High-magnitude innovators as keystone individuals in the evolution of culture

Borrowing from the concept of keystone species in ecological food webs, a recent focus in the field of animal behaviour has been keystone individuals: individuals whose impact on population dynamics is disproportionally larger than their frequency in the population. In populations evolving culture, such may be the role of high-magnitude innovators: individuals whose innovations are a major departure from the population's existing behavioural repertoire. Their effect on cultural evolution is twofold: they produce innovations that constitute a ‘cultural leap' and, once copied, their innovations may induce further innovations by conspecifics (socially induced innovations) as they explore the new behaviour themselves. I use computer simulations to study the coevolution of independent innovations, socially induced innovations and innovation magnitude, and show that while socially induced innovation is assumed here to be less costly than independent innovation, it does not readily evolve. When it evolves, it may in some conditions select against independent innovation and lower its frequency, despite it requiring independent innovation in order to operate; at the same time, however, it leads to much faster cultural evolution. These results confirm the role of high-magnitude innovators as keystones, and suggest a novel explanation for the low frequency of independent innovation. This article is part of the theme issue ‘Bridging cultural gaps: interdisciplinary studies in human cultural evolution'.</jats:p

High-magnitude innovators as keystone individuals in the evolution of culture

AbstractBorrowing from the concept of keystone species in ecological food webs, a recent focus in the field of animal behaviour has been keystone individuals: individuals whose impact on population dynamics is disproportionally larger than their frequency in the population. In populations evolving culture, such may be the role of high-magnitude innovators: individuals whose innovations are a major departure from the population’s existing behavioural repertoire. Their effect on cultural evolution is twofold: they produce innovations that constitute a ‘cultural leap’, and, once copied, their innovations may induce further innovations by conspecifics (socially induced innovations), as they explore the new behaviour themselves. I use computer simulations to study the co-evolution of independent innovations, socially induced innovations, and innovation magnitude, and show that while socially induced innovation is assumed here to be less costly than independent innovation, it does not readily evolve. When it evolves, it may in some conditions select against independent innovation and lower its frequency, despite it requiring independent innovation in order to operate; at the same time, however, it leads to much faster cultural evolution. These results confirm the role of high-magnitude innovators as keystones, and suggest a novel explanation for low frequency of independent innovation.</jats:p

Understanding the evolution of learning by explicitly modeling learning mechanisms

Abstract Models of the evolution of learning often assume that learning leads to the best solution to any task, and disregard the details of the learning and decision-making process along with its potential pitfalls. These models therefore do not explain instances in the animal behavior literature in which learning leads to maladaptive behaviors. In recent years a growing number of theoretical studies use explicit models of learning mechanisms, offering a fresh perspective on the issue by revealing the dynamics of information acquisition and biases arising from it. These models have pointed out possible learning rules and their adaptive value, and shown that the value of learning may crucially depend on such factors as the layout of the physical environment to be learned, the structure of the payoffs offered by different alternatives, the risk of failure, characteristics of the learner and social interactions. This review considers the merits of explicit modeling in studying the evolution of learning, describes the kinds of results that can only be obtained from this modeling approach, and outlines directions for future research.</jats:p

The local enhancement conundrum:in search of the adaptive value of a social learning mechanism

Social learning mechanisms are widely thought to vary in their degree of complexity as well as in their prevalence in the natural world. While learning the properties of a stimulus that generalize to similar stimuli at other locations (stimulus enhancement) prima fade appears more useful to an animal than learning about a specific stimulus at a specific location (local enhancement), empirical evidence suggests that the latter is much more widespread in nature. Simulating populations engaged in a producer scrounger game, we sought to deploy mathematical models to identify the adaptive benefits of reliance on local enhancement and/or stimulus enhancement, and the alternative conditions favoring their evolution. Surprisingly, we found that while stimulus enhancement readily evolves, local enhancement is advantageous only under highly restricted conditions: when generalization of information was made unreliable or when error in social learning was high. Our results generate a conundrum over how seemingly conflicting empirical and theoretical findings can be reconciled. Perhaps the prevalence of local enhancement in nature is due to stimulus enhancement costs independent of the learning task itself (e.g. predation risk), perhaps natural habitats are often characterized by unreliable yet highly rewarding payoffs, or perhaps local enhancement occurs less frequently, and stimulus enhancement more frequently, than widely believed. (C) 2013 Elsevier Inc. All rights reserved.</p

Constructive anthropomorphism: a functional evolutionary approach to the study of human-like cognitive mechanisms in animals

Anthropomorphism, the attribution of human cognitive processes and emotional states to animals, is commonly viewed as non-scientific and potentially misleading. This is mainly because apparent similarity to humans can usually be explained by alternative, simpler mechanisms in animals, and because there is no explanatory power in analogies to human phenomena when these phenomena are not well understood. Yet, because it is also difficult to preclude real similarity and continuity in the evolution of humans' and animals’ cognitive abilities, it may not be productive to completely ignore our understanding of human behaviour when thinking about animals. Here we propose that in applying a functional approach to the evolution of cognitive mechanisms, human cognition may be used to broaden our theoretical thinking and to generate testable hypotheses. Our goal is not to ‘elevate’ animals, but rather to find the minimal set of mechanistic principles that may explain ‘advanced’ cognitive abilities in humans, and consider under what conditions these mechanisms were likely to enhance fitness and to evolve in animals. We illustrate this approach, from relatively simple emotional states, to more advanced mechanisms, involved in planning and decision-making, episodic memory, metacognition, theory of mind, and consciousness.</jats:p

The magnitude of innovation and its evolution in social animals

Innovative behaviour in animals, ranging from invertebrates to humans, is increasingly recognized as an important topic for investigation by behavioural researchers. However, what constitutes an innovation remains controversial, and difficult to quantify. Drawing on a broad definition whereby any behaviour with a new component to it is an innovation, we propose a quantitative measure, which we call the magnitude of innovation, to describe the extent to which an innovative behaviour is novel. This allows us to distinguish between innovations that are a slight change to existing behaviours (low magnitude), and innovations that are substantially different (high magnitude). Using mathematical modelling and evolutionary computer simulations, we explored how aspects of social interaction, cognition and natural selection affect the frequency and magnitude of innovation. We show that high-magnitude innovations are likely to arise regularly even if the frequency of innovation is low, as long as this frequency is relatively constant, and that the selectivity of social learning and the existence of social rewards, such as prestige and royalties, are crucial for innovative behaviour to evolve. We suggest that consideration of the magnitude of innovation may prove a useful tool in the study of the evolution of cognition and of culture

The magnitude of innovation and its evolution in social animals

Innovative behaviour in animals, ranging from invertebrates to humans, is increasingly recognized as an important topic for investigation by behavioural researchers. However, what constitutes an innovation remains controversial, and difficult to quantify. Drawing on a broad definition whereby any behaviour with a new component to it is an innovation, we propose a quantitative measure, which we call the magnitude of innovation, to describe the extent to which an innovative behaviour is novel. This allows us to distinguish between innovations that are a slight change to existing behaviours (low magnitude), and innovations that are substantially different (high magnitude). Using mathematical modelling and evolutionary computer simulations, we explored how aspects of social interaction, cognition and natural selection affect the frequency and magnitude of innovation. We show that high-magnitude innovations are likely to arise regularly even if the frequency of innovation is low, as long as this frequency is relatively constant, and that the selectivity of social learning and the existence of social rewards, such as prestige and royalties, are crucial for innovative behaviour to evolve. We suggest that consideration of the magnitude of innovation may prove a useful tool in the study of the evolution of cognition and of culture.</p

The local enhancement conundrum: In search of the adaptive value of a social learning mechanism

Social learning mechanisms are widely thought to vary in their degree of complexity as well as in their prevalence in the natural world. While learning the properties of a stimulus that generalize to similar stimuli at other locations (stimulus enhancement) prima fade appears more useful to an animal than learning about a specific stimulus at a specific location (local enhancement), empirical evidence suggests that the latter is much more widespread in nature. Simulating populations engaged in a producer scrounger game, we sought to deploy mathematical models to identify the adaptive benefits of reliance on local enhancement and/or stimulus enhancement, and the alternative conditions favoring their evolution. Surprisingly, we found that while stimulus enhancement readily evolves, local enhancement is advantageous only under highly restricted conditions: when generalization of information was made unreliable or when error in social learning was high. Our results generate a conundrum over how seemingly conflicting empirical and theoretical findings can be reconciled. Perhaps the prevalence of local enhancement in nature is due to stimulus enhancement costs independent of the learning task itself (e.g. predation risk), perhaps natural habitats are often characterized by unreliable yet highly rewarding payoffs, or perhaps local enhancement occurs less frequently, and stimulus enhancement more frequently, than widely believed. (C) 2013 Elsevier Inc. All rights reserved.</p

Gene–Culture Coevolution in the Cognitive Domain

Abstract Gene–culture coevolution in the cognitive domain is expected to occur whenever cultural phenomena select for genes that affect cognition. Such cultural selection would occur, for example, if the culture of making certain tools leads to selection that favours genetic variants that somehow make one better at learning to make these tools. While these coevolutionary processes seem probable and important, they are difficult to study because the genetic underpinnings of cognitive traits are often poorly understood. Indeed, most evidence for cognitive gene–culture coevolution are circumstantial or indirect, and the role of such processes is often a topic of debate. This chapter suggests, however, that a strong case for cognitive gene–culture coevolution can be made based on theoretical considerations, which can also guide future work and put current evidence in context. Using a process-based (mechanistic) approach to cognitive evolution, the authors distinguish between culturally selected genetic changes in innate knowledge, culturally selected genetic changes in attentional and learning mechanisms, and culturally selected genetic changes in the neuroanatomical substrate. In this light, the authors suggest a limited role to hypothesized culturally selected modules requiring complex innate knowledge. On the other hand, culturally selected modifications of attentional and learning parameters may be significant because they can jointly handle the computational challenges involved in the construction of complex cognitive representations. In turn, the development of such representations (in the form of associative networks), sets the demand for culturally selected changes in size and structure of neuroanatomical substrate, for which some evidence is accumulating.</jats:p

The local enhancement conundrum:in search of the adaptive value of a social learning mechanism

Social learning mechanisms are widely thought to vary in their degree of complexity as well as in their prevalence in the natural world. While learning the properties of a stimulus that generalize to similar stimuli at other locations (stimulus enhancement) prima fade appears more useful to an animal than learning about a specific stimulus at a specific location (local enhancement), empirical evidence suggests that the latter is much more widespread in nature. Simulating populations engaged in a producer scrounger game, we sought to deploy mathematical models to identify the adaptive benefits of reliance on local enhancement and/or stimulus enhancement, and the alternative conditions favoring their evolution. Surprisingly, we found that while stimulus enhancement readily evolves, local enhancement is advantageous only under highly restricted conditions: when generalization of information was made unreliable or when error in social learning was high. Our results generate a conundrum over how seemingly conflicting empirical and theoretical findings can be reconciled. Perhaps the prevalence of local enhancement in nature is due to stimulus enhancement costs independent of the learning task itself (e.g. predation risk), perhaps natural habitats are often characterized by unreliable yet highly rewarding payoffs, or perhaps local enhancement occurs less frequently, and stimulus enhancement more frequently, than widely believed. (C) 2013 Elsevier Inc. All rights reserved.</p