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: Lateralized control of song production in Bengalese finches may enhance acoustic complexity of song and facilitate the rapid modification of sound production following manipulations of auditory feedback.
Abstract: Background Singing in songbirds is a complex, learned behavior which shares many parallels with human speech. The avian vocal organ (syrinx) has two potential sound sources, and each sound generator is under unilateral, ipsilateral neural control. Different songbird species vary in their use of bilateral or unilateral phonation (lateralized sound production) and rapid switching between left and right sound generation (interhemispheric switching of motor control). Bengalese finches (Lonchura striata domestica) have received considerable attention, because they rapidly modify their song in response to manipulations of auditory feedback. However, how the left and right sides of the syrinx contribute to acoustic control of song has not been studied. Methodology Three manipulations of lateralized syringeal control of sound production were conducted. First, unilateral syringeal muscular control was eliminated by resection of the left or right tracheosyringeal portion of the hypoglossal nerve, which provides neuromuscular innervation of the syrinx. Spectral and temporal features of song were compared before and after lateralized nerve injury. In a second experiment, either the left or right sound source was devoiced to confirm the role of each sound generator in the control of acoustic phonology. Third, air pressure was recorded before and after unilateral denervation to enable quantification of acoustic change within individual syllables following lateralized nerve resection. Significance These experiments demonstrate that the left sound source produces louder, higher frequency, lower entropy sounds, and the right sound generator produces lower amplitude, lower frequency, higher entropy sounds. The bilateral division of labor is complex and the frequency specialization is the opposite pattern observed in most songbirds. Further, there is evidence for rapid interhemispheric switching during song production. Lateralized control of song production in Bengalese finches may enhance acoustic complexity of song and facilitate the rapid modification of sound production following manipulations of auditory feedback.
28 citations
Cites background from "Frequency Modulation During Song in..."
...In a suboscine, the Great Kiskadee (Pitangus sulfuratus), respiratory air pressure controls the fundamental frequency of sound, even in the absence of syringeal muscular control [28]....
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TL;DR: It is suggested that evolutionary history, morphology, and present‐day ecological processes shape the distribution of song diversity in these charismatic and important birds.
Abstract: Studying the macroevolution of the songs of Passeriformes (perching birds) has proved challenging. The complexity of the task stems not just from the macroevolutionary and macroecological challenge of modeling so many species, but also from the difficulty in collecting and quantifying birdsong itself. Using machine learning techniques, we extracted songs from a large citizen science dataset, and then analyzed the evolution, and biotic and abiotic predictors of variation in birdsong across 578 passerine species. Contrary to expectations, we found few links between life-history traits (monogamy and sexual dimorphism) and the evolution of song pitch (peak frequency) or song complexity (standard deviation of frequency). However, we found significant support for morphological constraints on birdsong, as reflected in a negative correlation between bird size and song pitch. We also found that broad-scale biogeographical and climate factors such as net primary productivity, temperature, and regional species richness were significantly associated with both the evolution and present-day distribution of bird song features. Our analysis integrates comparative and spatial modeling with newly developed data cleaning and curation tools, and suggests that evolutionary history, morphology, and present-day ecological processes shape the distribution of song diversity in these charismatic and important birds.
27 citations
Cites background from "Frequency Modulation During Song in..."
...In addition, the anatomy and neurology of the song system is known to differ between these clades, which could also lead to differential constraints on song evolution (Gahr 2000; Amador et al. 2008)....
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TL;DR: The ability to produce high pitched notes is an honest indicator of male genetic diversity in male-male aggressive interactions in ocellated antbirds.
Abstract: Animals use honest signals to assess the quality of competitors during aggressive interactions. Current theory predicts that honest signals should be costly to produce and thus reveal some aspects of the phenotypic or genetic quality of the sender. In songbirds, research indicates that biomechanical constraints make the production of some acoustic features costly. Furthermore, recent studies have found that vocal features are related to genetic diversity. We linked these two lines of research by evaluating if constrained acoustic features reveal male genetic diversity during aggressive interactions in ocellated antbirds (Phaenostictus mcleannani). We recorded the aggressive vocalizations of radiotagged males at La Selva Biological Station in Costa Rica, and found significant variation in the highest frequency produced among individuals. Moreover, we detected a negative relationship between the frequency of the highest pitched note and vocalization duration, suggesting that high pitched notes might constrain the duration of vocalizations through biomechanical and/or energetic limitations. When we experimentally exposed wild radiotagged males to simulated acoustic challenges, the birds increased the pitch of their vocalization. We also found that individuals with higher genetic diversity (as measured by zygosity across 9 microsatellite loci) produced notes of higher pitch during aggressive interactions. Overall, our results suggest that the ability to produce high pitched notes is an honest indicator of male genetic diversity in male-male aggressive interactions.
27 citations
Cites background from "Frequency Modulation During Song in..."
...In the Great Kiskadee (Pitangus sulphuratus; Tyrannidae), the pressure in the air sac needed during exhalation increases with vocalization frequency [41]....
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TL;DR: Frequency measurements on spectrograms have the advantage of resilience to noise and the correctness of conclusions in Cardoso & Atwell (2011a, Animal Behaviour) is reiterated.
26 citations
TL;DR: The morphological and physiological features of sound production in alligators are investigated and it is concluded that the alligator larynx represents a sound source with only two control parameters (subglottal pressure and vocal fold adduction) in contrast to the mammalian larynX in which three parameters can be altered to modulate frequency.
Abstract: Vocalization is rare among non-avian reptiles, with the exception of the crocodilians, the sister taxon of birds. Crocodilians have a complex vocal repertoire. Their vocal and respiratory system is not well understood but appears to consist of a combination of features that are also found in the extremely vocal avian and mammalian taxa. Anatomical studies suggest that the alligator larynx is able to abduct and adduct the vocal folds, but not to elongate or shorten them, and is therefore lacking a key regulator of frequency, yet alligators can modulate fundamental frequency remarkably well. We investigated the morphological and physiological features of sound production in alligators. Vocal fold length scales isometrically across a wide range of alligator body sizes. The relationship between fundamental frequency and subglottal pressure is significant in some individuals at some isolated points, such as call onset and position of maximum fundamental frequency. The relationship is not consistent over large segments of the call. Fundamental frequency can change faster than expected by pressure changes alone, suggesting an active motor pattern controls frequency and is intrinsic to the larynx. We utilized a two-mass vocal fold model to test whether abduction and adduction could generate this motor pattern. The fine-tuned interplay between subglottal pressure and glottal adduction can achieve frequency modulations much larger than those resulting from subglottal pressure variations alone and of similar magnitude, as observed in alligator calls. We conclude that the alligator larynx represents a sound source with only two control parameters (subglottal pressure and vocal fold adduction) in contrast to the mammalian larynx in which three parameters can be altered to modulate frequency (subglottal pressure, vocal fold adduction and length/tension).
25 citations
Cites background from "Frequency Modulation During Song in..."
..., 2010a); unlike in suboscines, where a more pressuredriven frequency control exists (Amador et al., 2008)....
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...…tension of the labia to achieve stereotypic oscillation rates (Goller and Suthers, 1996), extending the frequency range beyond what would be possible by pressure changes alone (Riede et al., 2010a); unlike in suboscines, where a more pressuredriven frequency control exists (Amador et al., 2008)....
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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. 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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