Andrea Ravignani

Chorusing, synchrony, and the evolutionary functions of rhythm

A central goal of biomusicology is to understand the biological basis of human musicality. One approach to this problem has been to compare core components of human musicality (relative pitch perception, entrainment, etc.) with similar capacities in other animal species. Here we extend and clarify this comparative approach with respect to rhythm. First, whereas most comparisons between human music and animal acoustic behavior have focused on spectral properties (melody and harmony), we argue for the central importance of temporal properties, and propose that this domain is ripe for further comparative research. Second, whereas most rhythm research in non-human animals has examined animal timing in isolation, we consider how chorusing dynamics can shape individual timing, as in human music and dance, arguing that group behavior is key to understanding the adaptive functions of rhythm. To illustrate the interdependence between individual and chorusing dynamics, we present a computational model of chorusing agents relating individual call timing with synchronous group behavior. Third, we distinguish and clarify mechanistic and functional explanations of rhythmic phenomena, often conflated in the literature, arguing that this distinction is key for understanding the evolution of musicality. Fourth, we expand biomusicological discussions beyond the species typically considered, providing an overview of chorusing and rhythmic behavior across a broad range of taxa (orthopterans, fireflies, frogs, birds, and primates). Finally, we propose an “Evolving Signal Timing” hypothesis, suggesting that similarities between timing abilities in biological species will be based on comparable chorusing behaviors. We conclude that the comparative study of chorusing species can provide important insights into the adaptive function(s) of rhythmic behavior in our “proto-musical” primate ancestors, and thus inform our understanding of the biology and evolution of rhythm in human music and language.

Action at a distance: dependency sensitivity in a New World primate

Sensitivity to dependencies (correspondences between distant items) in sensory stimuli plays a crucial role in human music and language. Here, we show that squirrel monkeys (Saimiri sciureus) can detect abstract, non-adjacent dependencies in auditory stimuli. Monkeys discriminated between tone sequences containing a dependency and those lacking it, and generalized to previously unheard pitch classes and novel dependency distances. This constitutes the first pattern learning study where artificial stimuli were designed with the species communication system in mind. These results suggest that the ability to recognize dependencies represents a capability that had already evolved in humans’ last common ancestor with squirrel monkeys, and perhaps before.

Non-adjacent visual dependency learning in chimpanzees

Humans have a strong proclivity for structuring and patterning stimuli: Whether in space or time, we tend to mentally order stimuli in our environment and organize them into units with specific types of relationships. A crucial prerequisite for such organization is the cognitive ability to discern and process regularities among multiple stimuli. To investigate the evolutionary roots of this cognitive capacity, we tested chimpanzees—which, along with bonobos, are our closest living relatives—for simple, variable distance dependency processing in visual patterns. We trained chimpanzees to identify pairs of shapes either linked by an arbitrary learned association (arbitrary associative dependency) or a shared feature (same shape, feature-based dependency), and to recognize strings where items related to either of these ways occupied the first (leftmost) and the last (rightmost) item of the stimulus. We then probed the degree to which subjects generalized this pattern to new colors, shapes, and numbers of interspersed items. We found that chimpanzees can learn and generalize both types of dependency rules, indicating that the ability to encode both feature-based and arbitrary associative regularities over variable distances in the visual domain is not a human prerogative. Our results strongly suggest that these core components of human structural processing were already present in our last common ancestor with chimpanzees.

Musical evolution in the lab exhibits rhythmic universals

Music exhibits some cross-cultural similarities, despite its variety across the world. Evidence from a broad range of human cultures suggests the existence of musical universals1, here defined as strong regularities emerging across cultures above chance. In particular, humans demonstrate a general proclivity for rhythm2, although little is known about why music is particularly rhythmic and why the same structural regularities are present in rhythms around the world. We empirically investigate the mechanisms underlying musical universals for rhythm, showing how music can evolve culturally from randomness. Human participants were asked to imitate sets of randomly generated drumming sequences and their imitation attempts became the training set for the next participants in independent transmission chains. By perceiving and imitating drumming sequences from each other, participants turned initially random sequences into rhythmically structured patterns. Drumming patterns developed into rhythms that are more structured, easier to learn, distinctive for each experimental cultural tradition and characterized by all six statistical universals found among world music1; the patterns appear to be adapted to human learning, memory and cognition. We conclude that musical rhythm partially arises from the influence of human cognitive and biological biases on the process of cultural evolution3.

Primate Drum Kit: A System for Studying Acoustic Pattern Production by Non-Human Primates Using Acceleration and Strain Sensors

The possibility of achieving experimentally controlled, non-vocal acoustic production in non-human primates is a key step to enable the testing of a number of hypotheses on primate behavior and cognition. However, no device or solution is currently available, with the use of sensors in non-human animals being almost exclusively devoted to applications in food industry and animal surveillance. Specifically, no device exists which simultaneously allows: (i) spontaneous production of sound or music by non-human animals via object manipulation, (ii) systematical recording of data sensed from these movements, (iii) the possibility to alter the acoustic feedback properties of the object using remote control. We present two prototypes we developed for application with chimpanzees (Pan troglodytes) which, while fulfilling the aforementioned requirements, allow to arbitrarily associate sounds to physical object movements. The prototypes differ in sensing technology, costs, intended use and construction requirements. One prototype uses four piezoelectric elements embedded between layers of Plexiglas and foam. Strain data is sent to a computer running Python through an Arduino board. A second prototype consists in a modified Wii Remote contained in a gum toy. Acceleration data is sent via Bluetooth to a computer running Max/MSP. We successfully pilot tested the first device with a group of chimpanzees. We foresee using these devices for a range of cognitive experiments.

More than one way to see it: Individual heuristics in avian visual computation

Graphical abstract

The evolutionary biology of dance without frills.

Recently psychologists have taken up the question of whether dance is reliant on unique human adaptations, or whether it is rooted in neural and cognitive mechanisms shared with other species [1,2]. In its full cultural complexity, human dance clearly has no direct analog in animal behavior. Most definitions of dance include the consistent production of movement sequences timed to an external rhythm. While not sufficient for dance, modes of auditory-motor timing, such as synchronization and entrainment, are experimentally tractable constructs that may be analyzed and compared between species. In an effort to assess the evolutionary precursors to entrainment and social features of human dance, Laland and colleagues [2] have suggested that dance may be an incidental byproduct of adaptations supporting vocal or motor imitation - referred to here as the imitation and sequencing hypothesis. In support of this hypothesis, Laland and colleagues rely on four convergent lines of evidence drawn from behavioral and neurobiological research on dance behavior in humans and rhythmic behavior in other animals. Here, we propose a less cognitive, more parsimonious account for the evolution of dance. Our timing and interaction hypothesis suggests that dance is scaffolded off of broadly conserved timing mechanisms allowing both cooperative and antagonistic social coordination.

Chimpanzees process structural isomorphisms across sensory modalities

Graphical abstract

Seeking Temporal Predictability in Speech: Comparing Statistical Approaches on 18 World Languages

Temporal regularities in speech, such as interdependencies in the timing of speech events, are thought to scaffold early acquisition of the building blocks in speech. By providing on-line clues to the location and duration of upcoming syllables, temporal structure may aid segmentation and clustering of continuous speech into separable units. This hypothesis tacitly assumes that learners exploit predictability in the temporal structure of speech. Existing measures of speech timing tend to focus on first-order regularities among adjacent units, and are overly sensitive to idiosyncrasies in the data they describe. Here, we compare several statistical methods on a sample of 18 languages, testing whether syllable occurrence is predictable over time. Rather than looking for differences between languages, we aim to find across languages (using clearly defined acoustic, rather than orthographic, measures), temporal predictability in the speech signal which could be exploited by a language learner. First, we analyse distributional regularities using two novel techniques: a Bayesian ideal learner analysis, and a simple distributional measure. Second, we model higher-order temporal structure—regularities arising in an ordered series of syllable timings—testing the hypothesis that non-adjacent temporal structures may explain the gap between subjectively-perceived temporal regularities, and the absence of universally-accepted lower-order objective measures. Together, our analyses provide limited evidence for predictability at different time scales, though higher-order predictability is difficult to reliably infer. We conclude that temporal predictability in speech may well arise from a combination of individually weak perceptual cues at multiple structural levels, but is challenging to pinpoint.

Chronometry for the chorusing herd: Hamilton's legacy on context-dependent acoustic signalling—a comment on Herbers (2013)

Biology Letters’ special feature on Hamiltons legacy pays due tribute to a brilliant mind. Herbers [1] and the other contributors paint a compelling picture of how Hamiltons work on inclusive fitness anticipated much contemporary evolutionary thinking, although sometimes not acknowledged until much later. n nA more recent, although equally cited work by Hamilton is the ‘Geometry for the selfish herd’ [2], an elegant mathematical description of why individuals aggregate in space. In the spirit of this special feature [1], I illustrate why Hamiltons herd model should be recognized as an early mathematical formalism applicable to unrelated, although crucial, biological phenomena. Notably, Hamiltons model of gregarious behaviour can be directly applied to the problem of context-dependent acoustic signalling as follows, with the potential to describe how interdependent individual calls combine into choruses. n nMany animals communicate acoustically, often with an emphasis on signal timing, rather than other acoustic properties [3]. Synchrony and chorusing occur in insects, amphibians, birds and mammals. An overarching question is how individuals ‘distribute’ their calls over time and why different individuals’ calls group together, leading to synchronous, alternating or phase-locked choruses [3]. Two hypotheses, suggested and tested in [4], predict clusters of calls: individuals could maximize overall sound intensity to attract females or, alternatively, individuals could call in quasi-synchrony to decrease the individual risk of predation. In both cases, individuals would tend to call close to each other, so to increase signal amplitude or alter individual conspicuousness (depending on the receiver), similarly to what happens in human applauding [5]. n nSuppose three frogs, A, B and C, call periodically in time, say every second, although with different relative phases (see figure 1). B and C occur within a short time interval (short silence). A precedes them by a long interval (long SILENCE). The resulting acoustic pattern is A-SILENCE-B-silence-C-SILENCE-A- … A can modify its conspicuousness by shortening its ‘domain of silence’, i.e. timing its signal so it co-occurs, on average, with others’ calls. The most noise-robust, error-resistant strategy for A is to delay its call and signal exactly halfway between B and C; A calls, on average, in an ‘acoustically dense’ time period. n n n nFigurexa01. n nClocks showing, for each individual, signalling time in two contiguous periods. Individual A signalled at 00.00, B at 02.00 and C at 03.00 (a). As agents choose when to call simultaneously, in the next time period (b), A remains silent for longer than ... n n n nAssume, after Hamilton, that individuals A, B and C are located on a circular lily pond [2]. Instead of delaying or anticipating their signal phase/timing, they try to hide in-between other individuals. For instance, A occupies an isolated position on the pond, making it vulnerable to predators. A therefore seeks to decrease its ‘domain of danger’: if B and C are closer to each other than A is to any of them, A will jump and land between B and C [2]. n nOnce formulated in these terms, it is clear how the mechanics of Hamiltons spatial predation model map one-to-one onto the acoustic signalling mechanism sketched here (table 1). The original spatial model featured a closed, circular space. Circular metaphors (e.g. clocks) are also appropriate to represent periodic events, and the ‘circular’ feature in [2] enables its direct application to periodic signals over time, as required in models of chorusing. n n n nTablexa01. n nComparison between parameters from the one-dimensional model in [2] and the mathematically equivalent, context-dependent signalling framework sketched here. n n n nIn both cases, a general model is derived from applying the basic ‘time shift’ mechanism to all individuals (cf. figure 1, A delays its call and C anticipates its), and dynamically over time (figure 1a versus b). At every time period, most individuals will have either changed location or adapted their calls, making previous decisions suboptimal and spurring individuals to compensate by jumping to a better location, or shifting the phase of their upcoming call to an acoustically denser period of time. Computer simulations for the predation model showed formation of clusters of individuals [2]. By analogy, group signalling dynamics should begin with randomly occurring individual calls scattered over time and converge towards a few, high-intensity acoustic peaks (produced by several near-synchronous individuals). n nAn additional, deeper mathematical link connects Hamiltons model of space with dynamical processes in time. Hamilton noted that only one initial configuration, three evenly spaced frogs, will prevent aggregation [2]; decades later, the mathematical investigation of rhythm and timing in biological systems found that the same initial configuration will prevent synchronization of oscillators in time [6]. n nHerbers admits that one volume cannot do full justice to Hamiltons genius, anticipating how his ideas will ‘influence the field over the coming 50 years’ [1]. Hopefully, as I show here, Hamiltons mathematical insights will inform future research on both rhythmic processes in humans, such as language and music, and context-dependent acoustic signalling in other species.