Collective cognition, in which groups display enhanced problem-solving abilities compared with individuals, is a hallmark of ant behaviour. For instance, in navigation tasks, such as the piano movers' problem, a short-term memory-like directional persistence emerges in large enough ant groups and aids them in implementing an effective wall-following heuristic. Here, we investigate the collective problem-solving abilities of Paratrechina longicornis by presenting groups of varying sizes with an array of piano movers' puzzles. We find that increasingly complex puzzles reveal performance differences between small and large groups. To benchmark these results, we compare the performance of the ants to a simulated physics-based null model incorporating gravity and noise. Although the null model performs comparably with ants in simple puzzles, it fails in more challenging ones. Introducing ant-inspired mechanisms, including attachment along edges or transient leadership, enables the simulated solver to match ant performance across group sizes and tasks. Beyond these features, we show that ants solve a broad spectrum of puzzles without prior knowledge of geometry, whereas the simulated solver requires parameter adjustments tailored to each puzzle. Our findings highlight the flexibility and robustness of collective cognition in ant groups and provide a framework for integrating biological strategies into artificial problem-solving systems.

The ant nest serves as the skeleton of the ant superorganism. Similar to a skeleton, the nest expands as the colony grows and requires repair after catastrophic events. We experimentally compared nest excavation in colonies seeded from a single mated queen and allowed to grow for 6 months to excavation triggered by a catastrophic event in colonies with fixed demographics, where the age of each worker, including the queen, is known. The areas excavated by equal group sizes differed significantly between these conditions: heterogeneous populations in naturally growing colonies as well as cohorts of young ants dig larger areas than old ant cohorts. Moreover, we find that younger ants tend to dig slanted tunnels while older ants dig straight down. This is a novel form of age polyethism, where an ant’s age dictates not only her likelihood to engage in a task but also the way she performs the task. We further present a quantitative model that predicts that under normal growth, digging is predominantly performed by the younger ants, while after a catastrophe, all ants dig to restore lost nest volume. The fact that the nests of naturally growing colonies exhibit slanted tunnels strengthens this prediction. Finally, our results indicate how a colony’s demographic and physical history are sketched into the current structure of its nest.

Honey bee (Apis mellifera) recruitment dances vary in the accuracy of information they convey about available food sources. We investigated whether honey bees can assess the reliability of waggle dances. During the “learning stage”, free-flying foragers were exposed to one of three treatments as they visited the feeder at location A. Treatments differed in the quality of information communicated in the recruitment dances performed by a chosen focal bee. Focal bees were always able to access sugar solution at the feeder, while we changed the reliability of her dances by manipulating the ability of non-focal bees to access the advertised food. Non-focal bees that arrived at the feeder during the “honest” treatment received the advertised food, but were met with an empty feeder during the “liar” treatment. The third “unverified” treatment eliminated the ability of non-focal bees to verify dance information by capturing them upon arrival at the feeder for the remainder of the experiment. During the “test stage” the feeder was moved to a novel location (B). While the same focal bee danced for the new location, all non-focal bees arriving at the feeder were caught, thus testing the implications of the imposed treatments. Effects were positive in the honest treatment but not negative in the liar treatment: “Honest” focal bees increased their recruitment effort by performing more circuits over time when advertising the new food source, unverified dancers decreased recruitment effort, and liar focal bees did not change over time. The increase in honest dancer recruitment effort suggests an ability to assess the reliability of their own signal and may provide an evaluation mechanism for followers. We did not, however, find differences in follower behavior in relation to treatment. In the test stage, non-focal bees followed more circuits overall than in the learning stage. The probability of dancing did not differ across treatments or stages, and the availability of nectar receivers was not affected by the experimental design. We provide evidence of the strength of the waggle dance as an autocatalytic reinforcement of useful information. Quality of personal, learned information varies throughout societies. It is advantageous to pass accurate information to conspecifics, while distributing degraded information amplifies mistakes. Gauging the reliability of shared information may be beneficial. Confidence sharing, the ability of an individual to modulate the signal strength with which they communicate collected information to group mates, has been theoretically and experimentally shown to be part of efficient information exchange. We tested whether honey bee colonies control information flow given that bees may hold information of variable quality. We manipulated waggle dancers to have three different information quality treatments and monitored interactions between dancer and followers. Information quality provided by dancers was associated with increased recruitment effort, but only if she provided true information about the food source. We provide empirical evidence for how honest signals are shared within a population of cooperative individuals.

It is widely recognized that biological collectives operate near criticality to amplify their capability of collective response. The peak in susceptibility near criticality renders these groups highly responsive to external stimuli. While this phenomenon has been recognized and supported by evidence from theory, a direct experimental demonstration has been elusive. To bridge this gap, here we record the response of a group of Paratrechina longicornis ants to external stimuli as they join efforts to carry food to their nest. Using a robotic system that mimics a transient leader, we apply tactile ant-scale forces and measure the group's response at sub, near, and supercritical regimes. Supported by theory and simulations, we provide direct experimental evidence to demonstrate that at critical group size, the collective response of the ants to an external force is maximally amplified.

Biological ensembles use collective intelligence to tackle challenges together, but suboptimal coordination can undermine the effectiveness of group cognition. Testing whether collective cognition exceeds that of the individual is often impractical since different organizational scales tend to face disjoint problems. One exception is the problem of navigating large loads through complex environments and toward a given target. People and ants stand out in their ability to efficiently perform this task not just individually but also as a group. This provides a rare opportunity to empirically compare problem-solving skills and cognitive traits across species and group sizes. Here, we challenge people and ants with the same “piano-movers” load maneuvering puzzle and show that while ants perform more efficiently in larger groups, the opposite is true for humans. We find that although individual ants cannot grasp the global nature of the puzzle, their collective motion translates into emergent cognitive skills. They encode short-term memory in their internally ordered state and this allows for enhanced group performance. People comprehend the puzzle in a way that allows them to explore a reduced search space and, on average, outperform ants. However, when communication is restricted, groups of people resort to the most obvious maneuvers to facilitate consensus. This is reminiscent of ant behavior, and negatively impacts their performance. Our results exemplify how simple minds can easily enjoy scalability while complex brains require extensive communication to cooperate efficiently.

One of the hallmarks of higher cognition is the ability to anticipate near-future events and effectively react to them. This requires perceiving events in a dynamic environment and adjusting the actions accordingly to suit the expected outcomes. Social insects exhibit various forms of emergent collective cognition; however, it is not clear whether such preplanning is one of them. We discovered that when longhorn crazy ants cooperatively carry a large food item to the nest, some ants clear the path ahead of the moving load from small debris. The obstacle clearing is nest-oriented, as it creates a clear path connecting the food load with the nest. We show that this anticipatory obstacle-clearing behavior is context specific and that it is functional in reducing the time needed to deliver the large food load to the nest. Importantly, we found that no personal knowledge of the food load is required for the ants to start clearing the obstacles. Individual ant tracking revealed that clearing is instead triggered by social cues in the form of freshly laid pheromone markings. Indeed, we observed that obstacle clearing was performed by ants that had never experienced the big food load and even in cases where no such load was present at all, in response to the pheromone marks alone. These results provide strong evidence that individual ants do not possess an internal representation of the final goal of obstacle clearing. On the other hand, the goal-directedness of obtacle clearing appears to emerge at the ant group level from collective cognition.

Weaver ants (Oecophylla smaragdina) construct their nests by gluing together leaves in tree canopies. They accomplish this by forming tool-like self-assemblages, which they use to bend the leaves into place. To study this, we developed a novel experimental approach that recreates nest building in the lab using artificial leaves and multi-viewpoint 3D reconstruction. In all tested conditions, the ants combine the leaves into viable, closed structures where all leaves bend either upward or downward. Leaf thickness and initial placement were major factors in determining the configuration adopted. We quantify the dynamics of ants’ self-assembled tools and the resulting leaf manipulation, and we show that local ant-scale rules operating under geometric constraints explain why some nests bend downward while others bend upward. We also compute the transition angle. Finally, we use differential geometry to suggest why, under our experimental conditions, nests are constrained to be convex, architecturally stable, sphere-like surfaces. Our findings provide insights into the interplay of geometry, biomechanics, and local-ant-scale rules in complex collective construction.

The physicality of the world in which the animal acts—its anatomical structure, physiology, perception, emotional states, and cognitive capabilities—determines the boundaries of the behavioral space within which the animal can operate. Behavior, therefore, can be considered as the subspace that remains after secluding all actions that are not available to the animal due to constraints. The very signature of being a certain creature is reflected in these limitations that shape its behavior. A major goal of ethology is to expose those constraints that carve the intricate structure of animal behavior and reveal both uniqueness and commonalities between animals within and across taxa. Exploratory behavior in an empty arena seems to be stochastic; nevertheless, it does not mean that the moving animal is a random walker. In this study, we present how, by adding constraints to the animal’s locomotion, one can gradually retain the ‘mousiness’ that characterizes the behaving mouse. We then introduce a novel phenomenon of high mirror symmetry along the locomotion of mice, which highlights another constraint that further compresses the complex nature of exploratory behavior in these animals. We link these findings to a known neural mechanism that could explain this phenomenon. Finally, we suggest our novel finding and derived methods to be used in the search for commonalities in the motion trajectories of various organisms across taxa.

Activity and autonomous motion are fundamental aspects of many living and engineering systems. Here, the scale of biological agents covers a wide range, from nanomotors, cytoskeleton, and cells, to insects, fish, birds, and people. Inspired by biological active systems, various types of autonomous synthetic nano- and micromachines have been designed, which provide the basis for multifunctional, highly responsive, intelligent active materials. A major challenge for understanding and designing active matter is their inherent non-equilibrium nature due to persistent energy consumption, which invalidates equilibrium concepts such as free energy, detailed balance, and time-reversal symmetry. Furthermore, interactions in ensembles of active agents are often non-additive and non-reciprocal. An important aspect of biological agents is their ability to sense the environment, process this information, and adjust their motion accordingly. It is an important goal for the engineering of micro-robotic systems to achieve similar functionality. With many fundamental properties of motile active matter now reasonably well understood and under control, the ground is prepared for the study of physical aspects and mechanisms of motion in complex environments, of the behavior of systems with new physical features like chirality, of the development of novel micromachines and microbots, of the emergent collective behavior and swarming of intelligent self-propelled particles, and of particular features of microbial systems. The vast complexity of phenomena and mechanisms involved in the self-organization and dynamics of motile active matter poses major challenges, which can only be addressed by a truly interdisciplinary effort involving scientists from biology, chemistry, ecology, engineering, mathematics, and physics.

The concept of emergence, or synergy in its simplest form, is widely used but lacks a rigorous definition. Our work connects information and set theory to uncover the mathematical nature of synergy as the failure of distributivity. For the trivial case of discrete random variables, we explore whether and how it is possible to get more information out of lesser parts. The approach is inspired by the role of set theory as the fundamental description of part–whole relations. If taken unaltered, synergistic behavior is forbidden by the set-theoretic axioms. However, random variables are not a perfect analogy of sets: we formalize the distinction, highlighting a single broken axiom—union/intersection distributivity. Nevertheless, it remains possible to describe information using Venn-type diagrams. The proposed multivariate theory resolves the persistent self-contradiction of partial information decomposition and reinstates it as a primary route toward a rigorous definition of emergence. Our results suggest that non-distributive variants of set theory may be used to describe emergent physical systems.

Animal groups need to achieve and maintain consensus to minimize conflict among individuals and prevent group fragmentation. An excellent example of a consensus challenge is cooperative transport, where multiple individuals cooperate to move a large item together. This behaviour, regularly displayed by ants and humans only, requires individuals to agree on which direction to move in. Unlike humans, ants cannot use verbal communication but most likely rely on private information and/or mechanical forces sensed through the carried item to coordinate their behaviour. Here, we investigated how groups of weaver ants achieve consensus during cooperative transport using a tethered-object protocol, where ants had to transport a prey item that was tethered in place with a thin string. This protocol allows the decoupling of the movement of informed ants from that of uninformed individuals. We showed that weaver ants pool together the opinions of all group members to increase their navigational accuracy. We confirmed this result using a symmetry-breaking task, in which we challenged ants with navigating an open-ended corridor. Weaver ants are the first reported ant species to use a ‘wisdom-of-the-crowd’ strategy for cooperative transport, demonstrating that consensus mechanisms may differ according to the ecology of each species.

Ant colonies regulate foraging in response to their collective hunger, yet the mechanism behind this distributed regulation remains unclear. Previously, by imaging food flow within ant colonies we showed that the frequency of foraging events declines linearly with colony satiation (Greenwald et al., 2018). Our analysis implied that as a forager distributes food in the nest, two factors affect her decision to exit for another foraging trip: her current food load and its rate of change. Sensing these variables can be attributed to the forager’s individual cognitive ability. Here, new analyses of the foragers’ trajectories within the nest imply a different way to achieve the observed regulation. Instead of an explicit decision to exit, foragers merely tend toward the depth of the nest when their food load is high and toward the nest exit when it is low. Thus, the colony shapes the forager’s trajectory by controlling her unloading rate, while she senses only her current food load. Using an agent-based model and mathematical analysis, we show that this simple mechanism robustly yields emergent regulation of foraging frequency. These findings demonstrate how the embedding of individuals in physical space can reduce their cognitive demands without compromising their computational role in the group.

Paratrechina longicornis ants are known for their ability to cooperatively transport large food items. Previous studies have focused on the behavioral rules of individual ants and explained the efficient coordination using the coupled-carrier model. In contrast to this microscopic description, we instead treat the transported object as a single self-propelled particle characterized by its velocity magnitude and angle. We experimentally observe P. longicornis ants cooperatively transporting loads of varying radii. By analyzing the statistical features of the load's movement, we show that its salient properties are well captured by a set of Langevin equations describing a self-propelled particle. We relate the parameters of our macroscopic model to microscopic properties of the system. While the autocorrelation time of the velocity direction increases with group size, the autocorrelation time of the speed has a maximum at an intermediate group size. This corresponds to the critical slowdown close to the phase transition identified in the coupled-carrier model. Our findings illustrate that a self-propelled particle model can effectively characterize a system of interacting individuals.

Social groups often need to overcome differences in individual interests and knowledge to reach consensus decisions. Here, we combine experiments and modeling to study conflict resolution in emigrating ant colonies during binary nest selection. We find that cohesive emigration, without fragmentation, is achieved only by intermediate-sized colonies. We then impose a conflict regarding the desired emigration target between colony subgroups. This is achieved using an automated selective gate system that manipulates the information accessible to each ant. Under this conflict, we find that individuals concede their potential benefit to promote social consensus. In particular, colonies resolve the conflict imposed by a persistent minority through "majority concession,"wherein a majority of ants that hold first-hand knowledge regarding the superior quality nest choose to reside in the inferior one. This outcome is unlikely in social groups of selfish individuals and emphasizes the importance of group cohesion in eusocial societies.

The echo-chamber effect is a common term in opinion dynamic modeling to describe how a person's opinion might be artificially enhanced as it is reflected back at her through social interactions. Here, we study the existence of this effect in statistical mechanics models, which are commonly used to study opinion dynamics. We show that the Ising model does not exhibit echo-chambers, but this result is a consequence of a special symmetry. We then distinguish between three types of models: (i) those with a strong echo-chamber symmetry, that have no echo-chambers at all; (ii) those with a weak echo-chamber symmetry that can exhibit echo-chambers but only if there are external fields in the system, and (iii) models without echo-chamber symmetry that generically have echo-chambers. We use these results to construct an efficient algorithm to efficiently and precisely calculate magnetization in arbitrary tree networks. Finally, We apply this algorithm to study two systems: phase transitions in the random field Ising model on a Bethe lattice and the influence optimization problem in social networks.

A new study relates the properties of Drosophila melanogaster social networks to group composition and demonstrates how they may be altered using behavioral priming and genetic manipulations.

Much like a single organism, colonies of social insects regulate their collective nutrition towards a specific intake of nutrients. Yet, the mechanisms behind this colony‐level regulation are not fully understood. Although foraging behaviour in social insects has been studied extensively, not much is known on its relation with feeding processes that occur within the nest. In the nest, food is commonly transferred between individuals in numerous oral feeding interactions (trophallaxis). Studies on the properties of these trophallactic networks have so far been limited by: (a) the difficulty to non‐intrusively measure food inside individual ants, let alone its nutritional composition, and (b) the meticulous manual labour involved in detecting trophallactic events. Our dual‐fluorescence imaging set‐up is designed to track two food sources, each labelled with a different fluorophore, as they are disseminated throughout a freely behaving colony of individually tagged ants. Additionally, our image‐based deep learning algorithm for automatic detection of ant trophallaxis events efficiently yields a detailed record of all food‐transfer interactions. Using a series of calibration experiments, we demonstrate the reliability of our measurements. We then exemplify the capabilities of our new method by tracking food dissemination in a colony of Camponotus sanctus ants supplied with two nutritionally distinct food sources. We follow the amount of each food type in the crop of each ant in the colony. Our data reveal the path in nutrient space that the colony took to reach its final nutrient intake, the contributions of the different forager ants to this path and the dynamic nutritional states of all ants in the colony. We further demonstrate the potential applicability of dual‐fluorescence food imaging to other insect species. Our system provides access to data that were previously unavailable and is crucial for a complete description of nutrient regulation by social insects. More broadly, the ability to simultaneously and efficiently track two material flows in a behaving colony provides new opportunities for tackling many open and emerging questions on topics other than nutrition, such as disease spread and social regulation of colony development.

Cooperative transport of large food loads by Paratrechina longicornis ants demands repeated decision-making. Inspired by the Evidence Accumulation (EA) model classically used to describe decision-making in the brain, we conducted a binary choice experiment where carrying ants rely on social information to choose between two paths. We found that the carried load performs a biased random walk that continuously alternates between the two options. We show that this motion constitutes a physical realization of the abstract EA model and exhibits an emergent version of the psychophysical Weber’s law. In contrast to the EA model, we found that the load’s random step size is not fixed but, rather, varies with both evidence and circumstances. Using theoretical modeling we show that variable step size expands the scope of the EA model from isolated to sequential decisions. We hypothesize that this phenomenon may also be relevant in neuronal circuits that perform sequential decisions.