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Reinforcement learning (RL) models usually assume a stationary internal model structure of agents, which consists of fixed learning rules and environment representations. However, this assumption does not allow accounting for real problem solving by individuals who can exhibit irrational behaviors or hold inaccurate beliefs about their environment. In this work, we present a novel framework called Dynamic Structure Learning (DSL), which allows agents to adapt their learning rules and internal representations dynamically. This structural flexibility enables a deeper understanding of how individuals learn and adapt in real-world scenarios. The DSL framework reconstructs the most likely sequence of agent structures—sourced from a pool of learning rules and environment models—based on observed behaviors. The method provides insights into how an agent’s internal structure model evolves as it transitions between different structures throughout the learning process. We applied our framework to study rat behavior in a maze task. Our results demonstrate that rats progressively refine their mental map of the maze, evolving from a suboptimal representation associated with repetitive errors to an optimal one that guides efficient navigation. Concurrently, their learning rules transition from heuristic-based to more rational approaches. These findings underscore the importance of both credit assignment and representation learning in complex behaviors. By going beyond simple reward-based associations, our research offers valuable insights into the cognitive mechanisms underlying decision-making in natural intelligence. DSL framework allows better understanding and modeling how individuals in real-world scenarios exhibit a level of adaptability that current AI systems have yet to achieve.

\"Humans are a puzzling species. On the one hand, we struggle to survive on our own in the wild, often failing to overcome even basic challenges, like obtaining food, building shelters, or avoiding predators. On the other hand, human groups have produced ingenious technologies, sophisticated languages, and complex institutions that have permitted us to successfully expand into a vast range of diverse environments. What has enabled us to dominate the globe, more than any other species, while remaining virtually helpless as lone individuals? This book [argues] that the secret of our success lies not in our innate intelligence, but in our collective brains--in the ability of human groups to socially interconnect and learn from one another over generations\"--Back cover.

Kinship is important for understanding the evolution of social behaviour in group living species. However, even solitary living individuals differentiate between kin and non-kin neighbours, which could lead to some form of cooperation, defined as both partners benefitting from each other. A simple form of cooperation is mutualism, where both partners benefit simultaneously. Here we tested whether there is mutual tolerance by sharing foraging grounds between kin in a solitary species. This would indicate the possibility of kin selection and mutual cooperation. We used mini-GPS data loggers to investigate range overlap in the solitary bush Karoo rat (Otomys unisulcatus) between kin- and non-kin neighbours. Next, we quantified the extent to which individuals shared foraging grounds containing food plants within their overlapping ranges. Lastly, using step selection functions applied to GPS fixes collected every five minutes, we analysed how individuals moved relative to each other. Kin-neighbours had larger home range overlap than non-kin neighbours (70.4% vs 29.6%) and shared more of their foraging grounds (63% vs 37%). Temporal analysis of spatial data found no indication that neighbours avoided each other, independent of kinship. Instead, activity was synchronised. In sum, we found mutual tolerance between neighbours with regards to sharing foraging grounds, and kin shared nearly double as much of their foraging grounds than non-kin. These data can be interpreted as a simple way of mutual cooperation between kin in a solitary species, where both members benefit from sharing a considerable part of their foraging grounds.

Come face to face with intelligent animals! Find out how dolphins communicate, if elephants really have good memory skills, and if there are any animals that can learn to speak our languages!

Selective harvest, such as poaching, impacts group-living animals directly through mortality of individuals with desirable traits, and indirectly by altering the structure of their social networks. Understanding the relationship between the structural network changes and group performance in wild animals remains an outstanding problem. To address this knowledge gap, we evaluate the immediate effect of disturbance on group sociality in African savanna elephants — an example, group-living species threatened by poaching. Drawing on static association data from one free ranging population, we constructed 100 virtual networks; performed a series of experiments ‘poaching’ the oldest, socially central or random individuals; and quantified the immediate change in the theoretical indices of network connectivity and efficiency of social diffusion. Although the virtual networks never broke down, targeted elimination of the socially central conspecifics, regardless of age, decreased network connectivity and efficiency. These findings hint at the need to further study resilience by modeling network reorganization and interaction-mediated socioecological learning, empirical data permitting. Our work is unique in quantifying connectivity together with global efficiency in multiple virtual networks that represent the sociodemographic diversity of elephant populations likely found in the wild. The basic design of our simulation platform makes it adaptable for hypothesis testing about the consequences of anthropogenic disturbance or lethal management on social interactions in a variety of group-living species with limited, real-world data. We consider the immediate response of animal groups to human disturbance by using the African savanna elephant as an example of a group-living species threatened by poaching. Previous research in one elephant population showed that poaching-induced mortality reduced social interaction among distantly related elephants, but not among close kin. Whether this type of resilience indicates that affected populations function similarity before and after poaching is an open problem. Understanding it is important because poaching often targets the largest and most socially and ecologically experienced group members. Drawing on empirical association data, we simulated poaching in 100 virtual elephant populations and eliminated the most senior or sociable members. Targeted poaching of sociable conspecifics was more impactful. Although it did not lead to population breakdown, it hampered theoretical features of interspecific associations that in other systems have been associated with group cohesion and the efficiency of transferring social information. Our findings suggest that further inquiry into the relationship between resilience to poaching and group performance is warranted. In addition, our simulation platform offers a generalizable basis for hypothesis testing in other social species, wild or captive, subject to exploitation by humans.

[This corrects the article DOI: 10.3389/fnins.2024.1360205.].

Since the late 1980s the dominant theory of human origins has been that a 'cognitive revolution' (C.50,000 years ago) led to the advent of our species, Homo sapiens. As a result of this revolution our species spread and eventually replaced all existing archaic Homo species, ultimately leading to the superiority of modern humans.Or so we thought.As Clive Finlayson explains, the latest advances in genetics prove that there was significant interbreeding between Modern Humans and the Neanderthals. All non-Africans today carry some Neanderthal genes. We have also discovered aspects of Neanderthal behaviour that indicate that they were not cognitively inferior to modern humans, as we once thought, and in fact had their own rituals and art. Finlayson, who is at the forefront of this research, recounts the discoveries of his team, providing evidence that Neanderthals caught birds of prey, and used their feathers for symbolic purposes. There is also evidence that Neanderthals practised other forms of art, as the recently discovered engravings in Gorham's Cave Gibraltar indicate.Linking all the recent evidence, The Smart Neanderthal casts a new light on the Neanderthals and the \"Cognitive Revolution\". Finlayson argues that there was no revolution and, instead, modern behaviour arose gradually and independently among different populations of Modern Humans and Neanderthals. Some practices were even adopted by Modern Humans from the Neanderthals. Finlayson overturns classic narratives of human origins, and raises important questions about who we really are.

Bumblebees rely on visual memories acquired during the first outbound flights to relocate their nest. While these learning flights have been extensively studied in sparse environments with few objects, little is known about how bees adapt their flight in more dense, cluttered, settings that better mimic their natural habitats. Here we investigated how environmental complexity influences the first outbound flights of bumblebees. In a large arena we tracked the bees' 3D positions to examine the flight patterns, body orientations, and nest fixations across environmental conditions characterised by different object constellations around the nest entrance. In cluttered environments, bees prioritised altitude gain over horizontal distance, suggesting a strategy to overcome obstacles and visual clutter. Body orientation patterns became more diverse in dense environments, indicating a balance between nest-oriented learning and obstacle avoidance. Notably, bees consistently preferred to fixate the location of the nest entrance from elevated positions above the dense environment across all conditions. Our results reveal significant changes in the 3D flight structure, body orientations, and nest fixation behaviours as object density increases. This highlights the importance of considering 3D space and environmental complexity in understanding insect navigation.Competing Interest StatementThe authors have declared no competing interest.Footnotes* We analysed the influence of the time window on the altitude distance ratio and found the same statistical results for time windows between 10 to 30 seconds. Figure 5 was revised as we changed from linear KDE to circular KDEs. Exemplary time series plots are provided and fixation events are depicted. (Figures 6A and S2). We added a figure showing the spatial categories in the arena (new Figure 1) and also relabelled the different categories to improve readability.* https://gitlab.ub.uni-bielefeld.de/a.sonntag/bumblebeelearningflights3d