Moving targets: collective decisions and flexible choices in house-hunting ants

Many decisions involve a trade-off between commitment and flexibility. We show here that the collective decisions ants make over new nest sites are sometimes sufficiently flexible that the ants can change targets even after an emigration has begun. Our findings suggest that, in this context, the ants’ procedures are such that they can sometimes avoid ‘negative information cascades’ which might lock them into a poor choice. The ants are more responsive to belated good news of a higher quality nest than they are when the nest they had initially chosen degraded to become worse than an alternative. Our study confirms, in a new way, that ant colonies can be very powerful “search engines”.     

Extrapolation of urn models via poissonization: accurate measurements of the microbial unknown

The availability of high-throughput parallel methods for sequencing microbial communities is increasing our knowledge of the microbial world at an unprecedented rate. Though most attention has focused on determining lower-bounds on the α-diversity i.e. the total number of different species present in the environment, tight bounds on this quantity may be highly uncertain because a small fraction of the environment could be composed of a vast number of different species. To better assess what remains unknown, we propose instead to predict the fraction of the environment that belongs to unsampled classes. Modeling samples as draws with replacement of colored balls from an urn with an unknown composition, and under the sole assumption that there are still undiscovered species, we show that conditionally unbiased predictors and exact prediction intervals (of constant length in logarithmic scale) are possible for the fraction of the environment that belongs to unsampled classes. Our predictions are based on a poissonization argument, which we have implemented in what we call the Embedding algorithm. In fixed i.e. non-randomized sample sizes, the algorithm leads to very accurate predictions on a sub-sample of the original sample. We quantify the effect of fixed sample sizes on our prediction intervals and test our methods and others found in the literature against simulated environments, which we devise taking into account datasets from a human-gut and -hand microbiota. Our methodology applies to any dataset that can be conceptualized as a sample with replacement from an urn. In particular, it could be applied, for example, to quantify the proportion of all the unseen solutions to a binding site problem in a random RNA pool, or to reassess the surveillance of a certain terrorist group, predicting the conditional probability that it deploys a new tactic in a next attack.     

Probabilistic lattice models of collective motion and aggregation: from individual to collective dynamics

We construct lattice gas automaton models to study collective dynamics and aggregation processes in biological systems. Corresponding transport equations are derived in appropriate space and time scaling under mean-field assumption and complemented with automation simulations.     

Towards a collective biology of the gene

Two features of heterochromatin: heteropycnosis (high values of chromatin condensation), and repressed genetic expression, force upon us the use of cooperative variables, rather than molecular ones. In particular a "repressor" hypothesis is formulated, in which a useful parameter is clearly identified. This enables us to discuss the synchronized repression of a large number, n, of genes (as in the case of the Barr body, in which n is larger than 100). The hypothesis is documented with phenomena known to occur in active chromatin. Possible tests are suggested.     

Towards a calculus of biomolecular complexes at equilibrium

An overview is presented of the construction and use of algebraic partition functions to represent the equilibrium statistical mechanics of multimolecular complexes and their action within a larger regulatory network. Unlike many applications of equilibrium statistical mechanics, multimolecular complexes may operate with various subsets of their components present and connected to the others, the rest remaining in solution. Thus they are variable-structure systems. This aspect of their behavior may be accounted for by the use of 'fugacity' variables as a representation within the partition functions. Four principles are proposed by which the combinatorics of molecular complex construction can be reflected in the construction of their partition functions. The corresponding algebraic operations on partition functions are multiplication, addition, function composition and a less commonly used operation called contraction. Each has a natural interpretation in terms of probability distributions on multimolecular structures. Possible generalizations to nonequilibrium statistical mechanics are briefly discussed.     

Computational models of collective foraging.

In this paper the behaviour of a bee colony is modelled as society of communicating agents acting in parallel and synchronising their behaviour. Two computational approaches for defining the agents behaviour are introduced and compared. Their common features as well as the complementary aspects making them suitable for merging together into a more complex model.     

The social biology of domiciliary cockroaches: colony structure, kin recognition and collective decisions

A substantial body of research on eusocial insects seen in the last decades has gone hand-in-hand with the development of social evolution theory. In contrast, little attention has been given to the non-eusocial insect species that nevertheless exhibit a rich spectrum of social behaviours, thus effectively skewing our vision of insect sociality. Recent studies on the behaviour, ecology and genetic of “gregarious” cockroaches (Blattodea) have revealed a diversity of social structures and group dynamics unique among insects, providing an important comparative model for the broader understanding of insect social evolution. Here, we present an overview of the social biology of the domiciliary cockroaches (ca. 25 species adapted to human habitats) based on research on two model species, Blattella germanica and Periplaneta americana. We discuss the evolution of these domiciliary cockroaches, considering them in the context of “social herds” within the insect sociality framework.     

Termites as models of swarm cognition

Eusociality has evolved independently at least twice among the insects: among the Hymenoptera (ants and bees), and earlier among the Isoptera (termites). Studies of swarm intelligence, and by inference, swarm cognition, have focused largely on the bees and ants, while the termites have been relatively neglected. Yet, termites are among the world’s premier animal architects, and this betokens a sophisticated swarm intelligence capability. In this article, I review new findings on the workings of the mound of Macrotermes which clarify how these remarkable structures work, and how they come to be built. Swarm cognition in these termites is in the form of “extended” cognition, whereby the swarm’s cognitive abilities arise both from interaction amongst the individual agents within a swarm, and from the interaction of the swarm with the environment, mediated by the mound’s dynamic architecture. The latter provides large scale “cognitive maps” which enable termite swarms to assess the functional state of their structure and to guide repair efforts where necessary. The crucial role of the built environment in termite swarm cognition also points to certain “swarm cognitive disorders”, where swarms can be pushed into anomalous activities by manipulating crucial structural and functional attributes of the termite system of “extended cognition.”     

Cooperative object transport with a swarm of e-puck robots: robustness and scalability of evolved collective strategies

Cooperative object transport in distributed multi-robot systems requires the coordination and synchronisation of pushing/pulling forces by a group of autonomous robots in order to transport items that cannot be transported by a single agent. The results of this study show that fairly robust and scalable collective transport strategies can be generated by robots equipped with a relatively simple sensory apparatus (i.e. no force sensors and no devices for direct communication). In the experiments described in this paper, homogeneous groups of physical e-puck robots are required to coordinate and synchronise their actions in order to transport a heavy rectangular cuboid object as far as possible from its starting position to an arbitrary direction. The robots are controlled by dynamic neural networks synthesised using evolutionary computation techniques. The best evolved controller demonstrates an effective group transport strategy that is robust to variability in the physical characteristics of the object (i.e. object mass and size of the longest object’s side) and scalable to different group sizes. To run these experiments, we designed, built, and mounted on the robots a new sensor that returns the agents’ displacement on a 2D plane. The study shows that the feedback generated by the robots’ sensors relative to the object’s movement is sufficient to allow the robots to coordinate their efforts and to sustain the transports for an extended period of time. By extensively analysing successful behavioural strategies, we illustrate the nature of the operational mechanisms underpinning the coordination and synchronisation of actions during group transport.     

Fellow travellers: emergent properties of collective cell migration

Cells can migrate individually or collectively. Collective movement is common during normal development and is also a characteristic of some cancers. This review discusses recent insights into features that are unique to collective cell migration, as well as properties that emerge from these features. The first feature is that cells of the collective affect each other through adhesion, force‐dependent and signalling interactions. The second feature is that cells of the collective differ from one another: leaders from followers, tip from stalk and front from back. These are dynamic differences that are important for directional movement. Last, an unexpected property is discussed: epithelial cells can rotate persistently in constrained spaces.     

Suprathreshold perceptual decisions constrain models of confidence

Perceptual confidence is an important internal signal about the certainty of our decisions and there is a substantial debate on how it is computed. We highlight three confidence metric types from the literature: observers either use 1) the full probability distribution to compute probability correct (Probability metrics), 2) point estimates from the perceptual decision process to estimate uncertainty (Evidence-Strength metrics), or 3) heuristic confidence from stimulus-based cues to uncertainty (Heuristic metrics). These metrics are rarely tested against one another, so we examined models of all three types on a suprathreshold spatial discrimination task. Observers were shown a cloud of dots sampled from a dot generating distribution and judged if the mean of the distribution was left or right of centre. In addition to varying the horizontal position of the mean, there were two sensory uncertainty manipulations: the number of dots sampled and the spread of the generating distribution. After every two perceptual decisions, observers made a confidence forced-choice judgement whether they were more confident in the first or second decision. Model results showed that the majority of observers were best-fit by either: 1) the Heuristic model, which used dot cloud position, spread, and number of dots as cues; or 2) an Evidence-Strength model, which computed the distance between the sensory measurement and discrimination criterion, scaled according to sensory uncertainty. An accidental repetition of some sessions also allowed for the measurement of confidence agreement for identical pairs of stimuli. This N-pass analysis revealed that human observers were more consistent than their best-fitting model would predict, indicating there are still aspects of confidence that are not captured by our modelling. As such, we propose confidence agreement as a useful technique for computational studies of confidence. Taken together, these findings highlight the idiosyncratic nature of confidence computations for complex decision contexts and the need to consider different potential metrics and transformations in the confidence computation.     

Towards translational rodent models of depression

Rodent models of depression have been developed in an effort to identify novel antidepressant compounds and to further our understanding of the pathophysiology of depression. Various rodent models of depression and antidepressant-like behaviour are currently used but, clearly, none of these current models fully recapitulate all features of depression. Moreover, these models have not resulted in the development of novel non-monoaminergic-based antidepressants with clinical efficacy. Thus, a refinement of the current models of depression is required. The present review outlines the most commonly used models of depression and antidepressant drug-like activity and suggests several factors that should be considered when refining these models.