How Can Evolution Learn?

Plasticity-led and mutation-led evolutions are different modes of the same developmental gene regulatory network

The standard theory of evolution proposes that mutations cause heritable variations, which are naturally selected, leading to evolution. However, this mutation-led evolution (MLE) is being questioned by an alternative theory called plasticity-led evolution (PLE). PLE suggests that an environmental change induces adaptive phenotypes, which are later genetically accommodated. According to PLE, developmental systems should be able to respond to environmental changes adaptively. However, developmental systems are known to be robust against environmental and mutational perturbations. Thus, we expect a transition from a robust state to a plastic one. To test this hypothesis, we constructed a gene regulatory network (GRN) model that integrates developmental processes, hierarchical regulation, and environmental cues. We then simulated its evolution over different magnitudes of environmental changes. Our findings indicate that this GRN model exhibits PLE under large environmental changes and MLE under small environmental changes. Furthermore, we observed that the GRN model is susceptible to environmental or genetic fluctuations under large environmental changes but is robust under small environmental changes. This indicates a breakdown of robustness due to large environmental changes. Before the breakdown of robustness, the distribution of phenotypes is biased and aligned to the environmental changes, which would facilitate rapid adaptation should a large environmental change occur. These observations suggest that the evolutionary transition from mutation-led to plasticity-led evolution is due to a developmental transition from robust to susceptible regimes over increasing magnitudes of environmental change. Thus, the GRN model can reconcile these conflicting theories of evolution.

A neural network-based model framework for cell-fate decisions and development

Gene regulatory networks (GRNs) fulfill the essential function of maintaining the stability of cellular differentiation states by sustaining lineage-specific gene expression, while driving the progression of development. However, accounting for the relative stability of intermediate differentiation stages and their divergent trajectories remains a major challenge for models of developmental biology. Here, we develop an empirical data-based associative GRN model (AGRN) in which regulatory networks store multilineage stage-specific gene expression profiles as associative memory patterns. These networks are capable of responding to multiple instructive signals and, depending on signal timing and identity, can dynamically drive the differentiation of multipotent cells toward different cell state attractors. The AGRN dynamics can thus generate diverse lineage-committed cell populations in a robust yet flexible manner, providing an attractor-based explanation for signal-driven cell fate decisions during differentiation and offering a readily generalizable modelling tool that can be applied to a wide variety of cell specification systems.

Spread of the cycles: a feedback perspective on the Anthropocene

What propelled the human 'revolutions' that started the Anthropocene? and what could speed humanity out of trouble? Here, we focus on the role of reinforcing feedback cycles, often comprised of diverse, unrelated elements (e.g. fire, grass, humans), in propelling abrupt and/or irreversible, revolutionary changes. We suggest that differential 'spread of the cycles' has been critical to the past human revolutions of fire use, agriculture, rise of complex states and industrialization. For each revolution, we review and map out proposed reinforcing feedback cycles, and describe how new systems built on previous ones, propelling us into the Anthropocene. We argue that to escape a bleak Anthropocene will require abruptly shifting from existing unsustainable 'vicious cycles', to alternative sustainable 'virtuous cycles' that can outspread and outpersist them. This will need to be complemented by a revolutionary cultural shift from maximizing growth to maximizing persistence (sustainability). To achieve that we suggest that non-human elements need to be brought back into the feedback cycles underlying human cultures and associated measures of progress. This article is part of the theme issue 'Evolution and sustainability: gathering the strands for an Anthropocene synthesis'.

Gene birth in a model of non-genic adaptation

BACKGROUND

Over evolutionary timescales, genomic loci can switch between functional and non-functional states through processes such as pseudogenization and de novo gene birth. Particularly, de novo gene birth is a widespread process, and many examples continue to be discovered across diverse evolutionary lineages. However, the general mechanisms that lead to functionalization are poorly understood, and estimated rates of de novo gene birth remain contentious. Here, we address this problem within a model that takes into account mutations and structural variation, allowing us to estimate the likelihood of emergence of new functions at non-functional loci.

RESULTS

Assuming biologically reasonable mutation rates and mutational effects, we find that functionalization of non-genic loci requires the realization of strict conditions. This is in line with the observation that most de novo genes are localized to the vicinity of established genes. Our model also provides an explanation for the empirical observation that emerging proto-genes are often lost despite showing signs of adaptation.

CONCLUSIONS

Our work elucidates the properties of non-genic loci that make them fertile for adaptation, and our results offer mechanistic insights into the process of de novo gene birth.

Agency, Goal-Directed Behavior, and Part-Whole Relationships in Biological Systems

In this essay we aim to present some considerations regarding a minimal but concrete notion of agency and goal-directed behavior that are useful for characterizing biological systems at different scales. These considerations are a particular perspective, bringing together concepts from dynamical systems, combinatorial problem-solving, and connectionist learning with an emphasis on the relationship between parts and wholes. This perspective affords some ways to think about agents that are concrete and quantifiable, and relevant to some important biological issues. Instead of advocating for a strict definition of minimally agential characteristics, we focus on how (even for a modest notion of agency) the agency of a system can be more than the sum of the agency of its parts. We quantify this in terms of the problem-solving competency of a system with respect to resolution of the frustrations between its parts. This requires goal-directed behavior in the sense of delayed gratification, i.e., taking dynamical trajectories that forego short-term gains (or sustain short-term stress or frustration) in favor of long-term gains. In order for this competency to belong to the system (rather than to its parts or given by its construction or design), it can involve distributed systemic knowledge that is acquired through experience, i.e., changes in the organization of the relationships among its parts (without presupposing a system-level reward function for such changes). This conception of agency helps us think about the ways in which cells, organisms, and perhaps other biological scales, can be agential (i.e., more agential than their parts) in a quantifiable sense, without denying that the behavior of the whole depends on the behaviors of the parts in their current organization.

Promises and limits of an agency perspective in evolutionary developmental biology

An agent-based perspective in the study of complex systems is well established in diverse disciplines, yet is only beginning to be applied to evolutionary developmental biology. In this essay, we begin by defining agency and associated terminology formally. We then explore the assumptions and predictions of an agency perspective, apply these to select processes and key concept areas relevant to practitioners of evolutionary developmental biology, and consider the potential epistemic roles that an agency perspective might play in evo devo. Throughout, we discuss evidence supportive of agential dynamics in biological systems relevant to evo devo and explore where agency thinking may enrich the explanatory reach of research efforts in evolutionary developmental biology.

Recovering from trampling: The role of dauciform roots to functional traits response of Carex filispica in alpine meadow

In the natural habitats of China, dauciform roots were only described in degraded alpine meadows. It was found that the presence of dauciform roots of Carex filispica was related to the advantage of multiple functional traits after trampling, reflecting short-term resistance. However, the long-term response of dauciform roots to trampling and the recovery of C. filispica with and without dauciform roots to trampling require further studies. In this study, different intensities of trampling (0, 50, 200 and 500 passages) were performed in an alpine meadow. One year later, individuals with and without dauciform roots were separated and their functional traits related to the economic spectrum of leaves and roots were measured as a reflection of recovery from trampling. The results showed that: (1) 1 year after trampling, the number of dauciform roots showed an increase with trampling intensity; (2) 1 year later, there was no significant difference in the response of economic spectrum traits among trampling intensities, or between plants with and without dauciform roots; (3) the number of dauciform roots was positively correlated with the leaf area of both individuals with and without dauciform roots, as well as with the biomass of those without dauciform roots; and (4) plants with more resource-conservative roots showed an advantage after trampling recovery: specifically, plants with dauciform roots showed such an advantage in the control group, which was lost with a leaning towards resource-acquisitive roots and an increased density of dauciform roots once trampled. In contrast, plants without dauciform roots showed a significant advantage of conservative roots only after trampling. In conclusion, the presence of dauciform roots is related to the plants' position on the root economic spectrum, thereby influencing the recovery of C. filispica from trampling. Carex filispica showed strong recovery from trampling after 1 year, which makes it an adequate choice for ecological restoration in alpine meadows. Dauciform roots showed a positive correlation with the aboveground growth of both plants with and without them, however, it requires a lab-controlled study to confirm whether there is indeed a positive effect on the growth of neighbouring plants.

On the roles of function and selection in evolving systems

Physical laws-such as the laws of motion, gravity, electromagnetism, and thermodynamics-codify the general behavior of varied macroscopic natural systems across space and time. We propose that an additional, hitherto-unarticulated law is required to characterize familiar macroscopic phenomena of our complex, evolving universe. An important feature of the classical laws of physics is the conceptual equivalence of specific characteristics shared by an extensive, seemingly diverse body of natural phenomena. Identifying potential equivalencies among disparate phenomena-for example, falling apples and orbiting moons or hot objects and compressed springs-has been instrumental in advancing the scientific understanding of our world through the articulation of laws of nature. A pervasive wonder of the natural world is the evolution of varied systems, including stars, minerals, atmospheres, and life. These evolving systems appear to be conceptually equivalent in that they display three notable attributes: 1) They form from numerous components that have the potential to adopt combinatorially vast numbers of different configurations; 2) processes exist that generate numerous different configurations; and 3) configurations are preferentially selected based on function. We identify universal concepts of selection-static persistence, dynamic persistence, and novelty generation-that underpin function and drive systems to evolve through the exchange of information between the environment and the system. Accordingly, we propose a "law of increasing functional information": The functional information of a system will increase (i.e., the system will evolve) if many different configurations of the system undergo selection for one or more functions.

Dauciform roots affect the position of the neighboring plants on the economic spectrum in degraded alpine meadows

Background and aims

Special root structures that can dissolve insoluble phosphorus locked in soil are supposed to contribute not only to the growing status of themselves but also to the neighbouring plants. However, whether dauciform roots have any effect on the neighbouring plants and how does it respond to meadow degradation had not been studied.

Methods

Alpine meadows with different degradation statuses were selected and the functional traits of Carex filispica and the co-occurring species Polygonum viviparum were measured to explore their response to degradation, as well as the response of Polygonum viviparum to the dauciform roots of Carex filispica.

Results

The results showed that 1) the number of dauciform roots decreased with the intensifying degradation, positively related to available phosphorus in the soil and negatively related to the aboveground phosphorus of Carex filispica. 2) Carex filispica and Polygonum viviparum are similar in specific leaf area and specific root area, yet different in the phosphorus content. The available phosphorus in the soil was negatively related to the aboveground phosphorus of Carex filispica and positively related to that of Polygonum viviparum. 3) When lightly degraded, the proportion of dauciform roots had positive effects on the aboveground resource-acquiring traits of Polygonum viviparum, which were no longer significant at heavy degradation. 4) Polygonum viviparum and Carex filispica without dauciform roots have similar performance: a decrease of belowground carbon with the increasing degradation, and a trend toward resource conservation with the increasing proportion of dauciform roots, which did not exist in Carex filispica with dauciform roots.

Conclusion

Our study found that dauciform roots had a beneficial effect on the resource acquisition of their neighbouring plants. However, due to the uncontrollable nature of natural habitats, whether this effect is stable and strong enough to be performed in ecological restoration requires further lab-controlled studies.

Biological Robots: Perspectives on an Emerging Interdisciplinary Field

Advances in science and engineering often reveal the limitations of classical approaches initially used to understand, predict, and control phenomena. With progress, conceptual categories must often be re-evaluated to better track recently discovered invariants across disciplines. It is essential to refine frameworks and resolve conflicting boundaries between disciplines such that they better facilitate, not restrict, experimental approaches and capabilities. In this essay, we address specific questions and critiques which have arisen in response to our research program, which lies at the intersection of developmental biology, computer science, and robotics. In the context of biological machines and robots, we explore changes across concepts and previously distinct fields that are driven by recent advances in materials, information, and life sciences. Herein, each author provides their own perspective on the subject, framed by their own disciplinary training. We argue that as with computation, certain aspects of developmental biology and robotics are not tied to specific materials; rather, the consilience of these fields can help to shed light on issues of multiscale control, self-assembly, and relationships between form and function. We hope new fields can emerge as boundaries arising from technological limitations are overcome, furthering practical applications from regenerative medicine to useful synthetic living machines.

From autopoiesis to self-optimization: Toward an enactive model of biological regulation

The theory of autopoiesis has been influential in many areas of theoretical biology, especially in the fields of artificial life and origins of life. However, it has not managed to productively connect with mainstream biology, partly for theoretical reasons, but arguably mainly because deriving specific working hypotheses has been challenging. The theory has recently undergone significant conceptual development in the enactive approach to life and mind. Hidden complexity in the original conception of autopoiesis has been explicated in the service of other operationalizable concepts related to self-individuation: precariousness, adaptivity, and agency. Here we advance these developments by highlighting the interplay of these concepts with considerations from thermodynamics: reversibility, irreversibility, and path-dependence. We interpret this interplay in terms of the self-optimization model, and present modeling results that illustrate how these minimal conditions enable a system to re-organize itself such that it tends toward coordinated constraint satisfaction at the system level. Although the model is still very abstract, these results point in a direction where the enactive approach could productively connect with cell biology.

Variations and patterns of C and N stoichiometry in the first five root branch orders across 218 woody plant species

Despite the vital role in carbon (C) sequestration and nutrient retention, variations and patterns in root C and nitrogen (N) stoichiometry of the first five root orders across woody plant species remains unclear. We compiled a dataset to explore variations and patterns of root C and N stoichiometry in the first five orders of 218 woody plant species. Across the five orders, root N concentrations were greater in deciduous, broadleaf, and arbuscular mycorrhizal species than in evergreen, coniferous species, and ectomycorrhizal association species, respectively. Contrasting trends were found for root C : N ratios. Most root branch orders showed clear latitudinal and altitudinal trends in root C and N stoichiometry. There were opposite patterns in N concentrations between latitude and altitude. Such variations were mainly driven by plant species, and climatic factors together. Our results indicate divergent C and N use strategies among plant types and convergence and divergence in the patterns of C and N stoichiometry between latitude and altitude across the first five root orders. These findings provide important data on the root economics spectrum and biogeochemical models to improve understanding and prediction of climate change effects on C and nutrient dynamics in terrestrial ecosystems.

Cellular signaling pathways as plastic, proto-cognitive systems: Implications for biomedicine

Many aspects of health and disease are modeled using the abstraction of a "pathway"-a set of protein or other subcellular activities with specified functional linkages between them. This metaphor is a paradigmatic case of a deterministic, mechanistic framework that focuses biomedical intervention strategies on altering the members of this network or the up-/down-regulation links between them-rewiring the molecular hardware. However, protein pathways and transcriptional networks exhibit interesting and unexpected capabilities such as trainability (memory) and information processing in a context-sensitive manner. Specifically, they may be amenable to manipulation via their history of stimuli (equivalent to experiences in behavioral science). If true, this would enable a new class of biomedical interventions that target aspects of the dynamic physiological "software" implemented by pathways and gene-regulatory networks. Here, we briefly review clinical and laboratory data that show how high-level cognitive inputs and mechanistic pathway modulation interact to determine outcomes in vivo. Further, we propose an expanded view of pathways from the perspective of basal cognition and argue that a broader understanding of pathways and how they process contextual information across scales will catalyze progress in many areas of physiology and neurobiology. We argue that this fuller understanding of the functionality and tractability of pathways must go beyond a focus on the mechanistic details of protein and drug structure to encompass their physiological history as well as their embedding within higher levels of organization in the organism, with numerous implications for data science addressing health and disease. Exploiting tools and concepts from behavioral and cognitive sciences to explore a proto-cognitive metaphor for the pathways underlying health and disease is more than a philosophical stance on biochemical processes; at stake is a new roadmap for overcoming the limitations of today's pharmacological strategies and for inferring future therapeutic interventions for a wide range of disease states.

Darwin's agential materials: evolutionary implications of multiscale competency in developmental biology

A critical aspect of evolution is the layer of developmental physiology that operates between the genotype and the anatomical phenotype. While much work has addressed the evolution of developmental mechanisms and the evolvability of specific genetic architectures with emergent complexity, one aspect has not been sufficiently explored: the implications of morphogenetic problem-solving competencies for the evolutionary process itself. The cells that evolution works with are not passive components: rather, they have numerous capabilities for behavior because they derive from ancestral unicellular organisms with rich repertoires. In multicellular organisms, these capabilities must be tamed, and can be exploited, by the evolutionary process. Specifically, biological structures have a multiscale competency architecture where cells, tissues, and organs exhibit regulative plasticity-the ability to adjust to perturbations such as external injury or internal modifications and still accomplish specific adaptive tasks across metabolic, transcriptional, physiological, and anatomical problem spaces. Here, I review examples illustrating how physiological circuits guiding cellular collective behavior impart computational properties to the agential material that serves as substrate for the evolutionary process. I then explore the ways in which the collective intelligence of cells during morphogenesis affect evolution, providing a new perspective on the evolutionary search process. This key feature of the physiological software of life helps explain the remarkable speed and robustness of biological evolution, and sheds new light on the relationship between genomes and functional anatomical phenotypes.

Evolution with recombination as Gibbs sampling

This work presents a population genetic model of evolution, which includes haploid selection, mutation, recombination, and drift. The mutation-selection equilibrium can be expressed exactly in closed form for arbitrary fitness functions without resorting to diffusion approximations. Tractability is achieved by generating new offspring using n-parent rather than 2-parent recombination. While this enforces linkage equilibrium among offspring, it allows analysis of the whole population under linkage disequilibrium. We derive a general and exact relationship between fitness fluctuations and response to selection. Our assumptions allow analytical calculation of the stationary distribution of the model for a variety of non-trivial fitness functions. These results allow us to speak to genetic architecture, i.e., what stationary distributions result from different fitness functions. This paper presents methods for exactly deriving stationary states for finite and infinite populations. This method can be applied to many fitness functions, and we give exact calculations for four of these. These results allow us to investigate metastability, tradeoffs between fitness functions, and even consider error-correcting codes.

Scaffolding individuality: coordination, cooperation, collaboration and community

Processes of evolutionary transition (ET), becoming part of a new reproducing collective while losing the capacity of independent reproduction, seem difficult to track without circularity, since their features-units of selection, individuality, inheritance at multiple levels (MLS1, MLS2)-are products of one process. We describe ET in a non-circular way, noting kinds of interactions among community members necessary for such major transitions that are not instances of those same interactions within community members. Reproducing 'systems' tend to hybridize with environmental components, employing eco-devo scaffolding interactions forming communities. Communities are developmentally scaffolded systems of diverse members engaged in heterogeneous interactions. They may become individuals in their own right with the potential to evolve an inheritance system at the emergent community level. We argue for the explanatory benefits of treating 'individuality' as a special case of 'collectivity'. We characterize an idealized sequence of collective processes-coordination, cooperation and collaboration (3Cs)-which scaffolds transitions to new forms of collective individuality: communities. Hominid evolution and learning draw attention to developmental interactions driving both dimensions of ET: new 'levels of individuality' and inherited 'information systems'. Here, we outline a theoretical perspective that we suggest applies across a wide range of cases and scenarios. This article is part of the theme issue 'Human socio-cultural evolution in light of evolutionary transitions'.

There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines

The applicability of computational models to the biological world is an active topic of debate. We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Such a view dissolves the contingent dichotomies driven by human cognitive biases (e.g., a tendency to oversimplify) and prior technological limitations in favor of a more continuous view, necessitated by the study of evolution, developmental biology, and intelligent machines. Form and function are tightly entwined in nature, and in some cases, in robotics as well. Thus, efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. This is challenging for many reasons, one of which is that living systems perform multiple functions in the same place at the same time. We refer to this as “polycomputing”—the ability of the same substrate to simultaneously compute different things, and make those computational results available to different observers. This ability is an important way in which living things are a kind of computer, but not the familiar, linear, deterministic kind; rather, living things are computers in the broad sense of their computational materials, as reported in the rapidly growing physical computing literature. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of mesoscale events, as it has already done at quantum and relativistic scales. To develop our understanding of how life performs polycomputing, and how it can be convinced to alter one or more of those functions, we can first create technologies that polycompute and learn how to alter their functions. Here, we review examples of biological and technological polycomputing, and develop the idea that the overloading of different functions on the same hardware is an important design principle that helps to understand and build both evolved and designed systems. Learning to hack existing polycomputing substrates, as well as to evolve and design new ones, will have massive impacts on regenerative medicine, robotics, and computer engineering.

What role do dauciform roots play? Responses of Carex filispica to trampling in alpine meadows based on functional traits

In China, dauciform roots were hardly studied and only reported in alpine meadows, where sedges showed a different tendency from other functional groups such as grasses and forbs with degradation. In addition, Carex species were proved to have shifting scaling relationships among LES (leaf economics spectrum) traits under disturbance. So, are these unique performances of sedges related to the presence of dauciform roots, and if so, how? An alpine meadow dominated by Carex filispica in Baima Snow Mountain was selected, and quantitative trampling was performed (0, 50, 200, and 500 passes). The cover and dauciform root properties of Carex filispica were measured, as well as the morphological, chemical traits and biomass of leaves and roots, their correlations and the differences between individuals with and without dauciform roots were analyzed. After the trampling, individuals with dauciform roots showed multiple resource-acquisitive traits: Larger, thicker leaves, more aboveground biomass, higher efficiency of nutrient utilization, and slenderer roots. Additionally, they had a tighter correlation among belowground biomass, morphological and chemical traits, as well as dauciform root properties and morphology of leaves, suggesting that their traits were more related than those without dauciform roots. The presence of dauciform roots in Carex filispica was related to advantages in multiple traits after trampling, which is consistent with and might be responsible for the unique performances of sedges.

Cells as the first data scientists

The concepts that we generally associate with the field of data science are strikingly descriptive of the way that life, in general, processes information about its environment. The 'information life cycle', which enumerates the stages of information treatment in data science endeavours, also captures the steps of data collection and handling in biological systems. Similarly, the 'data-information-knowledge ecosystem', developed to illuminate the role of informatics in translating raw data into knowledge, can be a framework for understanding how information is constantly being transferred between life and the environment. By placing the principles of data science in a broader biological context, we see the activities of data scientists as the latest development in life's ongoing journey to better understand and predict its environment. Finally, we propose that informatics frameworks can be used to understand the similarities and differences between abiotic complex evolving systems and life.

An integrative systems biology view of host-pathogen interactions: The regulation of immunity and homeostasis is concomitant, flexible, and smart

The systemic bio-organization of humans and other mammals is essentially "preprogrammed", and the basic interacting units, the cells, can be crudely mapped into discrete sets of developmental lineages and maturation states. Over several decades, however, and focusing on the immune system, we and others invoked evidence - now overwhelming - suggesting dynamic acquisition of cellular properties and functions, through tuning, re-networking, chromatin remodeling, and adaptive differentiation. The genetically encoded "algorithms" that govern the integration of signals and the computation of new states are not fully understood but are believed to be "smart", designed to enable the cells and the system to discriminate meaningful perturbations from each other and from "noise". Cellular sensory and response properties are shaped in part by recurring temporal patterns, or features, of the signaling environment. We compared this phenomenon to associative brain learning. We proposed that interactive cell learning is subject to selective pressures geared to performance, allowing the response of immune cells to injury or infection to be progressively coordinated with that of other cell types across tissues and organs. This in turn is comparable to supervised brain learning. Guided by feedback from both the tissue itself and the neural system, resident or recruited antigen-specific and innate immune cells can eradicate a pathogen while simultaneously sustaining functional homeostasis. As informative memories of immune responses are imprinted both systemically and within the targeted tissues, it is desirable to enhance tissue preparedness by incorporating attenuated-pathogen vaccines and informed choice of tissue-centered immunomodulators in vaccination schemes. Fortunately, much of the "training" that a living system requires to survive and function in the face of disturbances from outside or within is already incorporated into its design, so it does not need to deep-learn how to face a new challenge each time from scratch. Instead, the system learns from experience how to efficiently select a built-in strategy, or a combination of those, and can then use tuning to refine its organization and responses. Efforts to identify and therapeutically augment such strategies can take advantage of existing integrative modeling approaches. One recently explored strategy is boosting the flux of uninfected cells into and throughout an infected tissue to rinse and replace the infected cells.

A common mechanism drives the alignment between the micro- and macroevolution of primate molars

A central challenge for biology is to reveal how different levels of biological variation interact and shape diversity. However, recent experimental studies have indicated that prevailing models of evolution cannot readily explain the link between micro- and macroevolution at deep timescales. Here, we suggest that this paradox could be the result of a common mechanism driving a correlated pattern of evolution. We examine the proportionality between genetic variance and patterns of trait evolution in a system whose developmental processes are well understood to gain insight into how such alignment between morphological divergence and genetic variation might be maintained over macroevolutionary time. Primate molars present a model system by which to link developmental processes to evolutionary dynamics because of the biased pattern of variation that results from the developmental architecture regulating their formation. We consider how this biased variation is expressed at the population level, and how it manifests through evolution across primates. There is a strong correspondence between the macroevolutionary rates of primate molar divergence and their genetic variation. This suggests a model of evolution in which selection is closely aligned with the direction of genetic variance, phenotypic variance, and the underlying developmental architecture of anatomical traits.

Computational modelling of plasticity-led evolution

Plasticity-led evolution is a form of evolution where a change in the environment induces novel traits via phenotypic plasticity, after which the novel traits are genetically accommodated over generations under the novel environment. This mode of evolution is expected to resolve the problem of gradualism (i.e., evolution by the slow accumulation of mutations that induce phenotypic variation) implied by the Modern Evolutionary Synthesis, in the face of a large environmental change. While experimental works are essential for validating that plasticity-led evolution indeed happened, we need computational models to gain insight into its underlying mechanisms and make qualitative predictions. Such computational models should include the developmental process and gene-environment interactions in addition to genetics and natural selection. We point out that gene regulatory network models can incorporate all the above notions. In this review, we highlight results from computational modelling of gene regulatory networks that consolidate the criteria of plasticity-led evolution. Since gene regulatory networks are mathematically equivalent to artificial recurrent neural networks, we also discuss their analogies and discrepancies, which may help further understand the mechanisms underlying plasticity-led evolution.

Cognition as Morphological/Morphogenetic Embodied Computation In Vivo

Cognition, historically considered uniquely human capacity, has been recently found to be the ability of all living organisms, from single cells and up. This study approaches cognition from an info-computational stance, in which structures in nature are seen as information, and processes (information dynamics) are seen as computation, from the perspective of a cognizing agent. Cognition is understood as a network of concurrent morphological/morphogenetic computations unfolding as a result of self-assembly, self-organization, and autopoiesis of physical, chemical, and biological agents. The present-day human-centric view of cognition still prevailing in major encyclopedias has a variety of open problems. This article considers recent research about morphological computation, morphogenesis, agency, basal cognition, extended evolutionary synthesis, free energy principle, cognition as Bayesian learning, active inference, and related topics, offering new theoretical and practical perspectives on problems inherent to the old computationalist cognitive models which were based on abstract symbol processing, and unaware of actual physical constraints and affordances of the embodiment of cognizing agents. A better understanding of cognition is centrally important for future artificial intelligence, robotics, medicine, and related fields.

Phenotype-first hypotheses, spandrels and early metazoan evolution

Against the neo-Darwinian assumption that genetic factors are the principal source of variation upon which natural selection operates, a phenotype-first hypothesis strikes us as revolutionary because development would seem to constitute an independent source of variability. Richard Watson and his co-authors have argued that developmental memory constitutes one such variety of phenotypic variability. While this version of the phenotype-first hypothesis is especially well-suited for the late metazoan context, where animals have a sufficient history of selection from which to draw, appeals to developmental memory seem less plausible in the evolutionary context of the early metazoans. I provide an interpretation of Stuart Newman's account of deep metazoan phylogenesis that suggests that spandrels are, in addition to developmental memory, an important reservoir of phenotypic variability. I conclude by arguing that Gerd Müller's "side-effect hypothesis" is an illuminating generalization of the proposed non-Watsonian version of the phenotype-first hypothesis.

Endless forms most beautiful 2.0: teleonomy and the bioengineering of chimaeric and synthetic organisms

The rich variety of biological forms and behaviours results from one evolutionary history on Earth, via frozen accidents and selection in specific environments. This ubiquitous baggage in natural, familiar model species obscures the plasticity and swarm intelligence of cellular collectives. Significant gaps exist in our understanding of the origin of anatomical novelty, of the relationship between genome and form, and of strategies for control of large-scale structure and function in regenerative medicine and bioengineering. Analysis of living forms that have never existed before is necessary to reveal deep design principles of life as it can be. We briefly review existing examples of chimaeras, cyborgs, hybrots and other beings along the spectrum containing evolved and designed systems. To drive experimental progress in multicellular synthetic morphology, we propose teleonomic (goal-seeking, problem-solving) behaviour in diverse problem spaces as a powerful invariant across possible beings regardless of composition or origin. Cybernetic perspectives on chimaeric morphogenesis erase artificial distinctions established by past limitations of technology and imagination. We suggest that a multi-scale competency architecture facilitates evolution of robust problem-solving, living machines. Creation and analysis of novel living forms will be an essential testbed for the emerging field of diverse intelligence, with numerous implications across regenerative medicine, robotics and ethics.

A reaction norm framework for the evolution of learning: how cumulative experience shapes phenotypic plasticity

Learning is a familiar process to most people, but it currently lacks a fully developed theoretical position within evolutionary biology. Learning (memory and forgetting) involves adjustments in behaviour in response to cumulative sequences of prior experiences or exposures to environmental cues. We therefore suggest that all forms of learning (and some similar biological phenomena in development, aging, acquired immunity and acclimation) can usefully be viewed as special cases of phenotypic plasticity, and formally modelled by expanding the concept of reaction norms to include additional environmental dimensions quantifying sequences of cumulative experience (learning) and the time delays between events (forgetting). Memory therefore represents just one of a number of different internal neurological, physiological, hormonal and anatomical ‘states’ that mediate the carry‐over effects of cumulative environmental experiences on phenotypes across different time periods. The mathematical and graphical conceptualisation of learning as plasticity within a reaction norm framework can easily accommodate a range of different ecological scenarios, closely linking statistical estimates with biological processes. Learning and non‐learning plasticity interact whenever cumulative prior experience causes a modification in the reaction norm (a) elevation [mean phenotype], (b) slope [responsiveness], (c) environmental estimate error [informational memory] and/or (d) phenotypic precision [skill acquisition]. Innovation and learning new contingencies in novel (laboratory) environments can also be accommodated within this approach. A common reaction norm approach should thus encourage productive cross‐fertilisation of ideas between traditional studies of learning and phenotypic plasticity. As an example, we model the evolution of plasticity with and without learning under different levels of environmental estimation error to show how learning works as a specific adaptation promoting phenotypic plasticity in temporally autocorrelated environments. Our reaction norm framework for learning and analogous biological processes provides a conceptual and mathematical structure aimed at usefully stimulating future theoretical and empirical investigations into the evolution of plasticity across a wider range of ecological contexts, while providing new interdisciplinary connections regarding learning mechanisms.

Biology, Buddhism, and AI: Care as the Driver of Intelligence

Intelligence is a central feature of human beings’ primary and interpersonal experience. Understanding how intelligence originated and scaled during evolution is a key challenge for modern biology. Some of the most important approaches to understanding intelligence are the ongoing efforts to build new intelligences in computer science (AI) and bioengineering. However, progress has been stymied by a lack of multidisciplinary consensus on what is central about intelligence regardless of the details of its material composition or origin (evolved vs. engineered). We show that Buddhist concepts offer a unique perspective and facilitate a consilience of biology, cognitive science, and computer science toward understanding intelligence in truly diverse embodiments. In coming decades, chimeric and bioengineering technologies will produce a wide variety of novel beings that look nothing like familiar natural life forms; how shall we gauge their moral responsibility and our own moral obligations toward them, without the familiar touchstones of standard evolved forms as comparison? Such decisions cannot be based on what the agent is made of or how much design vs. natural evolution was involved in their origin. We propose that the scope of our potential relationship with, and so also our moral duty toward, any being can be considered in the light of Care—a robust, practical, and dynamic lynchpin that formalizes the concepts of goal-directedness, stress, and the scaling of intelligence; it provides a rubric that, unlike other current concepts, is likely to not only survive but thrive in the coming advances of AI and bioengineering. We review relevant concepts in basal cognition and Buddhist thought, focusing on the size of an agent’s goal space (its cognitive light cone) as an invariant that tightly links intelligence and compassion. Implications range across interpersonal psychology, regenerative medicine, and machine learning. The Bodhisattva’s vow (“for the sake of all sentient life, I shall achieve awakening”) is a practical design principle for advancing intelligence in our novel creations and in ourselves.

Metacognition as a Consequence of Competing Evolutionary Time Scales

Evolution is full of coevolving systems characterized by complex spatio-temporal interactions that lead to intertwined processes of adaptation. Yet, how adaptation across multiple levels of temporal scales and biological complexity is achieved remains unclear. Here, we formalize how evolutionary multi-scale processing underlying adaptation constitutes a form of metacognition flowing from definitions of metaprocessing in machine learning. We show (1) how the evolution of metacognitive systems can be expected when fitness landscapes vary on multiple time scales, and (2) how multiple time scales emerge during coevolutionary processes of sufficiently complex interactions. After defining a metaprocessor as a regulator with local memory, we prove that metacognition is more energetically efficient than purely object-level cognition when selection operates at multiple timescales in evolution. Furthermore, we show that existing modeling approaches to coadaptation and coevolution—here active inference networks, predator–prey interactions, coupled genetic algorithms, and generative adversarial networks—lead to multiple emergent timescales underlying forms of metacognition. Lastly, we show how coarse-grained structures emerge naturally in any resource-limited system, providing sufficient evidence for metacognitive systems to be a prevalent and vital component of (co-)evolution. Therefore, multi-scale processing is a necessary requirement for many evolutionary scenarios, leading to de facto metacognitive evolutionary outcomes.

Design for an Individual: Connectionist Approaches to the Evolutionary Transitions in Individuality

The truly surprising thing about evolution is not how it makes individuals better adapted to their environment, but how it makes individuals. All individuals are made of parts that used to be individuals themselves, e.g., multicellular organisms from unicellular organisms. In such evolutionary transitions in individuality, the organised structure of relationships between component parts causes them to work together, creating a new organismic entity and a new evolutionary unit on which selection can act. However, the principles of these transitions remain poorly understood. In particular, the process of transition must be explained by “bottom-up” selection, i.e., on the existing lower-level evolutionary units, without presupposing the higher-level evolutionary unit we are trying to explain. In this hypothesis and theory manuscript we address the conditions for evolutionary transitions in individuality by exploiting adaptive principles already known in learning systems. Connectionist learning models, well-studied in neural networks, demonstrate how networks of organised functional relationships between components, sufficient to exhibit information integration and collective action, can be produced via fully-distributed and unsupervised learning principles, i.e., without centralised control or an external teacher. Evolutionary connectionism translates these distributed learning principles into the domain of natural selection, and suggests how relationships among evolutionary units could become adaptively organised by selection from below without presupposing genetic relatedness or selection on collectives. In this manuscript, we address how connectionist models with a particular interaction structure might explain transitions in individuality. We explore the relationship between the interaction structures necessary for (a) evolutionary individuality (where the evolution of the whole is a non-decomposable function of the evolution of the parts), (b) organismic individuality (where the development and behaviour of the whole is a non-decomposable function of the behaviour of component parts) and (c) non-linearly separable functions, familiar in connectionist models (where the output of the network is a non-decomposable function of the inputs). Specifically, we hypothesise that the conditions necessary to evolve a new level of individuality are described by the conditions necessary to learn non-decomposable functions of this type (or deep model induction) familiar in connectionist models of cognition and learning.

Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds

Synthetic biology and bioengineering provide the opportunity to create novel embodied cognitive systems (otherwise known as minds) in a very wide variety of chimeric architectures combining evolved and designed material and software. These advances are disrupting familiar concepts in the philosophy of mind, and require new ways of thinking about and comparing truly diverse intelligences, whose composition and origin are not like any of the available natural model species. In this Perspective, I introduce TAME-Technological Approach to Mind Everywhere-a framework for understanding and manipulating cognition in unconventional substrates. TAME formalizes a non-binary (continuous), empirically-based approach to strongly embodied agency. TAME provides a natural way to think about animal sentience as an instance of collective intelligence of cell groups, arising from dynamics that manifest in similar ways in numerous other substrates. When applied to regenerating/developmental systems, TAME suggests a perspective on morphogenesis as an example of basal cognition. The deep symmetry between problem-solving in anatomical, physiological, transcriptional, and 3D (traditional behavioral) spaces drives specific hypotheses by which cognitive capacities can increase during evolution. An important medium exploited by evolution for joining active subunits into greater agents is developmental bioelectricity, implemented by pre-neural use of ion channels and gap junctions to scale up cell-level feedback loops into anatomical homeostasis. This architecture of multi-scale competency of biological systems has important implications for plasticity of bodies and minds, greatly potentiating evolvability. Considering classical and recent data from the perspectives of computational science, evolutionary biology, and basal cognition, reveals a rich research program with many implications for cognitive science, evolutionary biology, regenerative medicine, and artificial intelligence.

Cancer progression as a learning process

Drug resistance and metastasis—the major complications in cancer—both entail adaptation of cancer cells to stress, whether a drug or a lethal new environment. Intriguingly, these adaptive processes share similar features that cannot be explained by a pure Darwinian scheme, including dormancy, increased heterogeneity, and stress-induced plasticity. Here, we propose that learning theory offers a framework to explain these features and may shed light on these two intricate processes. In this framework, learning is performed at the single-cell level, by stress-driven exploratory trial-and-error. Such a process is not contingent on pre-existing pathways but on a random search for a state that diminishes the stress. We review underlying mechanisms that may support this search, and show by using a learning model that such exploratory learning is feasible in a high-dimensional system as the cell. At the population level, we view the tissue as a network of exploring agents that communicate, restraining cancer formation in health. In this view, disease results from the breakdown of homeostasis between cellular exploratory drive and tissue homeostasis.

Bayes and Darwin: How replicator populations implement Bayesian computations

Bayesian learning theory and evolutionary theory both formalize adaptive competition dynamics in possibly high-dimensional, varying, and noisy environments. What do they have in common and how do they differ? In this paper, we discuss structural and dynamical analogies and their limits, both at a computational and an algorithmic-mechanical level. We point out mathematical equivalences between their basic dynamical equations, generalizing the isomorphism between Bayesian update and replicator dynamics. We discuss how these mechanisms provide analogous answers to the challenge of adapting to stochastically changing environments at multiple timescales. We elucidate an algorithmic equivalence between a sampling approximation, particle filters, and the Wright-Fisher model of population genetics. These equivalences suggest that the frequency distribution of types in replicator populations optimally encodes regularities of a stochastic environment to predict future environments, without invoking the known mechanisms of multilevel selection and evolvability. A unified view of the theories of learning and evolution comes in sight.

Toward a theory of evolution as multilevel learning

Modern evolutionary theory gives a detailed quantitative description of microevolutionary processes that occur within evolving populations of organisms, but evolutionary transitions and emergence of multiple levels of complexity remain poorly understood. Here, we establish the correspondence among the key features of evolution, learning dynamics, and renormalizability of physical theories to outline a theory of evolution that strives to incorporate all evolutionary processes within a unified mathematical framework of the theory of learning. According to this theory, for example, replication of genetic material and natural selection readily emerge from the learning dynamics, and in sufficiently complex systems, the same learning phenomena occur on multiple levels or on different scales, similar to the case of renormalizable physical theories.

We apply the theory of learning to physically renormalizable systems in an attempt to outline a theory of biological evolution, including the origin of life, as multilevel learning. We formulate seven fundamental principles of evolution that appear to be necessary and sufficient to render a universe observable and show that they entail the major features of biological evolution, including replication and natural selection. It is shown that these cornerstone phenomena of biology emerge from the fundamental features of learning dynamics such as the existence of a loss function, which is minimized during learning. We then sketch the theory of evolution using the mathematical framework of neural networks, which provides for detailed analysis of evolutionary phenomena. To demonstrate the potential of the proposed theoretical framework, we derive a generalized version of the Central Dogma of molecular biology by analyzing the flow of information during learning (back propagation) and predicting (forward propagation) the environment by evolving organisms. The more complex evolutionary phenomena, such as major transitions in evolution (in particular, the origin of life), have to be analyzed in the thermodynamic limit, which is described in detail in the paper by Vanchurin et al. [V. Vanchurin, Y. I. Wolf, E. V. Koonin, M. I. Katsnelson, Proc. Natl. Acad. Sci. U.S.A. 119, 10.1073/pnas.2120042119 (2022)].

Selection principles for Gaia

The Gaia hypothesis considers the life-environment coupled system as a single entity that acts to regulate and maintain habitable conditions on Earth. In this paper we discuss three mechanisms which could potentially lead to Gaia: Selection by Survival, Sequential Selection and Entropic Hierarchy. We use the Tangled Nature Model of co-evolution as a common framework for investigating all three, using an extended version of the standard model to elaborate on Gaia as an example of an entropic hierarchy. This idea, which combines sequential selection together with a reservoir of diversity that acts as a 'memory', implies a tendency towards growth and increasing resilience of the Gaian system over time. We then discuss how Gaian memory could be realised in practice via the microbial seed bank, climate refugia and lateral gene transfer and conclude by discussing testable implications of an entropic hierarchy for the study of Earth history and the search for life in the universe. This paper adds to the existing taxonomy of Gaia hypotheses to suggest an "Entropic Gaia" where we argue that increasing biomass, complexity and enhanced habitability over time is a statistically likely feature of a co-evolving system.

A simple regulatory architecture allows learning the statistical structure of a changing environment

Bacteria live in environments that are continuously fluctuating and changing. Exploiting any predictability of such fluctuations can lead to an increased fitness. On longer timescales, bacteria can 'learn' the structure of these fluctuations through evolution. However, on shorter timescales, inferring the statistics of the environment and acting upon this information would need to be accomplished by physiological mechanisms. Here, we use a model of metabolism to show that a simple generalization of a common regulatory motif (end-product inhibition) is sufficient both for learning continuous-valued features of the statistical structure of the environment and for translating this information into predictive behavior; moreover, it accomplishes these tasks near-optimally. We discuss plausible genetic circuits that could instantiate the mechanism we describe, including one similar to the architecture of two-component signaling, and argue that the key ingredients required for such predictive behavior are readily accessible to bacteria.

Minimal physicalism as a scale-free substrate for cognition and consciousness

Theories of consciousness and cognition that assume a neural substrate automatically regard phylogenetically basal, nonneural systems as nonconscious and noncognitive. Here, we advance a scale-free characterization of consciousness and cognition that regards basal systems, including synthetic constructs, as not only informative about the structure and function of experience in more complex systems but also as offering distinct advantages for experimental manipulation. Our "minimal physicalist" approach makes no assumptions beyond those of quantum information theory, and hence is applicable from the molecular scale upwards. We show that standard concepts including integrated information, state broadcasting via small-world networks, and hierarchical Bayesian inference emerge naturally in this setting, and that common phenomena including stigmergic memory, perceptual coarse-graining, and attention switching follow directly from the thermodynamic requirements of classical computation. We show that the self-representation that lies at the heart of human autonoetic awareness can be traced as far back as, and serves the same basic functions as, the stress response in bacteria and other basal systems.

Novelty and imitation within the brain: a Darwinian neurodynamic approach to combinatorial problems

Efficient search in vast combinatorial spaces, such as those of possible action sequences, linguistic structures, or causal explanations, is an essential component of intelligence. Is there any computational domain that is flexible enough to provide solutions to such diverse problems and can be robustly implemented over neural substrates? Based on previous accounts, we propose that a Darwinian process, operating over sequential cycles of imperfect copying and selection of neural informational patterns, is a promising candidate. Here we implement imperfect information copying through one reservoir computing unit teaching another. Teacher and learner roles are assigned dynamically based on evaluation of the readout signal. We demonstrate that the emerging Darwinian population of readout activity patterns is capable of maintaining and continually improving upon existing solutions over rugged combinatorial reward landscapes. We also demonstrate the existence of a sharp error threshold, a neural noise level beyond which information accumulated by an evolutionary process cannot be maintained. We introduce a novel analysis method, neural phylogenies, that displays the unfolding of the neural-evolutionary process.

Adaptive and nonadaptive plasticity in changing environments: Implications for sexual species with different life history strategies

Populations adapt to novel environmental conditions by genetic changes or phenotypic plasticity. Plastic responses are generally faster and can buffer fitness losses under variable conditions. Plasticity is typically modeled as random noise and linear reaction norms that assume simple one-to-one genotype-phenotype maps and no limits to the phenotypic response. Most studies on plasticity have focused on its effect on population viability. However, it is not clear, whether the advantage of plasticity depends solely on environmental fluctuations or also on the genetic and demographic properties (life histories) of populations. Here we present an individual-based model and study the relative importance of adaptive and nonadaptive plasticity for populations of sexual species with different life histories experiencing directional stochastic climate change. Environmental fluctuations were simulated using differentially autocorrelated climatic stochasticity or noise color, and scenarios of directional climate change. Nonadaptive plasticity was simulated as a random environmental effect on trait development, while adaptive plasticity as a linear, saturating, or sinusoidal reaction norm. The last two imposed limits to the plastic response and emphasized flexible interactions of the genotype with the environment. Interestingly, this assumption led to (a) smaller phenotypic than genotypic variance in the population (many-to-one genotype-phenotype map) and the coexistence of polymorphisms, and (b) the maintenance of higher genetic variation-compared to linear reaction norms and genetic determinism-even when the population was exposed to a constant environment for several generations. Limits to plasticity led to genetic accommodation, when costs were negligible, and to the appearance of cryptic variation when limits were exceeded. We found that adaptive plasticity promoted population persistence under red environmental noise and was particularly important for life histories with low fecundity. Populations producing more offspring could cope with environmental fluctuations solely by genetic changes or random plasticity, unless environmental change was too fast.

The emergence of cooperation by evolutionary generalization

In principle, any cooperative behaviour can be evolutionarily stable as long as it is incentivized by a reward from the beneficiary, a mechanism that has been called reciprocal cooperation. However, what makes this mechanism so powerful also has an evolutionary downside. Reciprocal cooperation faces a chicken-and-egg problem of the same kind as communication: it requires two functions to evolve at the same time-cooperation and response to cooperation. As a result, it can only emerge if one side first evolves for another reason, and is then recycled into a reciprocal function. Developing an evolutionary model in which we make use of machine learning techniques, we show that this occurs if the fact to cooperate and reward others' cooperation become general abilities that extend beyond the set of contexts for which they have initially been selected. Drawing on an evolutionary analogy with the concept of generalization, we identify the conditions necessary for this to happen. This allows us to understand the peculiar distribution of reciprocal cooperation in the wild, virtually absent in most species-or limited to situations where individuals have partially overlapping interests, but pervasive in the human species.

Minimal Organizational Requirements for the Ascription of Animal Personality to Social Groups

Recently, psychological phenomena have been expanded to new domains, crisscrossing boundaries of organizational levels, with the emergence of areas such as social personality and ecosystem learning. In this contribution, we analyze the ascription of an individual-based concept (personality) to the social level. Although justified boundary crossings can boost new approaches and applications, the indiscriminate misuse of concepts refrains the growth of scientific areas. The concept of social personality is based mainly on the detection of repeated group differences across a population, in a direct transposition of personality concepts from the individual to the social level. We show that this direct transposition is problematic for avowing the nonsensical ascription of personality even to simple electronic devices. To go beyond a metaphoric use of social personality, we apply the organizational approach to a review of social insect communication networks. Our conceptual analysis shows that socially self-organized systems, such as isolated ant trails and bee's recruitment groups, are too simple to have social personality. The situation is more nuanced when measuring the collective choice between nest sites or foraging patches: some species show positive and negative feedbacks between two or more self-organized social structures so that these co-dependent structures are inter-related by second-order, social information systems, complying with a formal requirement for having social personality: the social closure of constraints. Other requirements include the decoupling between individual and social dynamics, and the self-regulation of collective decision processes. Social personality results to be sometimes a metaphorical transposition of a psychological concept to a social phenomenon. The application of this organizational approach to cases of learning ecosystems, or evolutionary learning, could help to ground theoretically the ascription of psychological properties to levels of analysis beyond the individual, up to meta-populations or ecological communities.

The role of non-genetic information in evolutionary frameworks

The evolution of organisms has been a subject of paramount debate for hundreds of years and though major advances in the field have been made, the precise mechanisms underlying evolutionary processes remain fragmentary. Strikingly, the majority of the core principles accepted across the many fields of biology only consider genetic information as the major - if not exclusive - biological information carrier and thus consider it as the main evolutionary avatar. However, the real picture appears far more complex than originally anticipated, as compelling data suggest that nongenetic information steps up when highly dynamic evolutionary frameworks are explored. In light of recent evidence, we discuss herein the dynamic nature and complexity of nongenetic information carriers, and their emerging relevance in the evolutionary process. We argue that it is possible to overcome the historical arguments which dismissed these carriers, and instead consider that they are indeed core to life itself as they support a sustainable, continuous source of rapid adaptation in ever-changing environments. Ultimately, we will address the intricacies of genetic and non-genetic networks underlying evolutionary models to build a framework where both core biological information concepts are considered non-negligible and equally fundamental.

Awareness and integrated information theory identify plant meristems as sites of conscious activity

Lacking an anatomical brain/nervous system, it is assumed plants are not conscious. The biological function of consciousness is an input to behaviour; it is adaptive (subject to selection) and based on information. Complex language makes human consciousness unique. Consciousness is equated to awareness. All organisms are aware of their surroundings, modifying their behaviour to improve survival. Awareness requires assessment too. The mechanisms of animal assessment are neural while molecular and electrical in plants. Awareness of plants being also consciousness may resolve controversy. The integrated information theory (IIT), a leading theory of consciousness, is also blind to brains, nerves and synapses. The integrated information theory indicates plant awareness involves information of two kinds: (1) communicative, extrinsic information as a result of the perception of environmental changes and (2) integrated intrinsic information located in the shoot and root meristems and possibly cambium. The combination of information constructs an information nexus in the meristems leading to assessment and behaviour. The interpretation of integrated information in meristems probably involves the complex networks built around [Ca2+]i that also enable plant learning, memory and intelligent activities. A mature plant contains a large number of conjoined, conscious or aware, meristems possibly unique in the living kingdom.

Human biases limit cumulative innovation

Is technological advancement constrained by biases in human cognition? People in all societies build on discoveries inherited from previous generations, leading to cumulative innovation. However, biases in human learning and memory may influence the process of knowledge transmission, potentially limiting this process. Here, we show that cumulative innovation in a continuous optimization problem is systematically constrained by human biases. In a large (n = 1250) behavioural study using a transmission chain design, participants searched for virtual technologies in one of four environments after inheriting a solution from previous generations. Participants converged on worse solutions in environments misaligned with their biases. These results substantiate a mathematical model of cumulative innovation in Bayesian agents, highlighting formal relationships between cultural evolution and distributed stochastic optimization. Our findings provide experimental evidence that human biases can limit the advancement of knowledge in a controlled laboratory setting, reinforcing concerns about bias in creative, scientific and educational contexts.

Evolution of the locomotor skeleton in Anolis lizards reflects the interplay between ecological opportunity and phylogenetic inertia

Anolis lizards originated in continental America but have colonized the Greater Antillean islands and recolonized the mainland, resulting in three major groups (Primary and Secondary Mainland and Greater Antillean). The adaptive radiation in the Greater Antilles has famously resulted in the repeated evolution of ecomorphs. Yet, it remains poorly understood to what extent this island radiation differs from diversification on the mainland. Here, we demonstrate that the evolutionary modularity between girdles and limbs is fundamentally different in the Greater Antillean and Primary Mainland Anolis. This is consistent with ecological opportunities on islands driving the adaptive radiation along distinct evolutionary trajectories. However, Greater Antillean Anolis share evolutionary modularity with the group that recolonized the mainland, demonstrating a persistent phylogenetic inertia. A comparison of these two groups support an increased morphological diversity and faster and more variable evolutionary rates on islands. These macroevolutionary trends of the locomotor skeleton in Anolis illustrate that ecological opportunities on islands can have lasting effects on morphological diversification.

Evolutionary transitions in learning and cognition

We define a cognitive system as a system that can learn, and adopt an evolutionary-transition-oriented framework for analysing different types of neural cognition. This enables us to classify types of cognition and point to the continuities and discontinuities among them. The framework we use for studying evolutionary transitions in learning capacities focuses on qualitative changes in the integration, storage and use of neurally processed information. Although there are always grey areas around evolutionary transitions, we recognize five major neural transitions, the first two of which involve animals at the base of the phylogenetic tree: (i) the evolutionary transition from learning in non-neural animals to learning in the first neural animals; (ii) the transition to animals showing limited, elemental associative learning, entailing neural centralization and primary brain differentiation; (iii) the transition to animals capable of unlimited associative learning, which, on our account, constitutes sentience and entails hierarchical brain organization and dedicated memory and value networks; (iv) the transition to imaginative animals that can plan and learn through selection among virtual events; and (v) the transition to human symbol-based cognition and cultural learning. The focus on learning provides a unifying framework for experimental and theoretical studies of cognition in the living world. This article is part of the theme issue 'Basal cognition: multicellularity, neurons and the cognitive lens'.