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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
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


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.
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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–
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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
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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