33
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Co-Evolution of Social Learning and Evolutionary Preparedness in Dangerous Environments

      research-article
      1 , 2 , * , 1 , 1
      PLoS ONE
      Public Library of Science

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Danger is a fundamental aspect of the lives of most animals. Adaptive behavior therefore requires avoiding actions, objects, and environments associated with danger. Previous research has shown that humans and non-human animals can avoid such dangers through two types of behavioral adaptions, (i) genetic preparedness to avoid certain stimuli or actions, and (ii) social learning. These adaptive mechanisms reduce the fitness costs associated with danger but still allow flexible behavior. Despite the empirical prevalence and importance of both these mechanisms, it is unclear when they evolve and how they interact. We used evolutionary agent-based simulations, incorporating empirically based learning mechanisms, to clarify if preparedness and social learning typically both evolve in dangerous environments, and if these mechanisms generally interact synergistically or antagonistically. Our simulations showed that preparedness and social learning often co-evolve because they provide complimentary benefits: genetic preparedness reduced foraging efficiency, but resulted in a higher rate of survival in dangerous environments, while social learning generally came to dominate the population, especially when the environment was stochastic. However, even in this case, genetic preparedness reliably evolved. Broadly, our results indicate that the relationship between preparedness and social learning is important as it can result in trade-offs between behavioral flexibility and safety, which can lead to seemingly suboptimal behavior if the evolutionary environment of the organism is not taken into account.

          Related collections

          Most cited references38

          • Record: found
          • Abstract: found
          • Article: not found

          Social learning strategies.

          In most studies of social learning in animals, no attempt has been made to examine the nature of the strategy adopted by animals when they copy others. Researchers have expended considerable effort in exploring the psychological processes that underlie social learning and amassed extensive data banks recording purported social learning in the field, but the contexts under which animals copy others remain unexplored. Yet, theoretical models used to investigate the adaptive advantages of social learning lead to the conclusion that social learning cannot be indiscriminate and that individuals should adopt strategies that dictate the circumstances under which they copy others and from whom they learn. In this article, I discuss a number of possible strategies that are predicted by theoretical analyses, including copy when uncertain, copy the majority, and copy if better, and consider the empirical evidence in support of each, drawing from both the animal and human social learning literature. Reliance on social learning strategies may be organized hierarchically, their being employed by animals when unlearned and asocially learned strategies prove ineffective but before animals take recourse in innovation.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The cultural niche: why social learning is essential for human adaptation.

            In the last 60,000 y humans have expanded across the globe and now occupy a wider range than any other terrestrial species. Our ability to successfully adapt to such a diverse range of habitats is often explained in terms of our cognitive ability. Humans have relatively bigger brains and more computing power than other animals, and this allows us to figure out how to live in a wide range of environments. Here we argue that humans may be smarter than other creatures, but none of us is nearly smart enough to acquire all of the information necessary to survive in any single habitat. In even the simplest foraging societies, people depend on a vast array of tools, detailed bodies of local knowledge, and complex social arrangements and often do not understand why these tools, beliefs, and behaviors are adaptive. We owe our success to our uniquely developed ability to learn from others. This capacity enables humans to gradually accumulate information across generations and develop well-adapted tools, beliefs, and practices that are too complex for any single individual to invent during their lifetime.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The paranoid optimist: an integrative evolutionary model of cognitive biases.

              Human cognition is often biased, from judgments of the time of impact of approaching objects all the way through to estimations of social outcomes in the future. We propose these effects and a host of others may all be understood from an evolutionary psychological perspective. In this article, we elaborate error management theory (EMT; Haselton & Buss, 2000). EMT predicts that if judgments are made under uncertainty, and the costs of false positive and false negative errors have been asymmetric over evolutionary history, selection should have favored a bias toward making the least costly error. This perspective integrates a diverse array of effects under a single explanatory umbrella, and it yields new content-specific predictions.
                Bookmark

                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                3 August 2016
                2016
                : 11
                : 8
                : e0160245
                Affiliations
                [1 ]Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
                [2 ]Department of Economics, University of Zurich, Zürich, Switzerland
                Centre for Coevolution of Biology & Culture, University of Durham, UNITED KINGDOM
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceived and designed the experiments: BL IS.

                • Performed the experiments: BL IS.

                • Analyzed the data: BL.

                • Wrote the paper: BL IS AO.

                Article
                PONE-D-15-38331
                10.1371/journal.pone.0160245
                4972391
                27487079
                586693e8-ff18-4d72-b575-38479023a381
                © 2016 Lindström et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 31 August 2015
                : 15 July 2016
                Page count
                Figures: 6, Tables: 0, Pages: 19
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100004359, Vetenskapsrådet;
                Award ID: 421-2010-2084
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000781, European Research Council;
                Award ID: 284366
                Award Recipient :
                This research was supported by a grant from the Swedish Science Council (Vetenskapsrådet; 421-2010-2084), and an Independent Starting Grant (284366; Emotional Learning in Social Interaction project) from the European Research Council to Andreas Olsson. Björn Lindström was partially supported by Forte (COFAS2: 2014-2785 FOIP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Psychology
                Learning
                Biology and Life Sciences
                Psychology
                Cognitive Psychology
                Learning
                Social Sciences
                Psychology
                Cognitive Psychology
                Learning
                Biology and Life Sciences
                Neuroscience
                Learning and Memory
                Learning
                Biology and Life Sciences
                Behavior
                Animal Behavior
                Foraging
                Biology and Life Sciences
                Zoology
                Animal Behavior
                Foraging
                Biology and Life Sciences
                Behavior
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Psychology
                Learning
                Human Learning
                Biology and Life Sciences
                Psychology
                Cognitive Psychology
                Learning
                Human Learning
                Social Sciences
                Psychology
                Cognitive Psychology
                Learning
                Human Learning
                Biology and Life Sciences
                Neuroscience
                Learning and Memory
                Learning
                Human Learning
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Genetics
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Processes
                Evolutionary Adaptation
                Research and Analysis Methods
                Simulation and Modeling
                Agent-Based Modeling
                Computer and Information Sciences
                Systems Science
                Agent-Based Modeling
                Physical Sciences
                Mathematics
                Systems Science
                Agent-Based Modeling
                Biology and Life Sciences
                Organisms
                Animals
                Vertebrates
                Amniotes
                Reptiles
                Squamates
                Snakes
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

                Uncategorized
                Uncategorized

                Comments

                Comment on this article