Risk preference as an outcome of evolutionarily adaptive learning mechanisms: An evolutionary simulation under diverse risky environments

The optimization of cognitive and learning mechanisms can reveal complicated behavioral phenomena. In this study, we focused on reinforcement learning, which uses different learning rules for positive and negative reward prediction errors. We attempted to relate the evolved learning bias to the complex features of risk preference such as domain-specific behavior manifests and the relatively stable domain-general factor underlying behaviors. The simulations of the evolution of the two learning rates under diverse risky environments showed that the positive learning rate evolved on average to be higher than the negative one, when agents experienced both tasks where risk aversion was more rewarding and risk seeking was more rewarding. This evolution enabled agents to flexibly choose more reward behaviors depending on the task type. The evolved agents also demonstrated behavioral patterns described by the prospect theory. Our simulations captured two aspects of the evolution of risk preference: the domain-specific aspect, behavior acquired through learning in a specific context; and the implicit domain-general aspect, corresponding to the learning rates shaped through evolution to adaptively behave in a wide range of environments. These results imply that our framework of learning under the innate constraint may be useful in understanding the complicated behavioral phenomena.

Exposure to humans and task difficulty levels affect wild raccoons (Procyon lotor) learning

Cognition helps wildlife exploit novel resources and environments. Raccoons (Procyon lotor) have successfully adapted to human presence, in part due to their cognitive abilities. However, interactions between humans and wildlife can create conflict. A better understanding of the raccoon's behavioral flexibility and learning ability could mitigate some conflicts. Our objective was to evaluate wild raccoons learning in contexts varying in terms of exposure to humans (recreational and preservation zoning within protected areas) and task difficulty. Learning can be evaluated over multiple exposures to a cognitive task. Across three years of experiment, we employed 2 food extraction tasks to gauge the change in problem-solving performance over trials. This assessment considered the success probability (the number of successful trials divided by the total number of trials) and the time taken to solve the puzzles. We also looked at the effects of 2 behavioral traits, exploratory diversity and persistence. We found strong evidence for learning over consecutive trials in terms of improved success probability. Improvement in terms of success probability and solving time was more pronounced with the initially easier task. We detected an increase in success probability over trials only in the recreation zones, and there was no evidence of an effect of behavioral traits. The improved performance attributed to learning was also maintained over consecutive years. We provide additional evidence that raccoons can learn how to solve a problem, resulting in a more effective solution in consecutive trials. Finally, we consider the management implications of dealing with raccoons accessing anthropogenic resources.

Behavioral selection in structured populations

The multilevel model of behavioral selection (MLBS) by Borgstede and Eggert (Behav Process 186:104370. 10.1016/j.beproc.2021.104370 , 2021) provides a formal framework that integrates reinforcement learning with natural selection using an extended Price equation. However, the MLBS is so far only formulated for homogeneous populations, thereby excluding all sources of variation between individuals. This limitation is of primary theoretical concern because any application of the MLBS to real data requires to account for variation between individuals. In this paper, I extend the MLBS to account for inter-individual variation by dividing the population into homogeneous sub-populations and including class-specific reproductive values as weighting factors for an individual's evolutionary fitness. The resulting formalism closes the gap between the theoretical underpinnings of behavioral selection and the application of the theory to empirical data, which naturally includes inter-individual variation. Furthermore, the extended MLBS is used to establish an explicit connection between the dynamics of learning and the maximization of individual fitness. These results expand the scope of the MLBS as a general theoretical framework for the quantitative analysis of learning and evolution.

Risk-sensitive learning is a winning strategy for leading an urban invasion

In the unpredictable Anthropocene, a particularly pressing open question is how certain species invade urban environments. Sex-biased dispersal and learning arguably influence movement ecology, but their joint influence remains unexplored empirically, and might vary by space and time. We assayed reinforcement learning in wild-caught, temporarily captive core-, middle-, or edge-range great-tailed grackles-a bird species undergoing urban-tracking rapid range expansion, led by dispersing males. We show, across populations, both sexes initially perform similarly when learning stimulus-reward pairings, but, when reward contingencies reverse, male-versus female-grackles finish 'relearning' faster, making fewer choice-option switches. How do male grackles do this? Bayesian cognitive modelling revealed male grackles' choice behaviour is governed more strongly by the 'weight' of relative differences in recent foraging payoffs-i.e., they show more pronounced risk-sensitive learning. Confirming this mechanism, agent-based forward simulations of reinforcement learning-where we simulate 'birds' based on empirical estimates of our grackles' reinforcement learning-replicate our sex-difference behavioural data. Finally, evolutionary modelling revealed natural selection should favour risk-sensitive learning in hypothesised urban-like environments: stable but stochastic settings. Together, these results imply risk-sensitive learning is a winning strategy for urban-invasion leaders, underscoring the potential for life history and cognition to shape invasion success in human-modified environments.

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.

Flexible learning in complex worlds

Cognitive flexibility can enhance the ability to adjust to changing environments. Here, we use learning simulations to investigate the possible advantages of flexible learning in volatile (changing) environments. We compare two established learning mechanisms, one with constant learning rates and one with rates that adjust to volatility. We study an ecologically relevant case of volatility, based on observations of developing cleaner fish Labroides dimidiatus that experience a transition from a simpler to a more complex foraging environment. There are other similar transitions in nature, such as migrating to a new and different habitat. We also examine two traditional approaches to volatile environments in experimental psychology and behavioral ecology: reversal learning, and learning set formation (consisting of a sequence of different discrimination tasks). These provide experimental measures of cognitive flexibility. Concerning transitions to a complex world, we show that both constant and flexible learning rates perform well, losing only a small proportion of available rewards in the period after a transition, but flexible rates perform better than constant rates. For reversal learning, flexible rates improve the performance with each successive reversal because of increasing learning rates, but this does not happen for constant rates. For learning set formation, we find no improvement in performance with successive shifts to new stimuli to discriminate for either flexible or constant learning rates. Flexible learning rates might thus explain increasing performance in reversal learning but not in learning set formation, and this can shed light on the nature of cognitive flexibility in a given system.

How to promote the sustainable development of virtual reality technology for training in construction filed: A tripartite evolutionary game analysis

In recent years, virtual reality training technology (VRTT) has been considered by many scholars as a new training method instead of traditional training (TT) to reduce unsafe behaviors ascribed to construction workers (CWs) and corporate accident rates. However, in this process, a conflict of interest arises among the government, construction enterprises (CEs), and CWs. Therefore, this study introduces a quantitative research method, the three-party evolutionary game and creatively combining them with the product life cycle (PLC) to solve this problem by analyzing the equilibrium and evolutionarily stable strategies of the system. Finally, collaborative players' decision-making behaviors and their sensitivity to critical factors are examined. This paper will illustrate these in each stage through numerical simulations. The results of the study indicate that the government plays a dominant role in the VRTT introduction stage. When the government gives CEs appropriate subsidies, CEs will eventually realize the importance of VRTT for CWs. Then the government will gradually reduce the amount of the subsidies in this process. In addition, we also find that the continually high cost will lead to negative policies by the government, which requires the active cooperation and attitude change from CEs and CWs. Ultimately, the government, CEs and CWs adopt the best strategy in the evolutionary process to facilitate the promotion, application and sustainability of VRTT in the construction industry.

Caenorhabditis elegans foraging patterns follow a simple rule of thumb

Rules of thumb are behavioral algorithms that approximate optimal behavior while lowering cognitive and sensory costs. One way to reduce these costs is by simplifying the representation of the environment: While the theoretically optimal behavior may depend on many environmental variables, a rule of thumb may use a smaller set of variables that performs reasonably well. Experimental proof of this simplification requires an exhaustive mapping of all relevant combinations of several environmental parameters, which we performed for Caenorhabditis elegans foraging by covering systematically combinations of food density (across 4 orders of magnitude) and food type (across 12 bacterial strains). We found that worms' response is dominated by a single environmental variable: food density measured as number of bacteria per unit surface. They disregard other factors such as biomass content or bacterial strain. We also measured experimentally the impact on fitness of each type of food, determining that the rule is near-optimal and therefore constitutes a rule of thumb that leverages the most informative environmental variable. These results set the stage for further investigations into the underlying genetic and neural mechanisms governing this simplification process, and into its role in the evolution of decision-making strategies.

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.

Mechanisms of collective learning: how can animal groups improve collective performance when repeating a task?

Learning is ubiquitous in animals: individuals can use their experience to fine-tune behaviour and thus to better adapt to the environment during their lifetime. Observations have accumulated that, at the collective level, groups can also use their experience to improve collective performance. Yet, despite apparent simplicity, the links between individual learning capacities and a collective's performance can be extremely complex. Here we propose a centralized and broadly applicable framework to begin classifying this complexity. Focusing principally on groups with stable composition, we first identify three distinct ways through which groups can improve their collective performance when repeating a task: each member learning to better solve the task on its own, members learning about each other to better respond to one another and members learning to improve their complementarity. We show through selected empirical examples, simulations and theoretical treatments that these three categories identify distinct mechanisms with distinct consequences and predictions. These mechanisms extend well beyond current social learning and collective decision-making theories in explaining collective learning. Finally, our approach, definitions and categories help generate new empirical and theoretical research avenues, including charting the expected distribution of collective learning capacities across taxa and its links to social stability and evolution. This article is part of a discussion meeting issue 'Collective behaviour through time'.

Strategy maps: Generalised giving-up densities for optimal foraging

Finding a common currency for benefits and hazards is a major challenge in optimal foraging theory, often requiring complex computational methods. We present a new analytic approach that builds on the Marginal Value Theorem and giving-up densities while incorporating the nonlinear effect of predation risk. We map the space of all possible environments into strategy regions, each corresponding to a discrete optimal strategy. This provides a generalised quantitative measure of the trade-off between foraging rewards and hazards. This extends a classic optimal diet choice rule-of-thumb to incorporate the hazard of waiting for better resources to appear. We compare the dynamics of optimal decision-making for three foraging life-history strategies: One in which fitness accrues instantly, and two with delays before fitness benefit is accrued. Foragers with delayed-benefit strategies are more sensitive to predation risk than resource quality, as they stand to lose more fitness from a predation event than instant-accrual foragers.

Hamilton's rule, the evolution of behavior rules and the wizardry of control theory

This paper formalizes selection on a quantitative trait affecting the evolution of behavior (or development) rules through which individuals act and react with their surroundings. Combining Hamilton's marginal rule for selection on scalar traits and concepts from optimal control theory, a necessary first-order condition for the evolutionary stability of the trait in a group-structured population is derived. The model, which is of intermediate level of complexity, fills a gap between the formalization of selection on evolving traits that are directly conceived as actions (no phenotypic plasticity) and selection on evolving traits that are conceived as strategies or function valued actions (complete phenotypic plasticity). By conceptualizing individuals as open deterministic dynamical systems expressing incomplete phenotypic plasticity, the model captures selection on a large class of phenotypic expression mechanisms, including developmental pathways and learning under life-history trade-offs. As an illustration of the results, a first-order condition for the evolutionary stability of behavior response rules from the social evolution literature is re-derived, strengthened, and generalized. All results of the paper also generalize directly to selection on multidimensional quantitative traits affecting behavior rule evolution, thereby covering neural and gene network evolution.

Sociosexuality and Sexual Behavior in Men During the COVID-19 Pandemic

Previous studies have shown that unrestricted sociosexuality is part of a quantitative strategy where individuals prioritize the search for multiple partners and is associated with earlier onset of sexual debut, infidelity, reduced sexual disgust, and risk-taking behaviors. Thus, we aimed to investigate the relationship between sociosexuality and sexual risky behaviors during the COVID-19 pandemic due to its potential effects in sexual relationships in a long-standing period of physical distancing. Five hundred eighty-three men completed a series of online questionnaires regarding their sociosexuality, sexual experiences, and adherence to physical distancing recommendations. We found that only a small proportion of the sample had fully adhered to distancing guidelines. Sociosexuality correlated negatively with age at first sexual experience and positively with number of casual partners during the pandemic and frequency of unprotected sex with strangers. Both single and non-heterosexual (gays and bisexuals) men showed higher sociosexuality when compared to men in a relationship and heterosexual ones, respectively. Inconsistent adherence to physical distancing was associated with earlier age of sexual experience, higher number of casual sex, and higher frequency of unprotected sex with strangers. The results indicate that a large proportion of men engaged in some extent in health risk-taking sexual behaviors during the pandemic. As expected, sociosexuality was associated with variables previously found in other studies and with new ones investigated in the present study. We highlight the importance to acknowledge individual differences in response to a long period of distancing and its implications in the welfare of individuals, groups, and the population.

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

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

House sparrows use learned information selectively based on whether reward is hidden or visible

Based on past experience, food-related-cues can help foragers to predict the presence and the expected quality of food. However, when the food is already visible there is no need to predict its presence or its other visible attributes, but only those that are still cryptic, such as expected handling time or taste. Optimal foragers should therefore use only knowledge that is relevant to the current setting. Nevertheless, the extent to which they do so is not clear. In a set of experiments, we examined how a change in setting, from hidden to visible reward, affects the reliance of house sparrows (Passer domesticus) on three previously learned attributes of food-related cues (sand colors): the setting of the cue (e.g., whether the food was hidden or exposed), the expected amount of the reward (number of seeds), and the expected handling time. We found that sparrows used all three attributes when the rewards were hidden but reached decisions mainly based on handling time when the rewards were visible. This selective use of cue-related information suggests that animals do not simply associate cues with their average expected value but rather learn different attributes of a cue and use all, or only some of them, in a context-appropriate manner.

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.

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.

Analysis across diverse fish species highlights no conserved transcriptome signature for proactive behaviour

Background

Consistent individual differences in behaviour, known as animal personalities, have been demonstrated within and across species. In fish, studies applying an animal personality approach have been used to resolve variation in physiological and molecular data suggesting a linkage, genotype-phenotype, between behaviour and transcriptome regulation. In this study, using three fish species (zebrafish; Danio rerio, Atlantic salmon; Salmo salar and European sea bass; Dicentrarchus labrax), we firstly address whether personality-specific mRNA transcript abundances are transferrable across distantly-related fish species and secondly whether a proactive transcriptome signature is conserved across all three species.

Results

Previous zebrafish transcriptome data was used as a foundation to produce a curated list of mRNA transcripts related to animal personality across all three species. mRNA transcript copy numbers for selected gene targets show that differential mRNA transcript abundance in the brain appears to be partially conserved across species relative to personality type. Secondly, we performed RNA-Seq using whole brains from S. salar and D. labrax scoring positively for both behavioural and molecular assays for proactive behaviour. We further enriched this dataset by incorporating a zebrafish brain transcriptome dataset specific to the proactive phenotype. Our results indicate that cross-species molecular signatures related to proactive behaviour are functionally conserved where shared functional pathways suggest that evolutionary convergence may be more important than individual mRNAs.

Conclusions

Our data supports the proposition that highly polygenic clusters of genes, with small additive effects, likely support the underpinning molecular variation related to the animal personalities in the fish used in this study. The polygenic nature of the proactive brain transcriptome across all three species questions the existence of specific molecular signatures for proactive behaviour, at least at the granularity of specific regulatory gene modules, level of genes, gene networks and molecular functions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-020-07317-z.

Cooperative responses in rats playing a 2 × 2 game: Effects of opponent strategy, payoff, and oxytocin

The present study tested cooperation in rats playing a 2 × 2 game (2 players, 2 responses) in an operant chamber, where players choose to cooperate or defect without knowledge of their partner's choice. We evaluated cooperative responses in rats (Subjects) playing different games [iterated Prisoner's Dilemma (IPD), Stag Hunt] with a Stooge partner utilizing different response strategies [Tit-for-tat (TFT), Win-stay, Lose-shift (WSLS), Random], and we determined the effects of oxytocin (OT). IPD trial outcomes and payoffs included mutual cooperation (reward, R, 3 sugar pellets each), mutual defection (punishment, P, 1 pellet each), or unilateral defection (temptation, T, 5 pellets) and cooperation (sucker, S, 0 pellets). Stag Hunt was similar, except that T = 2 pellets. We hypothesized that Subjects would make more cooperative responses when playing Stag Hunt vs IPD, when playing IPD with a Stooge using TFT vs WSLS or Random, and when treated with OT. At baseline, Subjects' overall likelihood of cooperation was unaffected by the game (IPD vs SH) or by the Stooges' response strategy (TFT, WSLS, Random). Cooperative responses earned Subjects more pellets, except when playing with a Stooge using a random strategy. Trial outcomes (R, T, S or P) also varied by game and strategy, although the mutual defection (P) was the most common. Systemic pretreatment with OT increased Subjects' cooperative responses, resulting in fewer P and more R outcomes. In particular, IPD-Random Subjects were more cooperative, even at the expense of earning fewer pellets. These results demonstrate that OT increases cooperative behavior in rats playing 2 × 2 games.

Long-term consistency despite cross-seasonal changes in personality traits of common waxbills

Tests of long-term consistency in personality differences are not abundant. We studied personality in common waxbills (Estrilda astrild) from one Autumn to the next, and also from Autumn to a subsequent Spring, using a behavioural assay for their proactive-reactive personality axis (mirror test) and for tonic immobility. From one Autumn to the next, individual differences were consistent and the population median behaviours did not change, indicating long-term stability of personality differences. From Autumn to Spring, behavioural differences remained consistent, despite the population shifting to more proactive behaviour and individual differences being accentuated in the proactive-reactive personality axis. This increase in personality differences was not found between the two previous Autumns, suggesting a seasonal rather than longitudinal change, and a possible role for personality differences during the Spring breeding season. We conclude that waxbill personality differences are stable in the long term, despite changes in behaviour between Autumn and Spring.

Behavioral flexibility: A review, a model, and some exploratory tests

This paper aimed to explore and clarify the concept of behavioral flexibility. A selective literature review explored how the concept of behavioral flexibility has been used in ways that range from acknowledging the fact that animals’ behavior is not always bounded by instinctual constraints, to describing the variation between species in their capacity for innovative foraging, a capacity that has repeatedly been linked to having a brain larger than would be predicted from body size. This wide range of usages of a single term has led to some conceptual confusion. We sought to find a more precise meaning for behavioral flexibility by representing it within a simple formal model of problem solving. The key to our model is to distinguish between an animal’s state of knowledge about the world and its observable behavior, using a construct of response strength to represent that underlying knowledge. We modelled behavioral flexibility as a parameter in the function that transforms response strengths into observable response probabilities. We tested this model in simulations based on some recent experimental work on animal problem solving. Initial results showed that parametric manipulation can mimic some of the behavioral effects that have been attributed to flexibility.

The relationship between monoaminergic gene expression, learning, and optimism in red junglefowl chicks

Intra-species cognitive variation is commonly observed, but explanations for why individuals within a species differ in cognition are still understudied and not yet clear. Cognitive processes are likely influenced by genetic differences, with genes in the monoaminergic systems predicted to be important. To explore the potential role of these genes in association with individual variation in cognition, we exposed red junglefowl (Gallus gallus) chicks to behavioural assays measuring variation in learning (discriminative learning, reversal learning, and cognitive flexibility) and optimism (measured in a cognitive judgement bias test). Following this, we analysed prefrontal cortex gene expression of several dopaminergic and serotonergic genes in these chicks. Of our explored genes, serotonin receptor genes 5HT2A and 5HT2B, and dopaminergic receptor gene DRD1 were associated with measured behaviour. Chicks that had higher 5HT2A were less flexible in the reversal learning task, and chicks with higher 5HT2B also tended to be less cognitively flexible. Additionally, chicks with higher DRD1 were more optimistic, whilst chicks with higher 5HT2A tended to be less optimistic. These results suggest that the serotonergic and dopaminergic systems are linked to observed cognitive variation, and, thus, individual differences in cognition can be partially explained by variation in brain gene expression.

Decision making in foraging bats

Foraging is a complex and cognitively demanding behavior. Although it is often regarded as a mundane task, foraging requires the continuous weighting and integration of many sources of information with varying levels of credence. Bats are extremely diverse in their ecology and behavior, and thus demonstrate a wide variety of foraging strategies. In this review, we examine the different factors influencing the decision process of bats during foraging. Technological developments of recent years will soon enable real-time tracking of environmental conditions, of the position and quality of food items, the location of conspecifics, and the bat's movement history. Monitoring these variables alongside the continuous movement of the bat will facilitate the testing of different decision-making theories such as the use of reinforcement learning in wild free ranging bats and other animals.

Deconstructing Evolutionary Game Theory: Coevolution of Social Behaviors with Their Evolutionary Setting

Evolution of social behaviors is one of the most fascinating and active fields of evolutionary biology. During the past half century, social evolution theory developed into a mature field with powerful tools to understand the dynamics of social traits such as cooperation under a wide range of conditions. In this article, I argue that the next stage in the development of social evolution theory should consider the evolution of the setting in which social behaviors evolve. To that end, I propose a conceptual map of the components that make up the evolutionary setting of social behaviors, review existing work that considers the evolution of each component, and discuss potential future directions. The theoretical work reviewed here illustrates how unexpected dynamics can happen when the setting of social evolution itself is evolving, such as cooperation sometimes being self-limiting. I argue that a theory of how the setting of social evolution itself evolves will lead to a deeper understanding of when cooperation and other social behaviors evolve and diversify.

Individual differences in behavior and heart rate variability across the preweaning period in the domestic horse in response to an ecologically relevant stressor

The study of individual differences in behavior and physiology has attracted considerable interest among behavioral biologists. Important questions include how early in life such differences emerge and to what extent they remain stable across development. Due to the demanding nature of longitudinal studies, there is still a lack of information on this in mammals, especially in large, long-lived species. Our aim in this study was to look for stable individual differences in behavior and physiology during early development in the domestic horse and for correlations between the two parameters. We tested 30 Azteca-breed foals kept under standard conditions by briefly separating them from their mother in four repeated tests beginning at the first postnatal week until the foals were six months old, before they were weaned. Individual differences in behavior and heart rate variability of foals in response to brief maternal separations were consistent and were correlated from a very early age. These findings contribute to an understanding of the expression of individual differences in physiology and behavior from a developmental perspective and may help in the future selection of horses for functional contexts that require different levels of reactivity, thereby contributing to horse welfare and to human safety and economy.

Adaptive principles of weight regulation: Insufficient, but perhaps necessary, for understanding obesity

We reflect on the major issues raised by a thoughtful and diverse set of commentaries on our target article. We draw attention to the need to differentiate between ultimate and proximate explanation; the insurance hypothesis (IH) needs to be understood as an ultimate-level argument, although we welcome the various suggestions made about proximate mechanisms. Much of this response is concerned with clarifying the interrelationships between adaptationist explanations like the IH, constraint explanations, and dysfunction explanations, in understanding obesity. We also re-examine the empirical evidence base, concurring that it is equivocal and only partially supportive. Several commentators offer additional supporting evidence, whereas others propose alternative explanations for the evidence we reviewed and suggest ways that our current knowledge could be strengthened. Finally, we take the opportunity to clarify some of the assumptions and predictions of our formal model.

Are all patches worth exploring? Foraging desert birds do not rely on environmental indicators of seed abundance at small scales

Background

Consumers should show strong spatial preferences when foraging in environments where food availability is highly heterogeneous and predictable. Postdispersal granivores face this scenario in most arid areas, where soil seed bank abundance and composition associates persistently with vegetation structure at small scales (decimetres to metres). Those environmental features should be exploited as useful pre-harvest information, at least to avoid patches predicted to be poor. However, we did not find the expected spatial association in the algarrobal of the central Monte desert by observing foraging seed-eating birds, a field technique influenced by how much they exploit visited patches. In this work we tested if the first stage of foraging by granivorous birds (patch visit, encounter or exploration) is positively associated with environmental indicators of patch quality by recording the removal of single seeds from 300 scattered experimental devices during seasonal trials. Spatial selectivity was analysed by comparing the structural characteristics of used vs. available microhabitats, and evaluated against bottom-up and top-down hypotheses based on our previous knowledge on local seed bank abundance, composition and dynamics. Their foraging activity was also explored for spatial autocorrelation and environmental correlates at bigger scales.

Results

Postdispersal granivorous birds were less selective in their use of foraging space than expected if microhabitat appearance were providing them relevant information to guide their search for profitable foraging patches. No microhabitat type, as defined by their vegetation structure and soil cover, remained safe from bird exploration. Analyses at bigger temporal and spatial scales proved more important to describe heterogeneity in seed removal.

Conclusions

Closeness to tall trees, probably related to bird territoriality and reproduction or to their perception of predation risk, seemed to determine a first level of habitat selection, constraining explorable space. Then, microhabitat openness (rather than seed abundance) exerted some positive influence on which patches were more frequently visited among those accessible. Selective patterns by birds at small scales were closer to our predictions of a top-down spatial effect, with seed consumption creating or strengthening (and not responding to) the spatial pattern and dynamics of the seed bank.

Electronic supplementary material

The online version of this article (10.1186/s12898-019-0242-z) contains supplementary material, which is available to authorized users.

Rapid environmental change in games: complications and counter-intuitive outcomes

Human-induced rapid environmental change (HIREC) has recently led to alterations in the fitness and behavior of many organisms. Game theory is an important tool of behavioral ecology for analyzing evolutionary situations involving multiple individuals. However, game theory bypasses the details by which behavioral phenotypes are determined, taking the functional perspective straight from expected payoffs to predicted frequencies of behaviors. In contrast with optimization approaches, we identify that to use existing game theoretic models to predict HIREC effects, additional mechanistic details (or assumptions) will often be required. We illustrate this in relation to the hawk-dove game by showing that three different mechanisms, each of which support the same ESS prior to HIREC (fixed polymorphism, probabilistic choice, or cue dependency), can have a substantial effect on behavior (and success) following HIREC. Surprisingly, an increase in the value of resources can lead to a reduction in payoffs (and vice versa), both in the immediate- and long-term following HIREC. An increase in expected costs also increases expected payoffs. Along with these counter-intuitive findings, this work shows that simply understanding the behavioral payoffs of existing games is insufficient to make predictions about the effects of HIREC.

Trust your gut: using physiological states as a source of information is almost as effective as optimal Bayesian learning

Approaches to understanding adaptive behaviour often assume that animals have perfect information about environmental conditions or are capable of sophisticated learning. If such learning abilities are costly, however, natural selection will favour simpler mechanisms for controlling behaviour when faced with uncertain conditions. Here, we show that, in a foraging context, a strategy based only on current energy reserves often performs almost as well as a Bayesian learning strategy that integrates all previous experiences to form an optimal estimate of environmental conditions. We find that Bayesian learning gives a strong advantage only if fluctuations in the food supply are very strong and reasonably frequent. The performance of both the Bayesian and the reserve-based strategy are more robust to inaccurate knowledge of the temporal pattern of environmental conditions than a strategy that has perfect knowledge about current conditions. Studies assuming Bayesian learning are often accused of being unrealistic; our results suggest that animals can achieve a similar level of performance to Bayesians using much simpler mechanisms based on their physiological state. More broadly, our work suggests that the ability to use internal states as a source of information about recent environmental conditions will have weakened selection for sophisticated learning and decision-making systems.

Collective decision making by rational individuals

The patterns and mechanisms of collective decision making in humans and animals have attracted both empirical and theoretical attention. Of particular interest has been the variety of social feedback rules and the extent to which these behavioral rules can be explained and predicted from theories of rational estimation and decision making. However, models that aim to model the full range of social information use have incorporated ad hoc departures from rational decision-making theory to explain the apparent stochasticity and variability of behavior. In this paper I develop a model of social information use and collective decision making by fully rational agents that reveals how a wide range of apparently stochastic social decision rules emerge from fundamental information asymmetries both between individuals and between the decision makers and the observer of those decisions. As well as showing that rational decision making is consistent with empirical observations of collective behavior, this model makes several testable predictions about how individuals make decisions in groups and offers a valuable perspective on how we view sources of variability in animal, and human, behavior.

Gene(s) and individual feeding behavior: Exploring eco-evolutionary dynamics underlying left-right asymmetry in the scale-eating cichlid fish Perissodus microlepis

The scale‐eating cichlid fish Perissodus microlepis is a textbook example of bilateral asymmetry due to its left or right‐bending heads and of negative frequency‐dependent selection, which is proposed to maintain this stable polymorphism. The mechanisms that underlie this asymmetry remain elusive. Several studies had initially postulated a simple genetic basis for this trait, but this explanation has been questioned, particularly by reports observing a unimodal distribution of mouth shapes. We hypothesize that this unimodal distribution might be due to a combination of genetic and phenotypically plastic components. Here, we expanded on previous work by investigating a formerly identified candidate SNP associated to mouth laterality, documenting inter‐individual variation in feeding preference using stable isotope analyses, and testing their association with mouth asymmetry. Our results suggest that this polymorphism is influenced by both a polygenic basis and inter‐individual non‐genetic variation, possibly due to feeding experience, individual specialization, and intraspecific competition. We introduce a hypothesis potentially explaining the simultaneous maintenance of left, right, asymmetric and symmetric mouth phenotypes due to the interaction between diverse eco‐evolutionary dynamics including niche construction and balancing selection. Future studies will have to further tease apart the relative contribution of genetic and environmental factors and their interactions in an integrated fashion.

The erroneous signals of detection theory

Signal detection theory has influenced the behavioural sciences for over 50 years. The theory provides a simple equation that indicates numerous 'intuitive' results; e.g. prey should be more prone to take evasive action (in response to an ambiguous cue) if predators are more common. Here, we use analytical and computational models to show that, in numerous biological scenarios, the standard results of signal detection theory do not apply; more predators can result in prey being less responsive to such cues. The standard results need not apply when the probability of danger pertains not just to the present, but also to future decisions. We identify how responses to risk should depend on background mortality and autocorrelation, and that predictions in relation to animal welfare can also be reversed from the standard theory.

Social Learning Strategies: Bridge-Building between Fields

While social learning is widespread, indiscriminate copying of others is rarely beneficial. Theory suggests that individuals should be selective in what, when, and whom they copy, by following 'social learning strategies' (SLSs). The SLS concept has stimulated extensive experimental work, integrated theory, and empirical findings, and created impetus to the social learning and cultural evolution fields. However, the SLS concept needs updating to accommodate recent findings that individuals switch between strategies flexibly, that multiple strategies are deployed simultaneously, and that there is no one-to-one correspondence between psychological heuristics deployed and resulting population-level patterns. The field would also benefit from the simultaneous study of mechanism and function. SLSs provide a useful vehicle for bridge-building between cognitive psychology, neuroscience, and evolutionary biology.