An overview of the evolutionary causes and consequences of behavioural plasticity
TL;DR: It is argued that fine-grained and coarse- grained variation may differentially select for activational and developmental plasticity, respectively, because environmental variation experienced by an organism is largely determined by behaviour.
About: This article is published in Animal Behaviour.The article was published on 2013-05-01. It has received 502 citations till now. The article focuses on the topics: Developmental plasticity & Phenotypic plasticity.
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TL;DR: It is presented both theoretical and empirical arguments to show that behavioural adjustments to urban habitats are widespread and that they may potentially be important in facilitating resource use, avoiding disturbances and enhancing communication.
510 citations
TL;DR: It is claimed that rigorous testing of predictions requires methods that allow for quantifying and comparing whole organism plasticity, as well as the ability to experimentally manipulate the level of and capacity for developmental plasticity and phenotypic flexibility independent of genetic variation.
Abstract: Much research has been devoted to identify the conditions under which selection favours flexible individuals or genotypes that are able to modify their growth, development and behaviour in response to environmental cues, to unravel the mechanisms of plasticity and to explore its influence on patterns of diversity among individuals, populations and species. The consequences of developmental plasticity and phenotypic flexibility for the performance and ecological success of populations and species have attracted a comparatively limited but currently growing interest. Here, I re-emphasize that an increased understanding of the roles of plasticity in these contexts requires a ‘whole organism' (rather than ‘single trait') approach, taking into consideration that organisms are integrated complex phenotypes. I further argue that plasticity and genetic polymorphism should be analysed and discussed within a common framework. I summarize predictions from theory on how phenotypic variation stemming from developmental plasticity and phenotypic flexibility may affect different aspects of population-level performance. I argue that it is important to distinguish between effects associated with greater interindividual phenotypic variation resulting from plasticity, and effects mediated by variation among individuals in the capacity to express plasticity and flexibility as such. Finally, I claim that rigorous testing of predictions requires methods that allow for quantifying and comparing whole organism plasticity, as well as the ability to experimentally manipulate the level of and capacity for developmental plasticity and phenotypic flexibility independent of genetic variation.
369 citations
Cites background or result from "An overview of the evolutionary cau..."
...…2010; Tuomainen and Candolin, 2011), for instance, as a result of learning and experience or adjustments depending on the presence or absence of predators, and encompasses both developmental behavioural plasticity and activational plasticity (for a thorough discussion see Snell-Rood, 2013)....
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...…Grime, 2003; Sultan, 2004; Miner et al., 2005; Pigliucci, 2005; Gray and McKinnon, 2007; Whitlock et al., 2007; Forsman et al., 2008; Naeem et al., 2009; Whitman and Agrawal, 2009; Pfennig et al., 2010; Reed et al., 2010; te Beest et al., 2011; Violle et al., 2012; Wund, 2012; Snell-Rood, 2013)....
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...Heredity and alternative terms are sometimes used for one and the same phenomenon (Piersma and Drent, 2003; Whitman and Agrawal, 2009; Stamps and Groothuis, 2010; Brommer, 2013; Snell-Rood, 2013)....
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...Recent reviews and discussions of behavioural flexibility are largely in accordance with this prediction (Tuomainen and Candolin, 2011; Snell-Rood, 2013), but the evidence is based almost exclusively on observational data and theoretical modelling....
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TL;DR: How between-individual differences in behavioural plasticity can result from additive and interactive effects of genetic make-up and past environmental conditions, and under which conditions natural selection might favour this form of between- individual variation is discussed.
331 citations
TL;DR: The need for cross-disciplinary collaborations is advocated to settle the question of whether plasticity will promote or retard species' rates of adaptation to ever-more stressful environmental conditions.
Abstract: How populations and species respond to modified environmental conditions is critical to their persistence both now and into the future, particularly given the increasing pace of environmental chang...
315 citations
TL;DR: The process of adaptive radiation—the proliferation of species from a single ancestor and diversification into many ecologically different forms—has been of great interest to evolutionary biologists since Darwin and it is time to synthesize ecological and evolutionary processes.
Abstract: The process of adaptive radiation—the proliferation of species from a single ancestor and diversification into many ecologically different forms—has been of great interest to evolutionary biologists since Darwin. Since the middle of the last century, ecological opportunity has been invoked as a potential key to understanding when and how adaptive radiation occurs. Interest in the topic of ecological opportunity has accelerated as research on adaptive radiation has experienced a resurgence, fueled in part by advances in phylogenetic approaches to studying evolutionary diversification. Nonetheless, what the term actually means, much less how it mechanistically leads to adaptive diversification, is currently debated; whether the term has any predictive value or is a heuristic useful only for post hoc explanation also remains unclear. Recent recognition that evolutionary change can occur rapidly and on a timescale commensurate with ecological processes suggests that it is time to synthesize ecological and evo...
301 citations
References
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TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
Abstract: Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
16,652 citations
TL;DR: A theoretical framework is proposed that explains expert performance in terms of acquired characteristics resulting from extended deliberate practice and that limits the role of innate (inherited) characteristics to general levels of activity and emotionality.
Abstract: because observed behavior is the result of interactions between environmental factors and genes during the extended period of development. Therefore, to better understand expert and exceptional performance, we must require that the account specify the different environmental factors that could selectively promote and facilitate the achievement of such performance. In addition, recent research on expert performance and expertise (Chi, Glaser, & Farr, 1988; Ericsson & Smith, 1991a) has shown that important characteristics of experts' superior performance are acquired through experience and that the effect of practice on performance is larger than earlier believed possible. For this reason, an account of exceptional performance must specify the environmental circumstances, such as the duration and structure of activities, and necessary minimal biological attributes that lead to the acquisition of such characteristics and a corresponding level of performance. An account that explains how a majority of individuals can attain a given level of expert performance might seem inherently unable to explain the exceptional performance of only a small number of individuals. However, if such an empirical account could be empirically supported, then the extreme characteristics of experts could be viewed as having been acquired through learning and adaptation, and studies of expert performance could provide unique insights into the possibilities and limits of change in cognitive capacities and bodily functions. In this article we propose a theoretical framework that explains expert performance in terms of acquired characteristics resulting from extended deliberate practice and that limits the role of innate (inherited) characteristics to general levels of activity and emotionality. We provide empirical support from two new studies and from already published evidence on expert performance in many different domains.
7,886 citations
TL;DR: Central issues of reinforcement learning are discussed, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
Abstract: This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word "reinforcement." The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.
6,895 citations
Posted Content•
TL;DR: A survey of reinforcement learning from a computer science perspective can be found in this article, where the authors discuss the central issues of RL, including trading off exploration and exploitation, establishing the foundations of RL via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
Abstract: This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.
5,970 citations
Book•
01 Jan 2003
4,928 citations
"An overview of the evolutionary cau..." refers background in this paper
...Adaptive phenotypic plasticity, the ability of a genotype to vary its phenotype across environments, and thus maintain high performance across that environmental gradient, is important for survival in variable environments (Schlichting & Pigliucci 1998; West-Eberhard 2003; Bateson & Gluckman 2011)....
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