Animals do have culture, maintains this delightfully illustrated and provocative book, which cites a number of fascinating instances of animal communication and learning. John Bonner traces the origins of culture back to the early biological evolution of animals and provides examples of five categories of behavior leading to nonhuman culture: physical dexterity, relations with other species, auditory communication within a species, geographic locations, and inventions or innovations. Defining culture as the transmission of information by behavioral rather than genetical means, he demonstrates the continuum between the traits we find in animals and those we often consider uniquely human.
Introduction My goal in this paper is, first, to collect together a number of themes and observations that have usually been kept apart, locked up in their respective disciplines. When these are brought together, some general and far reaching implications become really rather clear. In particular, I want to make a case for the implicit coherence of these themes in the idea that the ‘roots of human sociality’ lie in a special capacity for social interaction, which itself holds the key to human evolution, the evolution of language, the nature of much of our daily concerns, the building blocks of social systems, and much else besides.
In recent years, the phenomenon of cumulative cultural evolution (CCE) has become the focus of major research interest in biology, psychology and anthropology. Some researchers argue that CCE is unique to humans and underlies our extraordinary evolutionary success as a species. Others claim to have found CCE in non-human species. Yet others remain sceptical that CCE is even important for explaining human behavioural diversity and complexity. These debates are hampered by multiple and often ambiguous definitions of CCE. Here, we review how researchers define, use and test CCE. We identify a core set of criteria for CCE which are both necessary and sufficient, and may be found in non-human species. We also identify a set of extended criteria that are observed in human CCE but not, to date, in other species. Different socio-cognitive mechanisms may underlie these different criteria. We reinterpret previous theoretical models and observational and experimental studies of both human and non-human species in light of these more fine-grained criteria. Finally, we discuss key issues surrounding information, fitness and cognition. We recommend that researchers are more explicit about what components of CCE they are testing and claiming to demonstrate.
Andreea Tulbure, Carmen Scheidemann, Elias Steiner
et al.
Task-oriented handovers (TOH) are fundamental to effective human-robot collaboration, requiring robots to present objects in a way that supports the human's intended post-handover use. Existing approaches are typically based on object- or task-specific affordances, but their ability to generalize to novel scenarios is limited. To address this gap, we present AFT-Handover, a framework that integrates large language model (LLM)-driven affordance reasoning with efficient texture-based affordance transfer to achieve zero-shot, generalizable TOH. Given a novel object-task pair, the method retrieves a proxy exemplar from a database, establishes part-level correspondences via LLM reasoning, and texturizes affordances for feature-based point cloud transfer. We evaluate AFT-Handover across diverse task-object pairs, showing improved handover success rates and stronger generalization compared to baselines. In a comparative user study, our framework is significantly preferred over the current state-of-the-art, effectively reducing human regrasping before tool use. Finally, we demonstrate TOH on legged manipulators, highlighting the potential of our framework for real-world robot-human handovers.
Large Language Models (LLMs) are increasingly used to power autonomous agents for complex, multi-step tasks. However, human-agent interaction remains pointwise and reactive: users approve or correct individual actions to mitigate immediate risks, without visibility into subsequent consequences. This forces users to mentally simulate long-term effects, a cognitively demanding and often inaccurate process. Users have control over individual steps but lack the foresight to make informed decisions. We argue that effective collaboration requires foresight, not just control. We propose simulation-in-the-loop, an interaction paradigm that enables users and agents to explore simulated future trajectories before committing to decisions. Simulation transforms intervention from reactive guesswork into informed exploration, while helping users discover latent constraints and preferences along the way. This perspective paper characterizes the limitations of current paradigms, introduces a conceptual framework for simulation-based collaboration, and illustrates its potential through concrete human-agent collaboration scenarios.
Debora Firmino de Souza, Sonia Sousa, Kadri Kristjuhan-Ling
et al.
The Industry 5.0 transition highlights EU efforts to design intelligent devices that can work alongside humans to enhance human capabilities, and such vision aligns with user preferences and needs to feel safe while collaborating with such systems take priority. This demands a human-centric research vision and requires a societal and educational shift in how we perceive technological advancements. To better understand this perspective, we conducted a systematic literature review focusing on understanding how trust and trustworthiness can be key aspects of supporting this move towards Industry 5.0. This review aims to overview the most common methodologies and measurements and collect insights about barriers and facilitators for fostering trustworthy HRI. After a rigorous quality assessment following the Systematic Reviews and Meta-Analyses guidelines, using rigorous inclusion criteria and screening by at least two reviewers, 34 articles were included in the review. The findings underscores the significance of trust and safety as foundational elements for promoting secure and trustworthy human-machine cooperation. Confirm that almost 30% of the revised articles do not present a definition of trust, which can be problematic as this lack of conceptual clarity can undermine research efforts in addressing this problem from a central perspective. It highlights that the choice of domain and area of application should influence the choice of methods and approaches to fostering trust in HRI, as those choices can significantly affect user preferences and their perceptions and assessment of robot capabilities. Additionally, this lack of conceptual clarity can be a potential barrier to fostering trust in HRI and explains the sometimes contradictory findings or choice of methods and instruments used to investigate trust in robots and other autonomous systems in the literature.