamental rhythmic pattern—equal timing between event onsets (isochrony)—based on temporal relations between intervals rather than on absolute durations. Prior work suggests that vocal nonlearners (pigeons and rats) are quite limited in this regard and are biased to attend to absolute durations when listening to rhythmic sequences. In contrast, using naturalistic sounds at multiple stimulus rates, we show that male zebra finches robustly recognize isochrony independent of absolute time intervals, even at rates distant from those used in training. Our findings highlight the importance of comparative studies of rhythmic processing and suggest that vocal learning species are promising animal models for key aspects of human rhythm perception. Such models are needed to understand the neural mechanisms behind the positive effect of rhythm on certain speech and movement disorders.
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12
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Knörnschild M, Fernandez AA. Do Bats Have the Necessary Prerequisites for Symbolic Communication? Front Psychol 2020; 11:571678. [PMID: 33262725 PMCID: PMC7688458 DOI: 10.3389/fpsyg.2020.571678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/12/2020] [Indexed: 11/20/2022] Open
Abstract
Training animals such as apes, gray parrots, or dolphins that communicate via arbitrary symbols with humans has revealed astonishing mental capacities that may have otherwise gone unnoticed. Albeit bats have not yet been trained to communicate via symbols with humans, we are convinced that some species, especially captive Pteropodid bats ("flying foxes"), show the potential to master this cognitive task. Here, we briefly review what is known about bats' cognitive skills that constitute relevant prerequisites for symbolic communication with humans. We focus on social learning in general, trainability by humans, associative learning from humans, imitation, vocal production learning and usage learning, and social knowledge. Moreover, we highlight potential training paradigms that could be used to elicit simple "symbolic" bat-human communication, i.e., training bats to select arbitrary symbols on a touchscreen to elicit a desired behavior of the human caregiver. Touchscreen-proficient bats could participate in cognition research, e.g., to study their numerical competence or categorical perception, to further elucidate how nonhuman animals learn and perceive the world.
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Affiliation(s)
- Mirjam Knörnschild
- Museum für Naturkunde, Leibniz-Institute for Evolution and Biodiversity Science, Berlin, Germany
- Animal Behavior Lab, Freie Universität, Berlin, Germany
- Smithsonian Tropical Research Institute, Ancón, Panama
| | - Ahana A. Fernandez
- Museum für Naturkunde, Leibniz-Institute for Evolution and Biodiversity Science, Berlin, Germany
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13
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Mazar O, Yovel Y. A sensorimotor model shows why a spectral jamming avoidance response does not help bats deal with jamming. eLife 2020; 9:55539. [PMID: 32718437 PMCID: PMC7406351 DOI: 10.7554/elife.55539] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 07/21/2020] [Indexed: 12/05/2022] Open
Abstract
For decades, researchers have speculated how echolocating bats deal with masking by conspecific calls when flying in aggregations. To date, only a few attempts have been made to mathematically quantify the probability of jamming, or its effects. We developed a comprehensive sensorimotor predator-prey simulation, modeling numerous bats foraging in proximity. We used this model to examine the effectiveness of a spectral Jamming Avoidance Response (JAR) as a solution for the masking problem. We found that foraging performance deteriorates when bats forage near conspecifics, however, applying a JAR does not improve insect sensing or capture. Because bats constantly adjust their echolocation to the performed task (even when flying alone), further shifting the signals' frequencies does not mitigate jamming. Our simulations explain how bats can hunt successfully in a group despite competition and despite potential masking. This research demonstrates the advantages of a modeling approach when examining a complex biological system.
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Affiliation(s)
- Omer Mazar
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Yossi Yovel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Department of Zoology, Tel Aviv University, Tel Aviv, Israel
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Wirthlin M, Chang EF, Knörnschild M, Krubitzer LA, Mello CV, Miller CT, Pfenning AR, Vernes SC, Tchernichovski O, Yartsev MM. A Modular Approach to Vocal Learning: Disentangling the Diversity of a Complex Behavioral Trait. Neuron 2019; 104:87-99. [PMID: 31600518 PMCID: PMC10066796 DOI: 10.1016/j.neuron.2019.09.036] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 09/18/2019] [Accepted: 09/21/2019] [Indexed: 12/14/2022]
Abstract
Vocal learning is a behavioral trait in which the social and acoustic environment shapes the vocal repertoire of individuals. Over the past century, the study of vocal learning has progressed at the intersection of ecology, physiology, neuroscience, molecular biology, genomics, and evolution. Yet, despite the complexity of this trait, vocal learning is frequently described as a binary trait, with species being classified as either vocal learners or vocal non-learners. As a result, studies have largely focused on a handful of species for which strong evidence for vocal learning exists. Recent studies, however, suggest a continuum in vocal learning capacity across taxa. Here, we further suggest that vocal learning is a multi-component behavioral phenotype comprised of distinct yet interconnected modules. Discretizing the vocal learning phenotype into its constituent modules would facilitate integration of findings across a wider diversity of species, taking advantage of the ways in which each excels in a particular module, or in a specific combination of features. Such comparative studies can improve understanding of the mechanisms and evolutionary origins of vocal learning. We propose an initial set of vocal learning modules supported by behavioral and neurobiological data and highlight the need for diversifying the field in order to disentangle the complexity of the vocal learning phenotype.
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