Netlang: A software for the linguistic analysis of corpora by means of complex networks

Chimpanzee play sequences are structured hierarchically as games

Social play is ubiquitous in the development of many animal species and involves players adapting actions flexibly to their own previous actions and partner responses. Play differs from other behavioural contexts for which fine-scale analyses of action sequences are available, such as tool use and communication, in that its form is not defined by its function, making it potentially more unpredictable. In humans, play is often organised in games, where players know context-appropriate actions but string them together unpredictably. Here, we use the sequential nature of play elements to explore whether play elements in chimpanzees are structured hierarchically and follow predictable game-like patterns. Based on 5,711 play elements from 143 bouts, we extracted individual-level play sequences of 11 Western chimpanzees (Pan troglodytes verus) of different ages from the Bossou community. We detected transition probabilities between play elements that exceeded expected levels and show that play elements form hierarchically clustered and interchangeable groups, indicative of at least six games that can be identified from transition networks, some with different roles for different players. We also show that increased information about preceding play elements improved predictability of subsequent elements, further indicating that play elements are not strung together randomly but that flexible action rules underlie their usage. Thus, chimpanzee play is hierarchically structured in short games which limit acceptable play elements and allow players to predict and adapt to partners' actions. This "grammar of action" approach to social interactions can be valuable in understanding cognitive and communicative abilities within and across species.

NetFACS: Using network science to understand facial communication systems

Understanding facial signals in humans and other species is crucial for understanding the evolution, complexity, and function of the face as a communication tool. The Facial Action Coding System (FACS) enables researchers to measure facial movements accurately, but we currently lack tools to reliably analyse data and efficiently communicate results. Network analysis can provide a way to use the information encoded in FACS datasets: by treating individual AUs (the smallest units of facial movements) as nodes in a network and their co-occurrence as connections, we can analyse and visualise differences in the use of combinations of AUs in different conditions. Here, we present ‘NetFACS’, a statistical package that uses occurrence probabilities and resampling methods to answer questions about the use of AUs, AU combinations, and the facial communication system as a whole in humans and non-human animals. Using highly stereotyped facial signals as an example, we illustrate some of the current functionalities of NetFACS. We show that very few AUs are specific to certain stereotypical contexts; that AUs are not used independently from each other; that graph-level properties of stereotypical signals differ; and that clusters of AUs allow us to reconstruct facial signals, even when blind to the underlying conditions. The flexibility and widespread use of network analysis allows us to move away from studying facial signals as stereotyped expressions, and towards a dynamic and differentiated approach to facial communication.

Fate of Duplicated Neural Structures

Statistical physics determines the abundance of different arrangements of matter depending on cost-benefit balances. Its formalism and phenomenology percolate throughout biological processes and set limits to effective computation. Under specific conditions, self-replicating and computationally complex patterns become favored, yielding life, cognition, and Darwinian evolution. Neurons and neural circuits sit at a crossroads between statistical physics, computation, and (through their role in cognition) natural selection. Can we establish a statistical physics of neural circuits? Such theory would tell what kinds of brains to expect under set energetic, evolutionary, and computational conditions. With this big picture in mind, we focus on the fate of duplicated neural circuits. We look at examples from central nervous systems, with stress on computational thresholds that might prompt this redundancy. We also study a naive cost-benefit balance for duplicated circuits implementing complex phenotypes. From this, we derive phase diagrams and (phase-like) transitions between single and duplicated circuits, which constrain evolutionary paths to complex cognition. Back to the big picture, similar phase diagrams and transitions might constrain I/O and internal connectivity patterns of neural circuits at large. The formalism of statistical physics seems to be a natural framework for this worthy line of research.