Download Toward wild psychometrics: linking individual cognitive differences

Behavioral
Ecology
The official journal of the
ISBE
International Society for Behavioral Ecology
Behavioral Ecology (2014), 25(6), 1299–1301. doi:10.1093/beheco/aru095
Invited Ideas
Toward wild psychometrics: linking individual
cognitive differences to fitness
Alex Thornton,a Jess Isden,b and Joah R. Maddenb
aCentre for Ecology and Conservation, Department of Biosciences, University of Exeter, Penryn
Campus, Treliever Road, Penryn TR10 9FE, UK and bCentre for Research in Animal Behaviour, School
of Psychology, University of Exeter, Perry Road, Exeter EX4 4QG, UK
Received 18 October 2013; revised 7 April 2014; accepted 29 April 2014; Advance Access publication 3 June 2014.
Our understanding of the processes underlying animal cognition has improved dramatically in recent years, but we still know little
about how cognitive traits evolve. Following Darwinian logic, to understand how selection acts on such traits we must determine
whether they vary between individuals, influence fitness, and are heritable. A handful of recent studies have begun to explore the relationship between variation in individual cognitive performance and fitness under natural conditions. Such work holds great promise,
but its success is contingent on accurate characterization and quantification of the cognitive differences between individuals. Existing
research has typically adopted a “problem-solving” approach, assuming that individuals that complete novel tasks have greater cognitive prowess than those that do not. We argue that this approach is incapable of determining that individual differences are due to
cognitive factors. We propose the adoption of psychologically grounded psychometric testing and discuss the criteria necessary to
examine the fitness consequences of cognitive variation in the wild.
Key words: cognition, evolution, heritability, individual differences, natural selection, psychometrics.
The evolution of cognition remains one of the most poorly understood issues in biology. Traditionally, studies of cognitive evolution
have employed a comparative approach, relating between-species
or between-population differences in brain size or cognitive performance to differences in ecology, social systems, or life history (e.g.,
Balda and Kamil 2002; Pravosudov and Clayton 2002). A recent
upsurge of interest in cognition among behavioral ecologists has
stimulated the emergence of a powerful new approach, shifting the
focus to differences between individuals. Just as field studies linking individual phenotypic differences to reproductive fitness have
furthered our understanding of selection acting on behavioral and
morphological traits (Clutton-Brock and Sheldon 2010), a focus on
the causes and consequences of variation in individual cognitive
performance can provide crucial insights into cognitive evolution
(Thornton and Lukas 2012). A few pioneering studies have generated tantalizing indications of how cognitive differences may
impact on fitness correlates such as mating success (Keagy et al.
2009, 2011), reproductive success (Cole et al. 2012; Cauchard et al.
2013), and competitive ability (Cole and Quinn 2012). We support
this approach wholeheartedly, but are concerned that the cognitive
tests used in these field studies differ substantially from established
psychological theory and practice. Thus, it is questionable whether
they have reliably characterized individual cognitive differences in
Address correspondence to A. Thornton. E-mail: [email protected].
© The Author 2014. Published by Oxford University Press on behalf of
the International Society for Behavioral Ecology. All rights reserved. For
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wild animals. Here, we examine the criteria necessary to examine
the fitness consequences of individual cognitive variation and suggest refinements and improvements to current methods.
First and most importantly, we must ensure that we are measuring cognitive differences and not variation in other traits or random
noise. Cognition, broadly defined, encompasses the mechanisms
by which animals acquire, process, store, and act on information
from the environment (Shettleworth 2010). Whereas morphology
and behavior can be measured accurately under natural conditions, cognitive processes cannot be observed directly and must
be inferred through experimentation (Shettleworth 2010). Few
studies in the wild have employed experimental protocols that can
explicitly quantify individual differences in specific cognitive processes (but see Boogert et al. 2010; Nachev and Winter 2012; Isden
et al. 2013). Instead, research has more commonly relied on what
we term the “problem-solving approach,” whereby animals in the
field or temporarily brought into captivity are presented with novel
problems, under the explicit or implied assumption that individuals that solve tasks at all, or do so faster than others, possess higher
levels of cognitive ability (Keagy et al. 2009; Cole et al. 2012;
Cole and Quinn 2012; Cauchard et al. 2013). Some of these tests
elicit behavior that is part of the standard repertoire of the species (e.g., male satin bowerbirds removing red objects from their
bowers: Keagy et al. 2009; great tits removing obstructions from
their nestboxes: Cauchard et al. 2013). Others use more artificial
tasks to elicit apparently innovative behavior (e.g., pulling a lever to
1300
obtain food rewards; Cole et al. 2012). Such tests have been used to
great effect in animal innovation literature to determine the physical and behavioral factors that influence individual tendencies to
perform novel behaviors (e.g., Morand-Ferron et al. 2011), but we
strongly doubt their ability to measure cognitive variability reliably.
Under Shettleworth’s broad definition, all behavioral outcomes
involve some cognitive processing, but this does not imply that solvers are necessarily more generally cognitively adept than nonsolvers. Solving a new problem on a single presentation may be due to
chance (e.g., knocking away an obstacle or lever by luck) or variation in factors such as strength, dexterity, motivation, or persistence
(e.g., Overington et al. 2011; Benson-Amram and Holekamp 2012;
Thornton and Samson 2012). The studies we critique here certainly provide important controls for some of these variables, but
all suffer from shared problems: contrary to standard practice in
experimental psychology, they do not target any defined cognitive
process and make no theoretically grounded, quantitative predictions as to what constitutes a correct outcome (see also Healy 2012).
Consequently, there is no psychological basis for concluding that,
for instance, a bird that pulls a lever is more cognitively able than
one that does not. We suggest that future work should adopt lessons
from psychometric research, where tests are designed to target specific cognitive processes and test outcomes can be clearly evaluated
on the basis of correctness, giving quantitative scores of individual
performance. One obvious improvement to the current approach is
to present problem-solving tasks repeatedly to derive measures of
individual instrumental learning ability from rates of improvement
over successive trials. Tasks incorporating functionally relevant and
irrelevant features could be particularly useful in assessing not only
differences in speed of solving (which may reflect strength or dexterity) but also in the ability to learn the instrumental contingencies of a given task, and/or to transfer learned rules from one task
to others with similar features (Thornton and Samson 2012). An
additional approach is to design choice tests that make specific,
defined cognitive demands such that once a subject is engaged
in a task it must rely on its cognitive abilities to choose correctly
between alternative options. Such an approach forms the basis for
numerous robust psychometric tests (see Plomin 2001; Matzel et al.
2003) and has been applied in free-living animals. For instance,
animals’ choices in field experiments have been used to determine
whether they have remembered the location of food sources (Healy
and Hurly 1995; Flores-Abreu et al. 2012), learned to associate
rewards with specific shapes or colors (Boogert et al. 2010; Isden
et al. 2013), or selected tools with appropriate functional properties
(Visalberghi et al. 2009). The challenge is now to harness similar
paradigms to quantify cognitive differences across large numbers of
individuals, as is widely done in psychometric studies of humans
and other animals (Plomin 2001; Matzel et al. 2003; Herrmann
et al. 2010). Fortunately, recent technological advances have great
promise in enabling rigorous, standardized testing. For instance,
Nachev and Winter (2012) used computer-controlled artificial
flowers to quantify the ability of individual pit-tagged wild bats
to discriminate between different sugar concentrations. Similarly,
building on their automated problem-solving tasks (Morand-Ferron
et al. 2011; Cole et al. 2012), Morand-Ferron and colleagues are
developing instrumental conditioning choice tasks to record individual learning curves for hundreds of wild pit-tagged birds over
repeated visits (Morand-Ferron J, personal communication). These
innovative applications of established psychological test paradigms
to wild animals provide a much-needed impetus to the development of a robust science of wild psychometrics.
Behavioral Ecology
Having obtained robust measurements of individual cognitive differences, we must then determine whether these are associated with
differential fitness outcomes. The relationship between cognitive performance and fitness may not be straightforward. Neural and cognitive phenotypes are often highly plastic, so performance measures
may be sensitive to the conditions under which they are taken. Some
birds, for example, undergo seasonal changes in hippocampal volume, which is likely to affect spatial memory (Smulders et al. 1995).
“Problem-solving” measures in great tits are also sensitive to testing
conditions: individuals’ success in captivity did not predict their success
in the wild (Morand-Ferron et al. 2011). Where possible, we advocate
testing animals repeatedly to determine the consistency and plasticity
of cognitive traits. It is also important to note that investment in the
neural tissue necessary for improved cognitive processing may generate
concomitant costs for other important traits (Kawecki 2010; Kotrschal
et al. 2013). We thus need measurements across a range of fitness-associated parameters to determine whether elevated cognitive ability generates net benefits (Cole et al. 2012). Finally, we must consider whether
elevated cognitive performance could result in benefits accruing across
several behavioral contexts, or only in specific situations. Natural selection is often assumed to fine-tune distinct cognitive mechanisms to
meet specific ecological challenges (Shettleworth 2010). However, some
cognitive processes such as associative learning are deployed across a
broad range of contexts (Heyes 2012) and human psychometric tests
indicate that an individual’s performance across a battery of disparate
tasks can be summarized by a single measure of “general intelligence”
(g) (Plomin 2001; Boogert, Fawcett and Lefebvre 2011; and Thornton
and Lukas 2012 review the equivocal evidence for g in animals). To
help resolve long-standing debates regarding modularity versus domain
generality and begin to pinpoint the selective advantages of elevated
cognitive ability, we advocate the use of batteries of tests across different cognitive domains, coupled with measures of multiple fitness
parameters. Following psychometric conventions, test batteries should
incorporate tasks covering a range of predefined cognitive domains
(e.g., association formation, instrumental learning, transitive inference,
inhibitory control, spatial memory), with tasks designed to minimize
effects of experience in one task on performance in others (c.f., Matzel
et al. 2003; Herrmann et al. 2010; Isden et al. 2013).
Finally, selection can only act on cognitive traits if they are heritable, an issue which has attracted little attention beyond humans
and a handful of laboratory studies on other species (Plomin 2001;
Kawecki 2010). Measures of heritability in wild populations are
likely to be complicated, with nongenetic factors including developmental conditions (Buchanan et al. 2013) and past learning opportunities (Thornton and Lukas 2012) affecting cognitive phenotypes.
Nevertheless, there are grounds for optimism that future studies will
begin to disentangle ontogenetic and heritable traits. In particular,
there is much to be learned from the animal personality literature,
where techniques including cross-fostering, experimental manipulation of individual developmental trajectories, selection experiments,
and quantitative genetic analyses are beginning to unravel genetic
contributions to complex behavioral traits (Van Oers et al. 2005).
Individual-based studies offer huge potential to inform our
understanding of cognitive evolution. Laboratory research on
human and nonhuman animals has generated overwhelming evidence that individuals differ in their cognitive abilities (Boogert,
Fawcett and Lefebvre 2011; Thornton and Lukas 2012). A suite of
pioneering studies have recognized that to understand how selection acts on cognitive traits we must now step out of the laboratory and examine this variation and its fitness consequences under
natural conditions. However, the challenges of such an endeavor
Thornton et al. • Toward wild psychometrics
should not be underestimated. Critically, if this exciting new avenue
of research is to progress, we must develop rigorous, psychologically
grounded experimental methods to identify and quantify explicit
cognitive processes and their heritability, ideally using batteries of
tests that call upon cognitive decision-making processes, spanning
cognitive domains. Only then can we begin to examine how these
underlying cognitive processes, either independently or in concert
with others, can generate the behavior on which selection may act.
Funding
A.T. was supported by a BBSRC David Phillips Fellowship (BB/
H021817/1); J.R.M. was funded by a BBSRC Grant (BB/
G01387X/1) and J.I. was funded by an EGF studentship from the
University of Exeter.
We are grateful to N. Boogert for stimulating discussion and comments on
previous drafts of the paper.
Forum editor: Sue Healy
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