General Cognitive Ability Predicts Survival-Readiness in Genetically Heterogeneous Laboratory Mice

Abstract

Although genetically heterogeneous laboratory mice express individual differences in general cognitive ability (c.f., “intelligence”), it is unknown whether these differences are translated into behaviors that would promote survival. Here, genetically heterogeneous laboratory CD-1 mice were administered a series of cognitive tests from which their aggregate general cognitive ability was estimated. Subsequently, all animals were tested on nine (unlearned) tasks designed to assess behaviors that could contribute to survival in the wild. These tests included nest building (in the home and a novel environment), exploration, several indices of food finding, retrieval, and preference, and predator avoidance. Like general cognitive ability, a principal component analysis of these measures of survival-related behaviors (survival-readiness) yielded a general factor that accounted for ∼25% of the variance of mice across all of the tasks. An aggregate metric of general cognitive ability predicted an aggregate metric of general survival-readiness (r = 0.64), suggesting that more intelligent animals would be more suited for survival in natural environments. The nature of the pattern of correlations between general cognitive ability and performance on individual tests of survival-readiness (where tests conducted in previously unexplored contexts were more closely related to general cognitive ability) suggests the possibility that heightened attention (which is taxed in a novel environment) may be the common mediator of both of these classes of abilities, although other potential mediators are discussed. In total, these results suggest that performance on tasks that are explicitly intended to assess the likelihood of survival can be impacted by cognitive abilities.

Full text

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ORIGINAL RESEARCH
published: 05 November 2020
doi: 10.3389/fevo.2020.531014
Edited by:
Laure Cauchard,
University of Aberdeen,
United Kingdom
Reviewed by:
Rachael Caroline Shaw,
Victoria University of Wellington,
New Zealand
Kenneth R. Light,
Columbia University, United States
*Correspondence:
Louis D. Matzel
matzel@psych.rutgers.edu
Specialty section:
This article was submitted to
Behavioral and Evolutionary Ecology,
a section of the journal
Frontiers in Ecology and Evolution
Received: 30 January 2020
Accepted: 22 September 2020
Published: 05 November 2020
Citation:
Matzel LD, Patel HM, Piela MC,
Manzano MD, Tu A and Crawford DW
(2020) General Cognitive Ability
Predicts Survival-Readiness
in Genetically Heterogeneous
Laboratory Mice.
Front. Ecol. Evol. 8:531014.
doi: 10.3389/fevo.2020.531014
General Cognitive Ability Predicts
Survival-Readiness in Genetically
Heterogeneous Laboratory Mice
Louis D. Matzel*, Himali M. Patel, Monica C. Piela, Margarita D. Manzano, Alison Tu and
Dylan W. Crawford
Department of Psychology, Rutgers University, New Brunswick, NJ, United States
Although genetically heterogeneous laboratory mice express individual differences in
general cognitive ability (c.f., “intelligence”), it is unknown whether these differences are
translated into behaviors that would promote survival. Here, genetically heterogeneous
laboratory CD-1 mice were administered a series of cognitive tests from which their
aggregate general cognitive ability was estimated. Subsequently, all animals were
tested on nine (unlearned) tasks designed to assess behaviors that could contribute
to survival in the wild. These tests included nest building (in the home and a novel
environment), exploration, several indices of food finding, retrieval, and preference, and
predator avoidance. Like general cognitive ability, a principal component analysis of
these measures of survival-related behaviors (survival-readiness) yielded a general factor
that accounted for ∼25% of the variance of mice across all of the tasks. An aggregate
metric of general cognitive ability predicted an aggregate metric of general survival-
readiness (r= 0.64), suggesting that more intelligent animals would be more suited
for survival in natural environments. The nature of the pattern of correlations between
general cognitive ability and performance on individual tests of survival-readiness (where
tests conducted in previously unexplored contexts were more closely related to general
cognitive ability) suggests the possibility that heightened attention (which is taxed in a
novel environment) may be the common mediator of both of these classes of abilities,
although other potential mediators are discussed. In total, these results suggest that
performance on tasks that are explicitly intended to assess the likelihood of survival can
be impacted by cognitive abilities.
Keywords: intelligence, survival, fitness, nest building, hoarding, foraging, mice
INTRODUCTION
In response to the question “how do we know that our [IQ] tests are ‘good’ measures of
intelligence?,” Wechsler (1944) wrote:
“The only honest answer we can make is that our own experience has shown them to be so. If this
seems to be a very tenuous answer we need only remind the reader that it has been practical experience
which has given (or denied) final validity to every intelligence test. Regrettable as it may seem, empirical
judgements, here as elsewhere, play the role of ultimate arbiter.”
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To an empiricist, Wechsler’s comment might seem to lack
quantitative substance, instead relying on anecdotal observations
to support the utility of the intelligence test. However, to the
extent that IQ tests should predict functionally important life
outcomes, the decades since Wechsler’s statement have filled in
the empirical gaps. It is now well-established that IQ tests predict
a range of measures of academic success, such as grades, years of
education, and performance on other standardized tests (such as
the SAT or GRE; for review, see Gottfredson, 1998). A skeptic
might be concerned that these correlations are to be expected,
since all of these outcomes are dependent (to varying degrees)
on test-taking abilities. Thus it is much more impressive that IQ
test performance predicts outcomes that are not dependent on
formal test-taking abilities, such as rank and performance ratings
obtained in military service (Gottfredson, 2003), job performance
ratings and satisfaction (Schmidt and Hunter, 1998), income and
life-long earnings (Murray, 1998), and even such distantly related
outcomes as the inverse relationship between IQ and racist beliefs
(Dhont and Hodson, 2014), obesity (Richards et al., 2009), clinical
depression (Gale et al., 2009), the likelihood of developing cancer,
and even death by automobile accident (Leon et al., 2009). Given
these observations, it is not surprising that IQ is directly related
to longevity (Wilson et al., 2009). This list of outcomes predicted
by performance on the IQ test has been necessarily truncated,
but the predictive validity of IQ tests have been discussed more
extensively elsewhere (e.g., Gottfredson, 1998, 2003).
Like humans, it is now established that variations in general
cognitive performance can be detected across individuals in a
variety of animal species including mice (Galsworthy et al., 2002;
Matzel et al., 2003;Sauce et al., 2018), rats (Anderson, 1993;
Locurto and Scanlon, 1998), birds (Shaw et al., 2015; but see
Sorato et al., 2018), dogs (Arden and Adams, 2016), and several
non-human primates (Herndon et al., 1997;Banerjee et al., 2009;
Herrmann et al., 2010;Beran and Hopkins, 2018;Eisenreich
and Hayden, 2018;Damerius et al., 2019). Notably, while the
literature on human intelligence is replete with examples of the
relationship between IQ test performance and real-life outcomes,
very few such demonstrations have been reported in non-human
animals. The paucity of predictive validation of these studies of
animal “general intelligence” has been noted by Burkart et al.
(2016) and Locurto (2017), who wrote that “an important, even
critical limitation of such studies is that they lack something that
is commonplace in studies of human g[general intelligence] –
namely, what is called predictive validity,” and this paucity of
evidence compromises any assessment or conclusions about the
nature of general intelligence in non-human species.
To the extent that it validates the utility of an intelligence
test, the absence of data related to the predictive capacity of
tests of non-human animal (hereafter “animal”) intelligence is
certainly problematic. It is notable though that some limited data
suggests that at least specific cognitive abilities predict outcomes
that would have apparent survival value. For instance, mice
with characteristically high intelligence (assessed as aggregate
performance across a battery of cognitive tests) exhibit more
effective foraging for food (Wass et al., 2012) and better avoidance
of contact with aversive stimulation (Matzel et al., 2006),
and mice that are more intelligent exhibit more exploratory
behaviors in what is determined to be a “safe” environment
(a behavior that would promote better contact with critical
environmental contingencies; Light et al., 2011). Likewise, tests
of general intelligence in cotton-top tamarin monkeys have
included measures that have clear implications for effective social
interactions (Banerjee et al., 2009). In the wild (where more
direct evidence of survival skills can sometimes be obtained),
similar relationships have been observed. For instance, male
New Zealand robins with superior spatial memory have greater
breeding success and provide an increased proportion of larger
prey items to offspring (Shaw et al., 2019). Similarly, Cole et al.
administered problem solving tests to great tits (Parus major),
and successful problem solvers produced larger broods of chicks
and were more efficient foragers for food (Cole et al., 2012; also
see Ashton et al., 2018). Cauchard et al. (2017) demonstrated that
the link between cognitive abilities and brood size was causal
in nature, i.e., while birds of higher cognitive abilities tended to
maintain larger broods, direct manipulation of brood size did
not in itself promote increases in cognitive ability. Other than
reproductive behaviors, cognitive abilities sometimes predict
other survival-related skills. For instance, the performance of
mountain chickadees in a spatial learning task was predictive of
the likelihood of surviving the birds’ first winter (Sonnenberg
et al., 2019), and longevity was predicted by spatial learning
in male African striped mice, while in females of the species,
performance on the same spatial task predicted the speed of their
response to predators (Maille and Schradin, 2016).
Despite the seeming relationship between specific cognitive
skills and survival, in other instances, specific cognitive abilities
have not always predicted important functional outcomes such
as song repertoire in birds that are dependent on these songs
for reproductive success (MacKinlay and Shaw, 2019), and
performance on a problem solving task did not predict mating
success in male spotted bowerbirds (Isden et al., 2013). In one
instance, pheasants that learned a reversal task more quickly were
found to be less likely to survive in the wild (Madden et al., 2018).
Thus consistent with Locurto’s (2017) concern, the assessment
of the predictive validity of tests of animal intelligence has been
non-systematic and has yielded inconclusive results. In part, this
may be a reflection of the limited nature of the cognitive tests
that have previously been used to assess these relationships. Other
than in monkeys, studies of the relationship between survival-
related behaviors and cognitive abilities have tended to be limited
to the assessment of animals’ performance on single domain-
specific abilities, e.g., spatial memory. (For a review of these
and other relevant issues, see Orr, 2009;Thornton et al., 2014;
Shaw et al., 2015).
A difficulty for the assessment of the predictive validity of tests
of animal intelligence is that the administration of well-controlled
and sensitive cognitive test batteries are facilitated by their
administration to animals that are maintained in captivity, and
this has been common in tests of mice, rats, dogs, and primates
(although some exceptions have been reported in birds; e.g.,
Shaw et al., 2015). However, these captive (or protected) animals
will not typically face the same demands on survival that would
present themselves to wild animals, thus mitigating the study
of ethologically-relevant survival-related outcomes. Moreover,
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many of the outcomes predicted by human intelligence tests
are a product of the impact of intelligence on the integration
and evaluation of available evidence and relevant information,
e.g., the better diet selection (and less obesity/Type II diabetes)
exhibited by more intelligent individuals is most certainly the
product of more acquired awareness of dietary risks in addition
to the better development of plans for mitigating those risks (for
discussion, see Mackintosh, 1998). These same outcomes would
not reasonably be anticipated in captive (or laboratory) animals,
where things like diet availability are intentionally controlled
(and a history of differential experiences cannot be drawn upon
by the animals).
The present study represented our preliminary effort to
assess the “functional” consequences of variations in mouse
intelligence while still maintaining the laboratory conditions
necessary to administer a controlled assessment of general
cognitive abilities. Tests of survival-readiness were chosen that
did not explicitly depend on prior experience and which had
previously been suggested to have survival benefits (and/or which
could reasonably be expected to impact the likelihood of survival
in the wild, e.g., Deacon, 2006a,b). To this end, 56 genetically
heterogeneous CD-1 mice were raised (under homogeneous
conditions) in captivity and administered a diverse battery
of cognitive tests (designed to characterize general cognitive
ability) as young adults. Subsequently, these animals were
administered a series of tests to assess unlearned skills related
to survival, e.g., nest building in home and novel environments,
foraging efficiency, exploration and food source discovery,
food preference, and predator avoidance. Since these survival
skills were nominally unlearned (a reasonable expectation in
laboratory-reared animals), performance on these tests would
provide an index of the extent to which general cognitive ability
predicted functional (and survival-related) skills.
MATERIALS AND METHODS
Subjects
A total of 56 CD-1 outbred male mice from Harlan Laboratories
(Indianapolis, IN, United States) were used. Animals were housed
individually in standard shoebox home cages in a temperature-
controlled colony room using a standard 12 h light-dark
cycle. These animals are well-suited for studies of individual
differences as the CD-1 mouse genome displays patterns of
linkage disequilibrium and heterogeneity similar to wild-caught
mice (Aldinger et al., 2009). In this, our first attempt to assess
survival-related behaviors, we focused on only male animals
as we have extensive previous experience in the assessment of
general cognitive abilities in these animals. Animals arrived in the
laboratory at approximately 8–10 weeks of age and were given
ad libitum access to food and water except during testing that
required food deprivation, when animals were given 120 min
access to food starting on the day prior to testing, then each
day following data collection. Prior to the start of testing (which
began at approximately 12–14 weeks of age), animals were
handled (i.e., held by an experimenter while walking throughout
the laboratory test rooms) for 60 s/day for 7 days to minimize any
stress that arises from handling. All procedures were conducted
with approval with the Institutional Care and Use Committee
(IACUC) at Rutgers University.
Procedures
Two phases of testing were administered to all animals. The first
phase of testing was designed to assess general cognitive ability
and was comprized of three distinct cognitive tasks (that yielded
seven measures of cognitive performance) that depended on
different underlying processes. Performance measures (indicative
of rate of learning or problem resolution) from these tests were
entered into a principal component analysis, to (1) determine
the degree to which a general factor influenced performance
across all cognitive tests, and (2) to generate factor scores for
each animal. A factor score is essentially an average z-score of
each animal’s performance across all cognitive tests (where the
individual tests are weighted according to their loading on the
general factor). Thus these factor scores represent each animal’s
general cognitive performance relative to all of the animals that
contributed to this sample.
Upon completion of the cognitive assessment, all animals were
then subjected to a series of tests intended to assess performance
on tasks with clear implications for animals’ survival.
Cognitive Tests (Seven Dependent
Measures)
The battery of cognitive tests employed here to assess general
cognitive ability is somewhat different in nature than batteries
that have been previously used in our laboratory, and notably,
the performance in all of the cognitive tests in the current battery
was motivated by food deprivation (whereas in prior batteries,
several different motivational states were represented). It should
be noted, however, that the current battery was compared to prior
batteries and it was determined that the amount of cognitive
variance accounted for by the present battery of tests was
comparable in magnitude and structure to what has previously
been reported (Crawford et al., 2020).
The sequence and nature of the cognitive tests are illustrated
in Table 1.
The first three cognitive tests (that yielded six dependent
measures) were conducted in a single piece of apparatus
constructed as a convertible hybrid-style straight alley/Lashley
maze. An illustration of this maze is provided in Figure 1. The
tests administered in this maze included a simple discrimination
task, egocentric navigation in a Lashley maze, a reversal of
path direction in the Lashley maze, and two simple object-
permanence puzzles.
Puzzle Solving in a Straight Alley (Yielding One
Measure of Cognitive Performance)
Mice were placed in the start box of the maze for 5 s, after which
the exit door was opened and the mice were free to traverse the
alley. When mice reached the goal area of the maze, access to
the alley was blocked to enclose them in the goal area. The goal
area contained a single platform with a food dish holding one
piece of accessible food and one piece of inaccessible food. Time
taken by the mouse to retrieve the food was recorded. This was
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TABLE 1 | Cognitive tests were administered in three apparatus over a 17 day period.
Cognitive tests
Days 1 and 2:
Acclimation
Day 3:
Training (Trials 1–5) and
puzzle solving (Trial 6)
Days 4 and 5:
Simple discrimination
(Trials 1–12)
Day 6:
Egocentric navigation (Trials 1–6) Trial 6:
Object permanence puzzle
Day 7:
Egocentric navigation (Trials 7–12)
Day 8:
Egocentric refresher trials (Trials 13 and 14)
Egocentric reversal trials, disengagement
approaches (Trials 15–18)
Day 9:
One acclimation trial (Trial 1)
and one training trial (Trial 2)
Days 10–13:
Two training trials per day (Trials
3–10)
Straight alley (puzzle
solving/simple
discrimination)
Two day break Lashley maze (egocentric navigation/object
permanence/puzzle/egocentric
reversal/disengagement approaches)
Two day break Decision tree maze (inductive
reasoning)
FIGURE 1 | The straight alley and Lashley maze configurations of a single test chamber. The left panel illustrates the maze configuration, and the right illustrates the
straight alley configuration. The start box is located near the left side of the maze, and the goal location is near the right. Depending on the task, either 1, 2, or 4 food
cups were present at the goal location. In some tasks, discriminative cues were mounted on the wall behind the goal cups (illustrated by the star over Position 1).
The apparatus was constructed of black Plexiglas and measurements are in cm.
repeated for a total of five trials with a 6–10 min ITI for each
animal. The first five trials in this maze were merely intended to
establish directed approach to the food cup and an expectation
that food would be located there. Trial 6 served as the critical
measure of cognitive performance and proceeded as previous
trials, with the exception of an added hexagonal lid (a plastic
weigh boat) placed on top of the food cup. Of interest were the
number of failures (“errors”) to remove the lid in order to collect
the food reward. An error was scored if an animal placed at least
two paws on platform and withdrew from the platform or if the
animal made contact with the lid and failed to remove it. This
trial was considered complete when the animal removed the lid
and retrieved the food reward.
Simple Discrimination in Straight Alley (One Measure
of Cognitive Performance)
Mice were again placed in the start box of the straight alley for 5 s
and then released. On these trials, the goal box contained four
platforms, each with one food cup on it. Each cup was baited
with inaccessible food, while the cup marked by a discriminative
cue also contained accessible food. During Trial 1, one cup
(in position #3, counted from left to right) was covered with
a hexagonal lid and baited with accessible food. This trial
proceeded as previous trials in the straight alley, counting errors
as previously described. Trials 2–12 had four cups in the goal area,
all covered with hexagonal lids. One of the four cups were marked
by a white star (35 mm diameter) on the wall behind and 20 mm
above the food cup. This star served as the discriminative cue and
marked the location of accessible food. Position of the target cup
was randomly selected for each of these trials and standardized
for all animals. Lid errors were recorded as previously described,
and errors were also counted any time that the mouse made
contact with a non-target lid. Errors could occur multiple times at
a single lid provided that the animal stepped off the platform after
making initial error (i.e., attempts at same lid without leaving the
platform count as a single error). Average number of errors on
Trials 6–11 served as each animal’s index of performance.
Lashley Maze (Yielding Four Measures of Cognitive
Performance)
For this portion of testing the straight alley maze was converted
to its Lashley maze configuration. A single platform with an
uncovered food cup baited with reinforcer was placed in the
center back of the goal area. This phase of training took place over
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3 days in total, with 2 days of acquisition testing (Trials 1–12)
followed by 1 day of testing (Trials 13–18) in which the learned
response requirements were reversed.
For Trials 1–6 (Day 1), animals were placed in the start
box, released, and allowed to traverse maze until it entered the
arena and food was retrieved. Errors were recorded for the
following actions: (1) back-tracking (complete reversal involving
movement), (2) missed turn, i.e., passing a door without entering,
or (3) wrong direction of turn (i.e., exiting a door and turning
in the wrong direction). Errors were not compounded, i.e., if an
animal missed a door (error), then back-tracked to return to that
door, the back-track was “necessary,” and thus was not scored as
an error. Likewise, if an animal made a wrong turn (error), it
must back-track to return to the proper course and thus back-
tracking was not an error. Errors could only occur if an animal
was moving toward the goal, i.e., if the animal made an error that
leads back through several doors before correcting and moving
toward the goal, only the first error in the series was counted.
Once the animal again progressed toward the goal, errors were
again counted. Trials 7–12 (Day 2) followed this same procedure.
A 20 min ITI intervened between each trial. The average number
of errors committed on Trials 3–5 (Day 1) served as each animal’s
index of acquisition. Trials 3–5 were used for this analysis as we
have previously found that performance on these trials effectively
capture differences between slow and faster learners (A complete
description and rationale for our scoring methods can be found
in Kolata et al., 2008).
On Trial 6 only, the food cup was covered by a blue ping-
pong ball constituting a “puzzle” to be solved in order to obtain
food. We recorded errors to enter the arena (as on all trials), then
errors committed in solving the puzzle. An error was scored if an
animal placed at least two paws on platform and withdrew from
the platform or made contact with the ball and failed to remove
it. Thus, errors were recorded if an animal approached a cup or
made contact with the lid without retrieving the food. The trial
was completed (and no error scored) if an animal successfully
removed the ball to retrieve the food reward. Trials 7–12 were
identical to Trials 1–5.
At the start of Day 3 in the Lashley maze, two “refresher”
trials were administered (Trials 13–14) that followed the same
procedure as the first 12 acquisition trials. The subsequent four
trials (Trial 15–18) required that the animal reverse its previously
learned path in order to obtain the food reward. A baited food
cup was placed in what was previously the start box. Animals
began the trial placed in the center of the goal area facing
an empty food cup in the location of the previously baited
cup. Two types of errors were recorded: (1) Approach errors
were recorded every time the animal approached the old
(now empty) food cup. This was constituted any time that
an animal placed at least two paws on the platform and
withdrew or when its nose crossed the plane of the cup wall
(in cases where the animal did not step on to the platform).
(2) Maze errors were recorded as the animal traversed the
maze toward the new goal location (in what was previously
the start box). These errors were scored as they were during
forward Lashley maze training, although in this instance, the
correct route was reversed. Animals were allowed to find and
consume the food, and then removed to begin their inter-
trial interval.
Decision Tree Maze (One Measure of Cognitive
Performance)
Upon completion of testing in the Lashley maze, an additional
cognitive test was administered in a distinct piece of apparatus (a
decision tree). The Decision Tree maze is a “tree” shaped maze
constructed from black Plexiglass with a start box and series of
bifurcating arms at seven symmetric locations, “nodes,” after an
initial split dividing the maze in two symmetrical halves (see
Wass et al., 2012, for an illustration of the maze). Before the
initial division in the maze sits an alley that originates from
a starting box with a removable door where mice begin the
test. At each of the 14 nodes within the maze (located at splits
and at the end of arms), a small hole (3 mm wide ×3 mm
deep) was drilled to hold a recessed 14 mg Noyes pellet that
serves as the food reward, a random selection of which were
baited on any given trial. This test involves mice navigating
the branch-like structured maze to inspect each node for food.
The object of this test is for mice learn the overall structure
of the maze and formulate the optimally efficient path through
which it can search each potential food deposit while using the
lowest amount of time and energy possible. Mice with high
general intelligence will explore the maze in efficient paths (i.e.,
cross the same node only en route to an unexplored node)
while mice with lower intelligence will often take meandering
paths and make many errors (unnecessarily crossing a node) in
exploring the maze. This maze has previously been shown to load
heavily (0.49) on a factor analysis describing a general intelligence
factor, and the efficiency with which an animal searches the
maze has been said to be emblematic of inductive reasoning
(Wass et al., 2012).
Animals were food deprived for approximately 16 h before
testing began. Testing in this maze lasted 5 days, with animals
being tested in two trials per day for a total of 10 trials. On Trial 1,
mice were placed in the start box for 10 s before opening the gate
to the maze. On this trial, all 14 nodes were baited with food. The
path taken by the animal was recorded until all food pellets were
retrieved, and then the mice remained in the maze for 12 min to
allow further exploration. Mice were then removed and placed
back in its home cage in the testing room for the 10 min ITI.
Trials 2–10 involved a similar procedure to trial one, with
two exceptions. The first exception is that during these trials,
only four to eight of the nodes were baited with food. Standard
arrangements of the food baiting were used to ensure consistency
among animals. The number and location of nodes to be baited
during each trial were selected randomly. The second exception
from Trial 1 was that these trials were not subjected to the 12 min
time requirement. Rather, these trials ended when all available
food had been eaten and all nodes had been explored.
The path an animal takes to explore each node in the maze
were recorded. On each trial we recorded the “streak,” or number
of node crossings an animal made before making an unnecessary
node crossing. For the present purpose, the average streak length
on best two of Trials 7–10 served as the dependent measure of
each animal’s performance.
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Survival-Related Tests (Nine Measures)
Survival-related measures were obtained in the animals’ home
cages as well as three novel environments. In total, nine measures
of survival-readiness were obtained. The sequence and nature of
these tasks are illustrated in Table 2.
Nest Building in the Home Cage (One Measure)
This procedure is based on one described by Deacon (2006a),
who argued that proper nest building (which is performed
by both male and female mice) conserves body heat and
protects animals from predation, both of which facilitate
survival in the wild.
Mice in this study had no prior experience with nesting
material other than the shredded cob bedding that lined their
home cages. Approximately 60 min prior to the onset of the
dark cycle, a 3 gcompressed cotton pad (Oasis Shred-a-Bed) was
placed against the front wall of the animals’ home cages. Even
with no prior experience, mice characteristically shred these pads
and use the material to construct nests. The pad remained in the
home cage throughout the ensuing dark cycle. 2 h after the onset
of the light cycle, the quality of the nest was recorded using the
5-point scale recommended by Deacon (2006a), where a score of
1 is assigned to an animal that has not noticeably touched the pad
(more than 90% intact), and a score of 5 is assigned to an animal
that has constructed a near-perfect nest with more than 90% of
the pad shredded, and the nest forming a crater occupying 25%
or less of the cage floor, with at least 50% of the walls higher than
the prone mouse’ body height. Intermediate scores (i.e., between
two whole numbers) were used when a nest was judged to be
intermediate between any two points on the rating scale.
Exploration and Food Retrieval in a Burrowing Box
(Two Measures)
Two adjoining white Plexiglas boxes (20 cm l ×13 cm w ×10 cm
h) were connected by a 2.5 cm diameter ×10 cm long tube that
emerged through the floor of each box on either side of the
adjoining wall. The wall that divided the two boxes was perforated
to facilitate the transmission of odors between the two sides of
the box. Testing in this box was performed on two consecutive
days. On Day 1, each mouse was placed in one box and allowed
to freely explore throughout a 12 min session. The dependent
measure obtained was the latency for the animal to first cross the
tube and enter to other box (recorded when all four paws made
contact with the floor of the box). This measure was essentially
an index of exploratory tendencies in a novel environment. Upon
completion of Day 1 testing, each animal began a 20 h period of
food deprivation. Day 2 of testing was similar to Day 1 with two
exceptions. First, the tube that connected the two boxes was filled
with shredded cob bedding. Second, the side of the box opposite
to the start side had in it one gram of standard lab chow. The
mouse was started in the empty side of the box, and of interest
was the latency to burrow through the occluded connecting tube
and retrieve a piece of food in the opposite box. This trial had
no time constraint and was ended when the mouse retrieved
the piece of food.
TABLE 2 | Survival-readiness tests were administered in four sets of apparatus over a 14 day period.
Survival readiness tests
Day 1:
Overnight nest building
Day 2:
Acclimation/exploration
measurement
Day 3:
Food retrieval
Day 4:
Two-h nest building
Day 5:
Latency to find food Amount of
food hoarded in 20 min
Day 6:
Food size preference
Day 7:
Acclimation to social
box Exploration
measure
Day 8:
Predator avoidance
Home cage (nest
building)
Two day break Burrow box
(exploration/food
retrieval)
Two day break Hoarding box (nest building/latency
to find food/food size preference)
Two day break Social box
(exploration/predator
avoidance)
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Food Retrieval and Nesting in a Novel Hoarding Box
(Four Measures)
Given access to a food supply outside of the home environment,
mice will typically retrieve that food and return it to their nesting
area. Moreover, when given a choice, mice tend to reject less
attractive food in favor of more attractive food. These species-
typical behaviors are said to have obvious survival value (Deacon,
2006b). To assess these behaviors in laboratory mice, Deacon
(2006b) developed a procedure in which mice are introduced to
and housed in a novel environment, and are given access to a
tunnel (that protrudes from the nest area) that terminates in a
batch of familiar food. In Deacon’s description of this task, the
mice were mildly food deprived and allowed approximately 12 h
to engage in food retrieval, after which was recorded the amount
of food (from a 100 gsource) that was returned to the home box.
In a preliminary attempt to employ this procedure, we observed
that all mice returned all food by the end of the 12 h interval.
Consequently, we made several modifications to the procedure
and also collected several other measures related to hoarding and
survival in a novel environment. First, we assessed nest building
shortly after introduction to the novel box. (This procedure was
identical to the procedure used in the home box except that the
quality of the nest was assessed 2 h after introduction in the novel
box, a behavior that would have important survival benefits when
an animal is moved from its familiar home nest.) Second, for
the first hoarding test animals were not food deprived and two
measures were obtained, including the latency to first contact
food after given access to the tunnel, and the amount of food
retrieved was recorded after 2 h of access. (In preliminary studies
we observed significant variability across animals in the amount
of food retrieved at this interval). Access to the tunnel was then
blocked. 20 h after the initial bout of hoarding, the mice were
again given access to the tunnel and again allowed to retrieve
food. However, in this instance the animals had access to 16 food
pellets, eight of which were 500 mg, and eight of which were
100 mg of interest was any observed preference for the large pellet
among the first eight pellets returned to the home cage.
Apparatus was similar to that described by Deacon (2006b).
A test box (30 ×13×15 cm) was constructed of white Plexiglas
with a clear lid covered in orange acetate (which darkened the
interior of the box). The floor of the box was lined with shredded
cob bedding. A 50 cm long ×5 cm diameter wire mesh (6 mm
grid size) tunnel extended from the end of the home box and
terminated in an 10 cm long black Plexiglas tube with a sealed
end (serving as a food cup). Access to the tunnel could be blocked
with a black rubber stopper.
On Day 1 (approximately 4 h after the start of the light
cycle), each mouse was transferred from its home cage to the test
apparatus (where it would remain for three consecutive days).
The home cage bedding was transferred to the test apparatus
along with 400 ml of additional fresh bedding and a Shred-a-
Bed nest pad. The test box contained the animal’s regular food
(four pellets) and a water spout. Four pieces of novel food (Hartz
Small Animal Diet for Guinea Pigs; two 100 mg pellets, and two
500 mg pellets, designated “small” and “large” test pellets) were
also present. (This food type would be used in a later test and
was provided at this time to mitigate any neophobic responses).
Entrance to wire tunnel was blocked. 2 h after introduction of
a mouse to the test box, nest quality was scored in the manner
described above.
On Day 2, 100 gof the animals’ standard diet (pellet size 1.5–
3.0 g) was placed in food cup at end of the wire tunnel. Mid-way
through the light cycle, the rubber plug was removed allowing
the animal access to the tunnel. The latency for each animal to
traverse the tunnel to reach the food was then recorded. 2 h later,
the tunnel was again blocked and food pellets that remained in the
tunnel were weighed; this weight was subtracted from the original
100 gto yield a measure of successfully hoarded food pellets (i.e.,
the pellets that had been returned to the nest area). After this test,
all food was removed from the nest box. Each animal was then
provided one large (500 mg) and one small (100 mg) test food
pellet (Hartz Small Animal Diet).
On Day 3 (18 h after removal of food from the home test
box) two columns of 8 test food pellets were placed in the food
cup at the end of the wire tunnel. The rows of the two columns
alternated between small and large pellets. The rubber plug was
then removed from the wire tunnel allowing each mouse access,
and the size of the first eight pellets retrieved was recorded. For
purposes of scoring, each large pellet was worth 1 point, whereas
small pellets were worth 0 points, yielding a maximum score of
“8” for each animal.
Predator Avoidance in a Social Box (Two Measures)
In their natural environments, mice are the target of predation by
rats. Even without prior experience with the predator, mice will
avoid areas marked by the odor of rats (Papes et al., 2010). This
native defensive tendency was assessed here where mice could
approach or withdraw from a live rat. A mouse was placed in
the center chamber of a 3-chamber box, in which the mouse
could freely move between the chambers. One of the chambers
that adjoined the center contained a rat restrained in a wire tube,
while the opposite chamber adjoining the center contained a
cotton wad similar in size to that of the rat inside a wire tube.
Two behaviors were of interest. First, we recorded the latency to
first exit the center chamber when both adjoining chambers were
empty (Day 1), and second (on Day 2), we recorded the difference
in time spent in the chamber containing the rat relative to the
chamber containing the cotton wad.
This test was conducted on two successive days, the first of
which was intended to acclimate the mouse to the test chambers
and to obtain a measure of exploration in a novel environment.
Each mouse (in its home cage) was placed in the testing room
under dim light for 10 min prior to testing. The predator
avoidance box was a 60 ×40 ×24 cm clear Plexiglas box divided
into three 20 ×40 ×24 cm sections. A small door (15 cm
square) allowed access from the center chamber into each of
the adjoining chambers. Each of the chambers that adjoined
the center contained a wire mesh container (6 mm grid, 18 cm
diameter ×22 cm high). These containers were empty on the
acclimation day. Each mouse was placed in the center chamber
and allowed to explore the box freely for 10 min. We recorded
the latency (in sec) for the animal to leave the center chamber
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and enter either of the two adjoining chambers (the mouse was
considered to have entered a new chamber when all four paws
were on its floor). At the conclusion of the 10 min of acclimation,
the mouse was returned to its home cage.
On Day 2, the box was arranged as during Day 1, with the
addition of a live Sprague-Dawley (Crl:SD) male rat in the wire
mesh container in one of the side chambers, and a rat-sized white
cotton bundle in the container in the opposite side chamber. The
mouse was again placed in the center chamber and allowed to
explore the box for 10 min. Of interest was the time spent in
the chamber with the cotton wad minus the time spent in the
chamber with the rat. A score approaching 10 min would reflect
maximum avoidance of the rat. After the 10 min test the mouse
was returned to its home cage.
Statistical Analyses
All statistical analyses were completed using IBM Statistics
Version 25. The dependent measures from each of the cognitive
tasks were entered into a principal component analysis to obtain
an estimate of the influence of a general cognitive factor and
to compute factors scores which reflect the aggregate (general)
cognitive performance of each mouse relative to the other mice
in this sample. Next, the performance measures of survival-
readiness were entered into a principal component analysis to
determine the existence of a “general survival-readiness factor”
and to compute factor scores on this dimension (indicative of
an animal’s relative survival-readiness). The degree of correlation
between general survival-readiness and general cognitive ability
could then be assessed. We also examined the correlations
between general cognitive factor scores and each measure of
survival-readiness to determine which, if any, of those individual
behaviors were predicted by general cognitive ability.
In some instances, better performance on a task was indicated
by a lower score (e.g., fewer errors), whereas in other instances,
better performance on a task was indicated by a higher score
(e.g., more food retrieved). This complicates the interpretation of
correlations, as although better performance on one task might
predict better performance on a second task, in one instance this
would be reflected in a negative correlation whereas in another
instance this would be reflected in a positive correlation. To
simplify the presentation (for instance, the correlation matrix
presented in Table 3), in all cases, correlations are presented
such that positive values mean better performance on one value
predicts better performance on the other. This was accomplished
by inverting raw values in some instances.
RESULTS
Fifty-six male mice contributed to this analysis. All 56
contributed data on all cognitive tests. For tests of survival-
readiness, one animal was removed from the study due to illness,
yielding ns = 55 on all tests except “Hoarded weight,” where due
to a procedural error, data from eight animals was lost, yielding
an n= 47 on that test. Principal component analyses require that
all subjects contribute to all dependent variables. Consequently,
the principal component analysis of cognitive abilities was based
TABLE 3 | Principal component analysis of seven cognitive tests (n= 56).
General factor
Straight alley lid puzzle errors 0.35
Straight alley discrimination errors 0.35
Lashley maze acquisition errors 0.64
Lashley maze lid puzzle errors 0.51
Lashley maze reversal errors 0.72
Lashley maze approach errors 0.65
Decision tree streak length 0.40
Variance explained 29.08%
Eigenvalue 2.03
Data for this analysis was entered such that in all tasks, lower values represented
better performance (requiring in several instances that raw values were inverted).
FIGURE 2 | Factor scores for general cognitive ability and general
survival-readiness are compared. A correlation was observed indicating that
better overall cognitive ability predicted better overall performance on tests of
survival skills (n= 47).
on an n= 56, whereas the analysis of survival abilities was
based on an n= 47. In one instance, we compared aggregate
performance on the cognitive tests (based on factor scores) to
aggregate performance on survival-readiness tests (see Figure 2,
described below). For this purpose, only the 47 animals that were
represented in both sets of tests were included in the analysis.
General Cognitive Ability
Table 3 presents the results of the principal component analysis
of all seven cognitive measures.
As evident from Table 3, the performance on all cognitive tests
loaded moderately to strongly on a principal factor, indicative of
a common influence on performance across all tests. This general
factor accounted for ∼29% of the variance in performance across
all tasks. This degree of variance is comparable to that observed
using batteries of tests that were very dissimilar to the one
used here (e.g., Kolata et al., 2008;Sauce et al., 2018). This is
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notable since in the present battery, all of the cognitive tests
were motivated by food rewards, which could make it difficult
to distinguish between a general cognitive factor and a general
motivational factor. However, the results obtained from other
test batteries were based on tasks that depended on at least three
different motivational systems. Thus it is reasonable to conclude
that the present results are a reflection more of cognitive ability
rather than a common motivational state.
General Survival-Readiness
Table 4 presents the results of the principal component analysis
of all nine measures of survival-related behaviors.
As evident from Table 4, the performance on many measures
of survival-readiness loaded moderately to strongly on a single
factor that accounted for ∼25% of the variance in performance
across all tests. This suggests the existence of a “general survival-
readiness” ability that would influence an individual’s capacity for
survival. Notably though, three of the measures loaded negligibly
or in the opposite direction on this factor, indicating a less than
universal influence on these survival-related behaviors.
The Relationship Between General
Cognitive Ability and General
Survival-Readiness
Forty-seven animals contributed scores on all of the cognitive
tests and all of the survival-readiness tests. Of particular interest
was the relationship between general cognitive abilities and
general survival-readiness. To understand this relationship, we
assessed the correlation between factor scores obtained on the
cognitive test battery and factor scores derived from tests of
survival-related behaviors, yielding r(46) = -0.64, p<0.01. This
correlation indicates that higher cognitive abilities (reflected in
lower values of performance, e.g., fewer errors, and thus lower
factor scores) predicts better performance in tests of survival-
readiness (reflected in higher values of performance, e.g., a higher
TABLE 4 | Principal component analysis of nine survival-related tests (eight
animals did not contribute a score on the amount of food hoarded, resulting in 47
animals that contributed scores to all tests).
General factor
Exploratory latency in burrow box −0.31
Food retrieval in burrow box 0.33
Nest quality in home environment −0.03
Nest quality in novel environment 0.82
Latency to find food in hoard box 0.27
Amount of food hoarded in 20 min 0.64
Preference for larger food 0.86
Exploration in social box 0.01
Predator avoidance in social box 0.35
Variance explained 24.96%
Eigenvalue 2.24
In some instances, higher raw values reflected better performance, and in other
instances, lower raw values reflected better performance. Thus to simplify the
presentation, values were in some instances inverted such that for all variables,
higher reported values represented better performance.
quality nest, thus higher factor scores). The relationship of these
two variables is illustrated in Figure 2.
To demonstrate the consistency of animals across tasks
in the cognitive battery and the survival-readiness battery, as
well as the relationship of these two trait, in Figure 3 we
illustrate the relative performances of two animals, one of
which (#29) was one of the best performers in the cognitive
battery, and one of which (#61) was the worst performer in
the cognitive battery. These designations were based on factor
scores extracted from performance on the battery of cognitive
tests. These scores ranged from -1.83 [best general cognitive
performance] to +2.67 [worst general cognitive performance].
Subject #29 received a factor score of -1.31, and Subject #61
received the factor score of +2.67. The subject that performed
best on the cognitive battery (factor score of -1.83) could not
be used for this analysis as it did not contribute one score
on the survival-readiness battery of tests). Subjects #29 and
61 were assigned a rank based on its performance relative
to all other animals on each of the cognitive tasks and each
of the survival-readiness tasks. These ranks are illustrated in
Figure 3. As this figure illustrates, Subject #29 performed
near above the median (near the top of the distribution) on
each of the cognitive tests, and also performed above the
median on each of the survival-readiness tests. Subject #61
performed below the median on all of the cognitive tests,
and also tended to perform poorly on most of the survival-
readiness tests.
General Cognitive Ability and Performance on
Individual Tests of Survival-Readiness
Finally, we examined the relationship between general cognitive
ability and performance on individual tests of survival-related
behaviors, as well the relationship between the various measures
of survival-readiness. A matrix of correlations of all relevant
variables (i.e., general cognitive factor scores and all measures
of performance on tests of survival-readiness) is presented
in Table 5.
The correlation matrix presented in Table 5 is a mixed
set of results. Most correlations were positive (suggesting a
common influence on general cognitive ability and all measures
of survival-readiness), although several negative correlations
were observed and most correlations were not significant (and
weak). However, general cognitive ability did significantly predict
several measures of survival-readiness, including nest building in
a novel environment, the amount of food hoarded in a 20 min
interval, the degree of preference for larger portions of food,
and predator avoidance. Notably, most of the strong correlations
between cognition and measures of survival-readiness were
observed in those cases where survival-related behaviors were
assessed in an environment that was unfamiliar to the mouse.
This suggests the possibility that distractions that arise in
a new environment (but which are minimal in a familiar
environment) are more easily overcome in animals with higher
cognitive abilities. This and other possibilities are considered
more fully below.
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StraightAlley,L
idPuzzle
StraighAlley,Discrimination
LashleyMaze,Acquisition
Lashley Maze,LidPuzzle
LashleyMaze,Reversal
LashleyMaze,A
pproachErrors
DecisionTree
StraightA
lley,L
idPuzzle
StraighAlley,Discrimination
Lashley Maze, Acquisition
Lashley Maze, LidPuzzle
LashleyMaze,R
eversal
LashleyMaze, ApproachErrors
DecisionTree
BurrowBox,ExploratoryLatency
Burrow Box,FoodRetrieval
NestQuality,H
omeEnvironment
NestQuality,NovelEnvironment
HoardBox,LatencytoFood
Hoard Box,AmountofF
ood
HoardBox,LargeFoodPreference
SocialBox,E
xploration
SocialBox,PredatorAvoidance
BurrowBox,ExploratoryLatency
BurrowBox,FoodRetrieval
NestQuality,HomeEnvironment
NestQuality,N
ovelEnvironment
HoardBox,LatencytoFood
Hoard Box,A
mountofF
ood
HoardBox,L
argeFoodPreference
SocialBox,E
xploration
SocialBox,PredatorAvoidance
0
20
40
Rank Performance (1 = best; 48 = worst)
Cognitive Tasks Survival-Readiness Tasks
#29, high cognitive ability
#61, low cognitive ability
median
FIGURE 3 | Subject #29 (high general cognitive abilities, as determined by factor scores obtained from the PCA of cognitive tasks) and Subject #61 (low general
cognitive abilities) were ranked relative to other animals in this sample (n= 47) on each cognitive (left sets of bars) and survival-readiness task (right sets of bars). The
heavy dashed line represents each animal’s mean rank on that set of tasks, and the light dashed line is the expected median for the sample of 47 animals. Subject
#29 performed well above the median on each of the cognitive tasks, and also performed well on each of the survival readiness tasks. Subject #61 performed
around the median or poorly on each class of tests.
GENERAL DISCUSSION
It had been noted by several authors (e.g., Burkart et al., 2016;
Locurto, 2017) that measures of “intelligence” in non-human
animals have suffered from the lack of independent verification of
the impact of those measures on outcomes that were independent
of the intelligence test itself. While some exceptions were noted
above, this criticism was certainly true of laboratory assessments
performed on mice, is thus it is an important concern to address.
An impediment to such an analysis arises when working with
laboratory mice, from which is difficult (or impossible) to assess
obvious practical outcomes of variations in intelligence, e.g., the
relationship of intelligence to survival in the wild (which could be
expected to be impacted by variations in cognitive abilities). Here
we took an intermediate approach, i.e., intelligence was assessed
in laboratory mice, and then these same mice were assessed for
performance on unlearned behaviors that could be reasonably
expected to impact survival in the wild.
In the present study, genetically heterogeneous laboratory
mice were assessed on a battery of cognitive tests (where
individual’s aggregate performance served as an index of
“intelligence”) and were then assessed on a number of
tasks relevant to nest building (in familiar and unfamiliar
environments), exploration in novel/familiar environments, food
discovery, efficiency of food retrieval and preference, and
predator avoidance. Better performance on these tasks might
promote survival-readiness under more natural (non-laboratory)
conditions. Several key observations arose from this analysis.
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TABLE 5 | A correlation matrix of cognitive factor scores (indicative of general cognitive ability) and all measures of survival-related behaviors.
Cognitive
FSs
Explore
latency
(burrow)
Food
latency
(burrow)
Nest in
home
Nest in
novel box
Food
latency
(tunnel)
Hoarded
weight
Food size
preference
Exploration
latency
(social)
Exploration latency −0.16
Food latency (burrow) 0.15 0.03
Nest in home 0.10 0.28* 0.09
Nest in novel box 0.42** −0.16 0.18 −0.08
Food latency (tunnel) 0.10 0.20 0.02 0.08 0.16
Hoarded weight 0.36* 0.00 0.10 0.06 0.43** 0.18
Food size preference 0.57** 0.41** 0.16 0.10 0.55** 0.00 0.43**
Explore latency (social) 0.04 −0.02 0.07 0.03 0.04 −0.19 0.20 0.15
Predator avoidance 0.28* −0.03 0.13 0.07 0.13 0.20 0.01 0.21 0.27*
In all cases, correlations are reported such that a positive correlation indicates that better performance on one measure predicts better performance on the other measure.
(This was done to simplify the presentation where in some instances, better performance was reflected in lower raw values whereas in other instances, better performance
was reflected in higher raw values). The ns for all comparisons are either 55 or 56 except for measures of “Hoarded weight,” where data from eight animals was lost,
yielding an n = 47. *p <0.05 (uncorrected). **p <0.01 (uncorrected).
First (and as in many previous reports, e.g., Matzel et al.,
2006, 2008;Kolata et al., 2007, 2008), a single factor was
found to contribute to performance on all cognitive tests.
Similarly (and second), a general “survival-readiness” trait was
also identified, i.e., a single source of variance was found to exert
an influence on some measures of unlearned survival-related
skills, accounting for 24.96% of the variance among individuals
across nine different tasks. Thirdly, and of principal relevance
to our intentions here, general cognitive abilities were strongly
predictive of general survival-readiness (r= -0.64). This latter
observation suggests that animals with higher cognitive abilities
(all other things equal) would indeed exhibit a higher likelihood
of survival in the wild.
The analysis of the relationship between general cognitive
ability and specific survival-related tasks is not entirely
straightforward. For instance, nest building in the home cage
was unrelated to general cognitive ability (r= 0.10), while nest
building in a novel environment was significantly predicted by an
individual’s general cognitive ability (r= 0.42). Although many
differences characterize these different tasks, a distinguishing
feature is that general cognitive ability appears to have a
stronger influence on tests of survival-readiness when those
later tests are administered in unfamiliar settings. We have
previously suggested that attentional abilities may be a principal
determinant of variations in mouse intelligence (Kolata et al.,
2005, 2007;Sauce et al., 2014; for review, see Matzel and
Kolata, 2010;Matzel and Sauce, 2017), a relationship that may
also exist among humans (Engle, 2002, 2018;Cowan et al.,
2005, 2006;Shipstead and Engle, 2013). Lapses in attention
(which could be exacerbated in unfamiliar environments with
new distractions) might explain the relationship between
general cognitive ability (and its dependence on attention) and
survival skills.
In addition to variations in general cognitive ability, other
“general” influences might account for the relationship between
performance on the cognitive battery and survival readiness.
Notably, it is conceivable that variations in stress reactivity
or anxiety might contribute to general cognitive performance
and thus might underly the relationship to survival readiness.
However, our prior work suggests that this is unlikely. In a
series of papers, general influences such as stress reactivity,
exploratory tendencies, and anxiety have been dissociated from
the principal factor in three ways. First, nominal measures of
fear/anxiety/stress do not load (or load weakly) on the principal
factor that captures general cognitive performance (Matzel
et al., 2006). Second, pharmacological reductions in anxiety or
stress reactivity do not promote increases in general cognitive
performance or change the pattern of loadings on the general
cognitive factor (where the cognitive tasks are differentially
dependent on fear-motivated responses; Grossman et al., 2007).
Lastly, Light et al. (2008) found that behavioral interventions
that reduce fear/stress/anxiety do not change the pattern of
loading on the principal factor that represents general cognitive
performance. These results have led us to favor the interpretation
that the general factor that describes performance across our
batteries of cognitive tests is reflective of a general influence on
cognitive abilities.
The potential relationship between attention and survival-
readiness might be best described with an anecdotal description
of mouse behavior. We have consistently observed that mice that
are classified as expressing low general cognitive abilities seem
to have difficulty maintaining directed behavior (an observation
that is consistent with empirical evidence, e.g., Kolata et al.,
2007;Matzel et al., 2008;Light et al., 2010). For example, in a
straight alley (a very simple test of learned behavior in which a
mouse runs in a straight line to a food reward), dull mice often
appear to lose focus, e.g., in the course of a run they may stop
and rear or engage in bouts of grooming. Thus even on such a
simple task, a dull mice will exhibit more between-trial variability,
and “worst” performance might best distinguish between high
and low intelligence individuals (a tendency that has been
repeatedly observed and quantified in humans’ performance
on many cognitive tasks; Juhel, 1993;Coyle, 2003). Relatedly,
it is possible that in a new environment, an animal could be
more distractible (and thus less directed) than would be the
case in a familiar environment. This suggests the possibility
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that distractions inherent to a new environment (but which are
absent from a familiar environment) are more easily overcome
in animals with higher cognitive abilities. In total, the possibility
that attentional variation is the common factor that mediates
the relationship between intelligence and expression of survival
skills seems both plausible and parsimonious with the range
of available data.
Of the nine tests of survival-readiness used here, three of those
tests (the latency to cross sides in the burrowing box, the latency
to traverse a tunnel in the hoarding box, and the latency to leave
the center compartment in the social box) were thought to be
primarily dependent on the propensity for exploration (which
of course can be influenced by stress, fear, or anxiety; Matzel
et al., 2006;Grossman et al., 2007). While other tests of survival-
readiness used here had obvious survival benefits, the relationship
of exploration to survival is more complex. Exploration (or
“novelty seeking”) could be beneficial under some circumstances,
but could be detrimental in others (necessitating a balance
between “too little” and “too much”). In this regard, we have
reported that variations in “exploration” may in fact be a
consequence of differences in rate of learning, such that more
competent animals evaluate their environment more quickly and
are thus more likely to engage in exploration when conditions
have been deemed appropriate (Light et al., 2008, 2011). This
interpretation is consistent with our regular observation that
exploration covaries with general cognitive ability (e.g., Matzel
et al., 2003). In this regard, it is quite surprising that none
of the measures of exploration employed here were related to
general cognitive performance and did not load appreciably on
the principal component analysis of survival-related behaviors.
One possible explanation of this result is consistent with previous
data of Light et al. (2011) who reported that the relationship
of exploration and general cognitive ability is diminished as
animals spend more time in a novel environment, i.e., after the
environment becomes familiar. In the present study, animals had
extensive experience in environments (during tests of cognitive
abilities) similar to those utilized for later tests of exploration,
and thus their novelty might have been minimized. Regardless
of the explanation, the results obtained here suggest that the
relationship between cognitive abilities and exploration might be
less universal than we had previously believed. Importantly, we
note that previous research suggests general cognitive abilities
of individuals may be differentially impacted by socialization
paradigms present in natural environments (e.g., Fitchett et al.,
2005;Chida et al., 2006). One of us has previously reported that
predisposition to submission within a social hierarchy predicts
superior cognitive performance (Matzel et al., 2017). One possible
explanation for this result was that survival-readiness strategies
reliant on cognitive abilities may have evolved to facilitate the
survival of those not predisposed to benefit from strategies
that favor dominant physical or social abilities. As such, the
social environment of an animal likely plays an integral role
in its survival strategy. Based on the results reported in the
present study it is possible, for example, that an animal with a
predisposition for high cognitive ability reared in an environment
free of socially dominant peers may enjoy a high degree of
survival-readiness (perhaps even enhanced by socialization, given
the relationship between socialization and improved cognitive
ability; see Voiker et al., 2005;Chida et al., 2006), while that same
animal reared with exposure to social submission may experience
decreased survival-readiness (given that social submission can
impair cognitive performance; see Fitchett et al., 2005;Colas-
Zelin et al., 2012).
In a general sense, it is unlikely that genes common to both
survival skills and intelligence could mediate the relationship
between these two sets of variables. In any nominal way, it seems
unlikely that the same genes could regulate tasks as dissimilar as
those that constitute tests of cognition (e.g., the Lashley Maze)
and tests of survival skills (e.g., nest building). However, while
these different traits are almost surely regulated independently,
they may also be regulated in common by networks of genes that
could impact processes like attention. While the heritability of
mouse intelligence (e.g., Galsworthy et al., 2002, 2005;Sauce et al.,
2018;Matzel et al., 2019) suggests a strong genetic influence on
the expression of this trait, the heritability of survival skills in
mice is presently unknown (but is currently under investigation
in our laboratory).
DATA AVAILABILITY STATEMENT
The datasets generated for this study are available on request to
the corresponding author.
ETHICS STATEMENT
The animal study was reviewed and approved by Rutgers IACUC.
AUTHOR CONTRIBUTIONS
LM and DC performed data analyses and wrote the manuscript.
LM, DC, HP, MP, MM, and AT maintained animals and collected
data. All authors contributed to the article and approved the
submitted version.
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
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