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University of St Andrews School of Biology Research Fellow in Evolutionary Modelling of Innovation, Intelligence and Brain Evolution SB1089 Further Particulars for Applicants School of Biology PRINCIPAL INVESTIGATORS: Kevin N. Laland is a Professor of Biology at the University of St Andrews, where he heads the Social Evolution and Learning Group (SEAL) and is a core member of the Centre for Social Learning and Cognitive Evolution (SLACE). His research focuses on animal behaviour and evolution, employing both experimental and theoretical methods, and he has 8 books and over 170 scientific articles published or in press. Over the past 20 years, Laland has carried out extensive experimental and theoretical research into animal social learning, including in humans. Further information can be found at: http://lalandlab.st-andrews.ac.uk/ Simon M. Reader is an Assistant Professor at McGill University in Montreal, and holds an affiliate professorship at Utrecht University, The Netherlands. He studies the causes and consequences of innovation and social learning in animals and humans, with 46 scientific publications. In particular, he has been looking at (i) the neuro-cognitive processes underlying the creation and acquisition of novel behaviour patterns, (ii) the diffusion dynamics of acquired information, (iii) the causes of individual, population and species-level variation in innovation and social learning propensities, (iv) the role of innovation and social learning in brain evolution, and (v) the evolutionary consequences of behavioural flexibility and the active role of behaviour in evolution. Further information can be found at: http://biology.mcgill.ca/faculty/reader/ UNIVERSITY OF ST ANDREWS St Andrews is the third oldest University in the UK, and was recently named best university in Scotland (Sunday Times University Guide), where it was also ranked number 9 in the UK national league. The University is home to the Centre for Social Learning and Cognitive Evolution, a world-leading centre of excellence in research related to this project. The Centre brings together a unique group of researchers in the Schools of Biology and Psychology who share common interests in the study of social learning and the evolution of complex cognition, with over 70 academic staff, post-docs and graduate students, and numerous national and international collaborators. On-going observational and experimental studies are currently being conducted on a wide range of animal groups, ranging from fish to birds, cetaceans and primates, including human beings. The Centre provides a highly stimulating and supportive setting for this research project. Further information about the Centre for Social Learning and Cognitive Evolution can be found at http://culture.st-and.ac.uk/solace. A description of the proposed research can be found at Appendix I. Job Description Job Title: Mathematical Modeller Hours of work: Full Time School/Unit: Biology Grade/Salary Range: Grade 6/£29,972-£32,751 per annum Reporting to: Prof Kevin Laland Reference No: SB1089 Job Family: Academic (Research) Duration of Post: 24 months Start Date: 9 January 2012 or as soon as possible thereafter Main Purpose of Role To develop evolutionary models of human and animal innovation. Key Duties and Responsibilities Develop evolutionary models of human and animal innovation. Prepare papers for journals/presentations either in-house or at national/international conferences or seminars to disseminate research findings. Read academic papers, journals and textbooks and attend conferences to keep abreast of developments. Co-ordinate and liaise with other members of the research group over work progress. A further 3 year post is available for a statistical modeller on the same project (see SB1090) Please note that this job description is not exhaustive, and the role holder may be required to undertake other relevant duties commensurate with the grading of the post. Activities may be subject to amendment over time as the role develops and/or priorities and requirements evolve. Person Specification This section details the attributes e.g. skills, knowledge/qualifications and competencies which are required in order to undertake the full remit of this post. Attributes Essential Education & Qualifications EITHER a PhD in biology OR a PhD in another relevant discipline (technical, professional, academic qualifications and training required) Experience & Knowledge (examples of specific experience and knowledge sought) Desirable Familiarity with scientific research into behavioural innovation, social learning and social evolution Experience of highlevel evolutionary mathematical modelling Means of Assessment (i.e. application form, interview, test, presentation etc) Certificate, interview Interview Experience of presenting research at conferences and of writing up research for publication Competencies & Skills (e.g. effective communication skills, initiative, flexibility, leadership etc) Good communication skills Commitment to highquality research, including hardworking with excellent attention to detail Ability to work within a small team and to integrate activities within the framework of a larger project Interview Strong organisational skills and personal initiative Essential Criteria – requirements without which a candidate would not be able to undertake the full remit of the role. Applicants who have not clearly demonstrated in their application that they possess the essential requirements will normally be rejected at the short listing stage. Desirable Criteria – requirements which would be useful for the candidate to hold. When short listing, these criteria will be considered when more than one applicant meets the essential requirements. Other Information We encourage applicants to apply online at www.vacancies.st-andrews.ac.uk/welcome.aspx, however if you are unable to do this, please call +44 (0)1334 462571 for a paper application form. For all applications, please quote ref: SB1089 The University is committed to equality of opportunity. The University of St Andrews is a charity registered in Scotland (No SC013532). Obligations as an Employee You have a duty to carry out your work in a safe manner in order not to endanger yourself or anyone else by your acts or omissions. You are required to comply with the University health and safety policy as it relates to your work activities, and to take appropriate action in case of an emergency. You are responsible for applying the University’s equality and diversity policies and principles in your own area of responsibility and in your general conduct. You have a responsibility to promote high levels of customer care within your own area of work/activities. You should be adaptable to change, and be willing to acquire new skills and knowledge as applicable to the needs of the role. You may, with reasonable notice, be required to work within other Schools/Units within the University of St Andrews. You have the responsibility to engage with the University’s commitment to Environmental Sustainability in order to reduce its waste, energy consumption and carbon footprint. The University & Town Founded in the 15th century, St Andrews is Scotland’s first university and the third oldest in the English speaking world. Situated on the east coast of Scotland and framed by countryside, beaches and cliffs, the City of St Andrews was once the centre of the nation’s political and religious life. Today it is known around the world as the Home of Golf and a vibrant academic town with a distinctively cosmopolitan feel where students and university staff account for more than 30% of the local population. The University of St Andrews is a diverse and international community of over 9000, comprising students and staff of over 100 nationalities. It has 7500 students, 6100 of them undergraduates, and employs approximately 1840 staff - made up of c.700 academic and c.1140 support personnel. St Andrews has approximately 50,000 living graduates, among them Scottish First Minister Alex Salmond and the novelist Fay Weldon. It has 1000 Honorary graduates, including Bob Dylan, Benjamin Franklin, The Dalai Lama and Jack Nicklaus. The University is one of Europe’s most research intensive seats of learning – over 40% of its turnover comes from research grants and contracts. It is the top rated University in Scotland for teaching quality and student satisfaction and among the top rated in the UK for research. St Andrews is consistently held to be one of the United Kingdom’s top ten universities in university league tables compiled by The Times, The Sunday Times, The Guardian and The Independent Complete University Guide. It has five times been named the top multi-faculty university in the UK in the National Student Survey. The Times Higher World University Rankings 2010 ranked St Andrews as one of the world’s top 20 Arts and Humanities universities. Its international reputation for delivering high quality teaching and research and student satisfaction make it one of the most sought after destinations for prospective students from the UK, Europe and overseas. In 2010 the University received on average 11 applications per place. St Andrews has not entered clearing for several years and sets highly challenging asking rates to attract only the most academically potent students in the Arts, Sciences, Medicine and Divinity. The University is closely integrated with the town. The Main Library, many academic Schools and Service Units are located centrally while the growth in research-active physical and mathematical sciences has been accommodated at the North Haugh on the western edge of St Andrews. As it prepares to celebrate its 600th anniversary from 2011 to 2013, the University is pursuing a varied programme of capital investment, including the refurbishment of its Main Library and a major investment in its collections, a new Biomolecular research facility, the refurbishment of the Students’ Union, the development of a wind-farm to offset energy costs and a joint initiative to site the new Madras secondary school next to the science campus on North Haugh. APPENDIX I DESCRIPTION OF THE PROPOSED RESEARCH We humans are an extraordinary species. We exist at densities that far exceed what would be typical for a mammal of our size1, we have managed to colonize virtually every region of the terrestrial globe, and we exhibit unparalleled behavioural diversity. To a large extent, it is our capacity for culture that underlies this adaptability, allowing us to accommodate to, control, and regulate, our immediate environment. By culture, we mean the ability to acquire and transmit learned knowledge, beliefs and skills (henceforth ‘social learning’), to devise ever more efficient solutions to problems that build on this reservoir of shared intelligence (henceforth ‘innovation’), and to express that knowledge in our behavior, tools, technology and engineering. This adaptability is not a recent human phenomenon. In the last 100,000 years our species has spread from East Africa around the globe, endured an Ice age, negotiated a transition from a hunter-gatherer to an agricultural form of subsistence and witnessed rapid increases in densities. These represent dramatic changes in the selective environment experienced by humans. Yet, in spite of such testing conditions, our species has not only survived but flourished. What is striking about the challenges that our species has faced is that virtually all of them are self-imposed. Humans have modified natural selection, for instance by dispersing into new environments with different climates, devising agricultural practices that alter diets, or being exposed to animal pathogens by domesticating livestock. While humans did not create the last Ice Age, the ability of our species to cope with it was affected by our ability to manufacture clothes, find or build shelter, control fire, etc. Recent analyses of human genetic variation support the argument that human cultural activities have affected our evolution through modifying selective environments. Genetic studies reveal that hundreds, possibly thousands, of human genes have been subject to positive selection over the last 100kyr, and the primary hypothesis put forward is that these alleles spread as adaptive responses to human learning and cultural activities 2-5. It would seem that human inventiveness is longstanding, and that the propagation of our innovations through social learning, and their expression in environmental modification, has literally shaped the human genome. In recent years, prominent geneticists have proposed that the human capacity for learning and culture has not only co-directed human evolution, but speeded up the rate of evolutionary change, and promoted phenotypic diversity by relaxing purifying selection 6-8. Our creativity is seemingly not only central to understanding our current success, but also our history. Given that this capacity for innovation is manifestly such a critical factor underlying the accomplishments of our species, it is surprising that hitherto so little scientific attention has been given to how and why it evolved. A particular challenge is satisfactorily to explain the evolutionary transition from the relatively simple inventions of other animals to the extraordinary complexity, diversity and prevalence of human innovation. At first sight, there is a vast gulf between the foraging innovations of birds and monkeys and the mind-boggling complexity of human creativity, as exemplified by our computers, satellites and Large Hadron colliders, and equally by Shakespearean tragedies or Beethoven’s moonlight sonata. Yet the capacity for human innovation must itself have evolved. Recent research reveals that many animals will invent new behaviors or modify existing behaviors (e.g. devise more efficient foraging techniques), that such innovation is taxonomically widespread, and that there is considerable inter- and intra-specific variation in inventiveness9,10. Perhaps the best-known example of animal innovation is the drinking of cream from milk bottles by European birds 11,12. However, milk-bottle opening is just one of hundreds of innovations reported in animals, with examples ranging from the incorporation of new items or techniques into foraging repertoires, to novel courtship displays, vocalizations, deceptive acts, and tool use9,13,14. Many animal innovations are in response to changed circumstances, such as human impacts, but innovations are also produced in stable environments, where an animal discovers a new method of exploiting the environment 15. We have long argued that such behavior can sensibly be termed “innovation” (see also 15-19). We distinguish between two meanings of the term innovation, as product and process (Reader & Laland 20039, p14): Innovation (sensu product) is a new or modified learned behaviour not previously found in the population. Innovation (sensu process) is a process that results in new or modified learned behaviour and that introduces novel behavioral variants into a population’s repertoire. A detailed discussion of these definitions is given elsewhere 9. Although the consanguinity of animal and human innovation is a matter of debate9, we believe that experimental, comparative, and observational research on animal innovation can also inform studies of human innovation. Particularly instructive are two major studies of innovation that have documented over 2,200 examples of foraging innovations in birds18 and over 550 examples of innovations in primates15,19. Although such studies are vulnerable to reporting biases (i.e. some species are studied more than others), statistical methods for addressing these biases have been devised 14. Such studies allow particular species of birds and primates to be characterized according to their innovativeness, and have the advantage that tens, or even hundreds, of species can be compared, based on ecologically valid data (as opposed to the handful of species that can plausibly be compared in experimental studies). Nonetheless, confidence in this measure is lent by the observation that an animal’s innovation rate correlates strongly with its performance in laboratory tests of learning and cognition 20, supporting the idea that innovation rate is a reliable cognitive measure. Moreover, in both birds and primates, innovation rate has been shown to correlate positively with absolute and relative measures of brain size 14,19,20. This raises the possibility that increased innovativeness may have yielded a selective advantage, thereby driving the evolution of brain enlargement14,19,20. There are many accounts of animal innovations facilitating survival in changed circumstances 21. Innovation might also be of critical importance to those endangered species forced to adjust to impoverished environments22. Species characterized by the aforementioned statistical methods as innovative are more likely to survive and establish themselves when introduced to new locations21,23,24,25. Evidence is mounting that innovation plays an important role in ecology (e.g., range expansion), in evolution (e.g., species and subspecies diversification 26), and in cultural diversification10. This suggests that innovation is not only widespread in animals, but functionally important. Consistent with the aforementioned analyses of recent human evolution where human innovation is implicated as a driver, more generally, innovation has been proposed to have a key influence on the tempo and course of evolution in vertebrates. For instance, Wilson’s 27 “behavioural-drive” hypothesis argues that innovation combined with cultural transmission led animals to exploit the environment in new ways, exposing them to novel selection pressures and increasing the rate of genetic evolution (see also 28). Our studies of primate innovation support a key assumption of the behavioral drive hypothesis—that brain size, innovation rate, and social-learning rate are linked19,20 (see also29), as does related work in birds18. Moreover, innovation rate and brain size recently have been shown to correlate with avian species and subspecies richness, suggesting that, as the behavioral-drive hypothesis predicts, evolutionary rates are accelerated in large-brained, innovative taxa21,30,31. The diffusion of innovations requires two processes: the initial inception of the behavioral variant (innovation), and the spread of the novel trait between individuals (social learning). In recent years many books, conferences, and papers have been dedicated to social learning (e.g. 32-39). In comparison, innovation has received little attention. Similarly, while there has been extensive research into the evolution of social learning using mathematical models40-42, there have been few similar mathematical analyses of innovation. Fortunately, this neglect is now starting to change, and innovation is increasingly being recognized as an important research topic 43-45. We have been at the forefront of recent investigations of innovation9,10,15,19,20. For 20 years we have investigated social learning and innovation in a variety of animals and we are the authors of the first and only book on ‘Animal Innovation’9. In seeking to understand the evolution of innovation, it is natural to turn to other animals and to ask about their abilities. A comparative perspective is potentially a productive avenue for investigating the evolution of human creativity, as it is for many aspects of cognition (e.g. 46), although there are at least two obvious approaches that might be adopted. One might collate data on the incidence of innovation across a broad taxonomic group (e.g. primates) and deploy comparative statistical techniques to ask what ecological, social, or life-history traits co-vary with this capability, as a means to explore the selection pressures that favored innovativeness. Second, one might construct mathematical models to explore the processes that promote or hinder innovative change. We will pursue both of these approaches. Here we propose a package of empirical and theoretical analyses of innovation in primates, including humans, designed to develop a new understanding of the roots and character of human creativity. The project sets out to provide authoritative answers to the following questions: What makes an individual animal innovative? What factors led to the evolution of innovativeness and large brains? Which processes promote, and hinder, innovative change? What is the relationship between human and animal innovation? The project comprises two integrated parts: 1. Statistical models of primate innovation (3-year position in statistical modelling) We will investigate the evolution of innovation and brain size using modern comparative statistical methods47. Previously, using such methods, we established that the reported incidence of behavioral innovation co-varies strongly with relative and absolute brain size measures in nonhuman primates, controlling for phylogeny and other confounds19. In this study, we endorsed the hypothesis that social learning and innovation may have driven primate brain evolution. We have collated an extensive databank of cognitive (e.g. social learning, innovation, tool use, extractive foraging, deception), lifehistory (e.g. diurnal/nocturnal, dispersal, inter-birth interval), physical (body size, brain size and component volumes, sexual dimorphism), social (e.g. group size, alloparental care) and ecological (e.g. diet, diet breadth, climate, seasonality, predation risk) variables across >200 species of primates. We will also compile the largest and most reliable primate brain dataset ever, by integrating published serial sections and MRI data with newly collected high-resolution scans of our own. This will increase the power of our analyses, and provide an invaluable resource for other scientists. Using modern comparative statistical methods (e.g. CAIC, Phylogenetic Regression, BayesTraits), we will extend our work to explore the factors that co-vary with, and potentially explain, the evolution of innovation and brain size in primates. However, here researchers run into a problem: even if comparative statistical methods were deployed to identify factors that explain variation in innovativeness, we still would not know whether such relationships were causal. Moreover, such analyses, where previously deployed, have revealed several factors that co-vary with innovation and brain size19,20, and hitherto it has not been clear how to evaluate alternative explanatory hypotheses, as it is plausible that another factor favoured larger brains. Of course, this problem is not specific to innovation; it is a problem that bedevils comparative statistical methods in general 47. Fortunately, a new statistical procedure has been devised, drawing from research into artificial intelligence, that allows causal inferences to be made from correlational data, using causal (or directed) graphs48,49. A causal graph is a formal means of specifying the causal relationship between variables, which can be used to generate a list of conditional and unconditional independence relations that would be found if the graph were correct 49. This allows one to test specific causal models against observational data, or to search for a set of causal graphs that are consistent with the data50. We will resolve long-standing controversies by using causal graphs to identify the most likely cause of primate brain evolution, and to sort between alternative explanations for the evolution of innovation. The application of such procedures potentially renders comparative statistical analyses far more powerful, since they allow alternative explanatory hypotheses to be evaluated and causality to be inferred. To our knowledge such methods have not yet been employed to address any evolutionary question. Accordingly, their application to evolution of innovation and brain size will be pioneering, and could even be a revolutionary development in evolutionary and behavioral biology, and across the social sciences, where correlational data is extremely prevalent. Accurate brain size data from a breadth of species is obviously important to this project, and we propose to enhance the reliability and power of this work significantly by expanding our primate brain size database. We have already successfully established a robust MRI protocol, producing very highresolution images on a state-of-the-art 9.4 Tesla scanner at Utrecht University. We have completed some MRI scans of primate brains, but they require measuring and extraction of the relevant quantitative data, a lengthy procedure requiring training. We would aim to scan further brains and quantify these and the scans already completed, to give c. 50 further brains in total. This is a substantial increase to currently available databases. The brain tissue would be obtained from an established primate brain bank (www.primatebrainbank.org), which collects tissue from zoo animals that have died of natural causes, so no animals would be sacrificed for this project. 2. Lessons for understanding human creativity (2-year position in mathematical modelling) The aforementioned statistical analyses of primate innovation will shed important light on both the nature of animal innovation and the selection pressures that favor innovativeness in animals. While such studies potentially contribute to a broader understanding of the roots of human innovation, and help to place human creativity in a natural context, they do not, in and of themselves, promote a direct understanding of human innovation in its modern setting. We will address this shortcoming in two ways: (a) by developing specific human-orientated models of the evolution of innovation, designed to lead to a deeper understanding of the factors that promote human innovativeness, and (b) by organizing a workshop specifically designed to explore the relationship between human and animal innovation, inviting leading authorities on both human creativity and animal innovation, and following this with a special edition of a journal on the same topic. A theme of both workshop and special edition will be to ask what we can learn about human creativity from the study of innovation in animals. (a) Gene-culture co-evolutionary models of the evolution of innovation: Recent analyses of data from the human genome project report substantive evidence for recent (<100kyrs), rapid selection of human genes2,3,51,52, and one52 concluded “gene-culture interactions directly or indirectly shaped our genomic architecture”. Such findings endorse the view of practitioners of the field of gene-culture co-evolutionary modeling40,41,52-55, who model genes and culture as two interacting forms of inheritance, deploying population-genetic and game-theory methods40,41. Such theory has led to a number of important insights concerning the evolution of culture and the psychological processes that underpin it (see42 for a summary). However, this body of theory has not yet addressed the evolution of innovativeness 42. This shortcoming will be rectified here. Laland has a track record in this area, having developed gene-culture co-evolutionary models of human sexual selection with a culturally transmitted mating preference56, the effects of cultural practices such as sexbiased infanticide on the evolution of the sex-ratio57, the evolution of handedness58, the evolutionary ramifications of cultural niche construction59 and the evolution of human mating systems 60. We will use mathematical modeling to explore the evolution of innovation and the factors that promote or hinder innovative change. This aspect of the project will be designed specifically with humans in mind, and we will explore the effect of including (or not) features into the models’ assumptions (e.g. a capacity for cumulative culture, high-fidelity knowledge transmission through teaching/language) that are likely to be restricted to humans and their immediate ancestors. This component of the project will draw on insights from the primate statistical analyses, to incorporate into the model factors that these studies reveal to be key parameters affecting innovativeness. (b) We will organize an international workshop specifically designed to explore the relationship between human and animal innovation, inviting leading authorities on both human creativity and animal innovation to a fully funded meeting based at St Andrews, U.K. The workshop will dedicate significant time to discussion, and a key theme will be to explore what can be learned about human creativity from the study of innovation in animals. References: 1. Currie D & Fritz J 1993 Oikos 67:56–68. 2. Wang ET et al 2006 PNAS 103:135–40. 3. Voight BT et al 2006 PLoS Biol. 4:e72. 4. Laland KN et al 2010 Nat Rev Genet 11:137–48. 5. Richerson et al 2010 Proc Nat Acad Sci USA 107: 8985–92. 6. Hawks J et al 2007 Proc Natl Acad Sci USA 104: 20753–8. 7. Keightley PD et al 2005 PLoS Biol. 3:e42. 8. Varki A et al 2008 Nat Rev Gen 9:749–63. 9. Reader SM & Laland KN (eds) 2003 Animal Innovation. OUP. 10. Laland KN & Reader SM 2009 In O’Brien & Shennan, S (eds) Innovation in Cultural Systems. MIT. 37–51. 11. Fisher J & Hinde R 1949 Brit Birds 42: 347–57. 12. Hinde R & Fisher J 1951 Brit Birds 44:393–6. 13. Casanova CR et al 2008 Am J Primatol 70:54–61. 14. Lefebvre L et al 2004 Brain, Behav & Evol 63:233–46. 15. Reader SM & Laland KN 2001 Int J Primatol 22:787–805. 16. Biro D et al 2003 Anim Cognit 6:213–23. 17. Kummer H & Goodall J 1985 Phil Trans R Soc B 308:203–14. 18. Lefebvre L et al 1997 Anim Behav 53:549–60. 19. Reader SM & Laland KN 2002. Proc Natl Acad Sci USA 99:4436–41. 20. Reader SM et al 2011 Phil Trans R Soc B 366:1017–27. 21. Sol D 2003 In Reader & Laland (eds) Animal Innovation. OUP. 63–82. 22. Greenberg R & Mettke-Hofman C 2001 Curr Ornithol 16:119–78. 23. Sol D & Lefebvre L 2000 Oikos 90:599–605. 24. Sol D et al 2005 Nat Acad Sci Proc 102:5460–65. 25. Wright TF et al 2010 Ethol Ecol & Evol 22:393-404. 26. Tebbich S, et al 2010 Phil Trans R Soc B 365:1099109. 27.Wilson AC 1985 Sci Am 253:148–57. 28. Wyles JS et al 1983 Proc Nat Acad Sci 80: 4394– 7.27. 29. Whiten A & van Schaik CP 2007 Phil Trans R Soc B 362:603–20. 30. Nicolakakis N et al 2003 Anim Behav 65:445–52. 31. Sol D et al 2005 Evolution 59:2669–77. 32. Box HO & Gibson KR (eds) 1999 Mammalian Social Learning. CUP. 33. Fragaszy D & Perry S (eds) 2003 Traditions in Nonhuman Primates. Chicago. 34. Heyes CM & Galef BG Jr (eds) 1996 Social Learning in Animals: The Roots of Culture. Academic. 35. Galef BG & Heyes CM 2004 Social learning and imitation. Special Edition of Learning and Behavior 32(1). 36. Kendal RL et al 2010 PLoS One 4:e5192. 37. Laland KN & Galef BG (eds) 2010 The Question of Animal Culture. Harvard UP. 38. Zentall & Galef (eds) 1988 Social Learning: Psychological and Biological Perspectives. Erlbaum. 39. Avital E & Jablonka E 2000 Animal Traditions. Cambridge UP 40. Boyd R & Richerson PJ 1985 Culture and the Evolutionary Process. Chicago. 41. Cavalli-Sforza LL & Feldman MW 1981 Cultural Transmission and Evolution. Princeton UP. 42. Richerson PJ & Boyd R 2005 Not By Genes Alone. Chicago. 43. Huffman MA 1996 In Heyes & Galef (eds) Social Learning in Animals. Academic. 267–90. 44. Leca J et al 2007 J Hum Evol 52:691–708. 45. Ramsey G et al 2007 Behav Brain Sci 30:393–437. 46. Tomasello M & Call J 1997 Primate Cognition New York 47. Harvey PH & Pagel MD 1991 The Comparative Method in Evolutionary Biology. OUP. 48. Pearl J 1988 Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann. 49. Shipley B 2000 Cause and Correlation in Biology. CUP. 50. Spirtes P et al 2001 Causation, Prediction, and Search. 2nd ed MIT. 51. Williamson SH et al 2007 PLoS Genet 2007 3:e90. 52. Rogers AR 1988 Am Anthro 90:819–31. 53. Feldman MW & Laland KN 1996 TREE 11:453–57. 54. Henrich J & McElreath R 2003 Evol Anthro 12:123–35. 55. Enquist M et al 2007 Am Anthro 109:727–34. 56. Laland KN 1994 Theor Pop Biol 45:1–15. 57. Kumm J et al 1994 Theor Pop Biol 46:249–78. 58. Laland KN et al 1995 Behav Genet 25:433–45. 59. Laland KN et al 2001 J Evol Biol 14:22–33. 60. Mesoudi A & Laland KN 2007 Proc R Soc B 274:1273–8. Further reading: Reader SM & Laland KN (eds.) 2003 Animal Innovation. Oxford University Press