The evolution of decision rules in complex environments

Multiplayer videogames to analyze behavior during ecological interactions

Behavior shapes population and community dynamics through feedbacks with habitat configuration and interaction networks. Work on this interplay includes longitudinal surveys, experiments, and models. Multiplayer online video games foster real-time interactions among lots of players in virtual spaces. Data from these games could complement theoretical and empirical work, but research on them is only emerging now. We highlight how these games allow us to track individual movement, decisions, interactions, and performance in a tractable environment. We use our work on the game Dead by Daylight as an example to show that social and predator-prey interactions can generate complex ecoevolutionary dynamics favoring an array of behavioral traits we often study in nature. These games can foster progress in ecoevolutionary and behavioral research.

Choice overload and its consequences for animal decision-making

Animals routinely make decisions with important consequences for their survival and reproduction, but they frequently make suboptimal decisions. Here, we explore choice overload as one reason why animals may make suboptimal decisions, arguing that choice overload may have important ecological and evolutionary consequences, and propose future directions.

The evolution of division of labour: preconditions and evolutionary feedback

Division of Labour (DoL) among group members reflects the pinnacle of social complexity. The synergistic effects created by task specialization and the sharing of duties benefitting the group raise the efficiency of the acquisition, use, management and defence of resources by a fundamental step above the potential of individual agents. At the same time, it may stabilize societies because of the involved interdependence among collaborators. Here, I review the conditions associated with the emergence of DoL, which include the existence of (i) sizeable groups with enduring membership; (ii) individual specialization improving the efficiency of task performance; and (iii) low conflict of interest among group members owing to correlated payoffs. This results in (iv) a combination of intra-individual consistency with inter-individual variance in carrying out different tasks, which creates (v) some degree of mutual interdependence among group members. DoL typically evolves 'bottom-up' without external regulatory forces, but the latter may gain importance at a later stage of the evolution of social complexity. Owing to the involved feedback processes, cause and effect are often difficult to disentangle in the evolutionary trajectory towards structured societies with well-developed DoL among their members. Nevertheless, the emergence of task specialization and DoL may entail a one-way street towards social complexity, with retrogression getting increasingly difficult the more individual agents depend on each other at progressing stages of social evolution.This article is part of the theme issue 'Division of labour as key driver of social evolution'.

Chimpanzees adapt their exploration to key properties of the environment

Exploration is an important strategy for reducing the uncertainty that pervades daily life. Yet the evolutionary roots of adaptive exploration are poorly understood. We harness and adapt the human decisions-from-experience paradigm to investigate exploration under uncertainty in chimpanzees. In our study, chimpanzees (N = 15; eight females) are simultaneously confronted with an uncertain option (with outcome variance) and a safe option (without outcome variance) and tested in both stable and changing environments. Results reveal that, as in human exploration, how and how much chimpanzees explore depends on the environment. One key environmental property is change: Chimpanzees explore more across trials in changing than in stable conditions. Consistent with the assumption of classic economic models that variance indicates risk, chimpanzees also explore more when they experience variance in the options' outcomes. Individual risk and uncertainty preferences did not have a statistically significant effect on exploratory efforts. These findings suggest that chimpanzees and humans share key similarities in the way they respond to risk and uncertainty.

Prenatal Zika Virus Exposure Alters the Interaction Between Affective Processing and Decision-Making in Juvenile Rhesus Macaques (Macaca mulatta)

Many challenges during pregnancy can disrupt fetal development and have varying consequences on the subsequent psychological development of infants. Notably, exposure to infectious pathogens during fetal development, such as those encountered in viral pandemics, has been associated with persistent developmental consequences on infants' brains and behavior. However, the underlying mechanisms and the degree to which neural plasticity over infancy may accommodate fetal insults remain unclear. To address this gap, we investigated the interaction between affective processing and decision-making in a cohort of rhesus monkey juveniles exposed to Zika virus (ZIKV) during fetal development, a pathogen known to profoundly disrupt central nervous system development. Ten juveniles exposed to ZIKV during their fetal development and nine procedure-matched controls (CONs) completed a judgment bias task with and without a negative mood induction. Although ZIKV exposure did not impact the monkeys' decision-making processes during the task or the magnitude of their behavioral responses to the mood induction procedure, it did alter the influence of mood induction on decision-making. Although CON monkeys exhibited significantly more conservative decision-making following negative mood induction, the decision-making of Zika-exposed monkeys remained consistent among conditions. These findings suggest that fetal exposure to ZIKV impacts the neural systems involved in integrating affective and cognitive information, with potential long-term implications for learning, memory, and emotion regulation.

Pollinator cognition and the function of complex rewards

The cognitive ecology of pollination is most often studied using simple rewards, yet flowers often contain multiple types of chemically complex rewards, each varying along multiple dimensions of quality. In this review we highlight ways in which reward complexity can impact pollinator cognition, demonstrating the need to consider ecologically realistic rewards to fully understand plant-pollinator interactions. We show that pollinators' reward preferences can be modulated by reward chemistry and the collection of multiple reward types. We also discuss how reward complexity can mediate pollinator learning through a variety of mechanisms, both with and without reward preference being altered. Finally, we show how an understanding of decision-making strategies is necessary to predict how pollinators' evaluation of reward options depends on the other options available.

Environmental complexity and regularity shape the evolution of cognition

The environmental complexity hypothesis suggests that cognition evolves to allow animals to negotiate a complex and changing environment. By contrast, signal detection theory suggests cognition exploits environmental regularities by containing biases (e.g. to avoid dangerous predators). Therefore, two significant bodies of theory on cognitive evolution may be in tension: one foregrounds environmental complexity, the other regularity. Difficulty in reconciling these theories stems from their focus on different aspects of cognition. The environmental complexity hypothesis focuses on the reliability of sensors in the origin of cognition, while signal detection theory focuses on decision making in cognitively sophisticated animals. Here, we extend the signal detection model to examine the joint evolution of mechanisms for detecting information (sensory systems) and those that process information to produce behaviour (decision-making systems). We find that the transition to cognition can only occur if processing compensates for unreliable sensors by trading-off errors. Further, we provide an explanation for why animals with sophisticated sensory systems nonetheless disregard the reliable information it provides, by having biases for particular behaviours. Our model suggests that there is greater nuance than has been previously appreciated, and that both complexity and regularity can promote cognition.

Economic foraging in a floral marketplace: asymmetrically dominated decoy effects in bumblebees

While most models of decision-making assume that individuals assign options absolute values, animals often assess options comparatively, violating principles of economic rationality. Such 'irrational' preferences are especially common when two rewards vary along multiple dimensions of quality and a third, 'decoy' option is available. Bumblebees are models of decision-making, yet whether they are subject to decoy effects is unknown. We addressed this question using bumblebees (Bombus impatiens) choosing between flowers that varied in their nectar concentration and reward rate. We first gave bees a choice between two flower types, one higher in concentration and the other higher in reward rate. Bees were then given a choice between these flowers and either a 'concentration' or 'rate' decoy, designed to be asymmetrically dominated on each axis. The rate decoy increased bees' preference in the expected direction, while the concentration decoy did not. In a second experiment, we manipulated choices along two single reward dimensions to test whether this discrepancy was explained by differences in how concentration versus reward rate were evaluated. We found that low-concentration decoys increased bees' preference for the medium option as predicted, whereas low-rate decoys had no effect. Our results suggest that both low- and high-value flowers can influence pollinator preferences in ways previously unconsidered.

Emotional contagion and prosocial behaviour in fish: An evolutionary and mechanistic approach

In this review, we consider the definitions and experimental approaches to emotional contagion and prosocial behaviour in mammals and explore their evolutionary conceptualisation for studying their occurrence in the evolutionarily divergent vertebrate group of ray-finned fish. We present evidence for a diverse set of fish phenotypes that meet definitional criteria for prosocial behaviour and emotional contagion and discuss conserved mechanisms that may account for some preserved social capacities in fish. Finally, we provide some considerations on how to address the question of interdependency between emotional contagion and prosocial response, highlighting the importance of recognition processes, decision-making systems, and ecological context for providing evolutionary explanations.

Unpredictable benefits of social information can lead to the evolution of individual differences in social learning

Human ecological success is often attributed to our capacity for social learning, which facilitates the spread of adaptive behaviours through populations. All humans rely on social learning to acquire culture, but there is substantial variation across societies, between individuals and over developmental time. However, it is unclear why these differences exist. Here, we present an evolutionary model showing that individual variation in social learning can emerge if the benefits of social learning are unpredictable. Unpredictability selects for flexible developmental programmes that allow individuals to update their reliance on social learning based on previous experiences. This developmental flexibility, in turn, causes some individuals in a population to end up consistently relying more heavily on social learning than others. We demonstrate this core evolutionary mechanism across three scenarios of increasing complexity, investigating the impact of different sources of uncertainty about the usefulness of social learning. Our results show how evolution can shape how individuals learn to learn from others, with potentially profound effects on cultural diversity.

A critical review of risk-sensitive foraging

Foraging is risk sensitive if choices depend on the variability of returns from the options as well as their mean return. Risk-sensitive foraging is important in behavioural ecology, psychology and neurophysiology. It has been explained both in terms of mechanisms and in terms of evolutionary advantage. We provide a critical review, evaluating both mechanistic and evolutionary accounts. Some derivations of risk sensitivity from mechanistic models based on psychophysics are not convincing because they depend on an inappropriate use of Jensen's inequality. Attempts have been made to link risk sensitivity to the ecology of a species, but again these are not convincing. The field of risk-sensitive foraging has provided a focus for theoretical and empirical work and has yielded important insights, but we lack a simple and empirically defendable general account of it in either mechanistic or evolutionary terms. However, empirical analysis of choice sequences under theoretically motivated experimental designs and environmental settings appears a promising avenue for mapping the scope and relative merits of existing theories. Simply put, the devil is in the sequence.

Collective incentives reduce over-exploitation of social information in unconstrained human groups

Collective dynamics emerge from countless individual decisions. Yet, we poorly understand the processes governing dynamically-interacting individuals in human collectives under realistic conditions. We present a naturalistic immersive-reality experiment where groups of participants searched for rewards in different environments, studying how individuals weigh personal and social information and how this shapes individual and collective outcomes. Capturing high-resolution visual-spatial data, behavioral analyses revealed individual-level gains-but group-level losses-of high social information use and spatial proximity in environments with concentrated (vs. distributed) resources. Incentivizing participants at the group (vs. individual) level facilitated adaptation to concentrated environments, buffering apparently excessive scrounging. To infer discrete choices from unconstrained interactions and uncover the underlying decision mechanisms, we developed an unsupervised Social Hidden Markov Decision model. Computational results showed that participants were more sensitive to social information in concentrated environments frequently switching to a social relocation state where they approach successful group members. Group-level incentives reduced participants' overall responsiveness to social information and promoted higher selectivity over time. Finally, mapping group-level spatio-temporal dynamics through time-lagged regressions revealed a collective exploration-exploitation trade-off across different timescales. Our study unravels the processes linking individual-level strategies to emerging collective dynamics, and provides tools to investigate decision-making in freely-interacting collectives.

The use of social information in vulture flight decisions

Animals rely on a balance of personal and social information to decide when and where to move next in order to access a desired resource. The benefits from cueing on conspecifics to reduce uncertainty about resource availability can be rapidly overcome by the risks of within-group competition, often exacerbated toward low-ranked individuals. Being obligate soarers, relying on thermal updraughts to search for carcasses around which competition can be fierce, vultures represent ideal models to investigate the balance between personal and social information during foraging movements. Linking dominance hierarchy, social affinities and meteorological conditions to movement decisions of eight captive vultures, Gyps spp., released for free flights in natural soaring conditions, we found that they relied on social information (i.e. other vultures using/having used the thermals) to find the next thermal updraught, especially in unfavourable flight conditions. Low-ranked individuals were more likely to disregard social cues when deciding where to go next, possibly to minimize the competitive risk of social aggregation. These results exemplify the architecture of decision-making during flight in social birds. It suggests that the environmental context, the context of risk and the social system as a whole calibrate the balance between personal and social information use.

Resource choice during ontogeny enhances both the short- and longer-term welfare of laying hen pullets

We hypothesised that resource choice during early life contributes to both current and longer-term beneficial effects on animal welfare. We investigated this hypothesis in a longitudinal cross-over experiment with laying hen pullets (Gallus gallus domesticus) reared in pens with one or four litter and perch types, respectively (n = 8 pens/treatment, all providing ample and identical litter and perch space). After 4 weeks (chick period), half the pens were modified to provide the opposite treatment (juvenile period). After 11 more weeks, all groups were moved to novel, identical laying pens (adult period; Week 16-27). In support of our hypothesis, the opportunity to choose between multiple litter and perch variants was associated with higher levels of positively-valenced behaviours, including play as chicks and dustbathing as juveniles and adults, and lower levels of negatively-valenced behaviours, including feather pecking as chicks and juveniles and aggressive pecking as adults. Resource choice in the juvenile period also led to better juvenile and adult plumage condition, and greater growth as adults. We conclude that the opportunity to choose among different litter and perch types, instead of having only one type of each, had both short- and longer-term positive effects on the birds' affective states and physical condition.

Dynamic noise estimation: A generalized method for modeling noise fluctuations in decision-making

Computational cognitive modeling is an important tool for understanding the processes supporting human and animal decision-making. Choice data in decision-making tasks are inherently noisy, and separating noise from signal can improve the quality of computational modeling. Common approaches to model decision noise often assume constant levels of noise or exploration throughout learning (e.g., the ϵ -softmax policy). However, this assumption is not guaranteed to hold - for example, a subject might disengage and lapse into an inattentive phase for a series of trials in the middle of otherwise low-noise performance. Here, we introduce a new, computationally inexpensive method to dynamically infer the levels of noise in choice behavior, under a model assumption that agents can transition between two discrete latent states (e.g., fully engaged and random). Using simulations, we show that modeling noise levels dynamically instead of statically can substantially improve model fit and parameter estimation, especially in the presence of long periods of noisy behavior, such as prolonged attentional lapses. We further demonstrate the empirical benefits of dynamic noise estimation at the individual and group levels by validating it on four published datasets featuring diverse populations, tasks, and models. Based on the theoretical and empirical evaluation of the method reported in the current work, we expect that dynamic noise estimation will improve modeling in many decision-making paradigms over the static noise estimation method currently used in the modeling literature, while keeping additional model complexity and assumptions minimal.

A neural network model for the evolution of learning in changing environments

Learning from past experience is an important adaptation and theoretical models may help to understand its evolution. Many of the existing models study simple phenotypes and do not consider the mechanisms underlying learning while the more complex neural network models often make biologically unrealistic assumptions and rarely consider evolutionary questions. Here, we present a novel way of modelling learning using small neural networks and a simple, biology-inspired learning algorithm. Learning affects only part of the network, and it is governed by the difference between expectations and reality. We use this model to study the evolution of learning under various environmental conditions and different scenarios for the trade-off between exploration (learning) and exploitation (foraging). Efficient learning readily evolves in our individual-based simulations. However, in line with previous studies, the evolution of learning is less likely in relatively constant environments, where genetic adaptation alone can lead to efficient foraging, or in short-lived organisms that cannot afford to spend much of their lifetime on exploration. Once learning does evolve, the characteristics of the learning strategy (i.e. the duration of the learning period and the learning rate) and the average performance after learning are surprisingly little affected by the frequency and/or magnitude of environmental change. In contrast, an organism's lifespan and the distribution of resources in the environment have a clear effect on the evolved learning strategy: a shorter lifespan or a broader resource distribution lead to fewer learning episodes and larger learning rates. Interestingly, a longer learning period does not always lead to better performance, indicating that the evolved neural networks differ in the effectiveness of learning. Overall, however, we show that a biologically inspired, yet relatively simple, learning mechanism can evolve to lead to an efficient adaptation in a changing environment.

Spoilt for choice: Do female mosquitoes experience choice overload when deciding where to lay eggs?

Animals live in complex natural environments. Based on the effects of natural selection, theory on animal information use says that it is optimal for animals to make "rational" decisions, i.e., to choose alternatives which maximize fitness gains, irrespective of the number of alternatives presented to them. Yet, animals commonly make seemingly "irrational" choices in the face of complex and variable stimuli that challenge their cognitive machinery. Here, we test the choice overload hypothesis - decision-making is negatively affected when animals experience an overload of choice. Using simultaneous-choice trials that varied in choice repertoire size, we examined oviposition site selection behaviour in Aedes aegypti towards larval predators, the nymphs of Bradinopyga geminata. Based on the underlying fitness trade-offs of oviposition decision-making, we predicted that female oviposition preference would be weaker and variation in this response would be higher in complex, multiple-choice trials than in binary-choice trials. In partial support of our hypothesis, oviposition preference was weaker in the complex, multiple-choice trials, but the variation in response depended on predator density, and did not depend on choice repertoire size. We suggest that information overload can negatively affect certain aspects of animal decision-making, resulting in choices appearing as "irrational" if the complexity of the decision-making context is not incorporated. Information overload can potentially lead to alternative strategies, such as bet-hedging or decision-making with reduced discrimination.

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.

Mood fluctuations shift cost-benefit tradeoffs in economic decisions

Mood effects on economic choice seem blatantly irrational, but might rise from mechanisms adapted to natural environments. We have proposed a theory in which mood helps adapting the behaviour to statistical dependencies in the environment, by biasing the expected value of foraging actions (which involve taking risk, spending time and making effort to get more reward). Here, we tested the existence of this mechanism, using an established mood induction paradigm combined with independent economic choices that opposed small but uncostly rewards to larger but costly rewards (involving either risk, delay or effort). To maximise the sensitivity to mood fluctuations, we developed an algorithm ensuring that choice options were continuously adjusted to subjective indifference points. In 102 participants tested twice, we found that during episodes of positive mood (relative to negative mood), choices were biased towards better rewarded but costly options, irrespective of the cost type. Computational modelling confirmed that the incidental mood effect was best explained by a bias added to the expected value of costly options, prior to decision making. This bias is therefore automatically applied even in artificial environments where it is not adaptive, allowing mood to spill over many sorts of decisions and generate irrational behaviours.

To eat, or not to eat: a phantom decoy affects information-gathering behavior by a free-ranging mammalian herbivore

When foraging, making appropriate food choices is crucial to an animal's fitness. Classic foraging ecology theories assume animals choose food of greatest benefit based on their absolute value across multiple dimensions. Consequently, poorer options are considered irrelevant alternatives that should not influence decision-making among better options. But heuristic studies demonstrate that irrelevant alternatives (termed decoys) can influence the decisions of some animals, indicating they use a relative rather than absolute evaluation system. Our aim was to test whether a decoy influenced the decision-making process-that is, information-gathering and food choice-of a free-ranging mammalian herbivore. We tested swamp wallabies, Wallabia bicolor, comparing their behavior toward, and choice of, two available food options over time in the absence or presence of the decoy. We used a phantom decoy-unavailable option-and ran two trials in different locations and seasons. Binary preferences (decoy absent) for the two available food options differed between trials. Irrespective of this difference, across both trials the presence of the decoy resulted in animals more likely to overtly investigate available food options. But, the decoy only shifted food choice, weakly, in one trial. Our results indicate that the decoy influenced the information-gathering behavior during decision-making, providing the first evidence that decoys can affect decision-making process of free-ranging mammalian herbivores in an ecologically realistic context. It is premature to say these findings confirm the use of relative evaluation systems. Whether the foraging outcome is more strongly affected by other decoys, food dimensions, or ecological contexts, is yet to be determined.

How honey bees make fast and accurate decisions

Honey bee ecology demands they make both rapid and accurate assessments of which flowers are most likely to offer them nectar or pollen. To understand the mechanisms of honey bee decision-making, we examined their speed and accuracy of both flower acceptance and rejection decisions. We used a controlled flight arena that varied both the likelihood of a stimulus offering reward and punishment and the quality of evidence for stimuli. We found that the sophistication of honey bee decision-making rivalled that reported for primates. Their decisions were sensitive to both the quality and reliability of evidence. Acceptance responses had higher accuracy than rejection responses and were more sensitive to changes in available evidence and reward likelihood. Fast acceptances were more likely to be correct than slower acceptances; a phenomenon also seen in primates and indicative that the evidence threshold for a decision changes dynamically with sampling time. To investigate the minimally sufficient circuitry required for these decision-making capacities, we developed a novel model of decision-making. Our model can be mapped to known pathways in the insect brain and is neurobiologically plausible. Our model proposes a system for robust autonomous decision-making with potential application in robotics.

Arc regulates a second-guessing cognitive bias during naturalistic foraging through effects on discrete behavior modules

Foraging in animals relies on innate decision-making heuristics that can result in suboptimal cognitive biases in some contexts. The mechanisms underlying these biases are not well understood, but likely involve strong genetic effects. To explore this, we studied fasted mice using a naturalistic foraging paradigm and discovered an innate cognitive bias called "second-guessing." This involves repeatedly investigating an empty former food patch instead of consuming available food, which hinders the mice from maximizing feeding benefits. The synaptic plasticity gene Arc is revealed to play a role in this bias, as Arc-deficient mice did not exhibit second-guessing and consumed more food. In addition, unsupervised machine learning decompositions of foraging identified specific behavior sequences, or "modules", that are affected by Arc. These findings highlight the genetic basis of cognitive biases in decision making, show links between behavior modules and cognitive bias, and provide insight into the ethological roles of Arc in naturalistic foraging.

When should bees be flower constant? An agent-based model highlights the importance of social information and foraging conditions

Many bee species show flower constancy, that is, a tendency to visit flowers of one type during a foraging trip. Flower constancy is important for plant reproduction, but the benefits of constancy to bees is unclear. Social bees, which often use communication about food sources, show particularly strong flower constancy. We aimed to better understand the benefits of flower constancy in social bees and how these benefits depend on foraging conditions. We hypothesised that sharing social information increases the benefits of flower constancy because social foragers share information selectively about high-quality food sources, thereby reducing the need to sample alternatives. We developed an agent-based model that allowed us to simulate bee colonies with and without communication and flower constancy in different foraging environments. By varying key environmental parameters, such as food source numbers and reward size, we explored how the costs and benefits of flower constancy depend on the foraging landscape. Flower constancy alone performed poorly in all environments, while indiscriminate flower choice was often the most successful strategy. However, communication improved the performance of flower constant colonies considerably in most environments. This combination was particularly successful when high-quality food sources were abundant and competition was weak. Our findings help explain why social bees tend to be more flower constant than solitary bees and suggest that flower constancy can be an adaptive strategy in social bees. Simulations suggest that anthropogenic changes of foraging landscapes will have different effects on the foraging performance of bees that vary in flower constancy.

Testing the Social Function of Metacognition for Common-Pool Resource Use

Metacognition, the ability to monitor and evaluate our own cognitive processes, confers advantages to individuals and their own judgment. A more recent hypothesis, however, states that explicit metacognition may also enhance the collective judgment of groups, and may enhance human collaboration and coordination. Here, we investigate this social function hypothesis of metacognition with arguably one of the oldest collaboration problems humans face, common-pool resource use. Using an agent-based model that simulates repeated group interactions and the forming of collective judgments about resource extraction, we show that (1) in "kind" environments (where confidence and judgment accuracy correlate positively), explicit metacognition may allow groups to reach more accurate judgments compared to groups exchanging object-level information only; while (2) in "wicked" environments (where confidence and judgment accuracy correlate negatively), explicit metacognition may protect groups from the large judgment errors yielded by groups using metacognitive information for individual-level learning only (implicit metacognition). With explicit metacognition, this research highlights a novel mechanism which, among others, provides a testable explanation of the often-observed finding that groups all over the world communicate to enhance common property use.

Magnitude-sensitive reaction times reveal non-linear time costs in multi-alternative decision-making

Optimality analysis of value-based decisions in binary and multi-alternative choice settings predicts that reaction times should be sensitive only to differences in stimulus magnitudes, but not to overall absolute stimulus magnitude. Yet experimental work in the binary case has shown magnitude sensitive reaction times, and theory shows that this can be explained by switching from linear to multiplicative time costs, but also by nonlinear subjective utility. Thus disentangling explanations for observed magnitude sensitive reaction times is difficult. Here for the first time we extend the theoretical analysis of geometric time-discounting to ternary choices, and present novel experimental evidence for magnitude-sensitivity in such decisions, in both humans and slime moulds. We consider the optimal policies for all possible combinations of linear and geometric time costs, and linear and nonlinear utility; interestingly, geometric discounting emerges as the predominant explanation for magnitude sensitivity.

Analysis of decisions based on option value (e.g. which pile of coins would you like?) suggests that the optimal rules correspond to simple mechanisms also known to be optimal for perceptual decisions (e.g. which light is brighter?) But, crucially, these analyses assume that the cost of time is linear—when the more usual assumption is made that time discounts multiplicatively (e.g. ‘a bird in the hand is worth two in the bush (and so two in the hand are worth four in the bush)’) then optimal decision-making looks quite different—in particular, the theory predicts that decision-making should be sensitive to the absolute magnitude of the opportunities, such as coin pile sizes, under consideration, in a way that the optimal perceptual mechanisms are not. As well as the theory, we present novel experimental evidence from human decision-making experiments, and foraging slime mould, of precisely such magnitude-sensitivity. This is a rare example of theory in behaviour making a falsifiable prediction that is confirmed in two, highly divergent, species, one with a brain and one without.

Using optimal foraging theory to infer how groups make collective decisions

Studying animal behavior as collective phenomena is a powerful tool for understanding social processes, including group coordination and decision-making. However, linking individual behavior during group decision-making to the preferences underlying those actions poses a considerable challenge. Optimal foraging theory, and specifically the marginal value theorem (MVT), can provide predictions about individual preferences, against which the behavior of groups can be compared under different models of influence. A major strength of formally linking optimal foraging theory to collective behavior is that it generates predictions that can easily be tested under field conditions. This opens the door to studying group decision-making in a range of species; a necessary step for revealing the ecological drivers and evolutionary consequences of collective decision-making.

Once an optimist, always an optimist? Studying cognitive judgment bias in mice

Individuals differ in the way they judge ambiguous information: some individuals interpret ambiguous information in a more optimistic, and others in a more pessimistic way. Over the past two decades, such "optimistic" and "pessimistic" cognitive judgment biases (CJBs) have been utilized in animal welfare science as indicators of animals' emotional states. However, empirical studies on their ecological and evolutionary relevance are still lacking. We, therefore, aimed at transferring the concept of "optimism" and "pessimism" to behavioral ecology and investigated the role of genetic and environmental factors in modulating CJB in mice. In addition, we assessed the temporal stability of individual differences in CJB. We show that the chosen genotypes (C57BL/6J and B6D2F1N) and environments ("scarce" and "complex") did not have a statistically significant influence on the responses in the CJB test. By contrast, they influenced anxiety-like behavior with C57BL/6J mice and mice from the "complex" environment displaying less anxiety-like behavior than B6D2F1N mice and mice from the "scarce" environment. As the selected genotypes and environments did not explain the existing differences in CJB, future studies might investigate the impact of other genotypes and environmental conditions on CJB, and additionally, elucidate the role of other potential causes like endocrine profiles and epigenetic modifications. Furthermore, we show that individual differences in CJB were repeatable over a period of seven weeks, suggesting that CJB represents a temporally stable trait in laboratory mice. Therefore, we encourage the further study of CJB within an animal personality framework.

Biased Learning as a Simple Adaptive Foraging Mechanism

Adaptive cognitive biases, such as “optimism,” may have evolved as heuristic rules for computationally efficient decision-making, or as error-management tools when error payoff is asymmetrical. Ecologists typically use the term “optimism” to describe unrealistically positive expectations from the future that are driven by positively biased initial belief. Cognitive psychologists on the other hand, focus on valence-dependent optimism bias, an asymmetric learning process where information about undesirable outcomes is discounted (sometimes also termed “positivity biased learning”). These two perspectives are not mutually exclusive, and both may lead to similar emerging space-use patterns, such as increased exploration. The distinction between these two biases may becomes important, however, when considering the adaptive value of balancing the exploitation of known resources with the exploration of an ever-changing environment. Deepening our theoretical understanding of the adaptive value of valence-dependent learning, as well as its emerging space-use and foraging patterns, may be crucial for understanding whether, when and where might species withstand rapid environmental change. We present the results of an optimal-foraging model implemented as an individual-based simulation in continuous time and discrete space. Our forager, equipped with partial knowledge of average patch quality and inter-patch travel time, iteratively decides whether to stay in the current patch, return to previously exploited patches, or explore new ones. Every time the forager explores a new patch, it updates its prior belief using a simple single-parameter model of valence-dependent learning. We find that valence-dependent optimism results in the maintenance of positively biased expectations (prior-based optimism), which, depending on the spatiotemporal variability of the environment, often leads to greater fitness gains. These results provide insights into the potential ecological and evolutionary significance of valence-dependent optimism and its interplay with prior-based optimism.

Decision Making: a Theoretical Review

Decision-making is a crucial skill that has a central role in everyday life and is necessary for adaptation to the environment and autonomy. It is the ability to choose between two or more options, and it has been studied through several theoretical approaches and by different disciplines. In this overview article, we contend a theoretical review regarding most theorizing and research on decision-making. Specifically, we focused on different levels of analyses, including different theoretical approaches and neuropsychological aspects. Moreover, common methodological measures adopted to study decision-making were reported. This theoretical review emphasizes multiple levels of analysis and aims to summarize evidence regarding this fundamental human process. Although several aspects of the field are reported, more features of decision-making process remain uncertain and need to be clarified. Further experimental studies are necessary for understanding this process better and for integrating and refining the existing theories.

Magnitude-sensitivity: rethinking decision-making

Magnitude-sensitivity refers to the result that performance in decision-making, across domains and organisms, is affected by the total value of the possible alternatives. This simple result offers a window into fundamental issues in decision-making and has led to a reconsideration of ecological decision-making, prominent computational models of decision-making, and optimal decision-making. Moreover, magnitude-sensitivity has inspired the design of new robotic systems that exploit natural solutions and apply optimal decision-making policies. In this article, we review the key theoretical and empirical results about magnitude-sensitivity and highlight the importance that this phenomenon has for the understanding of decision-making. Furthermore, we discuss open questions and ideas for future research.

Acrobatic squirrels learn to leap and land on tree branches without falling

Arboreal animals often leap through complex canopies to travel and avoid predators. Their success at making split-second, potentially life-threatening decisions of biomechanical capability depends on their skillful use of acrobatic maneuvers and learning from past efforts. Here, we found that free-ranging fox squirrels (Sciurus niger) leaping across unfamiliar, simulated branches decided where to launch by balancing a trade-off between gap distance and branch-bending compliance. Squirrels quickly learned to modify impulse generation upon repeated leaps from unfamiliar, compliant beams. A repertoire of agile landing maneuvers enabled targeted leaping without falling. Unanticipated adaptive landing and leaping "parkour" behavior revealed an innovative solution for particularly challenging leaps. Squirrels deciding and learning how to launch and land demonstrates the synergistic roles of biomechanics and cognition in robust gap-crossing strategies.

Biases and Variability from Costly Bayesian Inference

When humans infer underlying probabilities from stochastic observations, they exhibit biases and variability that cannot be explained on the basis of sound, Bayesian manipulations of probability. This is especially salient when beliefs are updated as a function of sequential observations. We introduce a theoretical framework in which biases and variability emerge from a trade-off between Bayesian inference and the cognitive cost of carrying out probabilistic computations. We consider two forms of the cost: a precision cost and an unpredictability cost; these penalize beliefs that are less entropic and less deterministic, respectively. We apply our framework to the case of a Bernoulli variable: the bias of a coin is inferred from a sequence of coin flips. Theoretical predictions are qualitatively different depending on the form of the cost. A precision cost induces overestimation of small probabilities, on average, and a limited memory of past observations, and, consequently, a fluctuating bias. An unpredictability cost induces underestimation of small probabilities and a fixed bias that remains appreciable even for nearly unbiased observations. The case of a fair (equiprobable) coin, however, is singular, with non-trivial and slow fluctuations in the inferred bias. The proposed framework of costly Bayesian inference illustrates the richness of a 'resource-rational' (or 'bounded-rational') picture of seemingly irrational human cognition.

Irrational risk aversion in an ant

Animals must often decide between exploiting safe options or risky options with a chance for large gains. Both proximate theories based on perceptual mechanisms, and evolutionary ones based on fitness benefits, have been proposed to explain decisions under risk. Eusocial insects represent a special case of risk sensitivity, as they must often make collective decisions based on resource evaluations from many individuals. Previously, colonies of the ant Lasius niger were found to be risk-neutral, but the risk preference of individual foragers was unknown. Here, we tested individual L. niger in a risk sensitivity paradigm. Ants were trained to associate one scent with 0.55 M sucrose solution and another with an equal chance of either 0.1 or 1.0 M sucrose. Preference was tested in a Y-maze. Ants were extremely risk-averse, with 91% choosing the safe option. Based on the psychophysical Weber-Fechner law, we predicted that ants evaluate resources depending on their logarithmic difference. To test this hypothesis, we designed 4 more experiments by varying the relative differences between the alternatives, making the risky option less, equally or more valuable than the safe one. Our results support the logarithmic origin of risk aversion in ants, and demonstrate that the behaviour of individual foragers can be a very poor predictor of colony-level behaviour.

Spatiotemporal data mining: a survey on challenges and open problems

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

Polar bears are inefficient predators of seabird eggs

Climate-mediated sea-ice loss is disrupting the foraging ecology of polar bears (Ursus maritimus) across much of their range. As a result, there have been increased reports of polar bears foraging on seabird eggs across parts of their range. Given that polar bears have evolved to hunt seals on ice, they may not be efficient predators of seabird eggs. We investigated polar bears' foraging performance on common eider (Somateria mollissima) eggs on Mitivik Island, Nunavut, Canada to test whether bear decision-making heuristics are consistent with expectations of optimal foraging theory. Using aerial-drones, we recorded multiple foraging bouts over 11 days, and found that as clutches were depleted to completion, bears did not exhibit foraging behaviours matched to resource density. As the season progressed, bears visited fewer nests overall, but marginally increased their visitation to nests that were already empty. Bears did not display different movement modes related to nest density, but became less selective in their choice of clutches to consume. Lastly, bears that capitalized on visual cues of flushing eider hens significantly increased the number of clutches they consumed; however, they did not use this strategy consistently or universally. The foraging behaviours exhibited by polar bears in this study suggest they are inefficient predators of seabird eggs, particularly in the context of matching behaviours to resource density.

Misinformation tactics protect rare birds from problem predators

Efficient decision-making integrates previous experience with new information. Tactical use of misinformation can alter choice in humans. Whether misinformation affects decision-making in other free-living species, including problem species, is unknown. Here, we show that sensory misinformation tactics can reduce the impacts of predators on vulnerable bird populations as effectively as lethal control. We repeatedly exposed invasive mammalian predators to unprofitable bird odors for 5 weeks before native shorebirds arrived for nesting and for 8 weeks thereafter. Chick production increased 1.7-fold at odor-treated sites over 25 to 35 days, with doubled or tripled odds of successful hatching, resulting in a 127% increase in modeled population size in 25 years. We demonstrate that decision-making processes that respond to changes in information reliability are vulnerable to tactical manipulation by misinformation. Altering perceptions of prey availability offers an innovative, nonlethal approach to managing problem predators and improving conservation outcomes for threatened species.

Matching Behaviours and Rewards

Matching describes how behaviour is related to rewards. The matching law holds when the ratio of an individual's behaviours equals the ratio of the rewards obtained. From its origins in the study of pigeons working for food in the laboratory, the law has been applied to a range of species, both in the laboratory and outside it (e.g., human sporting decisions). Probability matching occurs when the probability of a behaviour equals the probability of being rewarded. Input matching predicts the distribution of individuals across habitats. We evaluate the rationality of the matching law and probability matching, expose the logic of matching in real-world cases, review how recent neuroscience findings relate to matching, and suggest future research directions.

Win-stay/lose-switch, prospecting-based settlement strategy may not be adaptive under rapid environmental change

Understanding animal responses to environmental change is crucial for management of ecological traps. Between-year habitat selection was investigated in red-necked grebes (Podiceps grisegena) breeding on semi-natural fish ponds, where differential stocking of fish created contrasting yet poorly predictable brood-stage food availabilities. Grebes lured to low-quality ponds were more likely to shift territories than birds nesting on high-quality ponds, and tended to move to ponds whose habitat quality had been high in the previous year, irrespective of the current quality of the new and old territories. The territory switchers typically visited their future breeding ponds during or immediately after the brood-rearing period. However, owing to rotation of fish stocks, the habitat quality of many ponds changed in the following year, and then switchers from low-quality ponds and stayers on previously high-quality ponds were ecologically trapped. Thus, although breeders were making an informed choice, their settlement decisions, based on the win-stay/lose-switch rule and prospecting a year in advance, were inappropriate in conditions of year-to-year habitat fluctuations. Effective adaptation to rapid environmental change may necessitate both learning to correctly evaluate uncertain environmental cues and abandonment of previously adaptive decision-making algorithms (here prioritizing past-year information and assuming temporal autocorrelation of habitat quality).

Variation in primate decision-making under uncertainty and the roots of human economic behaviour

Uncertainty is a ubiquitous component of human economic behaviour, yet people can vary in their preferences for risk across populations, individuals and different points in time. As uncertainty also characterizes many aspects of animal decision-making, comparative research can help evaluate different potential mechanisms that generate this variation, including the role of biological differences or maturational change versus cultural learning, as well as identify human-unique components of economic decision-making. Here, we examine decision-making under risk across non-human primates, our closest relatives. We first review theoretical approaches and current methods for understanding decision-making in animals. We then assess the current evidence for variation in animal preferences between species and populations, between individuals based on personality, sex and age, and finally, between different contexts and individual states. We then use these primate data to evaluate the processes that can shape human decision-making strategies and identify the primate foundations of human economic behaviour. This article is part of the theme issue 'Existence and prevalence of economic behaviours among non-human primates'.

Towards an Evolutionary Theory of Stress Responses

All organisms have a stress response system to cope with environmental threats, yet its precise form varies hugely within and across individuals, populations, and species. While the physiological mechanisms are increasingly understood, how stress responses have evolved remains elusive. Here, we show that important insights can be gained from models that incorporate physiological mechanisms within an evolutionary optimality analysis (the 'evo-mecho' approach). Our approach reveals environmental predictability and physiological constraints as key factors shaping stress response evolution, generating testable predictions about variation across species and contexts. We call for an integrated research programme combining theory, experimental evolution, and comparative analysis to advance scientific understanding of how this core physiological system has evolved.

Computational animal welfare: towards cognitive architecture models of animal sentience, emotion and wellbeing

To understand animal wellbeing, we need to consider subjective phenomena and sentience. This is challenging, since these properties are private and cannot be observed directly. Certain motivations, emotions and related internal states can be inferred in animals through experiments that involve choice, learning, generalization and decision-making. Yet, even though there is significant progress in elucidating the neurobiology of human consciousness, animal consciousness is still a mystery. We propose that computational animal welfare science emerges at the intersection of animal behaviour, welfare and computational cognition. By using ideas from cognitive science, we develop a functional and generic definition of subjective phenomena as any process or state of the organism that exists from the first-person perspective and cannot be isolated from the animal subject. We then outline a general cognitive architecture to model simple forms of subjective processes and sentience. This includes evolutionary adaptation which contains top-down attention modulation, predictive processing and subjective simulation by re-entrant (recursive) computations. Thereafter, we show how this approach uses major characteristics of the subjective experience: elementary self-awareness, global workspace and qualia with unity and continuity. This provides a formal framework for process-based modelling of animal needs, subjective states, sentience and wellbeing.

Behavioural flexibility and reputation formation

Limited flexibility in behaviour gives rise to behavioural consistency, so that past behaviour is partially predictive of current behaviour. The consequences of limits to flexibility are investigated in a population in which pairs of individuals play a game of trust. The game can either be observed by others or not. Reputation is based on trustworthiness when observed and acts as a signal of behaviour in future interactions with others. Individuals use the reputation of partner in deciding whether to trust them, both when observed by others and when not observed. We explore the effects of costs of exhibiting a difference in behaviour between when observed and when not observed (i.e. a cost of flexibility). When costs are low, individuals do not attempt to signal that they will later be trustworthy: their signal should not be believed since it will always pay them to be untrustworthy if trusted. When costs are high, their local optimal behaviour automatically acts as an honest signal. At intermediate costs, individuals are very trustworthy when observed in order to convince others of their trustworthiness when unobserved. It is hypothesized that this type of strong signalling might occur in other settings.

The Rules of Attraction: The Necessary Role of Animal Cognition in Explaining Conservation Failures and Successes

Integrating knowledge and principles of animal behavior into wildlife conservation and management has led to some concrete successes but has failed to improve conservation outcomes in other cases. Many conservation interventions involve attempts to either attract or repel animals, which we refer to as approach/avoidance issues. These attempts can be reframed as issues of manipulating the decisions animals make, which are driven by their perceptual abilities and attentional biases, as well as the value animals attribute to current stimuli and past learned experiences. These processes all fall under the umbrella of animal cognition. Here, we highlight rules that emerge when considering approach/avoidance conservation issues through the lens of cognitive-based management. For each rule, we review relevant conservation successes and failures to better predict the conditions in which behavior can be manipulated, and we suggest how to avoid future failures.

Life in fluctuating environments

Variability in the environment defines the structure and dynamics of all living systems, from organisms to ecosystems. Species have evolved traits and strategies that allow them to detect, exploit and predict the changing environment. These traits allow organisms to maintain steady internal conditions required for physiological functioning through feedback mechanisms that allow internal conditions to remain at or near a set-point despite a fluctuating environment. In addition to feedback, many organisms have evolved feedforward processes, which allow them to adjust in anticipation of an expected future state of the environment. Here we provide a framework describing how feedback and feedforward mechanisms operating within organisms can generate effects across scales of organization, and how they allow living systems to persist in fluctuating environments. Daily, seasonal and multi-year cycles provide cues that organisms use to anticipate changes in physiologically relevant environmental conditions. Using feedforward mechanisms, organisms can exploit correlations in environmental variables to prepare for anticipated future changes. Strategies to obtain, store and act on information about the conditional nature of future events are advantageous and are evidenced in widespread phenotypes such as circadian clocks, social behaviour, diapause and migrations. Humans are altering the ways in which the environment fluctuates, causing correlations between environmental variables to become decoupled, decreasing the reliability of cues. Human-induced environmental change is also altering sensory environments and the ability of organisms to detect cues. Recognizing that living systems combine feedback and feedforward processes is essential to understanding their responses to current and future regimes of environmental fluctuations.

This article is part of the theme issue ‘Integrative research perspectives on marine conservation’.