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Leadbeater, E., & Dawson, E. H. (2017). A social insect perspective on the evolution of social learning mechanisms. Proc. Natl. Acad. Sci. U.S.A., 114(30), 7838–7845.
Abstract: The social world offers a wealth of opportunities to learn from others, and across the animal kingdom individuals capitalize on those opportunities. Here, we explore the role of natural selection in shaping the processes that underlie social information use, using a suite of experiments on social insects as case studies. We illustrate how an associative framework can encompass complex, context-specific social learning in the insect world and beyond, and based on the hypothesis that evolution acts to modify the associative process, suggest potential pathways by which social information use could evolve to become more efficient and effective. Social insects are distant relatives of vertebrate social learners, but the research we describe highlights routes by which natural selection could coopt similar cognitive raw material across the animal kingdom.
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Sabou, M., Bontcheva, K., & Scharl, A. (2012). Crowdsourcing Research Opportunities: Lessons from Natural Language Processing. In Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies (pp. 1–18). i-KNOW '12. New York, NY, USA: Acm.
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Bücheler, T., & Sieg, J. H. (2011). Understanding Science 2.0: Crowdsourcing and Open Innovation in the Scientific Method. Proceedings of the 2nd European Future Technologies Conference and Exhibition 2011 (FET 11), 7, 327–329.
Abstract: The innovation process is currently undergoing significant change in many industries. The World Wide Web has created a virtual world of collective intelligence and helped large groups of people connect and collaborate in the innovation process [1]. Von Hippel [2], for instance, states that a large number of users of a given technology will come up with innovative ideas. This process, originating in business, is now also being observed in science. Discussions around “Citizen Science” [3] and “Science 2.0” [4] suggest the same effects are relevant for fundamental research practices. “Crowdsourcing” [5] and “Open Innovation” [6] as well as other names for those paradigms, like Peer Production, Wikinomics, Swarm Intelligence etc., have become buzzwords in recent years. However, serious academic research efforts have also been started in many disciplines. In essence, these buzzwords all describe a form of collective intelligence that is enabled by new technologies, particularly internet connectivity. The focus of most current research on this topic is in the for-profit domain, i.e. organizations willing (and able) to pay large sums to source innovation externally, for instance through innovation contests. Our research is testing the applicability of Crowdsourcing and some techniques from Open Innovation to the scientific method and basic science in a non-profit environment (e.g., a traditional research university). If the tools are found to be useful, this may significantly change how some research tasks are conducted: While large, apriori unknown crowds of “irrational agents” (i.e. humans) are used to support scientists (and teams thereof) in several research tasks through the internet, the usefulness and robustness of these interactions as well as scientifically important factors like quality and validity of research results are tested in a systematic manner. The research is highly interdisciplinary and is done in collaboration with scientists from sociology, psychology, management science, economics, computer science, and artificial intelligence. After a pre-study, extensive data collection has been conducted and the data is currently being analyzed. The paper presents ideas and hypotheses and opens the discussion for further input.
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Benson-Amram, S., & Holekamp, K. E. (2012). Innovative problem solving by wild spotted hyenas. Proc R Soc B, 279, 4087–4095.
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Briefer, E. F., Padilla de la Torre, M., & McElligott, A. G. (2012). Mother goats do not forget their kids' calls. Proc R Soc B, 279.
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Frère, C. H., Krützen, M., Mann, J., Connor, R. C., Bejder, L., & Sherwin, W. B. (2010). Social and genetic interactions drive fitness variation in a free-living dolphin population. Proc. Natl. Acad. Sci. U.S.A., 107(46), 19949–19954.
Abstract: The evolutionary forces that drive fitness variation in species are of considerable interest. Despite this, the relative importance and interactions of genetic and social factors involved in the evolution of fitness traits in wild mammalian populations are largely unknown. To date, a few studies have demonstrated that fitness might be influenced by either social factors or genes in natural populations, but none have explored how the combined effect of social and genetic parameters might interact to influence fitness. Drawing from a long-term study of wild bottlenose dolphins in the eastern gulf of Shark Bay, Western Australia, we present a unique approach to understanding these interactions. Our study shows that female calving success depends on both genetic inheritance and social bonds. Moreover, we demonstrate that interactions between social and genetic factors also influence female fitness. Therefore, our study represents a major methodological advance, and provides critical insights into the interplay of genetic and social parameters of fitness.
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Griebenow, K., & Klibanov, A. M. (1995). Lyophilization-induced reversible changes in the secondary structure of proteins. Proc Natl Acad Sci USA, 92(24), 10969–10976.
Abstract: Changes in the secondary structure of some dozen different proteins upon lyophilization of their aqueous solutions have been investigated by means of Fourier-transform infrared spectroscopy in the amide III band region. Dehydration markedly (but reversibly) alters the secondary structure of all the proteins studied, as revealed by both the quantitative analysis of the second derivative spectra and the Gaussian curve fitting of the original infrared spectra. Lyophilization substantially increases the beta-sheet content and lowers the alpha-helix content of all proteins. In all but one case, proteins become more ordered upon lyophilization.
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Smaers, J. B., Dechmann, D. K. N., Goswami, A., Soligo, C., & Safi, K. (2012). Comparative analyses of evolutionary rates reveal different pathways to encephalization in bats, carnivorans, and primates. Proc Natl Acad Sci U S A, 109.
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Fagot, J., & Cook, R. G. (2006). Evidence for large long-term memory capacities in baboons and pigeons and its implications for learning and the evolution of cognition. Proc Natl Acad Sci U S A, 103.
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Harris, F. (1978). On the Use of Windows for Harmonic Analysis with the Discrete Fourier Transform. Proc IEEE, 66.
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