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Research
Cite this article: Sauce B, Bendrath S,
Herzfeld M, Siegel D, Style C, Rab S,
Korabelnikov J, Matzel LD. 2018 The impact of
environmental interventions among mouse
siblings on the heritability and malleability
of general cognitive ability. Phil. Trans. R. Soc.
B373: 20170289.
http://dx.doi.org/10.1098/rstb.2017.0289
Accepted: 5 June 2018
One contribution of 15 to a theme issue
‘Causes and consequences of individual
differences in cognitive abilities’.
Subject Areas:
behaviour, genetics, cognition
Keywords:
individual differences, heritability, malleability,
intelligence, environment intervention, mouse
Author for correspondence:
Louis D. Matzel
e-mail: matzel@psych.rutgers.edu
The impact of environmental
interventions among mouse siblings
on the heritability and malleability
of general cognitive ability
Bruno Sauce1, Sophie Bendrath2, Margalit Herzfeld2, Dan Siegel2,
Conner Style2, Sayeeda Rab2, Jonathan Korabelnikov2and Louis D. Matzel2
1
Department of Neuroscience, Karolinska Institutet, Solnava
¨gen 9, Solna 171 65, Sweden
2
Department of Psychology, Rutgers University, 152 Frelinghuysen Road, Piscataway, NJ 08854, USA
BS, 0000-0002-9544-0150; LDM, 0000-0003-0462-7188
General cognitive ability can be highly heritable in some species, but at the
same time, is very malleable. This apparent paradox could potentially be
explained by gene–environment interactions and correlations that remain
hidden due to experimental limitations on human research and blind
spots in animal research. Here, we shed light on this issue by combining
the design of a sibling study with an environmental intervention adminis-
tered to laboratory mice. The analysis included 58 litters of four full-
sibling genetically heterogeneous CD-1 male mice, for a total of 232 mice.
We separated the mice into two subsets of siblings: a control group
(maintained in standard laboratory conditions) and an environmental-
enrichment group (which had access to continuous physical exercise and
daily exposure to novel environments). We found that general cognitive
ability in mice has substantial heritability (24% for all mice) and is also
malleable. The mice that experienced the enriched environment had a
mean intelligence score that was 0.44 standard deviations higher than
their siblings in the control group (equivalent to gains of 6.6 IQ points in
humans). We also found that the estimate of heritability changed between
groups (55% in the control group compared with non-significant 15% in the
enrichment group), analogous to findings in humans across socio-economic
status. Unexpectedly, no evidence of gene–environment interaction was
detected, and so the change in heritability might be best explained by
higher environmental variance in the enrichment group. Our findings, as
well as the ‘sibling intervention procedure’ for mice, may be valuable to
future research on the heritability, mechanisms and evolution of cognition.
This article is part of the theme issue ‘Causes and consequences of
individual differences in cognitive abilities’.
1. Introduction
Cognitive abilities can be separated into multiple factors (derived from domain-
specific cognitive processes) that, at least in some species, are influenced by a
common, general factor (derived from domain-general cognitive processes)
[1]. This general cognitive ability (GCA), sometimes interpreted as ‘intelli-
gence’, is defined as the general capacity to learn, reason, plan and solve
problems [2]. GCA can vary greatly across individuals, and studying these vari-
ations provide precious information on the genetic and environmental factors
that shape this trait [3]. In addition, heritable individual differences are the
necessary fuel for evolution via natural selection, and so studying the heritability
of intelligence can shed light on how cognition evolved [1].
Heritability is a statistic that captures how much of the variation in a trait is
due to genetic differences, and the metric ranges from 0.0 to 1.0. Because the
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genetic and environmental effects often covary (e.g. related
individuals often share some environments in addition to
some genes), studies of heritability exploit special cases
where the two effects can be separated, such as cases of
families with twins [4]. These studies usually find quite
high heritabilities for GCA: around 0.60 (or sometimes
higher) in adult humans [5], 0.50 in chimpanzees [6] and
0.40 in mice [7].
The influence of genes on GCA, however, might not be as
powerful as estimates of heritability might lead many to
believe. There is evidence in humans suggesting that despite
its high heritability, intelligence is also quite malleable, with a
great deal of intelligence’s variation being attributable to
environmental factors across families and socioeconomic
status [8]. Adoption studies, for example, often reveal an
average increase in intelligence of one standard deviation
(15 IQ points) within several years of adoption [9]. This rep-
resents a remarkable cognitive gain of the adopted children
over their biological, non-adopted siblings.
An interesting question arises from the above obser-
vations: how can intelligence be at the same time highly
heritable and highly malleable? One possible solution to
this odd paradox is the role of gene – environment inter-
actions between genetic and environmental factors [8]. In
one type of gene–environment interaction, genetically differ-
ent individuals will have a different subjective experience (i.e.
pay attention to, absorb or respond differently) to the same
objective experience, and this can lead to further increases
or reductions in intelligence. Gene–environment interactions
in intelligence can be especially elusive to detection, and are
often disregarded or ignored by researchers [10].
The difficulties associated with tests of gene– environ-
ment interactions are due in part to limitations on work
with humans, including being largely confined to the assess-
ment of heritability and malleability using only correlational
methods (i.e. methods without experimental manipulations
that focus on variation between individuals). With laboratory
animals, we can easily control the environment, and can com-
bine correlational and experimental designs to more precisely
understand the effects of genes, environment and their
interaction. However, there are relatively few studies on indi-
vidual difference in cognitive abilities in non-human animals
[11]. Most animal studies have primarily used experimental
approaches (i.e. studies with manipulation that look at
group-level effects and ignore inter-individual variation),
and focus on single cognitive domains [12]. While these
studies have proven fruitful in delineating certain neurobiolo-
gical substrates of task performance [13,14], they do not
capture how genetic and environmental factors contribute
to create the differences in general cognitive skills.
In previous research by our group, genetically diverse
mice were tested on batteries of learning tasks, each of
which with unique sensory, motor and motivational
demands [15,16]. Mice that do well in one task of the battery
tend to perform well in other tasks within the battery too,
revealing a positive correlation of each animal’s learning
across all tasks. The ‘general learning’ scores derived from a
factor analysis also covaries with other cognitive domains,
such as inductive and deductive reasoning [17], spatial ability
[18] and working memory and attention [19]. That means that
the common factor behind performance in the learning bat-
teries is capturing something cognitively more general
(across domains) than simply learning. In fact, others have
described our results as qualitatively analogous to what is
described in humans as intelligence [20].
Previous studies by our group also found that differences
in mouse intelligence correlate with difference in expression
of genes known to play a role in learning and synaptic plas-
ticity [21], and also correlate with dopamine-induced activity
in the prefrontal cortex [22,23]. Because these studies were per-
formed in laboratory mice living in a fairly homogeneous
environment, the results suggest that individual differences
in a mouse’s IQ have strong genetic influences. Similar to the
human literature, there is also evidence for the malleability
of mouse intelligence. For example, a study found that a com-
bination between exercise and novel environments in mice
increases neurogenesis in the hippocampus and retention of
these new neurons [24]. A review by van Praag et al. [25] con-
cluded that environmental enrichment in rodents (defined as
‘a combination of complex inanimate and social stimulation’)
can have lasting effects on learning and brain growth.
Here, we attempted to test the prediction (based on the
evidence above) that mouse intelligence can have both high
heritability and malleability. For this, we used groups of
full-sibling mice and exposed subsets of each sibling cohort
to different environments. In other words, our study com-
bined the design of a sibling study with a controlled
environmental intervention. This allowed us to estimate
how many of the differences in mouse intelligence are influ-
enced by genetic and the environmental factors, as well as
test for expected gene–environment interactions.
2. Material and methods
(a) Subjects
We used 232 CD-1 outbred male mice from Harlan Laboratories
(Indianapolis, IN, USA). Estimates of genetic variation in this line
have indicated that despite over 50 years of breeding, they are
very similar to wild mouse populations [26]. The mice arrived
in our laboratory between at four and five weeks of age, and
they were singly housed in clear shoe box cages inside a temp-
erature-controlled colony room under a 12 L : 12 D cycle. In
order to minimize any differential stress responses due to exper-
imenter handling, we handled the animals for 90 s a day for a
period of 7 days prior to the start of the experiment. Handling
consisted of removing the mice from their home cage and
holding them while walking throughout the laboratory space.
The 232 mice comprised 58 sets of four siblings (fraternal
quadruplets), totalling 58 families whose parents were unrelated
to each other (as guaranteed by the supplier Envigo). Two siblings
of a set, randomly chosen, stayed in the home environment
(control group) and the two other siblings received an environ-
mental ‘enrichment’ treatment consisting of physical exercise and
exposure to novel and engaging environments (enrichment group).
All mice had continuous access to both food and water. The
only exception was during the tests requiring food deprivation,
when mice were provided with food in their home cages for only
90 min a day, beginning on the day prior to testing. Although
mild, this level of deprivation is sufficient to maintain stable per-
formance on learning tasks [15]. All experiments were conducted
in accordance with protocols approved by the Rutgers University
Institutional Animal Care and Use Committee (IACUC).
(b) Environmental enrichment
The enrichment manipulation lasted for 16 days, and the two
groups of mice were maintained in separate, though nominally
identical, colony rooms. (During enrichment, it was necessary
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to separate the groups into two colony rooms owing to the audi-
tory stimulation associated with aspects of the enrichment
procedure.) The enrichment group lived in home cages contain-
ing a running wheel for exercise throughout the treatment.
These mice were exposed to one novel environment each day
for 30 min outside their home cage. Animals were individually
exposed to a new novel environment at approximately the
same time each day for each of 16 days.
The environments encountered by the enrichment group
were: (i) a large, black, plastic box with two concave towers on
each side and a platform in the centre reachable by jumping;
(ii) a narrow Plexiglas tube where the ends have two small
boxes where the mice could traverse between the boxes by
going through the tube; (iii) an eight-arm radial arm maze
with all doors left open; (iv) an acoustic chamber with foam on
the wall with a fan inside as the only sound mice would experi-
ence; (v) a black box with a white stripe on the walls and the
floor covered with soft, plastic spikes; (vi) a white box with six
different plastic toys; (vii) a large social box with a second
mouse inside a cylindrical cage to interact with; (viii) an open
rat shoe-box cage, with one-quarter of its depth filled with bed-
ding and with 15 marbles on the top of the bedding that mice are
prone to manipulate and then hide; (ix) a closed rat shoe-box
cage with standard level of bedding containing four pieces of
paper towel to be shredded; (x) a white box with a fixed
‘merry-go-round’ like structure inside; (xi) a metal pot with
holes on the sides for nose poking, and a cover closing the pot;
(xii) a large, white, plastic box with the two angled cylindrical
beams originating on the floor that the mice could climb;
(xiii) a closed mouse shoe-box cage put upside down with 10
strings of rope crossing the top of it creating a net where mice
could walk; (xiv) a white box containing a white PVC tube
with a mirror at one of its ends; (xv) an acoustic chamber with
foam on the wall with a metal plate inside containing jars
filled with small metal bells to produce sound whenever the
mice roll the jars; and (xvi) a large white plastic box with an
angled ramp which ended at a large metal grid that the animals
could climb onto.
Upon completion of the 16 days, the enrichment group was
moved back to standard cages in the colony room with the con-
trol siblings. All mice were then handled again for 90 s a day for
7 days. This ensured that both groups were receiving similar con-
tact with humans and with the surrounding laboratory before the
start of behavioural testing. Also, these 7 days of break would
function as ‘rest’ for mice in the enrichment group to minimize
any differences in metabolic levels between them and mice in
the control group (metabolic differences might arise as a conse-
quence of the environmental experience, and confound results
by affecting the state of attention and of blood glucose levels
during the learning tasks). After this, all mice were tested in a
battery of learning tasks to provide an index of their GCA.
(c) Learning battery to measure general cognitive
ability
All mice were tested on a battery of five learning tasks, presented
in the following order: Lashley maze, passive avoidance, T-maze
alternation, odour discrimination and spatial water maze. Our
group had used these tests in the past to estimate GCA, and
described them in detail elsewhere [15,27]. In each task, we
obtained an outcome variable of learning performance for later
analyses. These variables were meant to capture the differences
in rate of learning among mice, and so only consider a mouse’s
early performance. (As opposed to, for example, considering
performance in all trials, because mice are typically at high
levels of performance during later trials. In this study, we are
interested in learning rates in tasks, not maximum task
performance. In most instances, mice reach comparable levels
of asymptotic performance.) A brief description of each task is
provided below.
In the Lashley maze, mice must navigate four interconnected
alleys to reach a goal box that contains a food reward. This task is
designed to measure the learning of a stable route, involves
egocentric navigation, requires ambulation and has food as moti-
vator. During each of five total trials, we tracked the two types of
errors that could be committed: backtracking, which we define as
a mouse going from one alley opening to the prior alley opening,
and dead end, which we define as a mouse walking past an alley
opening towards a dead end. Between each trial, the mice were
placed back in their home cage for 20 min. We defined the
outcome variable in the Lashley maze as the mean errors
(backtracking and dead end combined) across the first three
trials after acclimation.
In the passive avoidance task, a mousewas confined to a ‘safe’
platform for 5 min, after which the exit door was opened. When a
mouse stepped from the safe platform onto a grid floor (i.e. base-
line latency), it would encounter a 5 s compound aversive
stimulus composed of a bright white light and noise (a loud oscil-
lating tone, or ‘siren’). During the aversive stimulus presentation,
the mice retreat onto the safe platform, where they were then con-
fined for a 5 min interval. At the end of this interval, the door from
the platform was again opened, so that the mouse was again free
to exit the platform (i.e. avoidance latency). This task is designed
to measure the learning of operant avoidance, involves fear,
requires passivity and has aversive light and sound as motivator.
We defined the outcome variable in the passive avoidance as the
ratio of avoidance latency divided by baseline latency. Mice
with better learning should have relatively longer latencies to
step from the platform during the avoidance period.
In the T-maze alternation task, mice must alternate their fora-
ging for a food reward between two arms. This task is designed
to measure the learning of choice alternation, involves attentional
capacity to ignore place preference (and the tendency to return to
the last location of reinforcement), requires ambulation and has
food as motivator. The apparatus was a start arm that intersected
at its extremity with two choice arms, forming a ‘T’ shape. To
help the mice distinguish between arms, one of the arms’ walls
had vertical white stripes, and the other had horizontal white
stripes. If an incorrect choice was made, the animal could correct
its mistake and find the food in the other arm. After the correct
choice was made, we placed the animal back in the start area
where it waited 20 s for the following trial. We administered 2
days of testing with 12 trials per day. We defined the outcome
variable in the T-maze alternation as the trial when a mouse
first made four correct choices in a row. This variable is meant
to capture early learning performance by looking at the begin-
ning of minimum competency in the task. (It is notable that
mice initially tend to return to a location previously reinforced,
and so a streak of four correct alternations is unlikely to be
only due to chance.)
In the odour discrimination task, mice had to use a specific
odour cue (mint) to find food. The task was administered in a
square box, where three of the box’s four corners always con-
tained cups, and the fourth corner served as a start location.
Immediately before each trial, fresh swabs were loaded with
lemon, almond or mint (the target) odorants. This task is
designed to measure the learning of odour discrimination,
involves olfactory stimuli, requires ambulation and has food as
motivator. Each mouse received a total of four trials. After each
trial, we rearranged the location of the food cups, and waited
6 min before another trial. An error was recorded any time a
mouse sampled an incorrect cup, or when it sampled the
target cup without retrieving the available food. We defined
the outcome variable in the odour discrimination as the mean
errors across the first three trials after acclimation.
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In the spatial water maze task (or Morris water maze), mice
are placed in a circular pool of opaque water and can find an
underwater, hidden escape platform using spatial cues for navi-
gation. Once the mice had all four of their paws on the platform,
it was allowed to stay on the platform for 5 s, and then was
removed from the pool for a 20 min inter-trial interval. If a
mouse could not find the platform after 90 s, we placed it on
the platform for 5 s. Each mouse received a total of six trials,
on which the starting location was changed on each trial (thereby
mitigating strategies based on egocentric navigation). This task is
designed to measure the learning of triangulation, involves
spatial navigation, requires swimming and has water immersion
as motivator. We recorded path lengths from the start position to
the platform during each trial as the measure of learning. We
defined the outcome variable in the spatial water maze as the
mean path lengths across the first two trials after acclimation.
(d) Statistical analyses
For all the tasks in the learning battery, we defined univariate
outliers as any values above or below two interquartile ranges.
We then applied the technique known as ‘bring it to the fence’
to modify the outliers to values at either the lower fence (first
quartile minus twice the interquartile range) for low outliers or
the upper fence (third quartile plus twice the interquartile
range) for high outliers [28]. We also tested all data for the pres-
ence of kurtosis and skewness to check if the variables
conformed to a normal distribution. These pre-analyses were
all done in SPSS 24. We treated missing data by estimating
values for each case with multiple imputation, a technique that
estimates missing data points based on the observed data. Mul-
tiple imputation provides less biased information than simpler
procedures for dealing with missing data such as listwise
deletion, pairwise deletion or imputation of means [29].
Each mouse’s value of learning performance was determined
for each of the learning tasks. Using an exploratory factor analysis,
a statistical method that is used to explore underlying factors cap-
turing the common covariation among variables, we assessed
individual differences in learning on all the tasks. An exploratory
factor analysis captures only the variance shared in common
between the variables, and therefore is ideal to reveal a common
construct influencing learning in all tasks (as opposed to tech-
niques such as principal component analyses that capture both
shared and non-shared variance, and that are better suited for pur-
poses of dimension reduction). From this analysis, each animal
was then assigned a factor score, which represents their GCA, or
intelligence score. In principal, our primary factor could have cap-
tured a common influence other than ‘general cognitive ability’,
such as exploratory tendencies, anxiety or stress reactivity.
While this is always a possibility, extensive prior analyses measur-
ing these traits suggest that ‘non-cognitive’ influences load onto
secondary factors independent of the primary factor [30,31].
We performed a parallel analysis in SPSS 24 to verify if the
GCA factor we obtained has meaningful exploratory value, by
contrasting its eigenvalue with a ‘meaningless’ eigenvalue
based on random data (1000 datasets) that recreate the same par-
ameters (five variables, n¼231) [32]. We also performed a
confirmatory factor analysis to test our assumption that there is
a single factor (GCA) explaining the common variance between
learning tasks of the battery. We used the maximum-likelihood
estimation in AMOS 24 to acquire the solution for the model.
We assessed model fit by using two absolute indices—model
x
2
(
x
2
M) and root mean square error of approximation (RMSEA).
For
x
2
M, the null hypothesis is the model itself, so failing to
reject it indicates a good fit [33]. Similarly, RMSEA values of
0.06 and below are considered good [34]. In addition to these
two absolute indices, we also assessed model fit with an incre-
mental index, the comparative fit index (CFI), which indicates
an adequate model fit at values of 0.95 or above [34]. We chose
these tests due to their statistical relevance and frequent use [33].
We used the framework of linear mixed models in SPSS 24
for all further analyses. We estimated the heritabilities of GCA
scores from both groups (enrichment and control) combined as
well as from each group separately. We followed the classic
full-sibling formulas by Falconer to obtain full-sib heritability
(h
FS
), its standard deviations (
s
h
FS
) and significance [35]. We
obtained the terms
s
2
Fand
s
2
w(and consequent full-sibling intra-
class correlation) from a mixed model with only a random effect
of sibling families. All the variance explained by this random
effect is
s
2
F, and thus represents genetic factors due to different
parental origin (which also includes any shared early environ-
ment effect, such as the womb environment, as we discuss
later). Meanwhile, all the residual variance is
s
2
w.
hFS ¼2
s
2
F
ð
s
2
Fþ
s
2
wÞand
s
hFS ¼22½1þðn1Þt2
nðn1ÞðN1Þ
()
1=2
,
where h
FS
is the full-sibling heritability,
s
h
FS
the standard devi-
ation of the full-sibling heritability,
s
2
Fthe difference between
the siblings of different families,
s
2
wthe difference between
siblings within a family, nthe number of individuals per
family, Nthe number of families, tthe full-sibling intraclass
correlation: 1
2h
FS
.
We also used the framework of linear mixed models in SPSS
24 to test for environmental effects in GCA scores. The model
included the group treatments as a fixed effect (i.e. independent
environmental effect), and sibling families as a random effect (i.e.
independent genetic effect), and was estimated with maximum
likelihood using an unstructured covariance structure. Lastly,
we used the linear mixed models framework to test for gene–
environment interactions in GCA. To accomplish that, we com-
pared a baseline mixed model with sibling families as a
random intercept (i.e. allowing for different family values of
GCA between control and environment group treatments; in
other words, a model with independent effects) against a
mixed model with sibling families as a random intercept and
group treatment as a random slope (i.e. allowing for different
rates of change between control and enrichment treatments in
GCA in each different genetic family; in other words, a model
with gene–environment interactions). We compared these two
models using a likelihood ratio (LR)
x
2
difference test [36]. An
LR test compares nested models, and here it will test if the
addition of the random slopes (gene –environment interaction
model) to the random-intercepts model (independence model)
results in a significantly improved fit.
3. Results
(a) Descriptive statistics
By examining all learning variables for the presence of univari-
ate outliers, we found up to eight cases of outliers in each of the
variables for Lashley maze, passive avoidance, T-maze and
odour discrimination. We did not find any outliers for spatial
water maze. We treated the outliers using the technique
‘bring it to the fence’ as described in Material and methods.
We also tested all variables for skewness and kurtosis, and
all variables had values of skewness and kurtosis well within
the recommend range for normality (22 and þ2).
Less than 8% of the whole sample was missing (due to
mice’s natural death/illness and to experimenter’s error
during tests), and that data were missing at random, Little’s
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MCAR test:
x
2
¼20.51, d.f. ¼19, p¼0.364. As described in
Material and methods, we estimated values for each missing
case by using multiple imputation.
The means and standard deviations for all learning vari-
ables used in the subsequent analyses are shown in table 1.
(b) Factor analyses of general cognitive ability
The unrotated exploratory factory analysis of the perform-
ance data for the five learning tasks of the learning battery
isolated a factor that accounted for a total of 19.5% of the
variance in performance (table 2). That is equivalent to
accounting for 28% of the total variance from a principal
component analysis, a value similar to what we have found
in the past. Performance from all of the learning tasks
loaded consistently on this factor, and in the same direction.
We used this first factor from the exploratory factor analysis,
then, to extract factor scores to represent the mice’s GCA. The
parallel analysis showed that the eigenvalue of our factor
(eigenvalue ¼0.98, n¼231) is greater than the eigenvalue
of a randomly created factor (eigenvalue ¼0.21, n¼231),
which suggests that GCA as a factor has meaningful explora-
tory value. We also performed a confirmatory factor analysis
to ensure that the measured variables of learning would form
a coherent latent variable. The model from the confirmatory
factory analysis had a good fit to the data (
x
2
M¼7:04, d.f. ¼
6, p¼0.317; RMSEA ¼0.03; CFI ¼0.96), with all measured
variables having significant factor loadings (p,0.05).
(c) Heritabilities of general cognitive ability
We estimated the heritabilities for each individual learning
task, as well as for GCA derived from the exploratory
factor analysis described above (table 1). The heritability for
all mice combined was moderate –low, with a value of 0.24,
n¼231, p¼0.017. By contrast, the mice from the enrichment
group expressed a heritability of 0.15, and was not signifi-
cantly different from zero, n¼115, p¼0.284, while the
mice in the control group had a moderate– high heritability
of 0.55, n¼116, p¼0.017. Thus, environmental enrichment
was associated with a decrease in the estimate of the heritability
of animals’ GCA.
(d) Environmental effects on general cognitive ability
The means and standard deviations of GCA scores in all
mice, in enrichment group, and the control group can be
seen in table 1. The linear mixed model (with group treat-
ments as a fixed effect, and sibling families as a random
effect) revealed a significant effect of group treatment on
mice’s GCA (t¼3.69, p,0.001). The estimate of the model
showed an effect size of 0.39 (s.e. ¼0.11). That represent a
gain in 0.44 standard deviations in GCA for the mice in the
enrichment group. This result suggests that experience with
the enriched environment had a positive influence on
animals’ overall cognitive performance.
We also checked for the existence of gene–environment
interactions by comparing a model with only a random
Table 1. Means, standard deviations, heritabilities (h
FS
) and standard deviations of heritability (
s
h
FS
) for all outcome variables of the learning battery, as well
as the extracted variable of GCA (factor scores, where ‘0’ is the anticipated median; values above 0 reflect performance better than the median) in all mice, and
in each group separately.
variable mean s.d. heritability s.d. of heritability
Lashley maze
(mean errors across trials 1–3)
11.73 4.78 0.27 0.15
passive avoidance
(ratio of avoidance latency by baseline latency)
1.92 0.96 0.16 0.13
T-maze alternation
(trial at first four correct choices in a row)
9.61 8.04 0.08 0.12
odour discrimination
(mean errors across trials 1–3)
5.73 4.57 0.20 0.14
spatial water maze
(mean path length in cm across trials 1– 2)
1214.91 606.15 0.18 0.14
general cognitive ability—all mice
(scores extracted from EFA of all learning tasks)
0 0.89 0.24 0.15
general cognitive ability—enrichment group 0.20 0.89 0.15 0.29
general cognitive ability—control group 20.20 0.85 0.55 0.34
Table 2. Factor loadings and variance explained by the first factor (general
cognitive ability, or intelligence) extracted from the five learning tasks
using an exploratory factor analysis. n¼231.
learning task general cognitive ability
Lashley maze 0.89
passive avoidance 0.33
T-maze 0.22
odour discrimination 0.15
spatial water maze 0.11
eigenvalue 0.98
proportion of common variance 19.5%
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intercept of sibling families (independent effects) with a
model with a random intercept of litter family and a
random slope of group treatment (gene –environment inter-
action effects). The difference in LR test statistic between
the models was 0.22, with degrees of freedom ¼2. The p-
value of this statistic was 0.896, which is not significant,
and where the null hypothesis is the simpler model. The
LR test indicates, therefore, that there was no gene–environ-
ment interaction because the independent effects model is
more parsimonious and explains the data equally well.
4. Discussion
Here, we found that GCA in mice has substantial heritability
and malleability. Overall, mice siblings had much more similar
intelligence scores, i.e. siblings were more similar to one
another. Mice that were exposed to enriched environments
and physical exercise exhibited better performance than their
siblings in a control group (that were maintained in the stan-
dard laboratory environment). To our knowledge, this is the
first study to show in any non-human animal that a trait analo-
gous to human’s general intelligence is both substantially
heritable and malleable. We also found that heritability itself
changed between groups, a result that, as some researchers
argue, sometimes can reflect gene–environment interactions.
Unexpectedly, however, our tests showed no gene – environ-
ment interactions in our study. A closer examination of these
results might help to clarify all of these conclusions.
Similar to our previous work, there was a positive corre-
lation of each mouse’s rate of acquisition across all learning
tasks. The GCA factor accounted for 19.5% of the common
variance in mice’s performance. This is equivalent to account-
ing for 28% of the total variance from a principal component
analysis, which is similar to what we have reported pre-
viously [16]. Relatedly, in prior work, we have determined
that this general factor is unrelated to differences in stress
reactivity, fear or anxiety [30,31]. Here, a parallel analysis
showed that the first factor from the exploratory factor analy-
sis had exploratory value much above one from a random
dataset. Furthermore, a confirmatory factor analysis also
revealed a good fit of the model with a single latent variable
influencing all learning tasks of our battery, and the loadings
were all significant. At first glance, 19.5% might seem a low
value for a general cognitive factor in comparison to typical
values of 50% in humans. Note, however, that the cognitive
tasks composing modern human IQ tests are the result of a
slow and gradual intentional selection for tasks that load
well with others [37]. Tasks that had poor correlations with
other tasks were changed or removed over the decades. The
mouse learning battery we used here is not the culmination
of a similar process, and thus reflect less of that ‘bias’. In
fact, the learning tasks in our battery were designed to be dis-
tinct in many parameters (described in Material and
methods), and so the existence of a single factor that explains
one-fifth of that variance is rather striking.
The present study combines a full-sibling design with a
procedure loosely analogous to a human adoption study, or
a randomized clinical trial, or school intervention. Two of
the siblings in a litter of four were removed from their
usual environment and experienced a new, more complex
environment. In contrast with human adoption studies,
here we had direct control of the environment into which
some siblings were immersed. We found that mice in the
environmental enrichment condition had GCA scores 0.44
standard deviations higher than their peers in the control
group. In humans, this difference would represent 6.6 IQ
points, which is a substantial and functionally important
gain. At first glance, the gains we found might seem large
in response to an ‘intervention’ which lasted for only 16
days. However, mice’s typical life-span is less than 2 years,
and a substantial part of their development occurs during
the first 10 weeks of life, at which point they have reached
sexual maturity. At birth, mice are hairless, blind, deaf,
have minimal motor skills and are fully dependent on their
mother, while by the sixth week, mice are already fully func-
tional, with fine motor skills, a broad and complex repertoire
of social behaviour and a remarkable capacity for learning
[38]. In that context, 16 days of environmental enrichment
during this maturation phase of development are probably
quite meaningful. And, our environmental enrichment
included substantial physical exercise. In total, this enrich-
ment protocol was a dramatic intervention relative to the
standard treatment of isolated laboratory mice.
Numerous theories have been proposed to account for the
beneficial effect of environmental enrichment on cognition.
Among them, the ‘learning and memory’ hypothesis seems
to be favoured. This theory holds that when animals are con-
fronted with novelty and environmental complexity, there are
physiological and morphological changes that impact the
mechanisms that underlie learning [25]. Physical exercise
alone, however, stimulates synaptogenesis and neurogenesis,
but does not seem to promote improvements in general intel-
ligence [39]. This can be explained by the ‘use it or lose it’
paradigm in neuroscience: new neurons need to be recruited
for a specific cognitive function if they are to last beyond a
few days [40]. Therefore, if physical exercise had an influence
on the intelligence gains that we found here, it is likely to
have been a synergistic influence combined with the exposure
to novel and complex stimuli. In past research, for example,
we found that physical exercise alone did not improve
mice’s GCA, but when physical exercise was combined
with cognitive training, the treatment had a greater effect
than cognitive training alone [41].
Instead of (or in addition to) the conclusion that GCA was
helped by environmental enrichment, it is possible that GCA
was harmed by adverse environments (such as the sterile
home conditions encountered by our control group). In typi-
cal laboratory conditions (as experienced by the control
mice), mice are inhabiting an environment not expected by
natural selection, while in our enrichment condition, mice
encounter an environment that is a little closer to what
their genotypes might be adapted to. Note, however, that
not everything that is ‘natural’ is helpful, and not everything
that is ‘artificial’ leads to harm. Mice in the laboratory
environment have free and guaranteed access to food,
water and shelter. In the wild, they do not. These ‘artificial’
experiences might reasonably be expected to help in promot-
ing cognitive performance. However, laboratory mice are also
deprived socially, are deprived sexually, are deprived from
exploration, and from physical exercise (among other
things). These ‘artificial’ experiences might reasonably be
expected to harm cognitive performance. This leads to the
question: which set of experiences matter more, the experi-
ences that help, or the experiences that harm? We cannot
answer this question with our current analysis. Nonetheless,
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our results here show that GCA is substantially malleable—
being helped by enrichment and/or harmed by adverse
environments. We hope that future studies can more directly
address the help/harmed distinction.
When considering all mice as part of one population, our
estimate of heritability was 0.24 (or 24%), a moderate–low
range comparable to the value (between 0.34 and 0.42)
obtained by the only other study that estimated heritability
of general intelligence in mice [7]. Note that both ours and
this other study used sibling designs, and so the family effect
we interpret as ‘genetic’ also include any shared early environ-
ment effect, such as in the womb. Because of that, our
heritability estimate is an upper-limit heritability, and will
reflect maximum genetic influences in the case of minimum
maternal/litter effects [35]. In other animals ( primarily pri-
mates), estimates of GCA tend to be moderate with values
ranging from 0.3 to 0.6 [1]. Like ours, some of these primate
studies also using sibling designs, and hence the estimates
that they generate reflect upper-limit heritabilities.
The separate learning tasks in our study had similar
heritabilities compared with GCA and among each other
(with the exception of the T-maze alternation). This result,
combined with the good model fit of a single factor explain-
ing the variation in the learning tasks, supports the view of a
general influence on cognitive abilities. By contrast, Sorato
et al. (this edition) [42] found no covariation between
discriminative and reversal learning tasks, which, as the
authors argue, support the view of independent modularity.
Of course, these two views are not mutually exclusive, as
domain-specific cognitive abilities (modularity) might
share domain-general processes (general intelligence) [1].
More empirical evidence will be critical to determine
where in the modularity spectrum particular species and
environments fall.
When considering mice in the study as part of two differ-
ent populations, the enrichment group had an estimated
heritability not significantly different from zero, while mice
in the control group had a moderate heritability of 0.55. Inter-
estingly, estimates of the heritability of intelligence in
humans also seem to change across environments. A recent
meta-analysis by Tucker-Drob et al. [43] showed that among
affluent families, most of IQ’s variation was associated with
genetic variation (heritability of 0.70). However, among the
poorest families, the reverse was true: most of variation in
IQ was associated with the shared familial environment,
and little of the variation was attributable to genetic
variation (heritability of 0.10). Those authors and others
argue that changes in heritability are likely to be cases of
gene–environment interactions; what is sometimes described
as the bioecological model of intelligence [44].
Note, however, that the direction of the changes in herit-
ability that we observed here (in response to environmental
enrichment) were opposite those that would be expected
based on studies of humans. Mice exposed to the more com-
plex environment had a lower estimated heritability than mice
maintained in the more sterile environment. In human popu-
lations, gene–environment interactions in wealthy groups are
believed to inflate the estimates of heritability [8]. Typical
methods in quantitative genetics usually give priority to
genetics, and so gene–environment interactions are counted
as independent genetic effects [4]. In our study, however,
there was no gene–environment interaction (discussed
below). A possible explanation for our results is that mice
in the enrichment group showed lower heritability because
of more independent environmental variance, while indepen-
dent genetic variance remained the same. Because the
enrichment group had a more complex environment than
the control group, this has the potential of decreasing the
estimate of heritability of the enrichment group. Regardless
of its source, the present results highlight the sensitivity of
estimates of heritability to the environment in which the
estimate is obtained.
To our surprise, there was no interaction between family
(genetic) effects and group (environment) effects. Even though
we did not find direct evidence for gene– environment
interactions, it is important to note that gene– environment
correlations might still have exerted some influence, given
our finding that heritability changed across the two environ-
ments. To directly test for these correlations and the
‘snowballing’ influence that they can foster, however,
would require us to specify what environmental factors influ-
ence intelligence, and also restrict individuals with particular
genes to get more/less of specific environments without cor-
relating it with confounding factors. Of course, this is much
more feasible to be tested in non-human, laboratory animals
and future studies might well follow such a strategy.
The results here could help laying the groundwork for
future studies identifying specific genes, neural and develop-
mental mechanisms associated with GCA, as well as the
development of interventions to improve cognition. A clear
understanding of the causes of variation of intelligence is
also critical for us to know how different species adopted
different cognitive capacities, what the related selective press-
ures were and how intelligence differs across populations
or species.
Ethics. All experiments were conducted in accordance with protocols
approved by the Rutgers University IACUC.
Data accessibility. All data are available at: Research Gate repository
http://dx.doi.org/10.13140/RG.2.2.25565.10723. All raw data are
available at https://www.researchgate.net/publication/323128667_
Data_-_Heritability_and_Malleability_of_mouse_intelligence_2018.
Authors’ contributions. B.S.: conception and design, acquisition of data,
and analysis and interpretation of data; drafting the article and revis-
ing it critically for important intellectual content; and final approval
of the version to be published. S.B.: (i) conception and design, and
acquisition of data; (ii) revising the article critically for important
intellectual content; and (iii) final approval of the version to be pub-
lished. M.H.: (i) conception and design, and acquisition of data;
(ii) revising the article critically for important intellectual content;
and (iii) final approval of the version to be published. D.S.: (i) acqui-
sition of data; (ii) revising the article critically for important
intellectual content; and (iii) final approval of the version to be pub-
lished. C.S.: (i) acquisition of data; (ii) revising the article critically for
important intellectual content; and (iii) final approval of the version
to be published. S.R.: (i) acquisition of data; (ii) revising the article
critically for important intellectual content; and (iii) final approval
of the version to be published. J.K.: (i) acquisition of data; (ii) revising
the article critically for important intellectual content; and (iii) final
approval of the version to be published. L.D.M.: (i) conception and
design, acquisition of data, interpretation of data; (ii) revising the
article critically for important intellectual content; and (iii) final
approval of the version to be published.
Competing interests. We declare we have no competing interests.
Funding. This work was supported by grants from the National Insti-
tute of Mental Health (R03MH108706), the Busch Foundation and
the Office of Naval Research (N000141210873).
Acknowledgements. Our thanks to Tracey Shors, Edward Selby and
Christopher Chabris for the helpful suggestions and comments on
this work.
rstb.royalsocietypublishing.org Phil. Trans. R. Soc. B 373: 20170289
7
on August 21, 2018http://rstb.royalsocietypublishing.org/Downloaded from
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