Introduction

DEEP SOCIALITY

The transition to multicellularity, recognized as one the major transitions in evolution, has occurred independently several times. While multicellular development has been extensively studied in zygotic organisms including plant and animal groups, just a few aggregative multicellular organisms have been employed as model organisms for the study of multicellularity. Studying different evolutionary origins and modes of multicellularity enables comparative analyses that can help identifying lineage-specific aspects of multicellular evolution and generic factors and mechanisms involved in the transition to multicellularity. Among aggregative multicellular organisms, myxobacteria are a valuable system to explore the particularities that aggregation confers to the evolution of multicellularity and mechanisms shared with clonal organisms. Moreover, myxobacteria species develop fruiting bodies displaying a range of morphological diversity. In this review, we aim to synthesize diverse lines of evidence regarding myxobacteria development and discuss them in the context of Evo-Devo concepts and approaches. First, we briefly describe the developmental processes in myxobacteria, present an updated comparative analysis of the genes involved in their developmental processes and discuss these and other lines of evidence in terms of co-option and developmental system drift, two concepts key to Evo-Devo studies. Next, as has been suggested from Evo-Devo approaches, we discuss how broad comparative studies and integration of diverse genetic, physicochemical, and environmental factors into experimental and theoretical models can further our understanding of myxobacterial development, phenotypic variation, and evolution.

Systematic extinctions can leave major morphological gaps between living crown‐group clades. Such morphological gaps would be perceived, from a neontological point of view, as major evolutionary transitions. In order to fill these morphological gaps and to map the evolutionary steps toward major evolutionary transitions, we need to integrate extinct stem‐group taxa in phylogenetic studies. However, the recognition of stem group has not been widely adopted in the study of early animal fossils, despite that all fossils are stem groups at one level or another. Part of the difficulty is that stem groups may not have all features that collectively diagnose the respective crown group, and they can have unique (autapomorphic) features, making them tantalizingly similar to and frustratingly different from the crown group (e.g., stem‐group eukaryotes can be prokaryotic and stem‐group animals can be protistan). The need to embrace stem groups and to implement the PhyloCode, in order to achieve phylogenetic clarity and to offer key paleontological insights into the origin and early animal evolution, is illustrated in debates on several controversial Ediacaran and Cambrian fossils.

Major transitions in the physiological machinery of cognition.

The recognition of the role of mathematics and computer science in modern biology has led to new terminology, as did chemistry with biochemistry, and physics with biophysics. We need to think only of bioinformatics, computational biology, and even system biology and genomics for example. These terms seem to strongly suggest that this is all rather new. Yet a short review of the work of those such as J.B.S. Haldane, Sewell Wright, DArcy Thompson and R.A. Fisher, to say nothing of scientists like Luria and Delbrueck or Hodgkin and Huxley or Thomas Hunt Morgan, is useful. Their work and foresight set the stage for modern applications of mathematical modeling and statistics in the biological sciences. It has often been said that the only difference between now and then is the increase in data-a lot more data. This is clearly not the full story. In addition, we have computational power unimaginable to these earlier researchers, as well as to anyone only forty years ago. So what are our challenges? Some are clear, including the modeling and analysis of biologys complex systems such as a cells signaling, metabolic and differentiation. Also needed are analysis and models of complex neural systems and ecological structures. The latter, for example, will require a nearly full revamping of the early field of population genetics and evolution in order to exploit both modern genomics and new field studies of multiple species and environmental interactions. And there will be more, much of which will only become apparent as new data and questions arise. One example would be RNAi and micro-arrays inducing the development of new analysis tools.

The last years have seen an explosion of publications on consistent, i.e. repeatable, behavioural differences among individuals and their potential adaptive significance and evolutionary origin. Stable, repeatable traits imply reduced plasticity and this is particularly interesting in the case of behaviour, which is the most flexible trait of an animal in its interaction with the biotic and abiotic environment. While the importance of consistent individual differences has been widely acknowledged, the lifetime development of these differences and their potentially adaptive or maladaptive consequences for individuals only recently came into the focus of research [1,2]. As a result, widespread evidence for the existence of changes in behaviour and behavioural plasticity over the lifetime of individuals begins to accumulate [3]. This has raised novel questions concerning the overwhelming importance of ontogenetic and trans-generational influences on the development and fitness of behavioural phenotypes and the mechanisms influencing changes during an individual’s lifetime. Much theoretical work has been published on the importance of and limits to individual plasticity, mostly in the framework of the reaction norm. Less empirical work has actually studied, how the reaction norm comes about in an individual’s lifetime and how or whether it may change over the lifetime [4]. This raises important questions about the adaptivity of the phenotypic plasticity of behaviour and the question when within a lifetime such plasticity might be particularly relevant. Do particular windows of plasticity exist, and if so why are they where they are within the lifetime of an organism? The potential lifetime plasticity also raises the question about the evolution of the reaction norm and, more recently, about the evolution of its plasticity, questions that so far have received but little attention. In these fields of investigation theory has moved far ahead of empirical studies. Often, if not always, individuals face uncertain environmental conditions throughout their life, forcing them to adapt plastically in order to maximise fitness. Under such uncertain conditions we expect natural selection to favour plasticity. However, plastic responses to the environment can only be adaptive, if informative environmental cues exist that allow prediction of future conditions with reasonably high probability [5]. Depending on the cues available and the ontogenetic stage of an individual we expect differential effects of such predictive cues: Early in life highly informative cues, whether derived from maternal influences or perceived directly, may potentially act in an organisational manner, whereas less informative cues or cues perceived at a later age may lead to more immediate plasticity (see [4] for a definition of different types of plasticity). This may lead to developmental influences on plasticity, i.e. early environments may particularly influence the reaction norm. This idea of plasticity of the reaction norm as a trait that develops and thereby influences the reaction norm expressed during the lifetime of an individual is rather novel [6]. However, it follows directly from Gene x Environment interactions (GxE) influencing the reaction norm. Along the developmental axis Gene x Age (GxA) and Individual x Age (IxA) interactions will then determine the expression of the developing reaction norm in interaction with the environments encountered by an individual (see [7]). As pointed out earlier [2] genes themselves provide an environment for other genes so that even more complex interactions are to be expected. As these interactions may change with the state and the age of an individual it becomes clear why ontogenetic processes are of prime * Correspondence: fritz.trillmich@uni-bielefeld.de Animal Behaviour, Bielefeld University, Morgenbreede 45, 33615 Bielefeld, Germany Full list of author information is available at the end of the article Trillmich et al. Frontiers in Zoology 2015, 12(Suppl 1):S1 http://www.frontiersinzoology.com/content/12/S1/S1

This commentary poses an evolutionary hypothesis about the nature of the human condition: that we are stalled part way through a major evolutionary transition from individuals to groups, a transition that may never be completed but that has already shaped our history, politics, psychology, and social life. The conditions causing the transition to stall include the decreasing congruence of group boundaries with kinship boundaries, growth in group size, increasing interdependence of groups, membership of individuals in several types of groups, divided loyalties of individuals among groups, and the emergence of institutions as novel entities uncoupled from the individuals who temporarily belong to them. Those conditions combine to decrease the ability of cultural group selection to effect genetic change in group-oriented traits. The theory supporting this hypothesis deals with major transitions (e.g., Maynard Smith and Szathmary 1995), hierarchical selection (e.g., Price 1970, 1972; Frank 1995, 2003; Rice 2004), conflicts and conflict resolution (e.g., Burt and Trivers 2006), and gene-culture coevolution (e.g., Boyd and Richerson 2005; Richerson and Boyd 2005). The evidence is diverse. It comes from biological anthropology (e.g., Hill and Hurtado 1995), behavioral economics (e.g., Hammerstein 2003; Bowles 2004; Henrich et al. 2004), evolutionary psychology (e.g., Barkow et al. 1992), and history. The research programs it suggests are at least in anthropology, history, and political science.

The evolutionary trajectory from non-human to human forms of social organization involves change from experiential- to relational-based systems of social interaction. Social organization derived from biologically and experientially grounded social interaction reached a hiatus with the great apes due to an expansion of individualization of behaviour. The hiatus ended with the introduction of relational-based social interaction, culminating in social organization based on cultural kinship. This evolutionary trajectory links biological origins to cultural outcomes and makes evident the centrality of distributed forms of information for both the boundary and internal structure of human societies as these evolved from prior forms of social organization.

Vegetative status of children as a territorial bio-indicator of ecological safety

In this chapter I briefly summarize views on adaptation and language, some relevant neurobiological and genetic facts, the presence or absence of recursion in animals, the possible role of genetic assimilation in language evolution, the prerequisites of language and the nature of the human adaptive suite, and the relative merits of proposed evolutionary scenarios for the origin of natural language. I highlight the special difficulty of this last major transition and a possible integrative modelling approach to the problem. Finally, I give a summary showing that the transition from early hominine societies with protolanguage to modern society with language indeed qualifies as a major transition.

Drawing on recent advances in evolutionary biology, prominent scholars return to the question posed in a pathbreaking book: how evolution itself evolved. In 1995, John Maynard Smith and Eörs Szathmáry published their influential book The Major Transitions in Evolution. The "transitions" that Maynard Smith and Szathmáry chose to describe all constituted major changes in the kinds of organisms that existed but, most important, these events also transformed the evolutionary process itself. The evolution of new levels of biological organization, such as chromosomes, cells, multicelled organisms, and complex social groups radically changed the kinds of individuals natural selection could act upon. Many of these events also produced revolutionary changes in the process of inheritance, by expanding the range and fidelity of transmission, establishing new inheritance channels, and developing more open-ended sources of variation. Maynard Smith and Szathmáry had planned a major revision of their work, but the death of Maynard Smith in 2004 prevented this. In this volume, prominent scholars (including Szathmáry himself) reconsider and extend the earlier book's themes in light of recent developments in evolutionary biology. The contributors discuss different frameworks for understanding macroevolution, prokaryote evolution (the study of which has been aided by developments in molecular biology), and the complex evolution of multicellularity.

From a system’s theoretical point of view, adaptive learning systems (ALS) for education and training contain in their core – in a simplified form – closed feedback control loops in which the control is determined by the measured users’ performance. Improving this performance can increase the learning outcome, especially for critical disciplines such as education or training for disaster risk management. However, for this special form of intelligent (e-learning) assistance systems, learning theories and behavioral models have to be considered, e.g., game flow theory, cognition models, or learning models. The research question is how adaptive interactive learning environments (ILE) such as serious games and computer simulations can be characterized and analyzed to determine optimal adaptation strategies. Adaptive learning environments should adapt to the context-related needs of the user in order to ensure and optimize learning success, especially for disaster management training. This contribution presents a concept for an interoperable, adaptive ILE framework which follows control theory and its models, contributing to the state of the art for adaptive games or simulations in disaster risk management.

Life: social to its core

The integration of intention recognition systems in industrial collaborative robotics is crucial for improving safety and efficiency in modern manufacturing environments. This review paper looks at frameworks that enable collaborative robots to understand human intentions. This ability is essential for providing effective robotic assistance and promoting seamless human–robot collaboration, particularly in enhancing safety, improving operational efficiency, and enabling natural interactions. The paper discusses learning techniques such as rule-based, probabilistic, machine learning, and deep learning models. These technologies empower robots with human-like adaptability and decision-making skills. It also explores cues for intention recognition, categorising them into physical, physiological, and contextual cues. It highlights how implementing these various sensory inputs sharpen the interpretation of human intentions. Additionally, the discussion assesses the limitations of current research, including the need for usability, robustness, industrial readiness, real-time processing, and generalisability across various industrial applications. This evaluation identifies future research gaps that could improve the effectiveness of these systems in industrial settings. This work contributes to the ongoing conversation about the future of collaborative robotics, laying the foundation for advancements that can bridge the gap between human and robotic interactions. The key findings point out the significance of predictive understanding in promoting safer and more efficient human–robot interactions in industrial environments and provide recommendations for its use.

We aim to develop a goal specification method that is semantically clear, spatially sensitive, domain-agnostic, and intuitive for human users to guide agent interactions in 3D environments. Specifically, we propose a novel cross-view goal alignment framework that allows users to specify target objects using segmentation masks from their camera views rather than the agent’s observations. We highlight that behavior cloning alone fails to align the agent’s behavior with human intent when the human and agent camera views differ significantly. To address this, we introduce two auxiliary objectives: cross-view consistency loss and target visibility loss, which explicitly enhance the agent's spatial reasoning ability. According to this, we develop ROCKET-2, a state-of-the-art agent trained in Minecraft, achieving an improvement in the efficiency of inference 3x to 6x. We demonstrate that ROCKET-2 can directly interpret goals from human camera views, enabling better human-agent interaction. Remarkably, ROCKET-2 demonstrates zero-shot generalization capabilities: despite being trained exclusively on the Minecraft dataset, it can adapt and generalize to other 3D environments like Doom, DMLab, and Unreal through a simple action space mapping.