Animal acoustic communication often takes the form of complex sequences, made up of multiple distinct acoustic units. Apart from the well-known example of birdsong, other animals such as insects, amphibians, and mammals (including bats, rodents, primates, and cetaceans) also generate complex acoustic sequences. Occasionally, such as with birdsong, the adaptive role of these sequences seems clear (e.g. mate attraction and territorial defence). More often however, researchers have only begun to characterise – let alone understand – the significance and meaning of acoustic sequences. Hypotheses abound, but there is little agreement as to how sequences should be defined and analysed. Our review aims to outline suitable methods for testing these hypotheses, and to describe the major limitations to our current and near-future knowledge on questions of acoustic sequences. This review and prospectus is the result of a collaborative effort between 43 scientists from the fields of animal behaviour, ecology and evolution, signal processing, machine learning, quantitative linguistics, and information theory, who gathered for a 2013 workshop entitled, “Analysing vocal sequences in animals”. Our goal is to present not just a review of the state of the art, but to propose a methodological framework that summarises what we suggest are the best practices for research in this field, across taxa and across disciplines. We also provide a tutorial-style introduction to some of the most promising algorithmic approaches for analysing sequences. We divide our review into three sections: identifying the distinct units of an acoustic sequence, describing the different ways that information can be contained within a sequence, and analysing the structure of that sequence. Each of these sections is further subdivided to address the key questions and approaches in that area. We propose a uniform, systematic, and comprehensive approach to studying sequences, with the goal of clarifying research terms used in different fields, and facilitating collaboration and comparative studies. Allowing greater interdisciplinary collaboration will facilitate the investigation of many important questions in the evolution of communication and sociality.

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Human language, as well as birdsong, relies on the ability to arrange vocal elements in novel sequences. However, little is known about the ontogenetic origin of this capacity. We tracked the development of vocal combinatorial capacity in three species of vocal learners, combining an experimental approach in zebra finches with an analysis of natural development of vocal transitions in Bengalese finches and pre-lingual human infants and found a common, stepwise pattern of acquiring vocal transitions across species. In our first study, juvenile zebra finches were trained to perform one song and then the training target was altered, prompting the birds to swap syllable order, or insert a new syllable into a string. All birds solved these permutation tasks in a series of steps, gradually approximating the target sequence by acquiring novel pair-wise syllable transitions, sometimes too slowly to fully accomplish the task. Similarly, in the more complex songs of Bengalese finches, branching points and bidirectional transitions in song-syntax were acquired in a stepwise manner, starting from a more restrictive set of vocal transitions. The babbling of pre-lingual human infants revealed a similar developmental pattern: instead of a single developmental shift from reduplicated to variegated babbling (i.e., from repetitive to diverse sequences), we observed multiple shifts, where each novel syllable type slowly acquired a diversity of pair-wise transitions, asynchronously over development. Collectively, these results point to a common generative process that is conserved across species, suggesting that the long-noted gap between perceptual versus motor combinatorial capabilities in human infants1 may arise from the challenges in constructing new pair-wise transitions.

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Alzheimer's disease (AD) is the most prevalent age-dependent form of dementia, characterized by extracellular amyloid deposits comprising amyloid β-peptide (Aβ) in the cerebral cortex. Increasing evidence has indicated that ganglioside GM1 (GM1) in lipid rafts plays a pivotal role in amyloid deposition of Aβ and the related cytotoxicity in AD. Despite recent efforts to characterize Aβ-lipid interactions, the effect of Aβ aggregation on dynamic properties and organization of lipid membranes is poorly understood. In this study, we examined the aggregation of Aβ on supported lipid bilayers containing raft components (i.e., cholesterol, sphingomyelin, and GM1) and its effects on the membrane properties. We showed that the lateral fluidity of membranes was significantly affected by membrane binding and subsequent aggregation of Aβ. Microscopic observations of the membrane surfaces demonstrated an enhancement in phase separation of lipids as a result of interactions between Aβ and GM1 during induced aggregation of Aβ. The uptake of GM1 into Aβ aggregates and the attendant membrane damage were also observed under a microscope when the membrane-anchored aggregates were formed. On the basis of these observations, we propose that Aβ aggregates formed in the presence of lipid membranes have a latent ability to trigger the uptake of raft components accompanied by phase separation of lipids.

While social media make it easy to connect with and access information from anyone, they also facilitate basic influence and unfriending mechanisms that may lead to segregated and polarized clusters known as “echo chambers.” Here we study the conditions in which such echo chambers emerge by introducing a simple model of information sharing in online social networks with the two ingredients of influence and unfriending. Users can change both their opinions and social connections based on the information to which they are exposed through sharing. The model dynamics show that even with minimal amounts of influence and unfriending, the social network rapidly devolves into segregated, homogeneous communities. These predictions are consistent with empirical data from Twitter. Although our findings suggest that echo chambers are somewhat inevitable given the mechanisms at play in online social media, they also provide insights into possible mitigation strategies.

Bird songs are acoustic communication signals primarily used in male-male aggression and in male-female attraction. These are often monotonous patterns composed of a few phrases, yet some birds have extremely complex songs with a large phrase repertoire, organized in non-random fashion with discernible patterns. Since structure is typically associated with function, the structures of complex bird songs provide important clues to the evolution of animal communication systems. Here we propose an efficient network-based approach to explore structural design principles of complex bird songs, in which the song networks–transition relationships among different phrases and the related structural measures–are employed. We demonstrate how this approach works with an example using California Thrasher songs, which are sequences of highly varied phrases delivered in succession over several minutes. These songs display two distinct features: a large phrase repertoire with a ‘small-world’ architecture, in which subsets of phrases are highly grouped and linked with a short average path length; and a balanced transition diversity amongst phrases, in which deterministic and non-deterministic transition patterns are moderately mixed. We explore the robustness of this approach with variations in sample size and the amount of noise. Our approach enables a more quantitative study of global and local structural properties of complex bird songs than has been possible to date.

Online social media are increasingly facilitating our social interactions, thereby making available a massive “digital fossil” of human behavior. Discovering and quantifying distinct patterns using these data is important for studying social behavior, although the rapid time-variant nature and large volumes of these data make this task difficult and challenging. In this study, we focused on the emergence of “collective attention” on Twitter, a popular social networking service. We propose a simple method for detecting and measuring the collective attention evoked by various types of events. This method exploits the fact that tweeting activity exhibits a burst-like increase and an irregular oscillation when a particular real-world event occurs; otherwise, it follows regular circadian rhythms. The difference between regular and irregular states in the tweet stream was measured using the Jensen-Shannon divergence, which corresponds to the intensity of collective attention. We then associated irregular incidents with their corresponding events that attracted the attention and elicited responses from large numbers of people, based on the popularity and the enhancement of key terms in posted messages or “tweets.” Next, we demonstrate the effectiveness of this method using a large dataset that contained approximately 490 million Japanese tweets by over 400,000 users, in which we identified 60 cases of collective attentions, including one related to the Tohoku-oki earthquake. “Retweet” networks were also investigated to understand collective attention in terms of social interactions. This simple method provides a retrospective summary of collective attention, thereby contributing to the fundamental understanding of social behavior in the digital era.