Vocal development through morphological computation

Abstract

The vocal behavior of infants changes dramatically during early life. Whether or not such a change results from the growth of the body during development-as opposed to solely neural changes-has rarely been investigated. In this study of vocal development in marmoset monkeys, we tested the putative causal relationship between bodily growth and vocal development. During the first two months of life, the spontaneous vocalizations of marmosets undergo (1) a gradual disappearance of context-inappropriate call types and (2) an elongation in the duration of context-appropriate contact calls. We hypothesized that both changes are the natural consequences of lung growth and do not require any changes at the neural level. To test this idea, we first present a central pattern generator model of marmoset vocal production to demonstrate that lung growth can affect the temporal and oscillatory dynamics of neural circuits via sensory feedback from the lungs. Lung growth qualitatively shifted vocal behavior in the direction observed in real marmoset monkey vocal development. We then empirically tested this hypothesis by placing the marmoset infants in a helium-oxygen (heliox) environment in which air is much lighter. This simulated a reversal in development by decreasing the effort required to respire, thus increasing the respiration rate (as though the lungs were smaller). The heliox manipulation increased the proportions of inappropriate call types and decreased the duration of contact calls, consistent with a brief reversal of vocal development. These results suggest that bodily growth alone can play a major role in shaping the development of vocal behavior.

Fig 1. Developmental trajectories of marmoset vocalization.

(A) Exemplars of marmoset infant babbling-like vocalization from different postnatal days and classification of distinct call types. (B) Comparison of the call duration of different call types from the first postnatal week (n = 1,208 contact call syllables, 121 trill syllables, 250 twitter syllables, F = 754.32, p = 1.52×10⁻²³⁰, ANOVA, each call type is different from other types). (C) Duration of contact calls over postnatal days (n = 13 subjects, 244 trials). (D–F) Proportions of contact calls, trill, and twitter over postnatal days (n = 13 subjects, 244 trials). (G) Duration and fundamental frequency (F0) change over time of three call types. Contact call increases in duration over time (p = 8×10^-10), trill does not show significant change (p = 0.06), and twitter decreases over time in duration (p = 7×10^-4). (H) Comparison of slopes for (G). The slope of the contact call change in duration is greater than trill and twitter (ANCOVA, F = 25.5, p = 3.6×10^-10). (I) None of the calls show significant change in F0 over the first two months (p = 0.10, p = 0.09, p = 0.47 respectively for contact calls, trills and twitters). (J) Comparison of slopes for (I). There is no difference between the slopes of the F0 change (ANCOVA, F = 1.72, p = 0.18). Data underlying this figure can be found in S1 Data. F0, fundamental frequency.

Fig 2. A central pattern generator model for infant vocal production.

(A) Setup of CPG model. Both laryngeal (top) and respiratory (bottom) CPGs receive a common input (arousal). The CPGs coupled with each other. They drive the variation of the laryngeal tension and subglottal pressure to generate sound. Lung capacity affects the damping of the respiratory CPG via somatosensory feedback. (B) Different temporal patterns of the laryngeal tension and respiratory activity can be generated as the drive linearly ramps up, and distinct call types are produced. Panels from top to bottom: drive (arousal), laryngeal tension, respiratory activity, simulated sound pressure, and spectrogram. (C) Mean respiratory EMG profiles for the different call types. (D) Phase portraits in (x, y)-space illustrating the dynamics of the CPG model at different arousal levels. In regions of I (arousal) where the values are high or low, fixed points appear in the laryngeal dynamics, yielding mature and immature contact calls (left panels). In regions of moderate values of I, limit cycles appear in the laryngeal dynamics, which modulate the respiratory dynamics to produce trill or twitter (right panels). Within a panel, the left subplot is the phase portrait of the respiratory CPG and the right subplot is the laryngeal CPG. MATLAB code is available for figures (B) and (D) in S1 Code. CPG, central pattern generator; EMG, electromyography.

Fig 3. Simulated lung’s growth reproduces the developmental trajectories of call proportions.

(A) Illustration of the impact of the lung’s growth on the respiratory CPG. (B) Simulated respiratory activity under ramping input with decreasing values of the time constant. Fast respiratory patterns diminish as time constant decreases. From top to bottom: γ₁ = 3.4, 3.1, 2.8. (C) Body mass growth versus postnatal days (n = 13). Points are data, grey lines are cubic spline–fitted data, and black line is the mean body mass over time. (D) Diagram of different call types, classified by duration, in the parameter space of time constant and drive I. (E) Simulated twitter and trill proportions based on the areas in (D) at different time constants. (F) Averaged twitter and trill proportions from data (n = 13). (G) Simulated contact call proportions based on the area in (D). (H) Averaged contact call proportions from data (n = 13). MATLAB code is available for panels B, D, E, and G in S2 Code. CPG, central pattern generator.

Fig 4. Heliox manipulation briefly reverses the developmental trend of vocal behavior.

(A) Experiment setup. Infants (n = 2) were placed in the box for 20 min with 10 min for each condition (air versus heliox). The order of the conditions was counterbalanced across days. Only the last 5 min of each condition was used in the analyses. (B) Mean abdominal movements in air (n = 25 traces) and heliox condition (n = 37 traces) during the production of contact calls extracted from video. Data are from 2 subjects. (C) Respiratory rates in air (n = 25 video traces) and heliox (n = 37) condition during contact call production. Data are mean ± SEM. ***P < 0.001 (unpaired 2-tailed t test). (D) Vocal sequences produced in air and heliox on P26, in comparison with vocal sequences produced on P10 in air. (E) Duration changes over postnatal days for air and heliox conditions for each subject. Data are fitted to linear models. Shaded areas indicate 1 SE intervals. (F) Comparison of population mean of fractional contact call duration normalized to the air condition. Bar height represents population mean. Error bars are SEM. Each line is one subject. ***P < 0.001 (GLM). (G, I) Proportions of trills and twitters over postnatal days for air and heliox conditions. Shaded areas are the bootstrapped 95% confidence interval. (H, J) Comparison of heliox effect on trill and twitter proportion. Bar height represents mean proportion of all subjects (n = 3) and all trials over the first two months. Error bars are SEM. Each line is one subject ***P < 0.001 (GLM). (K) Heliox effect on sound spectrum. Left panels: mean power spectral densities (PSDs) of different call types in air condition. Right panels: mean PSDs in heliox condition. Data underlying this figure can be found in S1 Data. GLM, generalized linear model; heliox, helium–oxygen; PSD, power spectral density.