Bayesian Models of Development

The diverse roles of insulin signaling in insect behavior

In insects and other animals, nutrition-mediated behaviors are modulated by communication between the brain and peripheral systems, a process that relies heavily on the insulin/insulin-like growth factor signaling pathway (IIS). Previous studies have focused on the mechanistic and physiological functions of insulin-like peptides (ILPs) in critical developmental and adult milestones like pupation or vitellogenesis. Less work has detailed the mechanisms connecting ILPs to adult nutrient-mediated behaviors related to survival and reproductive success. Here we briefly review the range of behaviors linked to IIS in insects, from conserved regulation of feeding behavior to evolutionarily derived polyphenisms. Where possible, we incorporate information from Drosophila melanogaster and other model species to describe molecular and neural mechanisms that connect nutritional status to behavioral expression via IIS. We identify knowledge gaps which include the diverse functional roles of peripheral ILPs, how ILPs modulate neural function and behavior across the lifespan, and the lack of detailed mechanistic research in a broad range of taxa. Addressing these gaps would enable a better understanding of the evolution of this conserved and widely deployed tool kit pathway.

The algorithmic origins of counting

The study of how children learn numbers has yielded one of the most productive research programs in cognitive development, spanning empirical and computational methods, as well as nativist and empiricist philosophies. This paper provides a tutorial on how to think computationally about learning models in a domain like number, where learners take finite data and go far beyond what they directly observe or perceive. To illustrate, this paper then outlines a model which acquires a counting procedure using observations of sets and words, extending the proposal of Piantadosi et al. (2012). This new version of the model responds to several critiques of the original work and outlines an approach which is likely appropriate for acquiring further aspects of mathematics.

Time-specific convergence and divergence in individual differences in behavior: Theory, protocols and analyzes

Over the years, theoreticians and empiricists working in a wide range of disciplines, including physiology, ethology, psychology, and behavioral ecology, have suggested a variety of reasons why individual differences in behavior might change over time, such that different individuals become more similar (convergence) or less similar (divergence) to one another. Virtually none of these investigators have suggested that convergence or divergence will continue forever, instead proposing that these patterns will be restricted to particular periods over the course of a longer study. However, to date, few empiricists have documented time-specific convergence or divergence, in part because the experimental designs and statistical methods suitable for describing these patterns are not widely known. Here, we begin by reviewing an array of influential hypotheses that predict convergence or divergence in individual differences over timescales ranging from minutes to years, and that suggest how and why such patterns are likely to change over time (e.g., divergence followed by maintenance). Then, we describe experimental designs and statistical methods that can be used to determine if (and when) individual differences converged, diverged, or were maintained at the same level at specific periods during a longitudinal study. Finally, we describe why the concepts described herein help explain the discrepancy between what theoreticians and empiricists mean when they describe the "emergence" of individual differences or personality, how they might be used to study situations in which convergence and divergence patterns alternate over time, and how they might be used to study time-specific changes in other attributes of behavior, including individual differences in intraindividual variability (predictability), or genotypic differences in behavior.

Developmental changes in exploration resemble stochastic optimization

Human development is often described as a 'cooling off' process, analogous to stochastic optimization algorithms that implement a gradual reduction in randomness over time. Yet there is ambiguity in how to interpret this analogy, due to a lack of concrete empirical comparisons. Using data from n = 281 participants ages 5 to 55, we show that cooling off does not only apply to the single dimension of randomness. Rather, human development resembles an optimization process of multiple learning parameters, for example, reward generalization, uncertainty-directed exploration and random temperature. Rapid changes in parameters occur during childhood, but these changes plateau and converge to efficient values in adulthood. We show that while the developmental trajectory of human parameters is strikingly similar to several stochastic optimization algorithms, there are important differences in convergence. None of the optimization algorithms tested were able to discover reliably better regions of the strategy space than adult participants on this task.

Developing the genotype-to-phenotype relationship in evolutionary theory: A primer of developmental features

For decades, there have been repeated calls for more integration across evolutionary and developmental biology. However, critiques in the literature and recent funding initiatives suggest this integration remains incomplete. We suggest one way forward is to consider how we elaborate the most basic concept of development, the relationship between genotype and phenotype, in traditional models of evolutionary processes. For some questions, when more complex features of development are accounted for, predictions of evolutionary processes shift. We present a primer on concepts of development to clarify confusion in the literature and fuel new questions and approaches. The basic features of development involve expanding a base model of genotype-to-phenotype to include the genome, space, and time. A layer of complexity is added by incorporating developmental systems, including signal-response systems and networks of interactions. The developmental emergence of function, which captures developmental feedbacks and phenotypic performance, offers further model elaborations that explicitly link fitness with developmental systems. Finally, developmental features such as plasticity and developmental niche construction conceptualize the link between a developing phenotype and the external environment, allowing for a fuller inclusion of ecology in evolutionary models. Incorporating aspects of developmental complexity into evolutionary models also accommodates a more pluralistic focus on the causal importance of developmental systems, individual organisms, or agents in generating evolutionary patterns. Thus, by laying out existing concepts of development, and considering how they are used across different fields, we can gain clarity in existing debates around the extended evolutionary synthesis and pursue new directions in evolutionary developmental biology. Finally, we consider how nesting developmental features in traditional models of evolution can highlight areas of evolutionary biology that need more theoretical attention.

A data-driven framework to model the organism-environment system

Organisms modify their development and function in response to the environment. At the same time, the environment is modified by the activities of the organism. Despite the ubiquity of such dynamical interactions in nature, it remains challenging to develop models that accurately represent them, and that can be fitted using data. These features are desirable when modeling phenomena such as phenotypic plasticity, to generate quantitative predictions of how the system will respond to environmental signals of different magnitude or at different times, for example, during ontogeny. Here, we explain a modeling framework that represents the organism and environment as a single coupled dynamical system in terms of inputs and outputs. Inputs are external signals, and outputs are measurements of the system in time. The framework uses time-series data of inputs and outputs to fit a nonlinear black-box model that allows to predict how the system will respond to novel input signals. The framework has three key properties: it captures the dynamical nature of the organism-environment system, it can be fitted with data, and it can be applied without detailed knowledge of the system. We study phenotypic plasticity using in silico experiments and demonstrate that the framework predicts the response to novel environmental signals. The framework allows us to model plasticity as a dynamical property that changes in time during ontogeny, reflecting the well-known fact that organisms are more or less plastic at different developmental stages.

Bet-hedging via dispersal aids the evolution of plastic responses to unreliable cues

Adaptive plasticity is expected to evolve when informative cues predict environmental variation. However, plastic responses can be maladaptive even when those cues are informative, if prediction mistakes are shared across members of a generation. These fitness costs can constrain the evolution of plasticity when initial plastic mutants use of cues of only moderate reliability. Here, we model the barriers to the evolution of plasticity produced by these constraints and show that dispersal across a metapopulation can overcome them. Constraints are also lessened, though not eliminated, when plastic responses are free to evolve gradually and in concert with increased reliability. Each of these factors be viewed as a form of bet-hedging: by lessening correlations in the fates of relatives, dispersal acts as diversifying bet-hedging, while producing submaximal responses to a cue can be understood as a conservative bet-hedging strategy. While poor information may constrain the evolution of plasticity, the opportunity for bet-hedging may predict when that constraint can be overcome.

Leveraging big data to uncover the eco-evolutionary factors shaping behavioural development

Mapping the eco-evolutionary factors shaping the development of animals' behavioural phenotypes remains a great challenge. Recent advances in 'big behavioural data' research-the high-resolution tracking of individuals and the harnessing of that data with powerful analytical tools-have vastly improved our ability to measure and model developing behavioural phenotypes. Applied to the study of behavioural ontogeny, the unfolding of whole behavioural repertoires can be mapped in unprecedented detail with relative ease. This overcomes long-standing experimental bottlenecks and heralds a surge of studies that more finely define and explore behavioural-experiential trajectories across development. In this review, we first provide a brief guide to state-of-the-art approaches that allow the collection and analysis of high-resolution behavioural data across development. We then outline how such approaches can be used to address key issues regarding the ecological and evolutionary factors shaping behavioural development: developmental feedbacks between behaviour and underlying states, early life effects and behavioural transitions, and information integration across development.

The evolution of personality disorders: A review of proposals

Personality disorders (PDs) are currently considered dysfunctions. However, personality differences are older than humanity and are ubiquitous in nature, from insects to higher primates. This suggests that a number of evolutionary mechanisms-other than dysfunctions-may be able to maintain stable behavioral variation in the gene pool. First of all, apparently maladaptive traits may actually improve fitness by enabling better survival or successful mating or reproduction, as exemplified by neuroticism, psychopathy, and narcissism. Furthermore, some PDs may harm important biological goals while facilitating others, or may be globally beneficial or detrimental depending on environmental circumstances or body condition. Alternatively, certain traits may form part of life history strategies: Coordinated suites of morphological, physiological and behavioral characters that optimize fitness through alternative routes and respond to selection as a whole. Still others may be vestigial adaptations that are no longer beneficial in present times. Finally, variation may be adaptative in and by itself, as it reduces competition for finite resources. These and other evolutionary mechanisms are reviewed and illustrated through human and non-human examples. Evolutionary theory is the best-substantiated explanatory framework across the life sciences, and may shed light on the question of why harmful personalities exist at all.

Evidence in Support of Analogical Reasoning Improvements with Executive Attention Intervention in Healthy Young Adults

Analogical reasoning improvement is important in educational outcome improvement. Inspired by recent ideas and evidence, we applied anti-saccade task training as an executive attention intervention and tested whether it could improve analogical reasoning performance. A serial-task paradigm was applied where participants performed an anti-saccade followed by an analogical reasoning task including a perception condition. The experimental group finished the anti-saccade task in which the ratio of anti-saccade trials to pro-saccade trials was 5:1 while the counterpart was 1:1 in the active control group. Also, a blank control group was established where participants merely finished the analogical reasoning task. Event-related electroencephalographic (EEG) data were recorded when participants were performing the executive attention and analogical reasoning tasks. In addition, their resting state EEG was collected before and after the executive attention intervention. Behaviorally, the experimental group reacted significantly faster than the other two groups in analogical reasoning but not in perception. At the neural level, in the experimental group alone, the anti-saccade trials elicited a smaller N2 than pro-saccade trials and the resting alpha power was improved after executive attention intervention. No significant difference in P2 was found between the two groups in analogical reasoning or perception but the experimental group showed a larger late positive component than the active control group in analogical reasoning. We also found that the late positive component mediated the relationship between the N2 of anti-saccade trials and analogical reasoning reaction times in the experimental group. We further discussed the role of executive attention in the analogical reasoning process, which may pave the way for the future reliable improvement of fluid intelligence.

Developmental feedbacks and the emergence of individuality

Behavioural individuality is a hallmark of animal life, with major consequences for fitness, ecology, and evolution. One of the most widely invoked explanations for this variation is that feedback loops between an animal's behaviour and its state (e.g. physiology, informational state, social rank, etc.) trigger and shape the development of individuality. Despite their often-cited importance, however, little is known about the ultimate causes of such feedbacks. Expanding on a previously employed model of adaptive behavioural development under uncertainty, we find that (i) behaviour-state feedbacks emerge as a direct consequence of adaptive behavioural development in particular selective environments and (ii) that the sign of these feedbacks, and thus the consequences for the development of behavioural individuality, can be directly predicted by the shape of the fitness function, with increasing fitness benefits giving rise to positive feedbacks and trait divergence and decreasing fitness benefits leading to negative feedbacks and trait convergence. Our findings provide a testable explanatory framework for the emergence of developmental feedbacks driving individuality and suggest that such feedbacks and their associated patterns of behavioural diversity are a direct consequence of adaptive behavioural development in particular selective environments.

Developmental feedbacks and the emergence of individuality

Behavioural individuality is a hallmark of animal life, with major consequences for fitness, ecology, and evolution. One of the most widely invoked explanations for this variation is that feedback loops between an animal's behaviour and its state (e.g. physiology, informational state, social rank, etc.) trigger and shape the development of individuality. Despite their often-cited importance, however, little is known about the ultimate causes of such feedbacks. Expanding on a previously employed model of adaptive behavioural development under uncertainty, we find that (i) behaviour-state feedbacks emerge as a direct consequence of adaptive behavioural development in particular selective environments and (ii) that the sign of these feedbacks, and thus the consequences for the development of behavioural individuality, can be directly predicted by the shape of the fitness function, with increasing fitness benefits giving rise to positive feedbacks and trait divergence and decreasing fitness benefits leading to negative feedbacks and trait convergence. Our findings provide a testable explanatory framework for the emergence of developmental feedbacks driving individuality and suggest that such feedbacks and their associated patterns of behavioural diversity are a direct consequence of adaptive behavioural development in particular selective environments.

Developmental feedbacks and the emergence of individuality

Behavioural individuality is a hallmark of animal life, with major consequences for fitness, ecology, and evolution. One of the most widely invoked explanations for this variation is that feedback loops between an animal's behaviour and its state (e.g. physiology, informational state, social rank, etc.) trigger and shape the development of individuality. Despite their often-cited importance, however, little is known about the ultimate causes of such feedbacks. Expanding on a previously employed model of adaptive behavioural development under uncertainty, we find that (i) behaviour-state feedbacks emerge as a direct consequence of adaptive behavioural development in particular selective environments and (ii) that the sign of these feedbacks, and thus the consequences for the development of behavioural individuality, can be directly predicted by the shape of the fitness function, with increasing fitness benefits giving rise to positive feedbacks and trait divergence and decreasing fitness benefits leading to negative feedbacks and trait convergence. Our findings provide a testable explanatory framework for the emergence of developmental feedbacks driving individuality and suggest that such feedbacks and their associated patterns of behavioural diversity are a direct consequence of adaptive behavioural development in particular selective environments.

The emergence and development of behavioral individuality in clonal fish

Behavioral individuality is a ubiquitous phenomenon in animal populations, yet the origins and developmental trajectories of individuality, especially very early in life, are still a black box. Using a high-resolution tracking system, we mapped the behavioral trajectories of genetically identical fish (Poecilia formosa), separated immediately after birth into identical environments, over the first 10 weeks of their life at 3 s resolution. We find that (i) strong behavioral individuality is present at the very first day after birth, (ii) behavioral differences at day 1 of life predict behavior up to at least 10 weeks later, and (iii) patterns of individuality strengthen gradually over developmental time. Our results establish a null model for how behavioral individuality can develop in the absence of genetic and environmental variation and provide experimental evidence that later-in-life individuality can be strongly shaped by factors pre-dating birth like maternal provisioning, epigenetics and pre-birth developmental stochasticity.

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.

Uncertainty about others' trustworthiness increases during adolescence and guides social information sampling

Adolescence is a key life phase for developing well-adjusted social behaviour. An essential component of well-adjusted social behaviour is the ability to update our beliefs about the trustworthiness of others based on gathered information. Here, we examined how adolescents (n = 157, 10-24 years) sequentially sampled information about the trustworthiness of peers and how they used this information to update their beliefs about others' trustworthiness. Our Bayesian computational modelling approach revealed an adolescence-emergent increase in uncertainty of prior beliefs about others' trustworthiness. As a consequence, early to mid-adolescents (ages 10-16) gradually relied less on their prior beliefs and more on the gathered evidence when deciding to sample more information, and when deciding to trust. We propose that these age-related differences could be adaptive to the rapidly changing social environment of early and mid-adolescents. Together, these findings contribute to the understanding of adolescent social development by revealing adolescent-emergent flexibility in prior beliefs about others that drives adolescents' information sampling and trust decisions.

Sensitive periods, but not critical periods, evolve in a fluctuating environment: a model of incremental development

Sensitive periods, during which the impact of experience on phenotype is larger than in other periods, exist in all classes of organisms, yet little is known about their evolution. Recent mathematical modelling has explored the conditions in which natural selection favours sensitive periods. These models have assumed that the environment is stable across ontogeny or that organisms can develop phenotypes instantaneously at any age. Neither assumption generally holds. Here, we present a model in which organisms gradually tailor their phenotypes to an environment that fluctuates across ontogeny, while receiving cost-free, imperfect cues to the current environmental state. We vary the rate of environmental change, the reliability of cues and the duration of adulthood relative to ontogeny. We use stochastic dynamic programming to compute optimal policies. From these policies, we simulate levels of plasticity across ontogeny and obtain mature phenotypes. Our results show that sensitive periods can occur at the onset, midway through and even towards the end of ontogeny. In contrast with models assuming stable environments, organisms always retain residual plasticity late in ontogeny. We conclude that critical periods, after which plasticity is zero, are unlikely to be favoured in environments that fluctuate across ontogeny.

Resource predictability drives interannual variation in migratory behavior in a long-lived bird

There is a growing awareness that experience may play a major role in migratory decisions, especially in long-lived species. However, empirical support remains to date scarce. Here, we use multiyear GPS-tracking data on 28 adult Lesser Black-backed Gulls (Larus fuscus), a long-lived species for which migratory strategies typically consist of a series of long stopovers, to assess how experience affects interannual variation in stopover selection. We expect that food source reliability should play a pivotal role, as it both reduces the uncertainty on food availability across years, and enables for more efficient foraging during stopovers by reducing searching efforts. We found that during stopovers gulls indeed developed high fidelity to particular foraging locations, which strongly reduced the daily distance travelled for foraging. When revisiting stopovers in consecutive years, birds used over 80% of foraging locations from the previous year. Although the average fidelity to stopovers across years was a high as 85%, stopovers where birds showed high foraging site fidelity were up to 60% more likely to be revisited compared to stopover with low foraging site fidelity. Accordingly, birds using more stopovers with reliable foraging opportunities showed significantly less interannual variation in their stopover use than birds using stopovers with less reliable foraging opportunities. Our results thus highlight the need to further deepen our understanding of the role of cognitive processes in individual variation in migratory behavior.

An evolutionary model of sensitive periods when the reliability of cues varies across ontogeny

Sensitive periods are widespread in nature, but their evolution is not well understood. Recent mathematical modeling has illuminated the conditions favoring the evolution of sensitive periods early in ontogeny. However, sensitive periods also exist at later stages of ontogeny, such as adolescence. Here, we present a mathematical model that explores the conditions that favor sensitive periods at later developmental stages. In our model, organisms use environmental cues to incrementally construct a phenotype that matches their environment. Unlike in previous models, the reliability of cues varies across ontogeny. We use stochastic dynamic programming to compute optimal policies for a range of evolutionary ecologies and then simulate developmental trajectories to obtain mature phenotypes. We measure changes in plasticity across ontogeny using study paradigms inspired by empirical research: adoption and cross-fostering. Our results show that sensitive periods only evolve later in ontogeny if the reliability of cues increases across ontogeny. The onset, duration, and offset of sensitive periods—and the magnitude of plasticity—depend on the specific parameter settings. If the reliability of cues decreases across ontogeny, sensitive periods are favored only early in ontogeny. These results are robust across different paradigms suggesting that empirical findings might be comparable despite different experimental designs.

Combining information from parental and personal experiences: Simple processes generate diverse outcomes

Experiences of parents and/or offspring are often assumed to affect the development of trait values in offspring because they provide information about the external environment. However, it is currently unclear how information from parental and offspring experiences might jointly affect the information-states that provide the foundation for the offspring phenotypes observed in empirical studies of developmental plasticity in response to environmental cues. We analyze Bayesian models designed to mimic fully-factorial experimental studies of trans and within- generational plasticity (TWP), in which parents, offspring, both or neither are exposed to cues from predators, to determine how different durations of cue exposure for parents and offspring, the devaluation of information from parents or the degradation of information from parents would affect offspring estimates of environmental states related to risk of predation at the end of such experiments. We show that the effects of different cue durations, the devaluation of information from parents, and the degradation of information from parents on offspring estimates are all expected to vary as a function of interactions with two other key components of information-based models of TWP: parental priors and the relative cue reliability in the different treatments. Our results suggest empiricists should expect to observe considerable variation in the patterns observed in experimental studies of TWP based on simple principles of information-updating, without needing to invoke additional assumptions about costs, tradeoffs, development constraints, the fitness consequences of different trait values, or other factors.

Understanding the unexplained: The magnitude and correlates of individual differences in residual variance

Behavioral and physiological ecologists have long been interested in explaining the causes and consequences of trait variation, with a focus on individual differences in mean values. However, the majority of phenotypic variation typically occurs within individuals, rather than among individuals (as indicated by average repeatability being less than 0.5). Recent studies have further shown that individuals can also differ in the magnitude of variation that is unexplained by individual variation or environmental factors (i.e., residual variation). The significance of residual variation, or why individuals differ, is largely unexplained, but is important from evolutionary, methodological, and statistical perspectives. Here, we broadly reviewed literature on individual variation in behavior and physiology, and located 39 datasets with sufficient repeated measures to evaluate individual differences in residual variance. We then analyzed these datasets using methods that permit direct comparisons of parameters across studies. This revealed substantial and widespread individual differences in residual variance. The magnitude of individual variation appeared larger in behavioral traits than in physiological traits, and heterogeneity was greater in more controlled situations. We discuss potential ecological and evolutionary implications of individual differences in residual variance and suggest productive future research directions.

Acoustic developmental programming: a mechanistic and evolutionary framework

Conditions experienced prenatally, by modulating developmental processes, have lifelong effects on individual phenotypes and fitness, ultimately influencing population dynamics. In addition to maternal biochemical cues, prenatal sound is emerging as a potent alternative source of information to direct embryonic development. Recent evidence suggests that prenatal acoustic signals can program individual phenotypes for predicted postnatal environmental conditions, which improves fitness. Across taxonomic groups, embryos have now been shown to have immediate adaptive responses to external sounds and vibrations, and direct developmental effects of sound and noise are increasingly found. Establishing the full developmental, ecological, and evolutionary impact of early soundscapes will reveal how embryos interact with the external world, and potentially transform our understanding of developmental plasticity and adaptation to changing environments.

The development of the O2-sensing system in an amphibious fish: consequences of variation in environmental O2 levels

Proper development of the O₂-sensing system is essential for survival. Here, we characterized the development of the O₂-sensing system in the mangrove rivulus (Kryptolebias marmoratus), an amphibious fish that transitions between hypoxic aquatic environments and O₂-rich terrestrial environments. We found that NECs formed in the gills and skin of K. marmoratus during embryonic development and that both NEC populations are retained from the embryonic stage to adulthood. We also found that the hyperventilatory response to acute hypoxia was present in embryonic K. marmoratus, indicating that functional O₂-sensing pathways are formed during embryonic development. We then exposed embryos to aquatic normoxia, aquatic hyperoxia, aquatic hypoxia, or terrestrial conditions for the first 30 days of embryonic development and tested the hypothesis that environmental O₂ availability during embryonic development modulates the development of the O₂-sensing system in amphibious fishes. Surprisingly, we found that O₂ availability during embryonic development had little impact on the density and morphology of NECs in the gills and skin of K. marmoratus. Collectively, our results demonstrate that, unlike the only other species of fish in which NEC development has been studied to date (i.e., zebrafish), NEC development in K. marmoratus is largely unaffected by environmental O₂ levels during the embryonic stage, indicating that there is interspecies variation in O₂-induced plasticity in the O₂-sensing system of fishes.

Insects Provide Unique Systems to Investigate How Early-Life Experience Alters the Brain and Behavior

Early-life experiences have strong and long-lasting consequences for behavior in a surprising diversity of animals. Determining which environmental inputs cause behavioral change, how this information becomes neurobiologically encoded, and the functional consequences of these changes remain fundamental puzzles relevant to diverse fields from evolutionary biology to the health sciences. Here we explore how insects provide unique opportunities for comparative study of developmental behavioral plasticity. Insects have sophisticated behavior and cognitive abilities, and they are frequently studied in their natural environments, which provides an ecological and adaptive perspective that is often more limited in lab-based vertebrate models. A range of cues, from relatively simple cues like temperature to complex social information, influence insect behavior. This variety provides experimentally tractable opportunities to study diverse neural plasticity mechanisms. Insects also have a wide range of neurodevelopmental trajectories while sharing many developmental plasticity mechanisms with vertebrates. In addition, some insects retain only subsets of their juvenile neuronal population in adulthood, narrowing the targets for detailed study of cellular plasticity mechanisms. Insects and vertebrates share many of the same knowledge gaps pertaining to developmental behavioral plasticity. Combined with the extensive study of insect behavior under natural conditions and their experimental tractability, insect systems may be uniquely qualified to address some of the biggest unanswered questions in this field.

The information provided by the absence of cues: insights from Bayesian models of within and transgenerational plasticity

Empirical studies of phenotypic plasticity often use an experimental design in which the subjects in experimental treatments are exposed to cues, while the subjects in control treatments are maintained in the absence of those cues. However, researchers have virtually ignored the question of what, if any, information might be provided to subjects by the absence of the cues in control treatments. We apply basic principles of information-updating to several experimental protocols used to study phenotypic plasticity in response to cues from predators to show why the reliability of the information provided by the absence of those cues in a control treatment might vary as a function of the subjects' experiences in the experimental treatment. We then analyze Bayesian models designed to mimic fully factorial experimental studies of trans and within-generational plasticity, in which parents, offspring, both or neither are exposed to cues from predators, and the information-states of the offspring in the different groups are compared at the end of the experiment. The models predict that the pattern of differences in offspring information-state across the four treatment groups will vary among experiments, depending on the reliability of the information provided by the control treatment, and the parent's initial estimate of the value of the state (the parental Prior). We suggest that variation among experiments in the reliability of the information provided by the absence of particular cues in the control treatment may be a general phenomenon, and that Bayesian approaches can be useful in interpreting the results of such experiments.

Toward a Science of Effective Cognitive Training

A long-standing question in the behavioral sciences is whether cognitive functions can be improved through dedicated training. It is uncontested that training programs can lead to near transfer, meaning increased performance on untrained tasks involving similar cognitive functions. However, whether training also leads to far transfer, meaning increased performance on loosely related untrained tasks or even activities of daily living, is still hotly debated. Here, we review the extant literature and, in particular, the most recent meta-analytic evidence and argue that the ongoing crisis in the field of cognitive-training research may benefit from taking a more mechanistic approach to studying the effectiveness of training. We propose that (a) adopting a more rigorous theoretical framework that builds on a process-based account of training and transfer, (b) considering the role of individual differences in the responsiveness to training, and (c) drawing on Bayesian models of development may help to solve controversial issues in the field and lead the way to designing and implementing more effective training protocols.

Fluctuating environments during early development can limit adult phenotypic flexibility: insights from an amphibious fish

The interaction between developmental plasticity and the capacity for reversible acclimation (phenotypic flexibility) is poorly understood, particularly in organisms exposed to fluctuating environments. We used an amphibious killifish (Kryptolebias marmoratus) to test the hypotheses that organisms reared in fluctuating environments (i) will make no developmental changes to suit any one environment because fixing traits to suit one environment could be maladaptive for another, and (ii) will be highly phenotypically flexible as adults because their early life experiences predict high environmental variability in the future. We reared fish under constant (water) or fluctuating (water-air) environments until adulthood and assessed a suite of traits along the oxygen cascade (e.g. neuroepithelial cell density and size, cutaneous capillarity, gill morphology, ventricle size, red muscle morphometrics, terrestrial locomotor performance). To evaluate the capacity for phenotypic flexibility, a subset of adult fish from each rearing condition was then air-exposed for 14 days before the same traits were measured. In support of the developmental plasticity hypothesis, traits involved with O2 sensing and uptake were largely unaffected by water-air fluctuations during early life, but we found marked developmental changes in traits related to O2 transport, utilization and locomotor performance. In contrast, we found no evidence supporting the phenotypic flexibility hypothesis. Adult fish from both rearing conditions exhibited the same degree of phenotypic flexibility in various O2 sensing- and uptake-related traits. In other cases, water-air fluctuations attenuated adult phenotypic flexibility despite the fact that phenotypic flexibility is hypothesized to be favoured when environments fluctuate. Overall, we conclude that exposure to environmental fluctuations during development in K. marmoratus can dramatically alter the constitutive adult phenotype, as well as diminish the scope for phenotypic flexibility in later life.

The Hidden Talents Approach: Theoretical and Methodological Challenges

It is well established that people living in adverse conditions tend to score lower on a variety of social and cognitive tests. However, recent research shows that people may also develop 'hidden talents', that is, mental abilities that are enhanced through adversity. The hidden talents program sets out to document these abilities, their development, and their manifestations in different contexts. Although this approach has led to new insights and findings, it also comes with theoretical and methodological challenges. Here, we discuss six of these challenges. We conclude that the hidden talents approach is promising, but there is much scope for refining ideas and testing assumptions. We discuss our goal to advance this research program with integrity despite the current incentives in science.

The impact of learning opportunities on the development of learning and decision-making: an experiment with passerine birds

Developmental context has been shown to influence learning abilities later in life, namely through experiments with nutritional and/or environmental constraints (i.e. lack of enrichment). However, little is known about the extent to which opportunities for learning affect the development of animal cognition, even though such opportunities are known to influence human cognitive development. We exposed young zebra finches (Taenopygia guttata) (n = 26) to one of three experimental conditions, i.e. an environment where (i) colour cues reliably predicted the presence of food (associative learning), (ii) a combination of two-colour cues reliably predicted the presence of food (conditional learning), or (iii) colour cues were non-informative (control). After conducting two different discrimination tasks, our results showed that experience with predictive cues can cause increased choice accuracy and decision-making speed. Our first learning task showed that individuals in the associative learning treatment outperformed the control treatment, while task 2 showed that individuals in the conditional learning treatment had shorter latencies when making choices compared with the control treatment. We found no support for a speed-accuracy trade-off. This dataset provides a rare longitudinal and experimental examination of the effect of predictive versus non-predictive cues during development on the cognition of adult animals. This article is part of the theme issue 'Life history and learning: how childhood, caregiving and old age shape cognition and culture in humans and other animals'.

Gull chicks grow faster but lose telomeres when prenatal cues mismatch the real presence of sibling competitors

During embryonic life, individuals should adjust their phenotype to the conditions that they will encounter after birth, including the social environment, if they have access to (social) cues that allow them to forecast future conditions. In birds, evidence indicates that embryos are sensitive to cues from clutch mates, but whether embryos adjust their development to cope with the expected level of sibling competition has not hitherto been investigated. To tackle this question, we performed a 'match versus mismatch' experimental design where we manipulated the presence of clutch mates (i.e. clutch size manipulation) and the real (postnatal) level of sibling competition (i.e. brood size manipulation) in the yellow-legged gull (Larus michahellis). We provide evidence that the prenatal cues of sibling presence induced developmental changes (such as epigenetic profiles) that had programming effects on chick begging behaviour and growth trajectories after hatching. While receiving mismatching information favoured chick begging and growth, this came at the cost of reduced antioxidant defences and a premature loss of telomeres. Our findings highlight the role of the prenatal social environment in developmental plasticity and suggest that telomere attrition may be an important physiological cost of phenotype-environment mismatch.

A case for environmental statistics of early-life effects

There is enduring debate over the question of which early-life effects are adaptive and which ones are not. Mathematical modelling shows that early-life effects can be adaptive in environments that have particular statistical properties, such as reliable cues to current conditions and high autocorrelation of environmental states. However, few empirical studies have measured these properties, leading to an impasse. Progress, therefore, depends on research that quantifies cue reliability and autocorrelation of environmental parameters in real environments. These statistics may be different for social and non-social aspects of the environment. In this paper, we summarize evolutionary models of early-life effects. Then, we discuss empirical data on environmental statistics from a range of disciplines. We highlight cases where data on environmental statistics have been used to test competing explanations of early-life effects. We conclude by providing guidelines for new data collection and reflections on future directions.

This article is part of the theme issue ‘Developing differences: early-life effects and evolutionary medicine'.

Modeling the evolution of sensitive periods

In the past decade, there has been monumental progress in our understanding of the neurobiological basis of sensitive periods. Little is known, however, about the evolution of sensitive periods. Recent studies have started to address this gap. Biologists have built mathematical models exploring the environmental conditions in which sensitive periods are likely to evolve. These models investigate how mechanisms of plasticity can respond optimally to experience during an individual's lifetime. This paper discusses the central tenets, insights, and predictions of these models, in relation to empirical work on humans and other animals. We also discuss which future models are needed to improve the bridge between theory and data, advancing their synergy. Our paper is written in an accessible manner and for a broad audience. We hope our work will contribute to recently emerging connections between the fields of developmental neuroscience and evolutionary biology.

The hierarchically mechanistic mind: an evolutionary systems theory of the human brain, cognition, and behavior

The purpose of this review was to integrate leading paradigms in psychology and neuroscience with a theory of the embodied, situated human brain, called the Hierarchically Mechanistic Mind (HMM). The HMM describes the brain as a complex adaptive system that functions to minimize the entropy of our sensory and physical states via action-perception cycles generated by hierarchical neural dynamics. First, we review the extant literature on the hierarchical structure of the brain. Next, we derive the HMM from a broader evolutionary systems theory that explains neural structure and function in terms of dynamic interactions across four nested levels of biological causation (i.e., adaptation, phylogeny, ontogeny, and mechanism). We then describe how the HMM aligns with a global brain theory in neuroscience called the free-energy principle, leveraging this theory to mathematically formulate neural dynamics across hierarchical spatiotemporal scales. We conclude by exploring the implications of the HMM for psychological inquiry.

The hierarchically mechanistic mind: A free-energy formulation of the human psyche

This article presents a unifying theory of the embodied, situated human brain called the Hierarchically Mechanistic Mind (HMM). The HMM describes the brain as a complex adaptive system that actively minimises the decay of our sensory and physical states by producing self-fulfilling action-perception cycles via dynamical interactions between hierarchically organised neurocognitive mechanisms. This theory synthesises the free-energy principle (FEP) in neuroscience with an evolutionary systems theory of psychology that explains our brains, minds, and behaviour by appealing to Tinbergen's four questions: adaptation, phylogeny, ontogeny, and mechanism. After leveraging the FEP to formally define the HMM across different spatiotemporal scales, we conclude by exploring its implications for theorising and research in the sciences of the mind and behaviour.

Transgenerational Plasticity in Human-Altered Environments

Our ability to predict how species will respond to human-induced rapid environmental change (HIREC) may depend upon our understanding of transgenerational plasticity (TGP), which occurs when environments experienced by previous generations influence phenotypes of subsequent generations. TGP evolved to help organisms cope with environmental stressors when parental environments are highly predictive of offspring environments. HIREC can alter conditions that favored TGP in historical environments by reducing parents' ability to detect environmental conditions, disrupting previous correlations between parental and offspring environments, and interfering with the transmission of parental cues to offspring. Because of the propensity to produce errors in these processes, TGP will likely generate negative fitness outcomes in response to HIREC, though beneficial fitness outcomes may occur in some cases.

Temporal autocorrelation: a neglected factor in the study of behavioral repeatability and plasticity

Quantifying individual variation in labile physiological or behavioral traits often involves repeated measures through time, so as to test for consistency of individual differences (often using repeatability, “R”) and/or individual differences in trendlines over time. Another form of temporal change in behavior is temporal autocorrelation, which predicts observations taken closely together in time to be correlated, leading to nonrandom residuals about individual temporal trendlines. Temporal autocorrelation may result from slowly changing internal states (e.g., hormone or energy levels), leading to slowly changing behavior. Autocorrelation is a well-known phenomenon, but has been largely neglected by those studying individual variation in behavior. Here, we provide two worked examples which show substantial temporal autocorrelation (r > 0.4) is present in spontaneous activity rates of guppies (Poecilia reticulata) and house mice (Mus domesticus) in stable laboratory conditions, even after accounting for temporal plasticity of individuals. Second, we show that ignoring autocorrelation does bias estimates of R and temporal reaction norm variances upwards, both in our worked examples and in separate simulations. This bias occurs due to the misestimation of individual-specific means and slopes. Given the increasing use of technologies that generate behavioral and physiological data at high sampling rates, we can now study among- and within-individual changes in behavior in more detailed ways, including autocorrelation, which we discuss from biological and methodological perspectives and provide recommendations and annotated R code to help researchers implement these models on their data.