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Abstract Intelligence is suggested to have evolved in primates in response to complexities in the environment faced by their ancestors. Corvids, a large-brained group of birds, have been suggested to have undergone a convergent evolution of intelligence [ Emery & Clayton (2004) Science, Vol. 306, pp. 1903–1907 ]. Here we review evidence for the proposal from both ultimate and proximate perspectives. While we show that many of the proposed hypotheses for the evolutionary origin of great ape intelligence also apply to corvids, further study is needed to reveal the selective pressures that resulted in the evolution of intelligent behaviour in both corvids and apes. For comparative proximate analyses we emphasize the need to be explicit about the level of analysis to reveal the type of convergence that has taken place. Although there is evidence that corvids and apes solve social and physical problems with similar speed and flexibility, there is a great deal more to be learned about the representations and algorithms underpinning these computations in both groups. We discuss recent comparative work that has addressed proximate questions at this level, and suggest directions for future research.
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CURRENT ISSUES – PERSPECTIVES AND REVIEWS
Intelligence in Corvids and Apes: A Case of Convergent
Evolution?
Amanda Seed*, Nathan Emery& Nicola Claytonà
* Department of Psychology, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
School of Biological & Chemical Sciences, Queen Mary University of London, London, UK
àDepartment of Experimental Psychology, University of Cambridge, Cambridge, UK
(Invited Review)
In the last few decades there has been a growing
interest in the evolution of intelligence, and increas-
ing evidence that it has evolved independently in
several vertebrate groups other than primates, such
as dolphins (Marino 2002), hyaenas (Holekamp
et al. 2007) and canids (Miklosi et al. 2004; Hare &
Tomasello 2005) (for a definition of ‘intelligence’,
see Box 1). Perhaps most strikingly, there is evidence
for impressive cognitive abilities in groups of large-
brained birds, such as corvids and parrots (Emery &
Clayton 2004). The discovery that these non-pri-
mate, non-mammalian animals are capable of feats
thought until not long ago to be uniquely human,
such as recalling specific past events (episodic-like
memory; Clayton & Dickinson 1998), planning for
the future (Raby et al. 2007), taking the visual per-
spective of conspecifics (Dally et al. 2006), coopera-
tive problem-solving (Seed et al. 2008) and creating
novel tools to solve problems (Weir et al. 2002), has
fascinated the scientific and non-scientific commu-
nity alike, but it has also prompted questions. What
exactly is it that has evolved convergently? Can
these birds with walnut-sized brains really be using
human-like reasoning to carry out these behaviours,
and if not, what cognitive processes allow for their
impressive flexibility? Did the same evolutionary
processes that shaped the intelligence of primates
(and ultimately humans) act upon corvid ancestors?
What were they, and can the answer tell us any-
thing about the evolution of the human mind? The
notion that intelligence has evolved independently
but convergently in corvids and apes has therefore
prompted questions from all of Tinbergen’s four lev-
els of explanation. In this review we will address the
Correspondence
Nicola Clayton, Department of Experimental
Psychology, University of Cambridge, Downing
Street, Cambridge CB23EB, UK.
E-mail: nsc22@cam.ac.uk
Received: November 13, 2008
Initial acceptance: December 26, 2008
Final acceptance: February 15, 2009
(M. Taborsky)
doi: 10.1111/j.1439-0310.2009.01644.x
Abstract
Intelligence is suggested to have evolved in primates in response to com-
plexities in the environment faced by their ancestors. Corvids, a large-
brained group of birds, have been suggested to have undergone a con-
vergent evolution of intelligence [Emery & Clayton (2004) Science, Vol.
306, pp. 1903–1907]. Here we review evidence for the proposal from
both ultimate and proximate perspectives. While we show that many of
the proposed hypotheses for the evolutionary origin of great ape intelli-
gence also apply to corvids, further study is needed to reveal the selec-
tive pressures that resulted in the evolution of intelligent behaviour in
both corvids and apes. For comparative proximate analyses we empha-
size the need to be explicit about the level of analysis to reveal the type
of convergence that has taken place. Although there is evidence that
corvids and apes solve social and physical problems with similar speed
and flexibility, there is a great deal more to be learned about the repre-
sentations and algorithms underpinning these computations in both
groups. We discuss recent comparative work that has addressed proxi-
mate questions at this level, and suggest directions for future research.
Ethology
Ethology 115 (2009) 401–420 ª2009 Blackwell Verlag GmbH 401
hypothesis first from an ultimate and then a proxi-
mate perspective.
Why Would Intelligence Evolve? Surviving and
Thriving in an Unpredictable Environment
Although we humans tend to see our complex cogni-
tion as one of the pinnacles of evolution, it is in many
ways a costly and inefficient way of acting in the
world. The more behaviour that is either fully intact
from an animal’s birth, or that can be acquired rapidly
through simple associative learning, the faster the ani-
mal can get on with finding a mate and reproducing.
In contrast, a cognitively complex strategy demands
more time to be spent learning about the world and
attaining the full complement of adult behaviours. As
philosophers such as Godfrey-Smith and Sterelny
have argued, the only sort of environment that could
possibly favour the use of such a costly strategy is a
complex and unpredictable one (Godfrey-Smith 2001;
Sterelny 2003). When the environment can change
quickly, hardwired behaviours and rigid stimulus–
response action patterns may become maladaptive, or
less adaptive when compared with the flexible
behaviour based on more abstract knowledge.
Potts (2004) has described the palaeoenvironmen-
tal conditions prevalent during the evolution of great
apes, and the consequences they would have had for
the evolution of cognition. Between the Late Mio-
cene and the Late Pliocene, a diminished number of
species of great apes became confined to the forests
and woodlands of the tropical latitudes of Africa and
Southeast Asia. During the following Pleistocene
period, climatic instability led to sharp oscillations in
these equatorial habitats. Unlike the cercopithecine
primates, the great apes did not respond to these
environmental challenges by changing their bodies,
allowing for a reduction in the reliance on ripe fruits
and an increase in the amount of plant material in
the diet. Instead, the great apes continued to rely on
Box 1 – What is Intelligence?
Ever since scholars began discussing animal intelli-
gence it has been a highly divisive issue, and
remains so today. At the poles of the debate are
two opposite views concerning ‘thinking’ in ani-
mals. The first, the origin of which is attributed to
Descartes, is that animals are essentially mindless
machines, with their behaviour triggered wholly by
external or internal stimuli. The other, most
famously articulated by Darwin, is that ‘the differ-
ence in mind between man and the higher ani-
malsis one of degree and not of kind’ (Darwin,
1882).
Both these views are to be found entrenched
within the different historical approaches to the
study of animal cognition (Dickinson 1980; Toma-
sello & Call 1997; Shettleworth 1998; Wasserman
& Zentall 2006): the behaviourist school (Watson
1913) and that of cognitive ethology (Griffin 1978).
The former illuminates in detail one powerful gen-
eral mechanism by which animals acquire, process,
store and act on environmental stimuli: associative
learning. The latter is concerned with natural
behaviours for which explanations based either on
classical conditioning or hardwired predispositions
seem to fall short, and seeks to explain them in
mentalistic terms. Most contemporary accounts of
animal cognition acknowledge the interplay
between the two views (e.g. Dickinson & Shanks
1995).
Complex cognition or intelligence in animals is,
therefore, usually defined by exclusion, rather than
by some positive assessment of the mechanisms
underpinning it. Identifying ‘intelligence’ in animals
in practice typically amounts to observing animals
performing complex behaviours in their environ-
ment, and looking for evidence for ‘behavioural flex-
ibility’ or the appearance of novel solutions that are
not part of the animal’s repertoire (Roth & Dicke
2005). The relative size of the brain (or brain area) of
the species or group in question is often used as a
proxy for intelligence or ‘cognitive potential’, but as
Healy & Rowe (2007) point out, ‘considerable cau-
tion should be exercised when interpreting correla-
tions between such multifunctional brain regions
and complex behaviours, owing to the problems
inherent in attributing a single function to such a
region’.
What makes the evolution of intelligence so diffi-
cult to study is the fact that the feature itself is an
unobservable property of an animal’s psychology
that has no positive definition; is unlikely to be
unitary; and is held by some as not existing in most
animals. Nevertheless, the seemingly ‘intelligent’
behaviours of animals are among the most fascinat-
ing. The emerging consensus is that the best way
to address questions about the (possibly conver-
gent) evolution of intelligence, and the fitness ben-
efits it confers, is through carefully focused
comparative experimentation (Reader et al. 2005;
Healy & Rowe 2007).
Convergent Evolution of Intelligence in Corvids and Apes A. Seed et al.
402 Ethology 115 (2009) 401–420 ª2009 Blackwell Verlag GmbH
ripe-fruit frugivory, and Potts (2004) argues that this
diet and habitat during such a period of instability
would have exposed them to the environmental var-
iability hypothesized to favour the evolution of
advanced cognitive capacities.
Convergent Evolution
Environmental complexity and variability such as
that faced by primates during the course of their
evolution have also been faced by other groups of
animals. Emery & Clayton (2004) argued that cogni-
tive abilities of a level comparable with that of the
great apes has evolved convergently in corvids, a rel-
atively recently evolved group of passerine birds.
Convergent evolution is defined as ‘evolutionary
change in two or more unrelated organisms that
results in the independent development of similar
adaptations to similar environmental conditions’
(Keeton & Gould 1986). For similar traits to be
shown to have evolved convergently, rather than
being homologous, it must be demonstrated that the
trait was not present in the common ancestor.
A useful analogy is the evolution of flapping flight
in vertebrates. The forelimbs of birds, bats and ptero-
saurs have all evolved into wings. Knowledge of
their divergent evolutionary history, and the discon-
tinuity of such an adaptation in their ancestry, sug-
gests that they must have arisen through a process
of convergent evolution. Moreover, dissecting the
wings reveals that they are in fact structured differ-
ently (Fig. 1). The bird wing is the result of an
extension of all the bones of the forelimb, while the
bats and pterosaurs support the wing through
extended digits: the fifth digit for pterosaurs; while
for bats it is the second, third, fourth and fifth.
Emery & Clayton (2004) pointed out that corvids,
like apes, have evolved large brains relative to their
body size, and that the areas of the brain thought to
be functionally equivalent to the neocortex of pri-
mates, the nidopallium and mesopallium, are specifi-
cally enlarged. Behavioural hallmarks of intelligence
in apes, such as the manufacture and use of tools
and social strategizing, have also been documented
for species of corvids, and laboratory experiments
suggest that these are underpinned by a complex
cognitive toolkit common to corvids and apes
(Emery & Clayton 2004). This trait is likely to have
arisen by convergent evolution, because the evolu-
tionary lines that led to birds and mammals diverged
around 280 million years ago, and have undergone
very different evolutionary histories. In addition to
the obvious differences in morphology, the avian
brain is organized very differently from the mamma-
lian brain. For example, while the mammalian brain
is laminar, with the cells organized into layers, the
avian brain is nuclear, and is comprised of clusters of
cells. The difference in the structure of the brain
between birds and mammals, much like the differ-
ences in the anatomical features of the different
types of vertebrate wings, gives a good indication
that if the intelligence of corvids and apes is indeed
similar, the similarity must be the result of conver-
gence. However, the hypothesis that intelligence in
corvids and apes is the result of convergent evolu-
tion needs further testing: were similar environmen-
tal conditions responsible for the evolution of the
trait in both groups, and is the trait really under-
pinned by independently evolved yet similar cogni-
tive mechanisms?
Ultimate Perspectives – Hypotheses for the
Evolution of Intelligence
Although many agree that the function of intelli-
gence is to produce flexible adaptive behaviour in
the face of environmental complexity and variability,
different theories place different emphases on the
challenges from the physical environment and those
from the social environment. We describe the six
predominant hypotheses for the evolution of great
ape intelligence. These theories have received differ-
ent degrees of support, but to date none has been
wholly supported or refuted, and so we discuss the
extent to which each one might apply to corvids. It
is worth emphasizing that, while we are concerned
with corvids and apes in this review, many of the
hypotheses outlined above apply to other species of
birds and mammals, including food-caching parids,
omnivorous bears, tool-using Galapagos finches,
extractive foraging parrots and monkeys, social car-
nivores such as hyaenas, wolves and meerkats to
name but a few. Another point to stress at the outset
is that these hypotheses are not mutually exclusive,
and indeed it seems likely that more than one of
Fig. 1: The convergent evolution of wings in vertebrates.
A. Seed et al. Convergent Evolution of Intelligence in Corvids and Apes
Ethology 115 (2009) 401–420 ª2009 Blackwell Verlag GmbH 403
these evolutionary pressures has shaped the minds
of corvids, apes, as well as other species.
Physical
Several aspects of the foraging strategies of primates
have been linked to increased brain size. The different
proposals place differing emphasis on where food is
located, what food is eaten and how it is processed:
Where and when – Reliance on spatiotemporally dis-
persed food resources (Clutton-Brock & Harvey
1980; Milton 1981).
What – Omnivory and extractive foraging (Parker &
Gibson 1977; Gibson 1986).
How – Complex foraging (Byrne 1996, 2004) and
tool use (Goodall 1964; Parker & Gibson 1977).
Where and When can Food be Found?
Spatiotemporally Dispersed Food Resources
Milton (1981) suggested that the primate diet, in par-
ticular, the reliance on tropical plant foods, provided
the evolutionary stimulus for the evolution of large
brains. Tropical plants are patchily distributed
throughout the forest, and plants may only provide
edible ripe fruit at certain times of the year. However,
this temporal and spatial patchiness is predictable;
plants remain in the same place and ripen at predict-
able intervals. She proposed that those primate spe-
cies feeding on the most patchily distributed plants
would have evolved the largest brains in order to do
so efficiently, perhaps through the use of a ‘cognitive
map’, given the energetic expenditure of travelling
long distances to find food. In support of this hypoth-
esis, Clutton-Brock & Harvey (1980) found a positive
correlation between range area and degree of frugi-
vory, with the size of the brain relative to body size
for primate species. However, two main problems
with this finding have been raised; first, that the use
of body size as a comparator may be inappropriate
given the fact that foliovores have a larger gut than
frugivores to extract enough nutriment from a diet of
leaves, which lowers their brain:body ratio indepen-
dently of the size of the brain (Deacon 1980; Byrne
2000). Secondly, Dunbar (1992, 1995) has argued
that the absolute size of the range area may be an
inappropriate measure, given that differences in body
size will modulate the ‘complexity’ of any given
range area. Dunbar instead correlated the ‘neocortex
ratio’ (volume of the neocortex volume of the rest of
the brain) with range area corrected for body size,
and found no relationship. A conceptual criticism is
that although apes, including humans, are dependent
on ripe fruit, monkeys can in fact process unripe fruit
and so the demand of locating the food within a
narrow time window does not apply so strictly.
What about corvids?
While fruit features in the diet of many species of cor-
vid, a reliance on ripe fruit does not, and rather most
corvids are renowned for their ability to exploit a wide
variety of food resources (Goodwin 1986). However, a
conspicuous feature of corvid ecology worth mention-
ing here is the fact that many species cache foods dur-
ing periods of seasonal abundance; indeed caching is
likely to have been a trait present in the common
ancestor (de Kort et al. 2006). Western scrub-jays not
only remember where they have cached, but can also
integrate information about what they cached and
when, in order to recover perishable food when it is
still edible (Clayton & Dickinson 1998, 1999; Clayton
et al. 2001, 2003; de Kort et al. 2005). Remembering
the location of thousands of caches, which vary in
terms of their perishability, seems a comparable
spatiotemporal challenge to locating ripe fruit.
What Food? Omnivory and Extractive Foraging
Parker & Gibson (1977) proposed two important ele-
ments of the primate diet for the evolution of intelli-
gence: the degree of dietary generalism and reliance
on foods that need to be extracted from a substrate.
They suggested that the expansion of the neocortex
was therefore favoured in order to exploit niches not
readily available to others, because extractive foods
tend to be high in nutritive value and available all
year round. The main objection to this theory is the
fact that extractive foraging per se does not help pre-
dict brain size. Dunbar (1995) found no relationship
between extractive foraging and neocortex ratio.
However, Parker and Gibson emphasized that it was
the conjunction of omnivory and extractive foraging,
and the consequent variety and complexity of the
sensorimotor coordinations used in the finding and
processing of food, that led to the evolution of intelli-
gence. They support this notion with the observation
that among primates, the species with the largest
brain size relative to body size are omnivores that
engage in extractive foraging, such as chimpanzees,
orang-utans and capuchin monkeys.
What about corvids?
Some corvids such as crows, rooks and ravens are
highly omnivorous. For example, from a review of
Convergent Evolution of Intelligence in Corvids and Apes A. Seed et al.
404 Ethology 115 (2009) 401–420 ª2009 Blackwell Verlag GmbH
the available literature, Cramp & Perrins (1994)
reported that rooks consume over 170 species of
plants and animals, including many parts of the
plant (roots, seeds, leaves and fruits); a number of
insects both as larvae and adults; worms; seafood
such as shrimps and mussels; the eggs and young
of ground-living birds; and adult vertebrates, many
as carrion, and also as small prey which are first
caught and killed (mice, voles, frogs and even fish).
They also rely on extractive foraging for a high
percentage of their dietary requirements. Over 50%
of their food is taken by digging in the soil for
grain and invertebrates, particularly earthworms
(Lockie 1955), and they also dig for roots and
tubers such as potatoes and turnips. Other forms of
extractive foraging among corvids include the drop-
ping of mussels, limpets or bone from the air until
the shell or enamel breaks. These foods are also
accessed by hammering and prising with the beak,
as are nuts such as walnuts and acorns. Many spe-
cies feed on rubbish dumps, a feeding habit likely
to involve both extraction and diversity (Cramp &
Perrins 1994).
How is the Food Obtained? Complex Foraging and
Tool-Use
The complexity of great ape feeding and tool-using
behaviour has been linked to the evolution of
intelligence by Byrne (1996, 2004). Byrne proposed
that foraging and tool-use in the great apes is char-
acterized by a complex organizational structure. In
support of this notion he referred to the foraging
skills of gorillas, and observations of great ape tool-
use. Gorillas feed on a diet of plants with physical
defences such as nettles, using a wide range of
techniques, each involving the coordinated use of
two hands in different roles. The techniques are
comprised of a number of hierarchically organized
steps (Byrne & Byrne 1991, 1993; Byrne 1996).
Likewise, tool-use in chimpanzees and orang-utans
is characterized by bimanual role differentiation,
and the sequential use of different tools to achieve
the same end (Byrne 1996, 2004). Several
researchers have posited that tool-use may have
selected for advanced cognitive capacities, given the
challenge of tracking events between objects out-
side the body (Goodall 1964). In support of the
connection between tool-use and brain evolution is
the correlation between relative ‘executive brain’
(neocortex and striatum) and amount of tool-use
reported for a given species (Reader & Laland
2002).
What about corvids?
The foraging behaviour of corvids has not been stud-
ied in detail and so comparisons with apes are pre-
mature, but corvids (and other birds, such as raptors
and parrots) use both beak and feet in the processing
of foods (in Cramp & Perrins 1994). Hierarchically
structured foraging techniques have been described
for ravens by Heinrich (1999). There are reports
of infrequent tool use by several species of corvids
(Lefebvre et al. 2002), and New Caledonian crows
are known to both manufacture and use tools rou-
tinely in the wild. They make two types of tools,
and use them to obtain insect larvae from holes in
living and dead wood, from the leaf litter, and from
the base of plants (Hunt 2000). Stepped-cut tools are
fashioned from pandanus and fern leaves in a series
of steps, with the resulting tool retaining a thick base
and tapering in steps to a narrow tip (Hunt & Gray
2004b). Hook tools are commonly made by trimming
twigs of their branches, leaves and bark, in a com-
plex series of steps (Hunt 1996; Hunt & Gray
2004a). They can also be fashioned from a variety of
other substrates, including the midribs of leaves,
bamboo stems, and thorny vines. This use of a range
of materials and techniques to achieve the same end
suggests flexibility (Hunt & Gray 2002).
However, some primate researchers have argued
that the range of both the materials used to build
tools and the uses to which they are put by New
Caledonian crows is not as great as that described for
chimpanzees and orang-utans (Mendes et al. 2007).
Furthermore, while all species of great apes make
and use tools, only one corvid has been reported to
do so routinely (so the trait is unlikely to be the
ancestral condition). However, it is not clear
whether this reflects an absence of cognitively medi-
ated foraging, or a lack of benefit to be gained from
tool-mediated foraging in corvids. Corvids can use
their beak for many of the sorts of tasks that apes
use tools for, such as digging and cracking open
nuts. Furthermore, the lack of hands and physical
strength presents a limitation to the type of tool-
using that a bird can perform; tasks such as
smashing open insect nests could not conceivably be
facilitated through tool-use. The ecological condi-
tions favouring the use of tools in avian species
might therefore be expected to be fairly uncommon,
and perhaps these are not faced by most corvids. For
example, in the Galapagos Islands, tool-use by
woodpecker finches is common in some habitats but
rare in others. It is most common in coastal zones,
where harsh and unpredictable conditions are seen
A. Seed et al. Convergent Evolution of Intelligence in Corvids and Apes
Ethology 115 (2009) 401–420 ª2009 Blackwell Verlag GmbH 405
in conjunction with the availability of particularly
large grubs embedded in tree holes (Tebbich et al.
2002). Tool-use may be the expression of a pre-
existing physical intelligence rather than a necessary
condition for it to evolve, but nevertheless the rou-
tine use of tools may provide selective pressure for
further cognitive adaptation, a pressure not faced by
most corvids. The similarities and differences
between the challenges from the physical environ-
ment faced by apes and corvids, along with some
relevant biological differences because of their diver-
gent evolutionary history, are summarized in
Table 1.
Social
The challenges and opportunities presented by the
social environment can also be divided into three
broad categories:
Competition – Machiavellian strategies for maximiz-
ing personal gain, resulting in social complexity
(Jolly, 1966; Humphrey, 1976; Byrne and Whiten,
1988; Byrne & Whiten 1997).
Cooperation – Pro-social or mutually beneficial
behavioural coordination (Strum et al. 1997; Boesch
& Boesch-Achermann 2000; Barret & Henzi 2005;
Connor 2007; Dunbar & Schultz 2007; Emery et al.
2007).
Social learning – Exploiting the opportunity to learn
from others (Russon 1997; Whiten & van Schaik
2007).
Competition – Machiavellian Manoeuvring
Humphrey (1976) suggested that group-living pri-
mates could benefit from intelligent social strategies
that circumvent the constraints of individual ability
when competing for mates and resources. Though
Table 1: Comparison of corvids and chimpanzees
Chimpanzees Corvid examples
Physical
When & where? Spatiotemporally
dispersed food
Fruits and leaves
1
Arable crops, insects
2
* Caching Not reported Yes
What? Dietary diversity High (328 foods, 198 plant species)
3
For example, rooks – high (>170 species)
2
Extractive foraging Habitual: fruits, nuts, nest-building insects
4
Opportunistic: e.g. bone marrow, honey
5
Habitual: ground living insects, seeds
2,6
Opportunistic: e.g. shellfish, nuts, fruit,
rubbish
2
Innovation High levels
7
High levels
8
How? Complex foraging Bimanual, hierarchically organized
manipulation
9
Not well studied, can involve coordination
of beak and foot
2
* Tool-use and manufacture Extensive: e.g. ant-dipping, termite
fishing, sponge making
10
Routine manufacture of hook tools by
New Caledonian crows
11
Social
Competition Group-size Fairly large (19–106 individuals)
12
Rooks and jackdaws: large (50 to 1000
individuals)
2
Alliance formation Yes, with several individuals, ‘tactical’
13
Rooks, ravens and jackdaws: yes, usually
with 1 or 2 individuals
14
Cooperation Post-conflict behaviour Reconciliation, third-party affiliation,
third-party punishment
15
Rooks: third-party affiliation
16
Social learning Social facilitation of feeding Yes, e.g. novel foods
17
Yes, e.g. crows, rooks, ravens
18,19,20
Social learning of foraging
techniques
Yes, e.g. nut-cracking
17
Yes, e.g. Florida scrub-jays
Evolutionary legacy
* Reproductive biology Viviparous, long gestation Oviparous, altricial young
* Mating system Promiscuity Varied, mostly long-term monogamy;
cooperative breeding
21
* Body size and locomotion Large (50–80 kg), arboreal clambering Relatively small (<1 kg), flight
* Morphology Hands Wings, beaks
Asterisks denote clear differences between them.
1
Clutton-Brock & Harvey 1980;
2
Cramp & Perrins 1994;
3
Nishida & Uehara 1983;
4
Parker & Gibson 1977;
5
Brewer & McGrew, 1990;
6
Lockie 1955;
7
Reader & Laland 2002;
8
Lefebvre et al. 1997;
9
Byrne 2004;
10
McGrew 1992;
11
Hunt 1996;
12
Yamagiwa 2004;
13
Harcourt 1992;
14
Emery et al. 2007;
15
de Waal 1982;
16
Seed et al. 2007;
17
Rapaport & Brown 2008;
18
Sonerud et al. 2001;
19
Waite 1981;
20
Marzluff et al. 1996;
21
Clayton & Emery 2007.
Convergent Evolution of Intelligence in Corvids and Apes A. Seed et al.
406 Ethology 115 (2009) 401–420 ª2009 Blackwell Verlag GmbH
not explicit in his proposal, emphasis has tended to
be placed on exploitative aspects of social manoeu-
vring for the evolution of primate intelligence. It is
in this competitive setting that Humphrey argued
that an ‘evolutionary ratchet’ can be set up. When
one individual gains a competitive advantage (and
fitness benefits) through intelligent activity, such
intelligence is likely to spread through the gene pool,
and this effect will be iterated until an upper limit is
set by natural selection, for example by the con-
straint of maximum daily calorific intake for use in
fuelling brains. In primates, neocortex ratio corre-
lates with group size, independently of other envi-
ronmental variables such as home-range size
(Dunbar 1992, 1995). More importantly for this
hypothesis, neocortex size also correlates with indi-
cators of social complexity, such as deception rate
(Byrne & Corp 2004), mating system (Sawaguchi &
Kudo 1990) and grooming-clique size (Kudo &
Dunbar 2001). Interestingly, male rank predicts
mating success less well for primate species with
relatively larger neocortices (Pawolski et al. 1998),
which suggests that social strategizing is effective in
circumventing individual competitive ability.
What about corvids?
Corvids display a variety of social organization, rang-
ing from pair-living territorial species such as the
Eurasian jay, to cooperative breeders such as the
Florida scrub-jay, to communal-living species such
as rooks and jackdaws (reviewed by Clayton &
Emery 2007). Some corvids therefore fulfil the basic
criteria for the evolution of social intelligence: ‘living
in large semi-permanent groups of long-lived indi-
viduals’ (Byrne & Whiten 1997, pp. 14). Emery et al.
(2007) argue that both rooks and jackdaws (in cap-
tivity) do indeed form long-term alliances with other
group members, which are maintained through the
use of affiliative behaviours, and employed in ago-
nistic conflicts. They report preliminary evidence
that rooks are sensitive to relationships between
third parties, as they are seen to redirect aggression
to the partner of an individual that they have
received aggression from.
Food caching also provides a stimulant for group-
living animals to maximize personal gain at the
expense of others. In the wild, corvids’ caches can
be lost not just to degradation over time, but also to
thieving conspecifics, which use observational spatial
memory to accurately locate the caches of others
(Bednekoff & Balda 1996a,b; Heinrich & Pepper
1998). A series of observations and experiments car-
ried out with western scrub-jays and ravens have
shown that the strategies used by storing corvids to
protect their caches are based not just on simple
rules of thumb (e.g. by only hiding food when there
is no competitor in sight), but are instead highly
flexible, and may depend on an ability to take the
visual perspective of the competitor into account
(reviewed in Clayton et al. 2007) .
However, living in a large group per se does not
appear to have been an important selective pressure
during the evolution of large brains in birds, for
unlike primates and other mammals, there is no
clear relationship between brain size and group size
(reviewed in Emery et al. 2007; c.f. Iwaniuk &
Arnold 2004). A fundamental difference between
birds and mammals worth mentioning here is their
divergent reproductive biology. Mammals are vivipa-
rous (give birth to live young, after an extended per-
iod of development in the womb), while birds are
oviparous (lay eggs). The asymmetry between male
and female mammals in the selective advantage to
providing parental care means that in the majority
of mammals, polygyny, and predominantly maternal
care is seen. This asymmetry is smaller in birds
(because the initial investment by the female is
smaller), and for the majority of species, monogamy
and biparental care are optimal. Competition for
mates will occur at some point in the life cycle of
birds (in birds such as rooks which pair for life, per-
haps just once), but it is a continuous and critical
feature in the social life of a polygamous primate
(such as chimpanzees), because a dominant male
has the potential to monopolize mating opportuni-
ties, leaving the female to care for the offspring.
Such a mating system has obvious implications for
social structure. Numerous species of primates are
reported to maintain a network of valuable relation-
ships: between males, for competition for alpha sta-
tus and therefore mating rights; and between
females, for protecting offspring from infanticidal
males. Many of the advantages of such a network of
relationships do not apply to a monogamous species,
and as a consequence, neither does the need to track
the changing relationships between different third
parties.
Cooperation and Behavioural Coordination
In the Machiavellian framework, competition is cen-
tral to animals that are ‘forced’ to live together, and
cooperation functions largely in the accrual of indi-
vidual benefits. However, other theorists have
emphasized the inverse view: aggression is merely a
A. Seed et al. Convergent Evolution of Intelligence in Corvids and Apes
Ethology 115 (2009) 401–420 ª2009 Blackwell Verlag GmbH 407
way for group-living animals to negotiate the terms
of their peaceable co-existence, and adaptations that
improve social cohesion are likely to be selected for.
As Barret & Henzi (2005) explain, such adaptations
may be the product of multi-level selection, wherein
the individual has a stake in the well-being of the
group (e.g. if large groups suffer less from preda-
tion). de Waal & van Roosmalen (1979) suggested
that selective pressure exists for behaviours that
minimize the likelihood of conflict occurring (behav-
iour reading, policing) or that ameliorate the costs of
conflict after it has taken place, and repair threa-
tened relationships (reconciliation, third-party affilia-
tion). Group living may also provide a platform for
animals to increase their fitness by combining their
efforts, either as a group (e.g. group hunting, group
defence), or as a smaller number of individuals
(social grooming, pro-social helping). When animals
start cooperating in this way, a situation which can
be referred to as a ‘biological market’ is set up, and
it would pay animals to select the most effective
and or reciprocal cooperative partners. Furthermore,
adaptations that improve cooperative efficiency
(such as coordinating with another’s actions) are
likely to have fitness benefits, not only because of
the increased yield from cooperative action, but also
because effective co-operators fare well in the bio-
logical market. At this point, a ratchet effect could
conceivably be set up, much like the one described
for effective competition by Humphrey (1976). There
is evidence that conflict management, cooperation
and biological markets (or ‘score keeping’) are
important features of primate groups (Noe & van
Hooff 2001). However, whether the pressure for
such behaviours to evolve has been a driving force
behind the evolution of primate brains is difficult to
assess. Dunbar & Schultz (2007) report that species
of primates that form coalitions have significantly
higher neocortex ratios (Dunbar & Schultz 2007).
However, there is no more consensus as to the
cognitive requirements of these behaviours than
there is for competitive behaviours such as ‘tactical
deception’.
What about corvids?
The difference between mammalian and avian
reproductive biology and predominant mating sys-
tem is also relevant to this discussion. In socially
monogamous birds, increasing the quality of paren-
tal care may lead to increasing pay-offs, if more
experienced pairs (that have paired for more than
one breeding season) will raise more chicks. In such
cases, mate retention (long-term monogamy) may
be favoured. Numerous studies indicate that
‘divorce’ in long-term monogamous species is more
likely when the pairs’ mating attempts have been
relatively unsuccessful (Mock & Fujioka 1990), indi-
cating that successful coordination is an important
determining factor in the decision to remain paired.
Interestingly, a recent analysis of brain size and mat-
ing system in birds found that the largest relative
brain sizes are found in long-term monogamous spe-
cies and cooperative breeders (Emery et al. 2007).
This relationship may indicate that these mating sys-
tems set up a platform on which adaptations that
increase coordination between pair mates (and help-
ers) are favoured. Most corvids form long-term
monogamous pairs which associate throughout the
year, and typically for life. Recent studies of captive
rooks suggest that pair members do coordinate their
actions, both during displays and outside this context
(Emery et al. 2007). Furthermore, rooks engage in
third-party affiliation with their partner, after one of
them has been involved in a conflict with another
group member (Seed et al. 2007). However, they do
not reconcile these conflicts (with non-partnered
individuals, they do not fight with their partners), a
finding that is line with the notion that they do not
form valuable relationships with group members
outside their partnership. Therefore, while the pres-
sure to increase the cooperative quality of social
relationships may apply to both corvids and apes,
the pressure to keep track of a number of collabora-
tive partners in a ‘biological market’ may be a less
relevant one for long-term monogamous corvids
(although it may apply to species which spend an
extended period of time in non-breeding flocks, such
as ravens).
Social Learning – Exploiting the Opportunity to
Learn from Others
Living socially also provides sources of information
that it would pay an individual to be able to exploit,
and this might select for intelligence (Byrne &
Whiten 1997; Russon 1997; Whiten & van Schaik
2007). Individual learning is costly, in terms of time,
and also risky (e.g. from eating poisonous foods). An
ability to learn from the behaviours of others would
therefore be beneficial, although not without poten-
tial costs, for example from copying an unreliable
model, or employing a copied behaviour in the
wrong context (Richardson & Boyd 1985). Further-
more, individual innovation may occur at a rela-
tively low frequency if it depends on fortuitous
Convergent Evolution of Intelligence in Corvids and Apes A. Seed et al.
408 Ethology 115 (2009) 401–420 ª2009 Blackwell Verlag GmbH
contingencies that happen rarely, and so learning
from others might mean the difference between
acquiring a useful behaviour and never doing so.
Could the pressure to exploit this source of informa-
tion have driven the evolution of large brains? Pri-
mates (as well as many other social vertebrates,
including fish and birds) are known to take advan-
tage of the opportunity to learn socially. Reader &
Laland (2002) correlated the relative size of the
‘executive brain’ (neocortex and striatum) in pri-
mates with the number of reports of social learning,
innovation and tool-use for a given species. They
found a positive correlation for all three. This
provides some indication that the pressure to learn
from others may have played a role in primate brain
evolution.
What about corvids?
Innovation rate correlates with brain size in birds,
just as it does in primates (Lefebvre et al. 1997),
with corvids emerging as one of the most innovative
groups, along with parrots. However, no correlation
has been performed with incidences of social learn-
ing, because only 72 cases of social learning were
recorded (compared with 1796 observations of inno-
vation). This contrasts with the ratio of 558 cases of
innovation and 451 of social learning in Reader &
Laland’s (2002) study. Lefebvre & Bouchard (2003)
suggest that the relative numbers indicate that feed-
ing innovations do not spread as readily to other
birds as they do in primates. However, Marler’s
(1996) cautionary note concerning the different
research perspectives of ornithologists and primatolo-
gists should be remembered here, especially given
the fact that all 76 avian experimental studies were
positive accounts of social learning. Indeed, there are
field studies indicating that corvids’ foraging strate-
gies are subject to social influence; for example, local
enhancement accounted for the decision of rooks to
forage near other birds (Waite 1981); Florida scrub-
jays learned a novel foraging technique via social
learning (Midford et al. 2000); and roosts function
as information centres in ravens (Marzluff et al.
1996) and hooded crows (Sonerud et al. 2001).
Hunt & Gray (2002) suggest that the differing
complexity of the stepped-cut tools made by New
Caledonian crows may be a case for cumulative
cultural evolution (increasing the number of steps
required to make a more complex tool), analogous
to minor technological innovations in humans. The
geographical distribution of the use of tools with
different numbers of steps varies across the island
in a way that is consistent with the idea that the
behaviour has spread through social learning from
a centre of innovation at the island’s centre, where
the tools are most complex (Hunt & Gray 2002).
The authors report an absence of ecological vari-
ability that could explain this pattern. However, it
is not yet clear whether or not the crows are capa-
ble of the sort of sophisticated social learning that
could result in the manufacture of a three-stepped,
rather than a two-stepped tool. Recent research has
shown that the crows develop tool-use and manu-
facture in the absence of a model, and furthermore
they do so as quickly as they do with a human
demonstrator (Kenward et al. 2005). However, it
should be noted that this study did not report the
manufacture of stepped-cut tools, and only made
use of human demonstrators. These researchers did
report an effect of social influence; the hand-raised
birds preferred to handle objects that their human
demonstrators had handled previously (Kenward
et al. 2006).
In summary, living in a complex social and physi-
cal environment creates both challenges and oppor-
tunities, and the pressure to respond to some or all
of these may have selected for large brains and com-
plex cognition in both apes and corvids. Table 1
summarizes these challenges and the evidence for an
evolutionary response to them in apes (chimpan-
zees) and corvids, as well as the biological and mor-
phological features that might facilitate or constrain
such a response.
The challenges from the physical environment,
while not identical (given the different geographic
distributions of the two groups), can be seen to be
comparable. However, the two groups face these
challenges from a very different starting point,
because of the 280 million years of divergent evolu-
tion that they have undergone. While the grasping
hands and physical strength of great apes favour the
use of tools in solving a great many problems, the
beaks and small size of corvids make many of these
uses unnecessary or impossible. Therefore, from the
information on corvid foraging that is available,
hypotheses citing omnivory and extractive foraging
predict complex cognition in corvids, but those that
emphasize tool-use do not to the same extent (only
New Caledonian crows routinely make and use
tools, and they use a smaller variety of tools than
great apes such as chimpanzees and orang-utans).
With regard to the pressures posed by social living, it
can be seen that while qualitative changes might
benefit species from both groups (the formation of
alliances for competition, or increased coordination
A. Seed et al. Convergent Evolution of Intelligence in Corvids and Apes
Ethology 115 (2009) 401–420 ª2009 Blackwell Verlag GmbH 409
for cooperative action), the challenges associated
with employing and tracking such strategies within a
complex and changing social network are relevant to
promiscuous apes but probably not to the (largely)
monogamous corvids.
It is clear that much further studies are needed
before the function of intelligent behaviour in the
two groups can be established. An extensive set of
phylogenetically controlled comparisons, both
within the corvids, within the primates, and across
a larger range of avian and mammalian taxa, could
reveal whether intelligence tends to go hand in
hand with sociality, dietary generalism, tool use or
some other variable. Extending the number of spe-
cies studied within the corvids seems particularly
important, because at present it is not clear
whether or not intelligence is a corvid-wide trait, or
rather is something seen in a few species that have
been exposed to particular evolutionary pressures
(e.g. social food-caching species such as scrub-jays
and ravens, and tool-users such as New Caledonian
crows). More studies of corvids are needed, not
only of more species but also of several populations.
Corvids show intra-species flexibility in their social
and ecological habits; for example, carrion crows
are largely territorial, but in harsh environments
(such as northern Spain) they engage in coopera-
tive breeding (Baglione et al. 2002). Social structure
in Florida scrub-jays is similarly flexible (Woolfen-
den & Fitzpatrick 1984). Likewise, there is great
variability in the feeding habits of corvid species
across their range, and indeed corvids are known
for high rates of feeding innovation (Lefebvre et al.
1998).
The evidence for the different hypotheses to date
(largely correlationary analyses with brain size) is
difficult to interpret. The effects of different pressures
are very difficult to disentangle, as is the direction of
cause and effect, for example, if extractive foragers
tend to have larger brains, was the pressure to
exploit these hidden food resources the cause of
increased brain size, or is the behaviour rather the
expression of a general intelligence selected for in
another domain? As Healy & Rowe (2007) have
argued, it will also be important to develop a depen-
dent variable other than relative brain size for mea-
suring intelligence (Box 1). For that, we will need to
have a better understanding of the proximate mech-
anisms underpinning intelligent behaviour, as well
as techniques for measuring them across divergent
species. The evidence that these mechanisms are
indeed similar in corvids and apes is reviewed in the
next section.
Proximate Mechanisms
In the case of morphological features such as wings
in vertebrates, it is relatively easy to identify the
level at which convergence has taken place: a sim-
ple dissection can reveal that the similarity is only
skin-deep, and that different structures lie beneath
(Fig. 1). When the feature is a psychological one,
the task is not so simple, because it is that much
less tangible. The difficulty is compounded in the
case of ‘intelligence’ because there is no universally
accepted definition of the term (Box 1). What does
it mean to say that there has been a convergent
evolution of a psychological feature such as intelli-
gence? For example, both chimpanzees and New
Caledonian crows can use a novel technique to
obtain a food reward at the bottom of a transpar-
ent tube (Weir et al. 2002; Mendes et al. 2007).
While both examples are clearly evidence of
intelligent behaviour as defined by the principle
of exclusion (the behaviour cannot easily be
explained in terms of simple conditioning, or hard-
wired action patterns, see Box 1), claims that the
cognition underpinning the behaviour in the two
species is the result of convergent evolution can
lead to controversy. Some researchers have argued
that the tool-using behaviour of the crows is likely
to be an adaptive specialization and therefore not
equivalent to that of apes, which is rather an
expression of a generalized intelligence (Mendes
et al. 2007). From another perspective, the case
can also be made that the behaviour in both
species is the outcome of conserved associative
learning processes combined with exploratory
behaviour, and therefore the result of homology or
parallelism rather than convergence. However,
Byrne & Bates (2006, 2007) have argued that
setting associative accounts against such cognitive
explanations for complex behaviours as, for exam-
ple, an animal using a representation of another’s
mental states to determine what it can and cannot
see, is really an unhelpful blurring of two different
levels of description.
Finding the Level of Analysis
The notion that complex systems can be most use-
fully viewed at distinctly different levels of analysis
is not a new one. Marr identified three kinds of
questions that can be asked about psychological
features from his study of vision (Marr 1982). The
first level, the computational, is concerned with the
goal of the cognitive process, and the logic by
Convergent Evolution of Intelligence in Corvids and Apes A. Seed et al.
410 Ethology 115 (2009) 401–420 ª2009 Blackwell Verlag GmbH
which it is carried out. The second level, representa-
tion and algorithm, models the way in which stimuli
are encoded and processed. The third level is the
implementation, or the physical realization of these
models. These three levels can be seen to be analo-
gous to those that we use intuitively when describ-
ing the convergent evolution of a morphological
feature such as the vertebrate wing. Comparing
them side-by-side allows us to see the appropriate-
ness of Marr’s levels for structuring our investi-
gations into convergent psychological evolution
(Fig. 2).
lThe first level (computation) is basically concerned
with the question ‘what?’ What do the forearms of
birds, bats and pterosaurs do? What kinds of deci-
sions can New Caledonian crows and apes make
when choosing a tool?
lThe second and third levels are concerned with
the question ‘how?’ The second level (representation
and algorithm) asks this question in the context of
the mechanism, or algorithm that performs the func-
tion, and we can talk about the biomechanical pro-
cesses by which species in the different groups
power their flight, or the way in which certain stim-
uli (e.g. tool length, diameter and material) are rep-
resented in the mind of the animal, and the
algorithms by which they are processed and fed into
behaviour.
lThe third level (implementation) asks how these
processes are physically realized in terms of bones
and muscles, or neural structures and pathways.
The challenge in the case of convergent evolution
is first, to establish a level at which different features
are similar in different species and secondly, to dis-
cover the differences at the other levels that reveal
their divergent evolutionary history, and may
explain limits to the similarities found.
McClamrock (1991) points out two important cau-
tionary notes to bear in mind with the use of the
three levels analogy. First, the three levels should
not be seen as a model for the actual levels of orga-
nization that make up the complex feature in ques-
tion; there may be more, and at each level there
may be nested levels of organization. For example,
beyond the third level described for the evolution of
wings are the proteins and even the genes responsi-
ble for building them in the different vertebrate
groups. In the case of cognition, there are probably
sub-levels within representation and algorithm, for
example, the algorithm could be modelled in terms
of stimuli and associations, or using connectionist
modelling (McClamrock 1991). Secondly, moving
between levels of analysis can sometimes consist of
‘zooming in’ to a more fine-grained level of analysis,
but it can sometimes consist of asking the question
in a completely different contextual framework. As
long as these points are held in consideration, sev-
eral authors agree that Marr’s levels are a powerful
tool for analysing complex systems (McClamrock
1991; Mitchell 2006), and we argue that they are
especially useful for investigating convergent evolu-
tion (Box 2 – Why are Levels Useful?).
Fig. 2: Levels of analysis for viewing the
convergent evolution of wing powered flight
and tool selectivity. Images of New
Caledonian crow tool use and rook brain
taken from Emery & Clayton (2004). Reprinted
with permission from AAAS.
A. Seed et al. Convergent Evolution of Intelligence in Corvids and Apes
Ethology 115 (2009) 401–420 ª2009 Blackwell Verlag GmbH 411
Computational Convergence
The ethological validity of the studies that have been
conducted in recent years with apes and with cor-
vids (those capitalizing on naturally occurring
behaviours such as food caching, tool-use and food
competition) have allowed for insights into the abil-
ity of the animals to employ complex computations.
However, little work has been done on both corvids
and apes using paradigms that are directly compara-
ble. Given the many possible sources of variation
unrelated to cognition involved in comparing such
evolutionarily distant species (such as differences in
perception, attention and motivation; Bitterman
1960, 1965), assessing results from different method-
ologies is especially problematic. Recent work both
by ourselves and other groups has attempted to ford
this gap by conducting experiments using compara-
tive methodology. The aim is to go beyond broad
comparisons (such as observing that species of both
corvids and apes manufacture tools) to ask in more
detail the range of problems that the animals are
capable of solving (e.g. making or using a tool for a
particular purpose).
Box 2 – Why are Levels Useful?
1. Designing Appropriate Experiments
The field of animal cognition has experienced a
great surge of interest since the cognitive revolu-
tion, and with it has come dozens of new
discoveries concerning the computational abilities
of animals, and their impressive complexity and
flexibility. These findings have prompted research
into all four of Tinbergen’s questions (function,
phylogeny, causation and development).
Researchers focusing on causation (the proximate
mechanisms underpinning behaviour) aim at
designing ethologically valid experiments by con-
sidering Tinbergen’s other three levels of explana-
tion, for example by bearing in mind the role of
individual experience over development on per-
formance. Marr’s three levels (computation, rep-
resentation and algorithm, and implementation)
further sub-divide Tinbergen’s ‘causation’, and
research at one level can inform that at the other
two. The computational level is particularly
important for two reasons. First, it is logical to
know what a system is capable of before attempt-
ing to study how that is possible, in order to
focus one’s efforts on the right features of the
system. Marr gives a useful example:
Trying to understand bird flight by studying
only feathersjust cannot be donewe have
to understand aerodynamics, only then do the
structure of the feathers and the different
shapes of the birds’ wings make sense. (Marr
1982, p. 27)
Secondly, knowledge of a species’ intelligence at
the computational level can allow researchers to
use paradigms that are most likely to tap into the
animal’s information-processing skills, and isolate
the variable under question without confounding it
with other variables (e.g. the social cognition of
primates might best be demonstrated in competitive
contexts rather than cooperative ones, because
competition features more widely in their social
lives; Hare 2001). Being explicit about the level of
analysis is particularly important when the aim is
comparative. The chief reason for this is that differ-
ent experimental approaches are appropriate for
addressing convergence at different levels, because
different task features need to be kept comparable
depending on the type of question being asked.
2. Interpreting Results and Avoiding Needless
Controversy
A common response to ‘cognitive’ descriptions of
behaviour, such as describing a foraging chimpan-
zee or a caching scrub-jay as reasoning about men-
tal states, is that these behaviours may be ‘just the
result of associative learning’. A dichotomy emerges
between explanations based on the vocabulary
used to describe the abilities of language-using ani-
mals (such as understanding, reasoning and ratio-
nalizing), and explanations based on associative
models of animal learning. Byrne & Bates (2006)
argue that different levels of description are being
blurred in these debates, meaning that researchers
are talking at cross purposes. They argue that the
cognitive terms offer a useful framework with
which to study the feats that animals such as cor-
vids and apes are capable of (at what we have
referred to as Marr’s computational level). How-
ever, making the level of analysis explicit is impor-
tant, especially when discussing convergent
evolution, because the parallels being drawn can be
misinterpreted.
Convergent Evolution of Intelligence in Corvids and Apes A. Seed et al.
412 Ethology 115 (2009) 401–420 ª2009 Blackwell Verlag GmbH
Tool Use – Selectivity and Flexibility
Great ape and New Caledonian crow researchers
have used some paradigms that allow for direct com-
parisons. In very similar tasks, both crows and apes
have shown evidence for selectivity in their choice
and manufacture of tools for a given problem. Two
crows selected a tool of sufficient length to reach
inaccessible food (Chappell & Kacelnik 2004), and
they also manufactured a tool of the appropriate
diameter (Chappell & Kacelnik 2002). Mulcahy et al.
(2005) found that gorillas and orang-utans also
choose a tool of sufficient length (Mulcahy et al.
2005), and similar to crows there was no evidence
that their selectivity arose through trial-and-error
learning over the course of the experiment. Meta-
tool use, that is, using one tool to get another, is
thought to be a steep cognitive challenge, and its
emergence in the hominid tool-use record (in the
form of stone-knapping) is an important landmark
for anthropologists. Monkeys such as capuchins and
Japanese macaques have performed poorly in tests
requiring meta-tool use, often persistently directing
their tool-using behaviour directly towards the food
reward. Building on the earlier studies of Kohler
(1927) and Rensch & Do
¨hl (1968), Mulcahy et al.
(2005) presented gorillas and orang-utans with a
task in which they needed to use a small stick tool
to get a longer one. Most apes displayed meta-tool
use in the first trial when it was needed to get the
food reward (Mulcahy et al. 2005). A recent study
found that New Caledonian crows are also capable
of spontaneously using a small tool to get a longer
one (Taylor et al. 2007). Similar to the apes, most of
the crows acted directly on the longer tool, instead
of attempting to recover the food reward with the
short tool. In addition to displaying tool selectivity,
apes and corvids have also shown flexibility when
faced with a similar problem (gaining access to a
food reward at the bottom of a thin vertical tube).
Betty the New Caledonian crow bent straight pieces
of wire in order to hook out the reward, after her
mate stole the hooked piece (Weir et al. 2002).
Recently, orang-utans have also been reported to
obtain food placed out of their reach at the bottom
of a tube through spontaneous problem solving, by
spitting their drinking water into the tube so that
the food floats to the top (Mendes et al. 2007).
Using Social Cues to Solve Problems
Comparative work has also been done in the social
domain. Two paradigms used to test primates’ use of
social cues are gaze following, and the object choice
test. Similar to all species of great apes (Brauer et al.
2005), ravens not only visually co-orient with the
look-ups of a human experimenter but also reposi-
tion themselves to follow the experimenter’s gaze
around a visual barrier (Bugnyar et al. 2004). Apes
have difficulty using a human gaze cue to locate a
food item hidden under one of two cups, although
they can use an iconic marker to do so (Call et al.
1998, 2000; Barth et al. 2005; Herrmann et al.
2006). Similarly, a few ravens can use a pointing
gesture to locate food, but not a human gaze cue or
the cues possibly given by a conspecific that could
see the food (Schloegl et al. 2007). Both apes
and ravens are suggested to have difficulty in this
paradigm because of its cooperative nature; the loca-
tion of a piece of food is not often pointed out by
conspecifics in the competitive ecologies of these
species (Hare 2001). Interestingly, jackdaws have
recently been found capable of using the communi-
catory gestures of a familiar human (A. M. P. von
Bayern & N. J. Emery, unpubl. data) and conspecific
gaze cues in the object choice task, in the latter
when paired with their social partner, but not with
another group mate (A. M. P. von Bayern & N. J.
Emery, unpubl. data). This makes sense in the light
of the high levels of food sharing seen between affil-
iated pairs of jackdaws (von Bayern et al. 2007).
Corvids have also displayed primate-like social
skills in competitive foraging paradigms. Jackdaws
steal food more quickly from a human competitor
who is either glancing away or has his her eyes
closed than one who is looking directly at the food
(A. M. P. von Bayern and N. J. Emery, unpubl.
data). Similarly, chimpanzees take into account the
direction of a human competitor’s gaze, and try to
hide their approach to food (Hare et al. 2006). Inter-
estingly, concealing auditory information (avoiding a
noisy food or caching site) has also been docu-
mented for both chimpanzees (Melis et al. 2006a)
and western scrub-jays (Stulp G., Emery N. J. &
Clayton N. S., pers. obs.). Ravens lead conspecifics
away from boxes baited with food (Bugnyar & Ko-
trschal 2004), just as chimpanzees were shown to do
in the classic experiments of Menzel (1974). Ravens
also rush to recover a piece of food that a dominant
raven has seen being hidden, but delay their
approach if the dominant raven’s view of the baiting
process was obstructed (Bugnyar & Heinrich 2005).
Chimpanzees also differentiate between a dominant
chimpanzee that has seen food being hidden and
one that has not when deciding whether or not to
approach, although unlike the ravens the chimpanzees
A. Seed et al. Convergent Evolution of Intelligence in Corvids and Apes
Ethology 115 (2009) 401–420 ª2009 Blackwell Verlag GmbH 413
approached the food more often when the dominant
chimpanzee had not seen the food (Hare et al.
2001).
Apes and corvids also attend to the actions of their
conspecifics in order to coordinate with them to
solve a problem. Seed et al. (2008) tested rooks on a
task that has been used to test chimpanzees’ ability
to cooperate. The task requires the simultaneous
pulling of both ends of a rope to bring in a platform
containing food (Hirata & Fuwa 2007). Pulling just
one end causes the rope to become unconnected
from the platform. Tolerant pairs of chimpanzees
(that would feed together) were able to spontane-
ously find the solution to the cooperative task (Melis
et al. 2006a). Similarly, the eight rooks quickly
solved the problem without training when paired
with their social partner. However, although chim-
panzees delayed acting on the apparatus while their
partner gained access to the test room, and did so
more often when two individuals were needed to
solve the task than when one individual could pre-
vail alone (Melis et al. 2006b), rooks did not delay
acting on the apparatus over the course of 15 trials
(Seed et al. 2008). Furthermore, given a choice
between an apparatus that could be operated indi-
vidually over one that required the action of two
individuals, four of six individuals showed no prefer-
ence. Further work is needed to support the idea
that the difference between rooks and chimpanzees
represents a real limitation in the computations that
rooks are capable of in a cooperative setting. If this
is the case, it remains to be seen what the limitations
are (a failure to compute some of the requirements
of the task, or the properties of the cooperative
agent).
These experiments have shown that both corvids
and apes are capable of selective and flexible prob-
lem-solving, and that their behaviour in very similar
experiments can be strikingly convergent. Many of
these experiments have also been able to give an
indication of the representations and algorithms
underpinning the behaviour (e.g. when the tests are
novel to the animals and performance is good from
the first trial, then associative learning of actions in
response to task-specific stimuli and differential rein-
forcement cannot explain the results). However, for
a positive assessment of the representations and
algorithms at work further study is required. Have
the animals acquired the knowledge needed to solve
the tasks through past associative learning or do
inborn predispositions play a role? Do they solve the
novel tasks through a process of stimulus generaliza-
tion, or do they have a representation of the abstract
properties at hand (such as object properties, anima-
cy, or mental states such as seeing)? Are processes
such as ‘insight’ or ‘reasoning’ involved, and what
does this mean in algorithmic terms? Crucially for
this discussion, are the answers to these questions
similar for both corvids and apes? A few experiments
have attempted to address this level of analysis, and
those that have done so in a way that allows for
direct comparisons between corvids and apes are
described below.
Representation and Algorithm
When testing the cognition of a single species at the
algorithmic level, it is difficult to find a paradigm
that isolates the process under question, without
confounding it with other variables. The problem
becomes more difficult when the aim of the research
is comparative, and an appropriate test of a given
ability in one species is not necessarily suitable for
another. There are two important ways in which this
difficulty can be addressed: ascertaining task equiva-
lence, and triangulation. Species do not need to be
tested on the exact same piece of apparatus for
meaningful comparisons to be drawn. It is more
important that the same conceptual question is
asked using tasks that the species can solve, or learn
to solve, before investigations into the underlying
cognition are made. Using a task that the majority of
subjects can learn to solve should minimize the
number of ‘false-negatives’ that occur because of
limitations associated with perception and motiva-
tion. Once paradigms have been found, different
conceptual features can be varied systematically in
transfer tasks, in order to ascertain which of the pos-
sible features the subjects used to solve the task. This
approach is referred to as ‘triangulation’, and has
been advocated by Heyes (1993), among others.
We used this approach in a study of problem-solv-
ing in rooks. Visalberghi & Limongelli (1994) exam-
ined whether or not tool-users form representations
about causal relations in a task which has since been
widely employed: the trap-tube task. In this task, the
subject must use a tool to push a food reward out
from a horizontal tube, which has a trap along its
length into which the food will drop if pushed over
it. Seed et al. (2006) aimed to test the null hypothe-
sis: ‘a successful animal will use an arbitrary cue to
solve the task’. Eight birds were tested on a version
of the trap problem that featured two ‘traps’ along a
horizontal tube. One of the traps was functional
(sealed with a black disc at the bottom) and would
trap the reward if the rooks pulled the food over it.
Convergent Evolution of Intelligence in Corvids and Apes A. Seed et al.
414 Ethology 115 (2009) 401–420 ª2009 Blackwell Verlag GmbH
The other was non-functional; in Design A it had a
black disc at the top, which the food could pass over;
in Design B it had no black disc, so the food could fall
through it. Seven of the eight birds learned to avoid
the functional trap, in between 30 and 140 trials.
This was evidence for convergence at the computa-
tional level; like capuchin monkeys and chimpan-
zees, rooks were able to learn to avoid the trap.
All seven rooks immediately solved task B once
they had learned to solve A, and vice versa. How-
ever, both these tasks could have been solved by
learning to avoid the trap with the black disc at the
bottom, without anything about the properties of
the objects being encoded. The seven birds were
therefore given two transfer tasks, both featuring the
two previously non-functional traps (pass-across or
fall-through). In Design C both ends of the tube
were blocked with bungs, so the food could not be
recovered from the end of the tube, and the birds
needed to pull away from the trap with the black
disc at the top; in Design D the tube was lowered to
the surface of the testing shelf, so that the food
could not be recovered from beneath, and the rooks
needed to pull towards the trap with the black disc
at the top to be successful. Crucially, therefore, both
tasks featured the same familiar cue, but each
required the opposite response to it (pull away from
the black disc in Task C, pull towards it in Task D).
The birds were given 20 trials on both of these trans-
fer tasks. Six of the subjects performed at chance on
both tasks, but one bird was able to solve these trans-
fers, suggesting that it did not solve the two-trap task
simply by using the appearance of the functional trap
as an arbitrary cue (Seed et al. 2006).
Seed et al. (in press) recently conducted a similar
experiment with chimpanzees. Instead of having to
use a tool to move the food, small holes cut into the
front of the puzzle allowed them to use their fingers
instead. All of the eight chimpanzees tested learned
to avoid the trap. Furthermore, we found that one
chimpanzee passed both Designs C and D. We com-
pared the performance of these experienced chim-
panzees with that of naı
¨ve ones on a new version of
the task, which differed from the original task in
size, shape, colour and material. Strikingly, the expe-
rienced subjects solved the task rapidly, but the
inexperienced subjects failed to do so in 150 trials.
Similarly, Taylor et al. (in press) recently found that
New Caledonian crows can also learn to solve the
two-trap problem, and that successful subjects were
able to transfer to a version of the task that was as
different from the original problem as the second
task given to chimpanzees was. These results suggest
that rooks, chimpanzees and New Caledonian crows
do not use a simple cue-based rule to solve the trap
task. We propose that instead they extracted causally
relevant functional information (such as surface con-
tinuity, or the solidity of barriers). However, further
work is required to uncover the exact nature of their
object representations, and the algorithms by which
they are fed into behaviour.
Other comparative work has shown that corvids
and apes are capable of solving problems by attend-
ing to a cue that is arbitrarily linked to the success-
ful solution. Helme et al. tested rooks and bonobos
on a task in which a disc attached to a stick needed
to be pulled into contact with a food reward in
order to get it out of a tube. Both rooks and bono-
bos learned to solve this task, and transfer tasks
revealed that they did so by learning rules based on
the relative length of the stick at either end, rather
than using information about contact (Helme et al.
2006a,b).
Although it is arguably easier to manipulate the
type of information given in physical tasks; this has
also been done in experiments of visual perspective
taking. Both western scrub-jays and chimpanzees
behave differently when a competitor saw food hid-
den, and when they did not (Hare et al. 2000; Dally
et al. 2005). What sorts of information are encoded
for this differentiation to be made; is it done using
an ‘evil eye’ strategy, by simply linking the presence
of eyes to the hiding of the food? Both teams of
researchers have ruled out this possibility, by swap-
ping the dominant animal that witnessed the hiding
event for another conspecific at the time of food
recovery. Despite the fact that the food was observed
at the time of hiding, western scrub-jays and chim-
panzees treat this new observer as ignorant of food
location. In the case of the scrub-jays, this new dom-
inant had also seen a (different) hiding process, and
so differential behavioural cuing cannot explain the
results. Both species therefore encode not only the
presence, but also the identity, of the observer (Hare
et al. 2001; Dally et al. 2006).
Finding a paradigm which can reveal the content
of mental representations that animals use to solve
problems is a difficult task, and especially so in the
case of comparative work. Triangulation is a power-
ful tool for establishing the sorts of information that
are being used, and work so far has shown that cor-
vids and apes seem to be capable of going beyond
simple perceptual information and using more
abstract representations, in both the social and the
physical domain. This approach could be employed
comparatively for a broad range of computational
A. Seed et al. Convergent Evolution of Intelligence in Corvids and Apes
Ethology 115 (2009) 401–420 ª2009 Blackwell Verlag GmbH 415
problems. The question of algorithm is still more dif-
ficult. While the use of abstract representations may
mean that a completely novel task can be solved in
very few trials, does this mean that anything other
than the learning of arbitrary associations was used
to build the behaviour in the first context? Are cor-
vids and apes capable of encoding information about
the causal power of particular events or the animacy
of their conspecifics? Penn et al. (2008) have argued
that such ‘unobservable’ concepts are unavailable to
non-human animals. This is undoubtedly an impor-
tant area for future research in both corvids and
apes, and our hope is that research will continue to
use paradigms that allow for comparisons to be
drawn between the two groups.
Conclusion
Convergent evolution is said to have occurred when
distantly related organisms respond to similar evolu-
tionary pressures by the development of similar
traits. We have shown that corvids and apes have
been exposed to similar evolutionary pressures dur-
ing their evolutionary histories, but note that their
divergent biologies may constrain an evolutionary
response to some of them. However, the evidence
for the effect of such pressures in both groups is
restricted to correlationary analyses with brain size,
and we agree with Healy & Rowe (2007) that the
next step for identifying the evolutionary pressures
causing intelligence will be phylogenetically con-
trolled comparative experimentation. At the proxi-
mate level, convergent evolution is characterized not
only by similarities, but also by differences. We have
argued that Marr’s levels can help us to structure
the study of convergence in a slippery and conten-
tious feature: intelligence. In the case of intelligence
in corvids and apes, while the similarities are fasci-
nating, we should also celebrate the differences,
because identifying both the similarities and differ-
ences in the cognition of corvids and apes, and
assessing their life-history correlates, may enable us
to pinpoint the features of ape cognition that served
as crucial pre-adaptations for the evolution of
human intelligence. Both of these can best be stud-
ied by being clear about the level of analysis being
addressed.
Acknowledgements
We would like to thank Josep Call, Michael Toma-
sello and Anne Helme for useful discussion, and Wil-
liam McGrew and Juan Carlos Gomez for comments
on an earlier version of the manuscript. AMS was
supported by a BBSRC studentship, a Royal Com-
mission for the Exhibition of 1851 Fellowship and
Clare College, Cambridge, and NJE was supported by
a Royal Society University Research Fellowship.
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... Although the majority of research interrogating the SIH and EIH has focused on primates, birds have emerged as a major model system in cognitive evolution over the last 20 years (eg. [21,[33][34][35]). Some species of bird show convergent cognitive performance to primates [33,35], yet birds have divergent neuroanatomy [33] and differing constraints on brain size, such as those imposed by long-range migration [36]. ...
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... Executive functions support individuals' flexibility in response to the ever-changing environment. Although birds and mammals can solve cognitively demanding problems with similar speed and flexibility [59], their performance is achieved with different-looking brains. In this section, relevant homologies between mammalian and avian brains, as well as the relevance of key brain areas in executive functions research will be discussed. ...
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Simple Summary: Everyday functioning requires dealing with a lot of information, usually so smoothly that we barely notice it. The processes that support smooth processing of such information are called executive functions. In recent years, researchers have become interested in these processes in birds, whom, although long considered "bird-brained" and less clever than mammals, actually parallel mammals in tests of intellectual prowess. Interest in birds' brains and performance is increasing , but an overview of relevant previous findings is lacking. Therefore, in this paper, relevant findings are collected and organized to support further investigations of executive functions in birds. Abstract: Executive functions comprise top-down cognitive processes that exert control over information processing, from acquiring information to issuing a behavioural response. These cognitive processes of inhibition, working memory and shifting underpin complex cognitive skills, such as episodic memory and planning, which have been repeatedly investigated in several bird species in recent decades. Until recently, avian executive functions were studied in relatively few bird species, but have gained traction in comparative cognitive research following MacLean and colleagues' large-scale study (2014). Therefore, in this review paper, relevant previous findings are collected and organized to facilitate further investigations of these core cognitive processes in birds. This review can assist in integrating findings from avian and mammalian cognitive research and further current understanding of executive functions' significance and evolution.
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