Bridging developmental systems theory and evolutionary psychology using dynamic optimization

Basic Emotions or Constructed Emotions: Insights From Taking an Evolutionary Perspective

The ongoing debate between basic emotion theories (BETs) and the theory of constructed emotion (TCE) hampers progress in the field of emotion research. Providing a new perspective, here we aim to bring the theories closer together by dissecting them according to Tinbergen's four questions to clarify a focus on their evolutionary basis. On the basis of our review of the literature, we conclude that whereas BETs focus on the evolution question of Tinbergen, the TCE is more concerned with the causation of emotion. On the survival value of emotions both theories largely agree: to provide the best reaction in specific situations. Evidence is converging on the evolutionary history of emotions but is still limited for both theories-research within both frameworks focuses heavily on the causation. We conclude that BETs and the TCE explain two different phenomena: emotion and feeling. Therefore, they seem irreconcilable but possibly supplementary for explaining and investigating the evolution of emotion-especially considering their similar answer to the question of survival value. Last, this article further highlights the importance of carefully describing what aspect of emotion is being discussed or studied. Only then can evidence be interpreted to converge toward explaining emotion.

Art Value Creation and Destruction

I present a theory of creative and destructive value state referring to abstract art. Value is a probabilistic state held as a mixture of its expectation and information forces that coexist in a give-and-take relationship. Expectations are driven by the disclosure of novel information about the value state of various events of desire. Each bit of accumulated information contributes to the improvement of perception up to a threshold level, beyond which begin conscious states. The desire to disclose a value state triggers a triadic system of evaluation which uses concepts, observables and approaches. While the triadic valuation mechanisms can be used to assess various commodities, the scope of this work is limited to the case of artworks, in particular abstract paintings. I assume that art value is basically mediated by the interplay between these value state mechanisms of creation and destruction. Expectations in artwork develop attraction by challenging its contemplator to evaluate (predict) its meaning. Once the relevant information, corresponding to its creative expectations, is acquired (and conditioned), emotional states of indifference, disinterest and desensitization develop.

Biology, Society, or Choice: How Do Non-Experts Interpret Explanations of Behaviour?

Explanations for human behaviour can be framed in many different ways, from the social-structural context to the individual motivation down to the neurobiological implementation. We know comparatively little about how people interpret these explanatory framings, and what they infer when one kind of explanation rather than another is made salient. In four experiments, UK general-population volunteers read vignettes describing the same behaviour, but providing explanations framed in different ways. In Study 1, we found that participants grouped explanations into 'biological', 'psychological' and 'sociocultural' clusters. Explanations with different framings were often seen as incompatible with one another, especially when one belonged to the 'biological' cluster and the other did not. In Study 2, we found that exposure to a particular explanatory framing triggered inferences beyond the information given. Specifically, psychological explanations led participants to assume the behaviour was malleable, and biological framings led them to assume it was not. In Studies 3A and 3B, we found that the choice of explanatory framing can affect people's assumptions about effective interventions. For example, presenting a biological explanation increased people's conviction that interventions like drugs would be effective, and decreased their conviction that psychological or socio-political interventions would be effective. These results illuminate the intuitive psychology of explanations, and also potential pitfalls in scientific communication. Framing an explanation in a particular way will often generate inferences in the audience-about what other factors are not causally important, how easy it is to change the behaviour, and what kinds of remedies are worth considering-that the communicator may not have anticipated and might not intend.

Integrating developmental plasticity into eco-evolutionary population dynamics

There are increasing calls to incorporate developmental plasticity into the framework of eco-evolutionary dynamics. The current way is via genotype-specified reaction norms in which inheritance and phenotype expression are gene-based. I propose a developmental system perspective in which phenotypes are formed during individual development in a process comprising a complex set of interactions that involve genes, biochemistry, somatic state, and the (a)biotic environment, and where the developmental system is the unit of phenotype evolution. I explain how the two perspectives differ in assumptions and predictions, which can be contrasted using cue-and-response systems of anticipatory or mitigating developmental plasticity. This can lead to new ways of eco-evolutionary thinking, and deliver important explanations of how populations respond to environmental change through evolved developmental plasticity.

Expectable Environments in Early Life

Humans develop in the context of environmental information that can be considered either experience-expectant or experience-dependent. Though the exact timing of sensitive period closures and consequences of environmental experiences have not been well delineated, early life is a period of increased vulnerability. While some forms of care (e.g., institutional care for children; representing the absence of experience-expectant caregiving) are not present in the evolutionary history of humans, it is likely that what is considered significant hardship today may have been more typical experience-dependent environmental information in the evolutionary timescale. Thus, assumptions that threatening or neglectful experiences are unexpected for the human child may not fit well in the scope of the broader timescale of human history. We argue that it is important to consider early caregiving experiences from the context of what has been expected in our evolutionary past rather than what is expected in modern sociocultural terms.

The importance of life history and population regulation for the evolution of social learning

Social learning and life history interact in human adaptation, but nearly all models of the evolution of social learning omit age structure and population regulation. Further progress is hindered by a poor appreciation of how life history affects selection on learning. We discuss why life history and age structure are important for social learning and present an exemplary model of the evolution of social learning in which demographic properties of the population arise endogenously from assumptions about per capita vital rates and different forms of population regulation. We find that, counterintuitively, a stronger reliance on social learning is favoured in organisms characterized by 'fast' life histories with high mortality and fertility rates compared to 'slower' life histories typical of primates. Long lifespans make early investment in learning more profitable and increase the probability that the environment switches within generations. Both effects favour more individual learning. Additionally, under fertility regulation (as opposed to mortality regulation), more juveniles are born shortly after switches in the environment when many adults are not adapted, creating selection for more individual learning. To explain the empirical association between social learning and long life spans and to appreciate the implications for human evolution, we need further modelling frameworks allowing strategic learning and cumulative culture. 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'.

The evolution of distorted beliefs vs. mistaken choices under asymmetric error costs

Why do people sometimes hold unjustified beliefs and make harmful choices? Three hypotheses include (a) contemporary incentives in which some errors cost more than others, (b) cognitive biases evolved to manage ancestral incentives with variation in error costs and (c) social learning based on choice frequencies. With both modelling and a behavioural experiment, we examined all three mechanisms. The model and experiment support the conclusion that contemporary cost asymmetries affect choices by increasing the rate of cheap errors to reduce the rate of expensive errors. Our model shows that a cognitive bias can distort the evolution of beliefs and in turn behaviour. Unless the bias is strong, however, beliefs often evolve in the correct direction. This suggests limitations on how cognitive biases shape choices, which further indicates that detecting the behavioural consequences of biased cognition may sometimes be challenging. Our experiment used a prime intended to activate a bias called 'hyperactive agency detection', and the prime had no detectable effect on choices. Finally, both the model and experiment show that frequency-dependent social learning can generate choice dynamics in which some populations converge on widespread errors, but this outcome hinges on the other two mechanisms being neutral with respect to choice.

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.

After phrenology: Time for a paradigm shift in cognitive science

Anderson (2014) uses an impressive, consolidating review of the literature to argue for major changes in cognitive science. Arguably, however, much of what he proposes is not particularly new. He also neglects important predictive coding approaches that call his perspective of the brain into question, and his misconstrual of evolutionary psychology devalues an influential paradigm that promises to complement his own.

Open Science Is Liberating and Can Foster Creativity

Some scholars think that Open Science practices constrain researchers in ways that reduce their creativity, arguing, for instance, that preregistration discourages data exploration and so stifles discovery. In this article, we argue the opposite: Open Science practices are liberating and can foster creativity. Open Science practices are liberating because they (a) enable us to explore data transparently and comfortably; (b) reward quality, which is under our control, rather than outcomes, which are not; and (c) reduce the choke hold of needing to find "positive" results for career advancement. Open Science practices can foster creativity because they cultivate an open and flexible mind-set, create a more collaborative and constructive climate, and generate more accurate information and make it more accessible. In sum, Open Science liberates researchers more than it constrains them.

Echoes of Early Life: Recent Insights From Mathematical Modeling

In the last decades, developmental origins of health and disease (DOHaD) has emerged as a central framework for studying early‐life effects, that is, the impact of fetal and early postnatal experience on adult functioning. Apace with empirical progress, theoreticians have built mathematical models that provide novel insights for DOHaD. This article focuses on three of these insights, which show the power of environmental noise (i.e., imperfect indicators of current and future conditions) in shaping development. Such noise can produce: (a) detrimental outcomes even in ontogenetically stable environments, (b) individual differences in sensitive periods, and (c) early‐life effects tailored to predicted future somatic states. We argue that these insights extend DOHaD and offer new research directions.

Does Exposure to Hostile Environments Predict Enhanced Emotion Detection?

We used a Face-in-the-Crowd task to examine whether hostile environments predict enhanced detection of anger, and whether such enhanced cognition occurs for a different negative emotion, sadness, as well. We conducted a well-powered, preregistered study in 100 college students and 100 individuals from a community sample with greater exposure to hostile environments. At the group level, the community sample was less accurate at detecting both angry and sad faces than students; and, only students discriminated anger more accurately than sadness. At the individual level, having experienced more violence did not predict enhanced anger detection accuracy. In general, participants had a lower threshold (i.e., a more liberal criterion) for detecting emotion in response to anger than sadness. And, students had a higher threshold (i.e., a more conservative criterion) for detecting emotion than the community sample in response to both anger and sadness. Overall, these findings contradict our hypothesis that exposure to hostile environments predicts enhanced danger detection. Rather, our community sample was more prone to over-perceiving emotions, consistent with previous studies showing bias in threat-exposed populations. Future work is needed to tease apart the conditions in which people exposed to social danger show enhanced accuracy or bias in their perception of emotions.

Answering Schrödinger's question: A free-energy formulation

The free-energy principle (FEP) is a formal model of neuronal processes that is widely recognised in neuroscience as a unifying theory of the brain and biobehaviour. More recently, however, it has been extended beyond the brain to explain the dynamics of living systems, and their unique capacity to avoid decay. The aim of this review is to synthesise these advances with a meta-theoretical ontology of biological systems called variational neuroethology, which integrates the FEP with Tinbergen's four research questions to explain biological systems across spatial and temporal scales. We exemplify this framework by applying it to Homo sapiens, before translating variational neuroethology into a systematic research heuristic that supplies the biological, cognitive, and social sciences with a computationally tractable guide to discovery.

Enriching behavioral ecology with reinforcement learning methods

This article focuses on the division of labor between evolution and development in solving sequential, state-dependent decision problems. Currently, behavioral ecologists tend to use dynamic programming methods to study such problems. These methods are successful at predicting animal behavior in a variety of contexts. However, they depend on a distinct set of assumptions. Here, we argue that behavioral ecology will benefit from drawing more than it currently does on a complementary collection of tools, called reinforcement learning methods. These methods allow for the study of behavior in highly complex environments, which conventional dynamic programming methods do not feasibly address. In addition, reinforcement learning methods are well-suited to studying how biological mechanisms solve developmental and learning problems. For instance, we can use them to study simple rules that perform well in complex environments. Or to investigate under what conditions natural selection favors fixed, non-plastic traits (which do not vary across individuals), cue-driven-switch plasticity (innate instructions for adaptive behavioral development based on experience), or developmental selection (the incremental acquisition of adaptive behavior based on experience). If natural selection favors developmental selection, which includes learning from environmental feedback, we can also make predictions about the design of reward systems. Our paper is written in an accessible manner and for a broad audience, though we believe some novel insights can be drawn from our discussion. We hope our paper will help advance the emerging bridge connecting the fields of behavioral ecology and reinforcement learning.

What Do Evolutionary Models Teach Us About Sensitive Periods in Psychological Development?

Abstract. Sensitive periods in development are widespread in nature. Many psychologists and biologists regard sensitive periods as byproducts of developmental processes. Although this view may be correct in some cases, it is unlikely to be the whole story. There is large variation in sensitive periods (a) between species in the same trait ( Beecher & Brenowitz, 2005 ), (b) between individuals of the same species ( Frankenhuis, Panchanathan, & Belsky, 2016 ), and (c) between different traits within a single individual ( Zeanah, Gunnar, McCall, Kreppner, & Fox, 2011 ). In this article, we discuss recent insights provided by formal models of the evolution of sensitive periods. These models help to identify the conditions in which sensitive periods are likely to evolve, and make predictions about the factors that affect their development. We conclude by discussing future directions for empirical research.

Brain adaptation and alternative developmental trajectories

Resilience and adaptation in the face of early genetic or environmental risk has become a major interest in child psychiatry over recent years. However, we still remain far from an understanding of how developing human brains as a whole adapt to the diffuse and widespread atypical synaptic function that may be characteristic of some common developmental disorders. The first part of this paper discusses four types of whole-brain adaptation in the face of early risk: redundancy, reorganization, niche construction, and adjustment of developmental rate. The second part of the paper applies these adaptation processes specifically to autism. We speculate that key features of autism may be the end result of processes of early brain adaptation, rather than the direct consequences of ongoing neural pathology.

The evolution of sensitive periods in a model of incremental development

Sensitive periods, in which experience shapes phenotypic development to a larger extent than other periods, are widespread in nature. Despite a recent focus on neural-physiological explanation, few formal models have examined the evolutionary selection pressures that result in developmental mechanisms that produce sensitive periods. Here, we present such a model. We model development as a specialization process during which individuals incrementally adapt to local environmental conditions, while receiving a constant stream of cost-free, imperfect cues to the environmental state. We compute optimal developmental programmes across a range of ecological conditions and use these programmes to simulate developmental trajectories and obtain distributions of mature phenotypes. We highlight four main results. First, matching the empirical record, sensitive periods often result from experience or from a combination of age and experience, but rarely from age alone. Second, individual differences in sensitive periods emerge as a result of stochasticity in cues: individuals who obtain more consistent cue sets lose their plasticity at faster rates. Third, in some cases, experience shapes phenotypes only at a later life stage (lagged effects). Fourth, individuals might perseverate along developmental trajectories despite accumulating evidence suggesting the alternate trajectory is more likely to match the ecology.

What can cross-cultural correlations teach us about human nature?

Many recent evolutionary psychology and human behavioral ecology studies have tested hypotheses by examining correlations between variables measured at a group level (e.g., state, country, continent). In such analyses, variables collected for each aggregation are often taken to be representative of the individuals present within them, and relationships between such variables are presumed to reflect individual-level processes. There are multiple reasons to exercise caution when doing so, including: (1) the ecological fallacy, whereby relationships observed at the aggregate level do not accurately represent individual-level processes; (2) non-independence of data points, which violates assumptions of the inferential techniques used in null hypothesis testing; and (3) cross-cultural non-equivalence of measurement (differences in construct validity between groups). We provide examples of how each of these gives rise to problems in the context of testing evolutionary hypotheses about human behavior, and we offer some suggestions for future research.

Adaptive explanations for sensitive windows in development

Development in many organisms appears to show evidence of sensitive windows—periods or stages in ontogeny in which individual experience has a particularly strong influence on the phenotype (compared to other periods or stages). Despite great interest in sensitive windows from both fundamental and applied perspectives, the functional (adaptive) reasons why they have evolved are unclear. Here we outline a conceptual framework for understanding when natural selection should favour changes in plasticity across development. Our approach builds on previous theory on the evolution of phenotypic plasticity, which relates individual and population differences in plasticity to two factors: the degree of uncertainty about the environmental conditions and the extent to which experiences during development (‘cues’) provide information about those conditions. We argue that systematic variation in these two factors often occurs within the lifetime of a single individual, which will select for developmental changes in plasticity. Of central importance is how informational properties of the environment interact with the life history of the organism. Phenotypes may be more or less sensitive to environmental cues at different points in development because of systematic changes in (i) the frequency of cues, (ii) the informativeness of cues, (iii) the fitness benefits of information and/or (iv) the constraints on plasticity. In relatively stable environments, a sensible null expectation is that plasticity will gradually decline with age as the developing individual gathers information. We review recent models on the evolution of developmental changes in plasticity and explain how they fit into our conceptual framework. Our aim is to encourage an adaptive perspective on sensitive windows in development.

A mathematical model of the evolution of individual differences in developmental plasticity arising through parental bet-hedging

Children vary in the extent to which their development is shaped by particular experiences (e.g. maltreatment, social support). This variation raises a question: Is there no single level of plasticity that maximizes biological fitness? One influential hypothesis states that when different levels of plasticity are optimal in different environmental states and the environment fluctuates unpredictably, natural selection may favor parents producing offspring with varying levels of plasticity. The current article presents a mathematical model assessing the logic of this hypothesis--specifically, it examines what conditions are required for natural selection to favor parents to bet-hedge by varying their offspring's plasticity. Consistent with existing theory from biology, results show that between-individual variation in plasticity cannot evolve when the environment only varies across space. If, however, the environment varies across time, selection can favor differential plasticity, provided fitness effects are large (i.e. variation in individuals' plasticity is correlated with substantial variation in fitness). Our model also generates a novel restriction: Differential plasticity only evolves when the cost of being mismatched to the environment exceeds the benefits of being well matched. Based on mechanistic considerations, we argue that bet-hedging by varying offspring plasticity, if it were to evolve, would be more likely instantiated via epigenetic mechanisms (e.g. pre- or postnatal developmental programming) than genetic ones (e.g. mating with genetically diverse partners). Our model suggests novel avenues for testing the bet-hedging hypothesis of differential plasticity, including empirical predictions and relevant measures. We also discuss several ways in which future work might extend our model.

From computers to cultivation: reconceptualizing evolutionary psychology

Does evolutionary theorizing have a role in psychology? This is a more contentious issue than one might imagine, given that, as evolved creatures, the answer must surely be yes. The contested nature of evolutionary psychology lies not in our status as evolved beings, but in the extent to which evolutionary ideas add value to studies of human behavior, and the rigor with which these ideas are tested. This, in turn, is linked to the framework in which particular evolutionary ideas are situated. While the framing of the current research topic places the brain-as-computer metaphor in opposition to evolutionary psychology, the most prominent school of thought in this field (born out of cognitive psychology, and often known as the Santa Barbara school) is entirely wedded to the computational theory of mind as an explanatory framework. Its unique aspect is to argue that the mind consists of a large number of functionally specialized (i.e., domain-specific) computational mechanisms, or modules (the massive modularity hypothesis). Far from offering an alternative to, or an improvement on, the current perspective, we argue that evolutionary psychology is a mainstream computational theory, and that its arguments for domain-specificity often rest on shaky premises. We then go on to suggest that the various forms of e-cognition (i.e., embodied, embedded, enactive) represent a true alternative to standard computational approaches, with an emphasis on “cognitive integration” or the “extended mind hypothesis” in particular. We feel this offers the most promise for human psychology because it incorporates the social and historical processes that are crucial to human “mind-making” within an evolutionarily informed framework. In addition to linking to other research areas in psychology, this approach is more likely to form productive links to other disciplines within the social sciences, not least by encouraging a healthy pluralism in approach.

Evolution, development, and the emergence of disgust

Evolutionary developmental psychology typically utilizes an evolutionary lens to explain various phenomena that occur throughout development. In this paper, I argue that the converse is also important: Developmental evidence can inform evolutionary theory. In particular, knowledge about the developmental origins of a psychological trait can be used to evaluate theoretical claims about its evolved function. I use the emotion of disgust as a case study to illustrate this approach. Disgust is commonly thought to be a behavioral adaptation for avoiding the ingestion of pathogens. Given this claim, disgust should be expected to develop at a time when humans are especially vulnerable to the dangers of ingesting pathogens, during the immediate post-weaning period from about 3 to 5 years of age. Despite a strong selective pressure at this point in development, research has suggested that the emotion of disgust and the recognition of the "disgust face" do not reliably emerge until later in ontogeny, at 5 years of age or after. Given the late developmental appearance of disgust, I re-evaluate claims about its adaptive role.

Why Are Childhood Family Factors Associated With Timing of Maturation? A Role for Internal Prediction

Children, particularly girls, who experience early familial adversity tend to go on to reach sexual maturity relatively early. This feature of adolescent development is believed to be an evolved strategy that arose because individuals with genes that caused them to mature relatively early under certain conditions left behind more descendants than those who did not. However, although much has been done to uncover the psychological and physiological mechanisms underlying this process, less attention has been paid to the evolutionary reasons behind why it might be advantageous. It has previously been suggested that this strategy evolved because early familial adversity accurately indicated later environmental adversity, under which conditions early reproduction would likely maximize evolutionary fitness. In this article, we contrast this “external prediction” model with an alternative explanation, which builds on the existing explanation and is mutually compatible with it but also distinct from it. We argue that accelerated development is advantageous because early adversity detrimentally affects the individual’s body, increasing later morbidity and mortality; individuals may adapt to this internal setback by accelerating their development. Unlike the external prediction model, this “internal prediction” relies not on temporal environmental continuity but on long-term effects of early circumstances on the body.