Frequency Modulation During Song in a Suboscine Does Not Require Vocal Muscles
01 May 2008-Journal of Neurophysiology (American Physiological Society)-Vol. 99, Iss: 5, pp 2383-2389
TL;DR: This work investigates sound production and control of sound frequency in the Great Kiskadee by recording air sac pressure and vocalizations during spontaneously generated song and assumes a nonlinear restitution force for the oscillating membrane folds in a two mass model of sound production to reproduce the frequency modulations of the observed vocalizations.
Abstract: The physiology of sound production in suboscines is poorly investigated. Suboscines are thought to develop song innately unlike the closely related oscines. Comparing phonatory mechanisms might therefore provide interesting insight into the evolution of vocal learning. Here we investigate sound production and control of sound frequency in the Great Kiskadee (Pitangus sulfuratus) by recording air sac pressure and vocalizations during spontaneously generated song. In all the songs and calls recorded, the modulations of the fundamental frequency are highly correlated to air sac pressure. To test whether this relationship reflects frequency control by changing respiratory activity or indicates synchronized vocal control, we denervated the syringeal muscles by bilateral resection of the tracheosyringeal nerve. After denervation, the strong correlation between fundamental frequency and air sac pressure patterns remained unchanged. A single linear regression relates sound frequency to air sac pressure in the intact and denervated birds. This surprising lack of control by syringeal muscles of frequency in Kiskadees, in strong contrast to songbirds, poses the question of how air sac pressure regulates sound frequency. To explore this question theoretically, we assume a nonlinear restitution force for the oscillating membrane folds in a two mass model of sound production. This nonlinear restitution force is essential to reproduce the frequency modulations of the observed vocalizations.
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TL;DR: This review discusses four variables that determine the oscillation frequency of the vibrating structures within a bird's syrinx: viscoelastic properties of the oscillating tissue, air sac pressure, neuromuscular control of movements and source-filter interactions.
Abstract: One major feature of the remarkable vocal repertoires of birds is the range of fundamental frequencies across species, but also within individual species. This review discusses four variables that determine the oscillation frequency of the vibrating structures within a bird’s syrinx. These are (1) viscoelastic properties of the oscillating tissue, (2) air sac pressure, (3) neuromuscular control of movements and (4) source-filter interactions. Our current understanding of morphology, biomechanics and neural control suggests that a complex interplay of these parameters can lead to multiple combinations for generating a particular fundamental frequency. An increase in the complexity of syringeal morphology from non-passeriform birds to oscines also led to a different interplay for regulating oscillation frequency by enabling control of tension that is partially independent of regulation of airflow. In addition to reviewing the available data for all different contributing variables, we point out open questions and possible approaches.
63 citations
Cites background or methods from "Frequency Modulation During Song in..."
...In a tyrannid suboscine, the great Kiskadee (Pitangus sulfuratus), air sac pressure is highly correlated with F0 of the 3 syllables (‘‘kis-ka-dee’’) of its territorial call sequence (Amador et al., 2008)....
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...This relationship could be reproduced in a syrinx model, if a non-linear restitution force was assumed (Amador et al., 2008)....
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TL;DR: In gibbons, dynamic control over the vocal tract configuration, rather than anatomical modifications, has been a dominant factor in determining call structure, allowing monogamous gibbons to produce pure-tonal melodious songs in the dense tropical forests with poor visibility.
Abstract: Diversifications in primate vocalization, including human speech, are believed to reflect evolutionary modifications in vocal anatomy and physiology. Gibbon song is acoustically unique, comprising loud, melodious, penetrating pure tone-like calls. In a white-handed gibbon, Hylobates lar, the fundamental frequency (f(0) ) of song sounds is amplified distinctively from the higher harmonics in normal air. In a helium-enriched atmosphere, f(0) does not shift, but it is significantly suppressed and 2f(0) is emphasized. This implies that the source is independent of the resonance filter of the supralaryngeal vocal tract (SVT) in gibbons, in contrast to musical wind instruments, in which the filter primarily determines f(0) . Acoustic simulation further supported that gibbons' singing is produced analogously to professional human soprano singing, in which a precise tuning of the first formant (F(1) ) of the SVT to f(0) amplifies exclusively the f(0) component of the source. Thus, in gibbons, as in humans, dynamic control over the vocal tract configuration, rather than anatomical modifications, has been a dominant factor in determining call structure. The varied dynamic movements were adopted in response to unique social and ecological pressures in gibbons, allowing monogamous gibbons to produce pure-tonal melodious songs in the dense tropical forests with poor visibility.
50 citations
Cites background from "Frequency Modulation During Song in..."
...These parameters are based on a standard setting widely applied to human voice and animal vocalizations (Ishizaka and Flanagan, 1972; Steinecke and Herzel, 1995; Amador et al., 2008)....
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TL;DR: It is shown that the sound generating structures of the syrinx, the labia and the associated cartilaginous framework, also display sexual dimorphism, which illustrates a significant evolutionary step towards increased vocal complexity in birds.
Abstract: Background
In many songbirds the larger vocal repertoire of males is associated with sexual dimorphism of the vocal control centers and muscles of the vocal organ, the syrinx. However, it is largely unknown how these differences are translated into different acoustic behavior.
Methodology/Principal Findings
Here we show that the sound generating structures of the syrinx, the labia and the associated cartilaginous framework, also display sexual dimorphism. One of the bronchial half rings that position and tense the labia is larger in males, and the size and shape of the labia differ between males and females. The functional consequences of these differences were explored by denervating syringeal muscles. After denervation, both sexes produced equally low fundamental frequencies, but the driving pressure generally increased and was higher in males. Denervation strongly affected the relationship between driving pressure and fundamental frequency.
Conclusions/Significance
The syringeal modifications in the male syrinx, in concert with dimorphisms in neural control and muscle mass, are most likely the foundation for the potential to generate an enhanced frequency range. Sexually dimorphic vocal behavior therefore arises from finely tuned modifications at every level of the motor cascade. This sexual dimorphism in frequency control illustrates a significant evolutionary step towards increased vocal complexity in birds.
48 citations
Cites result from "Frequency Modulation During Song in..."
...Denervation of the syrinx leads only to minor frequency changes and does not cause a drastic change in the slope of the frequency-pressure relationship [39], unlike the results reported here for the male zebra finch....
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TL;DR: Recon reconstructing the physiological parameters that control an avian vocal organ during birdsong production using recorded song and integrating the model with reconstructed parameters leads to the synthesis of highly realistic songs.
Abstract: We reconstruct the physiological parameters that control an avian vocal organ during birdsong production using recorded song. The procedure involves fitting the time dependent parameters of an avian vocal organ model. Computationally, the model is implemented as a dynamical system ruling the behavior of the oscillating labia that modulate the air flow during sound production, together with the equations describing the dynamics of pressure fluctuations in the vocal tract. We tested our procedure for Zebra finch song with, simultaneously recorded physiological parameters: air sac pressure and the electromyographic activity of the left and right ventral syringeal muscles. A comparison of the reconstructed instructions with measured physiological parameters during song shows a high degree of correlation. Integrating the model with reconstructed parameters leads to the synthesis of highly realistic songs.
47 citations
TL;DR: It is hypothesized that the ability for emotional and interactional prosody (EIP) paved the way for the evolution of linguistic prosody – and perhaps also of music, continuing to play a vital role in the acquisition of language.
Abstract: Across a wide range of animal taxa, prosodic modulation of the voice can express emotional information and is used to coordinate vocal interactions between multiple individuals. Within a comparative approach to animal communication systems, I hypothesize that the ability for emotional and interactional prosody (EIP) paved the way for the evolution of linguistic prosody - and perhaps also of music, continuing to play a vital role in the acquisition of language. In support of this hypothesis, I review three research fields: i) empirical studies on the adaptive value of EIP in nonhuman primates, mammals, songbirds, anurans and insects; ii) the beneficial effects of EIP in scaffolding language learning and social development in human infants; iii) the cognitive relationship between linguistic prosody and the ability for music, which has often been identified as the evolutionary precursor of language.
46 citations
Cites background from "Frequency Modulation During Song in..."
...For instance, songbirds are able to change the shape of their vocal tract, tuning it to the fundamental frequency of their song (Riede et al., 2006; Amador et al., 2008)....
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References
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01 Jan 1978TL;DR: In this article, the authors compare two straight line regression models and conclude that the Straight Line Regression Equation does not measure the strength of the Straight-line Relationship, but instead is a measure of the relationship between two straight lines.
Abstract: 1. CONCEPTS AND EXAMPLES OF RESEARCH. Concepts. Examples. Concluding Remarks. References. 2. CLASSIFICATION OF VARIABLES AND THE CHOICE OF ANALYSIS. Classification of Variables. Overlapping of Classification Schemes. Choice of Analysis. References. 3. BASIC STATISTICS: A REVIEW. Preview. Descriptive Statistics. Random Variables and Distributions. Sampling Distributions of t, ?O2, and F. Statistical Inference: Estimation. Statistical Inference: Hypothesis Testing. Error Rate, Power, and Sample Size. Problems. References. 4. INTRODUCTION TO REGRESSION ANALYSIS. Preview. Association versus Causality. Statistical versus Deterministic Models. Concluding Remarks. References. 5. STRAIGHT-LINE REGRESSION ANALYSIS. Preview. Regression with a Single Independent Variable. Mathematical Properties of a Straight Line. Statistical Assumptions for a Straight-line Model. Determining the Best-fitting Straight Line. Measure of the Quality of the Straight-line Fit and Estimate ?a2. Inferences About the Slope and Intercept. Interpretations of Tests for Slope and Intercept. Inferences About the Regression Line ?YY|X = ?O0 + ?O1X . Prediction of a New Value of Y at X0. Problems. References. 6. THE CORRELATION COEFFICIENT AND STRAIGHT-LINE REGRESSION ANALYSIS. Definition of r. r as a Measure of Association. The Bivariate Normal Distribution. r and the Strength of the Straight-line Relationship. What r Does Not Measure. Tests of Hypotheses and Confidence Intervals for the Correlation Coefficient. Testing for the Equality of Two Correlations. Problems. References. 7. THE ANALYSIS-OF-VARIANCE TABLE. Preview. The ANOVA Table for Straight-line Regression. Problems. 8. MULTIPLE REGRESSION ANALYSIS: GENERAL CONSIDERATIONS. Preview. Multiple Regression Models. Graphical Look at the Problem. Assumptions of Multiple Regression. Determining the Best Estimate of the Multiple Regression Equation. The ANOVA Table for Multiple Regression. Numerical Examples. Problems. References. 9. TESTING HYPOTHESES IN MULTIPLE REGRESSION. Preview. Test for Significant Overall Regression. Partial F Test. Multiple Partial F Test. Strategies for Using Partial F Tests. Tests Involving the Intercept. Problems. References. 10. CORRELATIONS: MULTIPLE, PARTIAL, AND MULTIPLE PARTIAL. Preview. Correlation Matrix. Multiple Correlation Coefficient. Relationship of RY|X1, X2, !KXk to the Multivariate Normal Distribution. Partial Correlation Coefficient. Alternative Representation of the Regression Model. Multiple Partial Correlation. Concluding Remarks. Problems. References. 11. CONFOUNDING AND INTERACTION IN REGRESSION. Preview. Overview. Interaction in Regression. Confounding in Regression. Summary and Conclusions. Problems. References. 12. DUMMY VARIABLES IN REGRESSION. Preview. Definitions. Rule for Defining Dummy Variables. Comparing Two Straight-line Regression Equations: An Example. Questions for Comparing Two Straight Lines. Methods of Comparing Two Straight Lines. Method I: Using Separate Regression Fits to Compare Two Straight Lines. Method II: Using a Single Regression Equation to Compare Two Straight Lines. Comparison of Methods I and II. Testing Strategies and Interpretation: Comparing Two Straight Lines. Other Dummy Variable Models. Comparing Four Regression Equations. Comparing Several Regression Equations Involving Two Nominal Variables. Problems. References. 13. ANALYSIS OF COVARIANCE AND OTHER METHODS FOR ADJUSTING CONTINUOUS DATA. Preview. Adjustment Problem. Analysis of Covariance. Assumption of Parallelism: A Potential Drawback. Analysis of Covariance: Several Groups and Several Covariates. Comments and Cautions. Summary Problems. Reference. 14. REGRESSION DIAGNOSTICS. Preview. Simple Approaches to Diagnosing Problems in Data. Residual Analysis: Detecting Outliers and Violations of Model Assumptions. Strategies of Analysis. Collinearity. Scaling Problems. Diagnostics Example. An Important Caution. Problems. References. 15. POLYNOMIAL REGRESSION. Preview. Polynomial Models. Least-squares Procedure for Fitting a Parabola. ANOVA Table for Second-order Polynomial Regression. Inferences Associated with Second-order Polynomial Regression. Example Requiring a Second-order Model. Fitting and Testing Higher-order Model. Lack-of-fit Tests. Orthogonal Polynomials. Strategies for Choosing a Polynomial Model. Problems. 16. SELECTING THE BEST REGRESSION EQUATION. Preview. Steps in Selecting the Best Regression Equation. Step 1: Specifying the Maximum Model. Step 2: Specifying a Criterion for Selecting a Model. Step 3: Specifying a Strategy for Selecting Variables. Step 4: Conducting the Analysis. Step 5: Evaluating Reliability with Split Samples. Example Analysis of Actual Data. Issues in Selecting the Most Valid Model. Problems. References. 17. ONE-WAY ANALYSIS OF VARIANCE. Preview. One-way ANOVA: The Problem, Assumptions, and Data Configuration. for One-way Fixed-effects ANOVA. Regression Model for Fixed-effects One-way ANOVA Fixed-effects Model for One-way ANOVA. Random-effects Model for One-way ANOVA. -comparison Procedures for Fixed-effects One-way ANOVA. a Multiple-comparison Technique. Orthogonal Contrasts and Partitioning an ANOVA Sum of Squares. Problems. References. 18. RANDOMIZED BLOCKS: SPECIAL CASE OF TWO-WAY ANOVA. Preview. Equivalent Analysis of a Matched-pairs Experiment. Principle of Blocking. Analysis of a Randomized-blocks Experiment. ANOVA Table for a Randomized-blocks Experiment. Models for a Randomized-blocks Experiment. Fixed-effects ANOVA Model for a Randomized-blocks Experiment. Problems. References. 19. TWO-WAY ANOVA WITH EQUAL CELL NUMBERS. Preview. Using a Table of Cell Means. General Methodology. F Tests for Two-way ANOVA. Regression Model for Fixed-effects Two-way ANOVA. Interactions in Two-way ANOVA. Random- and Mixed-effects Two-way ANOVA Models. Problems. References. 20. TWO-WAY ANOVA WITH UNEQUAL CELL NUMBERS. Preview. Problem with Unequal Cell Numbers: Nonorthogonality. Regression Approach for Unequal Cell Sample Sizes. Higher-way ANOVA. Problems. References. 21. THE METHOD OF MAXIMUM LIKELIHOOD. Preview. The Principle of Maximum Likelihood. Statistical Inference Using Maximum Likelihood. Summary. Problems. 22. LOGISTIC REGRESSION ANALYSIS. Preview. The Logistic Model. Estimating the Odds Ratio Using Logistic Regression. A Numerical Example of Logistic Regression. Theoretical Considerations. An Example of Conditional ML Estimation Involving Pair-matched Data with Unmatched Covariates. Summary. Problems. References. 23. POLYTOMOUS AND ORDINAL LOGISTIC REGRESSION. Preview. Why Not Use Binary Regression? An Example of Polytomous Logistic Regression: One Predictor, Three Outcome Categories. An Example: Extending the Polytomous Logistic Model to Several Predictors. Ordinal Logistic Regression: Overview. A "Simple" Hypothetical Example: Three Ordinal Categories and One Dichotomous Exposure Variable. Ordinal Logistic Regression Example Using Real Data with Four Ordinal Categories and Three Predictor Variables. Summary. Problems. References. 24. POISSON REGRESSION ANALYSIS. Preview. The Poisson Distribution. Example of Poisson Regression. Poisson Regression: General Considerations. Measures of Goodness of Fit. Continuation of Skin Cancer Data Example. A Second Illustration of Poisson Regression Analysis. Summary. Problems. References. 25. ANALYSIS OF CORRELATED DATA PART 1: THE GENERAL LINEAR MIXED MODEL. Preview. Examples. General Linear Mixed Model Approach. Example: Study of Effects of an Air Polluion Episode on FEV1 Levels. Summary!XAnalysis of Correlated Data: Part 1. Problems. References. 26. ANALYSIS OF CORRELATED DATA PART 2: RANDOM EFFECTS AND OTHER ISSUES. Preview. Random Effects Revisited. Results for Random Effects Models Applied to Air Pollution Study Data. Second Example!XAnalysis of Posture Measurement Data. Recommendations about Choice of Correlation Structure. 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TL;DR: Human speech and birdsong have numerous parallels, with striking similarities in how sensory experience is internalized and used to shape vocal outputs, and how learning is enhanced during a critical period of development.
Abstract: Human speech and birdsong have numerous parallels. Both humans and songbirds learn their complex vocalizations early in life, exhibiting a strong dependence on hearing the adults they will imitate, as well as themselves as they practice, and a waning of this dependence as they mature. Innate predispositions for perceiving and learning the correct sounds exist in both groups, although more evidence of innate descriptions of species-specific signals exists in songbirds, where numerous species of vocal learners have been compared. Humans also share with songbirds an early phase of learning that is primarily perceptual, which then serves to guide later vocal production. Both humans and songbirds have evolved a complex hierarchy of specialized forebrain areas in which motor and auditory centers interact closely, and which control the lower vocal motor areas also found in nonlearners. In both these vocal learners, however, how auditory feedback of self is processed in these brain areas is surprisingly unclear. Finally, humans and songbirds have similar critical periods for vocal learning, with a much greater ability to learn early in life. In both groups, the capacity for late vocal learning may be decreased by the act of learning itself, as well as by biological factors such as the hormones of puberty. Although some features of birdsong and speech are clearly not analogous, such as the capacity of language for meaning, abstraction, and flexible associations, there are striking similarities in how sensory experience is internalized and used to shape vocal outputs, and how learning is enhanced during a critical period of development. Similar neural mechanisms may therefore be involved.
1,519 citations
TL;DR: It is concluded that Area X and LMAN contribute differently to song acquisition: the song variability that is typical of vocal development persists following early deafness or lesions of Area X but ends abruptly following removal of LMAN.
Abstract: Song production in song birds is controlled by an efferent pathway. Appended to this pathway is a “recursive loop” that is necessary for song acquisition but not for the production of learned song. Since zebra finches learn their song by imitating external models, we speculated that the importance of the recursive loop for learning might derive from its processing of auditory feedback during song acquisition. This hypothesis was tested by comparing the effects on song in birds deafened early in life and birds with early lesions in either of two nuclei--Area X and the lateral magnocellular nucleus of the anterior neostriatum (LMAN). These nuclei are part of the recursive loop. The three treatments affected song development differently, as reflected by various parameters of the adult song of these birds. Whereas LMAN lesions resulted in songs with monotonous repetitions of a single note complex, songs of Area X-lesioned birds consisted of rambling series of unusually long and variable notes. Furthermore, whereas song of LMAN lesioned birds stabilized early, song stability as seen in intact birds was never achieved in Area X-lesioned birds. Early deafness also resulted in poorly structured and unstable song. We conclude that Area X and LMAN contribute differently to song acquisition: the song variability that is typical of vocal development persists following early deafness or lesions of Area X but ends abruptly following removal of LMAN. Apparently, LMAN plays a crucial role in fostering the kinds of circuit plasticity necessary for learning.
921 citations
TL;DR: Lesions in the magnocellular nucleus of the anterior neostriatum of passerine birds disrupted song development in juvenile male zebra finches but did not affect maintenance of stable song patterns by adult birds.
Abstract: The magnocellular nucleus of the anterior neostriatum is a forebrain nucleus of passerine birds that accumulates testosterone and makes monosynaptic connections with other telencephalic nuclei that control song production in adult birds. Lesions in the magnocellular nucleus disrupted song development in juvenile male zebra finches but did not affect maintenance of stable song patterns by adult birds. These results represent an instance in which lesions of a discrete brain region during only a restricted phase in the development of a learned behavior cause permanent impairment. Because cells of the magnocellular nucleus accumulate androgens these findings raise the possibility that this learning is mediated by hormones.
834 citations