Content uploaded by Louis D Matzel
Author content
All content in this area was uploaded by Louis D Matzel
Content may be subject to copyright.
Behavioral/Systems/Cognitive
Individual Differences in the Expression of a “General”
Learning Ability in Mice
Louis D. Matzel,
1
Yu Ray Han,
1
Henya Grossman,
1
Meghana S. Karnik,
1
Dave Patel,
1
Nicholas Scott,
1
Steven M. Specht,
2
and Chetan C. Gandhi
3
1
Department of Psychology, Program in Behavioral Neuroscience, Rutgers University, Piscataway, New Jersey 08854,
2
Department of Psychology, Utica
College, Utica, New York 13502, and
3
Department of Neurobiology, Yale University School of Medicine, New Haven, Connecticut 06510
Human performance on diverse tests of intellect are impacted by a “general” regulatory factor that accounts for up to 50% of the variance
between individuals on intelligence tests. Neurobiological determinants of general cognitive abilities are essentially unknown, owing in
part to the paucity of animal research wherein neurobiological analyses are possible. We report a methodology with which we have
assessed individual differences in the general learning abilities of laboratory mice. Abilities of mice on tests of associative fear condition-
ing, operant avoidance, path integration, discrimination, and spatial navigation were assessed. Tasks were designed so that each made
unique sensory, motor, motivational, and information processing demands on the animals. A sample of 56 genetically diverse outbred
mice (CD-1) was used to assess individuals’ acquisition on each task. Indicative of a common source of variance, positive correlations
were found between individuals’ performance on all tasks. When tested on multiple test batteries, the overall performance ranks of
individuals were found to be highly reliable and were “normally” distributed. Factor analysis of learning performance variables deter-
mined that a single factor accounted for 38% of the total variance across animals. Animals’ levels of native activity and body weights
accounted for little of the variability in learning, although animals’ propensity for exploration loaded strongly (and was positively
correlated) with learning abilities. These results indicate that diverse learning abilities of laboratory mice are influenced by a common
source of variance and, moreover, that the general learning abilities of individual mice can be specified relative to a sample of peers.
Key words: intelligence; general intelligence; fluid intelligence; associative learning; memory; spatial learning; emotional learning; learn-
ing systems; genetic variation; behavioral phenotypes
Introduction
A “general” influence on humans’ performance across diverse
tests of cognitive abilities has been described as the most dominant
and one of the most heritable cognitive traits ever identified (Plomin,
1999; Plomin and Spinath, 2002). Although general cognitive abili-
ties are vigorously studied in human populations (for review, see
Jensen, 1998; Mackintosh, 1998), comparable studies of nonhuman
animals have been infrequent. Nevertheless, the topic has generated
attention within the broader neuroscience community (Plomin,
1999, 2001; Matzel and Gandhi, 2000; Gray et al., 2003). Given this
emerging interest, the underrepresentation of this work in studies of
laboratory animals is unfortunate, given the utility of such subjects
for the elucidation of the brain substrates for individual differences
in learning and intellect.
An individual’s proficiency on any test of mental ability re-
flects domain-specific attributes as well as a domain-independent
general influence on cognitive function (Sternberg and Kaufman,
1998). Although this conclusion has been based primarily on
studies of “intelligence,” subjects’ performance on intelligence
tests typically covary with performance on explicit tests of learn-
ing (Kolligian and Sternberg, 1987; Carroll, 1993). Although it is
thus likely that a general factor influences animals’ performance
on tests of learning, tasks presumed to impinge on specific do-
mains (e.g., “spatial” memory, “emotional” memory, “reflex”
memory) are typically the focus of investigators interested in un-
derlying brain mechanisms (Squire and Zola-Morgan, 1991;
Lavond et al., 1993; LeDoux, 2000; Gilbert et al., 2001). Despite
their utility for this purpose, these disparate tasks are not directly
useful for the estimation of animals’ general learning abilities,
because an unspecified proportion of the behavioral variance on
a particular task is attributable to such a factor. Thus a need exists
for practical and conceptually valid methods to quantify the gen-
eral learning abilities of animals. Such methods are imperative to
evaluations of individual differences in learning but are also nec-
essary to assess the effects on learning of manipulations (e.g.,
pharmacological or transgenic) presumed to impinge on cogni-
tive abilities. In the absence of a more systematic approach, such
efforts have relied on behavioral results obtained in different lab-
oratories, each using unique learning tasks with no deliberate
consideration of their unique or common properties (Staubli et
al., 1994; Shors et al., 1995; Hampson et al., 1998; Tang et al.,
1999).
Received Dec. 11, 2002; revised March 28, 2003; accepted April 1, 2003.
This work was supported by a Busch Foundation Award to L.D.M. Thanks are extended to Drs. Tracey Shors, Ralph
Miller, and Mike Galsworthy for discussions relevant to the development of these experiments, and to Randy Gallis-
tel, Ronald Gandelman, and Alex Kusnecov for their comments on an earlier version of this manuscript.
Correspondence should be addressed to Louis D. Matzel, Department of Psychology, Program in Biopsychology
and Behavioral Neuroscience, Rutgers University, Busch Campus, Piscataway, NJ 08854. E-mail:
matzel@rci.rutgers.edu.
Copyright © 2003 Society for Neuroscience 0270-6474/03/236423-11$15.00/0
The Journal of Neuroscience, July 23, 2003 •23(16):6423– 6433 • 6423
Attempts to isolate general learning abilities in individual lab-
oratory animals have been rare (cf. Galsworthy et al., 2002) and
results have been inconsistent (Locurto and Scanlon, 1998). Here
we report a methodology with which to characterize the general
learning abilities of individual mice on the basis of their perfor-
mance on a battery of learning assays. Tasks in this test battery
isolate basic learning skills that are presumed to underlie a broad
range of more complex forms of learning. Given their rudimen-
tary nature, learning on these tasks can be precisely quantified,
and the idiosyncratic properties of the tasks can be specified.
Tasks were designed on the basis of four considerations. (1) Task
diversity: tasks make different sensory, motor, motivational, and
information processing demands on the animal. (2) (Non)-
transfer of learning: tasks were designed such that an animal’s
experience with one task would not obviously impinge on its
performance on other tasks in the battery. (3) Time constraints:
to reduce any differential (between-animal) impact of the passage
of time (e.g., aging, cycles), tasks were designed so that the entire
battery could be administered in 16 d. (4) Sensitivity to variability
between animals: critically, we intended to assess learning during
acquisition, mitigating any differential influence of animals’ca-
pacity for long-term retention and ensuring sensitivity to real
differences between animals that might otherwise be obscured in
measures of asymptotic performance.
Here we report results obtained from a sample of 56 outbred
CD-1 adult male mice (and a matched sample of 8 animals that
contributed to relevant control procedures). Additionally, rudi-
mentary measures of exploration, activity, motor performance,
emotionality, and body weight were obtained. In combination
with the analysis regimen that we describe, we were able to esti-
mate that proportion of the variance between animals that is
uniquely attributable to a general influence on learning/cognitive
abilities.
Materials and Methods
Subjects
A sample of 64 male CD-1 mice (Harlan Sprague Dawley) were 80 –86 d
old at the start of experimentation. Fifty-six animals contributed to our
analysis of general learning abilities, and eight animals served in control
procedures. CD-1 animals exhibit considerably more between-animal
behavioral variability than several inbred strains that we have tested with
similar procedures. Animals were trained and tested in five independent
replications (n⫽8, 8, 8, 12, and 20). Two of the replications (n⫽8) were
composed of animals obtained from a single shipment, whereas the re-
maining replications were composed of animals drawn from separate
shipments. In the descriptions of individual tasks, the performance of
subjects 9 –16 is described, and these animals were trained and tested
concurrently with the eight subjects that served in various control
procedures.
Animals were acclimated to our laboratory for 20–26 d before testing
and were handled for 90 sec/d, 5 d/week during this period. This handling
ensured that differential stress responses to the experimenters and any
associated effects on learning were minimized. Animals were individually
housed in clear boxes with floors lined with wood shavings in a humidity-
and temperature-controlled vivarium adjacent to testing rooms. A 12 hr
light/dark cycle was maintained.
Behavioral training and testing
A total of 56 animals were tested on five learning tasks and in an open
field. The processes that are commonly asserted to underlie each task,
relevant stimuli, deprivation state, and relevant motor requirements are
summarized in Table 1. After the completion of each task, animals re-
ceived1dofrest. With 1–3 d required for each task, the entire test
regimen was completed (for each replication) in 16 d. With the exception
of fear conditioning and passive avoidance, the performance of all ani-
mals was stored on video tape and behavior was scored off-line. Different
experimenters were responsible for training and testing animals in each
of the five learning tasks, and no experimenter was aware of animals’
performance on other tasks until after the completion of the entire bat-
tery of tests.
Before testing on any task, the test chambers were “primed”by expos-
ing two nonexperimental animals to the apparatus and procedures. This
was intended to standardize the apparatus such that the first animals in a
test cycle encountered a chamber that was nominally identical (e.g., in
odor) to that experienced by subsequently tested animals. The surfaces of
every piece of apparatus were cleaned with a mild alcohol solution after
removal of every subject from the apparatus.
For the two tests requiring food deprivation, ad libitum food was re-
moved from the animals’home cages at the end of the light cycle on the
day before the rest day that preceded the start of training. During the
deprivation period, animals were provided with food in their home cages
for 90 min/d during the last 2 hr of the light cycle, and thus they were food
deprived for ⬃16 hr at the time of training or testing. Although mild, this
level of deprivation was sufficient to maintain stable performance on
these tasks. In the one task that required water deprivation, the same
schedule was followed except that ad libitum access to water was limited
to 60 min per day.
So that the time of day did not differentially impact animals’perfor-
mance, all animals were trained and tested during the middle 7 hr of the
light cycle, and procedures were administered to animals with as little
temporal dispersion as possible. All animals were trained and tested un-
der conditions that were as similar as possible.
Open-field exploration. A square field (46 ⫻46 cm) with 13-cm-high
walls was constructed of white Plexiglas and located in a brightly lit room
(400 Lux) with a background noise of 65 dB
c
. The field was conceptually
divided into a grid composed of 6 ⫻6 7.65 cm quadrants in which 20 of
the quadrants abutted the outer walls of the field (i.e., “wall”quadrants)
and 16 quadrants were displaced from the walls and composed the inte-
rior (i.e., “open”quadrants) of the field.
Animals were placed in the center of the field. After 20 sec had elapsed
(during which the animals self-selected a starting location), the animals’
behavior was monitored for 4 min. Throughout this time the animal’s
entries into wall and open quadrants were recorded. An entry was re-
corded whenever both front paws crossed the border of a quadrant.
Additionally, animals’running speed was estimated. In the open field,
rodents often exhibit “bursts”of uninterrupted running, typically along
the walls of the field. Here, running speed was calculated as those in-
stances in which an animal ran continuously (i.e., without stopping,
rearing, or overt head turning) along an outer wall (from corner to cor-
ner) of the field, but only on those instances in which the animal began
from a stationary start in one corner. Four such episodes were recorded
Table 1. Summary of task variables
Process Test stimulus Motor requirement
Organic
deprivation Reinforcer
Lashley maze Operant approach/egocentric navigation Egocentric/visual Ambulation Food BioServ Pellet (⫹)
Passive avoidance Operant avoidance Place Passivity None Noise/light (⫺)
Spatial water maze Operant escape/allocentric navigation Extramaze/visual Swimming None Water immersion (⫺)
Odor discrimination Discrimination Olfactory Cue-directed ambulation Food Rice (⫹)
Fear conditioning Association formation Auditory Suppression Water Foot shock (⫺)
6424 •J. Neurosci., July 23, 2003 •23(16):6423–6433 Matzel et al. •General Learning Abilities
for each animal during the last 3 min of the test interval (such bursts are
infrequent during the first minute of exposure), and the average of these
four instances served as the index of each animal’s running speed (cen-
timeters per second). (Because rates varied between bursts of running,
multiple instances were averaged to provide a more accurate estimate of
each animal’s“typical”rate. Four such instances were averaged because it
was determined that no animal in our sample made fewer than four
bursts of running that satisfied our criterion for inclusion.)
We have analyzed animals’open-field behavior in 1 min blocks but
have observed no systematic pattern of change across the 4 min of testing,
so here data are reported as the sum of the 4 min test. It should be noted
that a 4 min test was explicitly chosen (on the basis of pilot work) so that
appreciable changes in behavior (e.g., that which accompanies habitua-
tion) were not observed over time (as may occur during longer periods of
exposure to the field). This was intended to ensure that open-field per-
formance was most sensitive to unlearned behavioral tendencies.
Lashley III maze. The Lashley III maze consists of a start box, four
interconnected alleys, and a goal box containing a food reward. Over
trials, the latency of rats to locate the goal box decreases, as do their errors
(i.e., wrong turns or retracing). Lashley asserted that rats’performance in
this maze reflected a sequence of learned motor responses that were
dependent on egocentric navigation. Although there was much debate
associated with Lashley’s interpretation of rats’performance, it is con-
ceded that under certain conditions, animal’s rely heavily on fixed motor
patterns to navigate such a maze, a strategy that differentiates this per-
formance from that in the spatial water maze (task 4 below).
Here, the Lashley III maze was scaled for mice (as illustrated in Fig. 1),
and parameters were developed that supported rapid acquisition. The
maze was constructed of black Plexiglas.A2cmwide ⫻0.1 cm deep
white cup was located in the rear portion of the goal box, and 45 mg of
BioServe (rodent grain) pellets served as reinforcers. Illumination was 80
Lux at the floor of the maze. The maze was isolated behind a shield of
white Plexiglas to mitigate against extra-maze landmark cues.
Food-deprived animals were acclimated and trained on 2 successive
days. On the day before acclimation, all animals were provided with three
food pellets in their home cages to familiarize them with the novel rein-
forcer. On the acclimation day, each mouse was placed in the four alleys
of the maze, but the openings between the alleys were blocked so that the
animals could not navigate the maze. Each animal was confined to the
start and subsequent two alleys for 4 min and for 6 min in the last (goal)
alley, where three food pellets were present in the food cup. This accli-
mation period promotes stable and high levels of activity on the subse-
quent training day. On the training day, each animal was placed in the
start box and allowed to traverse the maze until it reached the goal box
and consumed the single food pellet present in the cup. After it consumed
the food, the animal was returned to its home cage for a 20 min interval
(ITI), after which it was returned to the start box to begin the next trial.
The apparatus was cleaned during each ITI, and the sequence was re-
peated for five trials. Both the latency and errors (i.e., a turn in an incor-
rect direction, including those that result in path retracing) to enter the
goal box were recorded on each trial.
Typically, on the first trial animals enter the goal box within 100–300
sec and make 15–25 “errors”before retrieving the food. On subsequent
trials, performance improves markedly. For purposes of ranking animals,
the average of performance on trials 3 and 4 served as the index of learn-
ing for each animal. We have adopted the practice of averaging behavior
over two trials to better represent animals’performance.
One-trial passive avoidance. Animals learn to suppress movement to
avoid contact with aversive stimuli. This “passive avoidance”response is
exemplified in step-down avoidance procedures, during which, com-
monly, an animal is placed on a platform whereupon it encounters a foot
shock when it steps off the platform. After just a single encounter with
shock, animals are subsequently reluctant to step off the safe platform.
The animals’reluctance to leave the platform is believed to not reflect
fear, because typical fear responses are not expressed in animals engaged
in the avoidance response (Bolles, 1969; Morris, 1974). We intended that
the tasks that comprise our test battery each use unique stimuli to moti-
vate responding. To not duplicate stimuli (i.e., shock) used to support
associative learning in task 6, here we use a variant of the step-down
avoidance task that does not rely on shock to motivate behavior. After
they step off the platform, animals are exposed to a compound of bright
light, noise, and vibration. Like more common procedures, our variant of
this task supports learning after only a single trial (i.e., subsequent step-
down latencies are markedly increased).
A chamber illuminated by dim (⬍5 fc) red light was used for training
and testing. At the rear of a 16 ⫻12 cm (length ⫻width) white grid floor
was an enclosed platform (70 ⫻45 ⫻45 cm; length ⫻width ⫻height)
constructed of black Plexiglas and closed on all sides except the side
facing the grid floor. The platform floor was 5 cm above the grid floor,
and a black Plexiglas sloping ramp extended 5 cm from the floor of the
platform to the grid floor. The exit from the platform could be blocked by
a remotely operated, clear Plexiglas sliding door. When an animal
stepped from the platform and contacted the grid floor, the compound
aversive stimulus composed of a bright (550 Lux) white light, noise, and
vibration was initiated. Noise and vibration were produced by a flexible
nylon rod attached to a motor outside of an exterior wall of the chamber
such that the rod struck the wall of the chamber twice during each revo-
lution (1400 rpm) of the motor, producing a noise 65 dBa above a 45 dBa
background and a 46 Hz vibration of the chamber surfaces.
Animals were placed on the platform behind the exit blocked by the
Plexiglas door. After 5 min of confinement, the door was retracted and
the latency of the animal to leave the platform and make contact with the
grid floor was recorded. Before training, step-down latencies typically
range from 8 to 20 sec. (This narrow range of baseline latencies reflects
the 5 min of confinement of the animal on the platform, as determined by
preliminary studies.) After contact with the floor, the door to the plat-
form was lowered, and the aversive stimulus (light, noise, and vibration)
was presented for 4 sec, at which time the platform door was opened to
allow animals to return to the platform, where they were again confined
for 5 min. At the end of this interval, the door was opened, and the latency
of the animal to exit the platform and step onto the grid floor (with no
aversive stimulation) was recorded, completing training and testing.
The ratios of post-training to pretraining step-down latencies were
calculated for each animal and served to index learning. In pilot experi-
ments, we determined that asymptotic performance was apparent in
group averages after two to three training trials; thus performance after a
single trial reflects (in most instances) subasymptotic learning.
Spatial water maze. For this task, animals are immersed in a round pool
of opaque water from which they can escape onto a hidden (i.e., sub-
merged) platform. The latency for animals to find the platform decreases
across successive trials. In this task, performance of animals can improve
across trials despite the animals beginning each trial from a new start
location. Such a procedure mitigates against egocentric navigation and
promotes the animals’dependence on extra-maze spatial landmarks. As
demonstrated by Morris (Morris, 1981), rats’performance in the water
maze does not rely on fixed motor patterns (i.e., performance improves
despite the animals irregular starting location) or the presence of discern-
able cues within the maze (e.g., visual, tactile, or olfactory signals). In-
stead, performance is dependent on the stability of extra-maze cues, or
“landmarks,”and is said to reflect the animals’representation of its en-
vironment as a “cognitive map.”
We have developed a protocol in which mice exhibit significant reduc-
tions in their latency to locate the escape platform within six training
trials. Because this is unusually rapid learning in this task, several relevant
modifications of the task should be emphasized. First, animals were con-
fined in a clear Plexiglas cylinder on the safe platform for 5 min on the day
before training. Second, a considerably longer ITI (10 min) was used than
is typical (90 sec). Last, the water in the maze was cooled (with submerged
tubes of circulating refrigerant) to 15°C (in a 22°C room). This latter
modification motivates the mice to remain on the escape platform after
locating it, whereas in room temperature water (22°C), mice often reen-
ter the water and continue swimming immediately after locating the
platform, complicating the interpretation of the animals’behavior.
A round white pool (140 cm diameter, 56 cm deep) was filled to within
20 cm of the top with water made opaque by the addition of a nontoxic,
water soluble, white paint. A hidden 12-cm-diameter perforated white
platform was in a fixed location 1.5 cm below the surface of the water
midway between the center and perimeter of the pool. The pool was
Matzel et al. •General Learning Abilities J.Neurosci., July 23, 2003 •23(16):6423– 6433 • 6425
enclosed within a ceiling-high white curtain on which six different 45- to
65-cm-high black geometric shapes (landmarks) were variously posi-
tioned at heights (relative to water surface) ranging from 90 to 150 cm. A
video camera lens extended through a 30-cm-diameter black circle 180
cm above the center of the water surface.
On the day before training, each animal was confined to the escape
platform for 300 sec. On the subsequent training day, animals were
started from a unique location on each of six trials. (The pool was con-
ceptually divided into four quadrants, and two starting points were lo-
cated in each of the three quadrants that did not contain the escape
platform. The starting point on each trial alternated between the three
available quadrants.) An animal was judged to have escaped from the
water (i.e., located the platform) at the moment at which four paws were
situated on the platform, provided that the animal remained on the
platform for at least 5 sec. Each animal was left on the platform for a total
of 20 sec, after which the trial was terminated. Trials were spaced at 10
min intervals, during which time the animals were held in a warmed
(26.5°C), opaque (5 Lux) box lined with cage paper. On each trial, a 90
sec limit on swimming was imposed, at which time any animal that had
not located the escape platform was placed by the experimenter onto the
platform, where it remained for 20 sec. Animals were observed from a
remote (outside of the pool’s enclosure) video monitor, and animals’
performance was recorded on video tape for subsequent analysis.
Odor discrimination and choice. Rodents learn rapidly to use odors to
guide appetitively reinforced behaviors. In a procedure based on one
designed by Sara (Sara et al., 2001) for rats, mice learn to navigate a
square field in which unique odor-marked (e.g., almond, lemon, mint)
food cups are located in three corners. Although food is present in each
cup, it is accessible to the animals in only one cup (e.g., that marked by
mint odor). An animal is placed in the empty corner of the field, after
which it will explore the field and eventually retrieve the single piece of
available food. On subsequent trials, the location of the food cups are
changed, but the accessible food is consistently marked by the same odor
(i.e., mint). On successive trials, animals require less time to retrieve the
food and make fewer approaches (i.e., “errors”) to those food cups in
which food is not available. We have adapted this procedure for use with
mice, and typically observe errorless performance within three to four
training trials. Control procedures (in which the target odor is not con-
sistent) indicate that odor is the principal determinant of animals’dis-
crimination (i.e., performance does not improve under conditions for
which the target odor is changed across trials).
A black Plexiglas 60-cm-square field with 30-cm-high walls was lo-
cated in a dimly lit (10 fc) testing room with a high ventilation rate (3 min
volume exchange). Three 4 ⫻4⫻2.0 cm (length, width, height) alumi-
num food cups were placed in three corners of the field. A food reinforcer
(30 mg portions of chocolate-flavored puffed rice) was placed in a 1.6-
cm-deep, 1-cm-diameter depression in the center of each cup. The food
in two of the cups was covered (1.0 cm below the surface of the cup) with
a wire mesh so that it was not accessible to the animal, whereas in the
third cup (the “target”cup), the food could be retrieved and consumed.
A cotton-tipped laboratory swab, located between the center and rear
corner of each cup, extended vertically 3 cm from the surface of the cups.
Immediately before each trial, fresh swabs were loaded with 25
lof
either lemon, almond, or mint odorants (McCormick flavor extracts).
The mint odor was always associated with the target food cup. (It should
be noted that in pilot studies, the odor associated with food was counter-
balanced across animals, and no discernible differences in performance
could be detected in response to the different odors.)
On the acclimation day, each food-deprived animal was placed in the
field for 20 min with no food cups present. At the end of that day’s light
cycle, three pieces of chocolate-flavored puffed rice that would subse-
quently serve as the reinforcer were placed in the animals’home cages to
acquaint them with the reinforcer. On the subsequent test day, animals
received four training trials in the field with three food cups present. On
each trial, an animal was placed in the empty corner of the field. On trial
1, the reinforcing food (rice) was available to the animal in the cup
marked by mint odor. On only this trial, an additional portion of food
was placed on the top surface of the same cup. The trial continued until
the animal retrieved and consumed the food from the target cup, after
which the animal was left in the chamber for an additional 20 sec and
then returned to its home cage to begin a 6 min ITI. On trials 2–4, the
location of the food cups was rearranged, but the baited cup remained
consistently marked by the mint odor. Both the corner location of the
mint odor and its position relative to the remaining odors were changed
on each trial.
On each trial, the latency to retrieve the food and errors were recorded.
An error was recorded any time that an animal made contact with an
incorrect cup or its nose crossed a plane parallel to the perimeter of a
incorrect cup. Similarly, an error was recorded when an animal sampled
(as above) the target cup but did not retrieve the available food. In this
task, latency to retrieve food and errors have yielded closely comparable
patterns of results, as indicated both in group means and in the perfor-
mance of individual animals. For the purpose of ranking animals for
analysis, errors served as the dependent measure to avoid the complica-
tion of differences between animals’speed of locomotion.
Associative fear conditioning. In such a procedure, animals are exposed
to a stimulus [i.e., a conditioned stimulus (CS); a tone] that terminates in
the onset of a mild foot shock [i.e., an unconditioned stimulus (US)].
These tone–shock (CS–US) pairings come to elicit conditioned fear re-
sponses when animals are subsequently presented with the tone. This
learned fear can be assessed in various ways. In the present studies, fear
was indexed by CS-elicited suppression of ongoing drinking, because this
measure is quantified easily and precisely. “Lick suppression”is concep-
tually analogous to the more commonly used measure of CS-elicited
generalized “freezing”(i.e., during that time in which an animal freezes it
necessarily is not capable of drinking from a lick tube). In our laboratory,
lick suppression has proven to be of greater utility, given that the gener-
alized freezing exhibited by mice is far less regular (and thus more am-
biguous) than that which we have typically observed in rats. To avoid any
interaction of the training context (which itself acquires an association
with shock) with the CS at the time of testing, training and testing were
conducted in separate distinct contexts.
Two distinct experimental chambers (i.e., contexts; 32 ⫻28 ⫻28 cm,
length⫻width ⫻height) were used, each of which was contained in a
sound- and light-attenuating enclosure. These boxes were designated as
“training”and “testing”contexts and differ as follows. The training con-
text was brightly illuminated (100 Lux), had clear Plexiglas walls, no lick
tube, and parallel stainless steel rods (5 mm, 10 mm spacing) forming the
floor. The test context was dimly illuminated (6 Lux), the walls were
covered with an opaque pattern of alternating black and white vertical
stripes (3 cm wide), and the floor was formed from stainless 1.5 mm rods
arranged at right angles to form a grid of 8 mm squares. A water-filled lick
tube protruded through a small hole in one wall of the test chamber, such
that the tip of the tube was flush with the interior surface of the wall at a
point 3 cm above the floor. After contacting the tube, the animal com-
pleted a circuit such that the number of licks per second could be re-
corded. This circuit was designed so that if an animal made continuous
contact with the tube (i.e., “mouthed”the tip), the circuit recorded eight
licks per second, a rate that approximates continuous licking.
In the training chamber, a 0.6 mA constant-current scrambled foot
shock (US) could be delivered through the grid floor. In both the training
and test chambers, a 40 dB above background tone could be generated by
the operation of Sonalert oscillators mounted on the top center of an end
wall of each chamber.
Water-deprived animals were acclimated to the training and test
chambers by placing them each in both contexts for 30 min on the day
before training. Within several minutes of their first placement in the test
context, water-deprived mice exhibited stable licking (for water). When
subsequently placed in the chamber, these animals typically initiated
licking within 5–10 sec and licked at relatively stable rates for the subse-
quent 4 –6 min. Training occurred in the training context in a single 40
min session during which each animal was administered a tone–shock
pairing 15 and 30 min after entering the chamber. Each 10 sec tone
terminated with the onset of a 500 msec foot shock. With our present
parameters, we have observed that asymptotic performance (as evident
in group means) is reached with four to six such pairings. Thus two
pairings (in most instances) support subasymptotic conditioned re-
sponding. At the end of the training session, animals were returned to
6426 •J. Neurosci., July 23, 2003 •23(16):6423– 6433 Matzel et al. •General Learning Abilities
their home cages for 60 min, after which they were reacclimated to the
test context for 20 min, where they were allowed ad libitum access to the
lick tubes. On the subsequent day (23–25 hr after training), animals were
tested. Each animal was placed in the test context, whereupon after they
made 50 licks the tone CS was presented continuously until the animal
completed an additional 25 licks. The latency to complete the last 25 licks
during the pre-tone interval and in the presence of the tone was recorded,
with a 600 sec limit imposed on the second 25 licks (a limit not reached by
any animal described here). With these measures, the latency to complete
25 licks in the presence of the tone CS serves as our index of learned fear,
and the latency to complete 25 licks before CS onset served as an index of
basal lick rates.
Results
Performance data from five independent replications (three
composed of 8 subjects, one of 12 subjects, and one of 20 subjects;
total sample ⫽56) contributed to the ultimate factor analysis.
First, results from each of the behavioral tasks that comprise the
test battery will be described. Summary data are presented from a
single replication of eight animals (subjects 9–16), as are sum-
mary data obtained from an additional group of eight animals
that were trained and tested concurrently (with subject 9 –16) on
certain control procedures. In addition to summary data for these
eight subjects, the performance data of two individual animals
from this sample are also provided that illustrate the relative con-
sistency of these two animals across each task. These two animals
were chosen for illustration because they were ultimately deter-
mined to be the most (subject 16) and least (subject 13) efficient
learners in this particular replication.
Subsequent to the descriptions and summaries of each of the
five learning tasks is the presentation and factor analysis of the
data obtained from a larger sample of 56 subjects. Inferences of
general learning abilities are derived from these later analyses.
Similarly, results of an experiment are described from which it is
possible to estimate the reliability of our estimates of animals’
general learning abilities. Finally, data relevant to the relationship
of native behavioral tendencies to general learning abilities are
presented.
Individual learning tasks
Lashley maze (Fig. 1)
The mean performance of animals 9–16 are illustrated in Figure
2, as are the responses of animals 13 and 16, which ranked last and
first (respectively) on general learning abilities in this replication.
Both latency and error measures are similarly representative of
animals’performance. For purposes of analysis, however, errors
serve as our index of learning because this measure is devoid of
any differences between animals in running speed.
One-trial passive avoidance
Group data as well as the performance of animals 13 (worst ag-
gregate learner) and 16 (best aggregate learner) are illustrated in
Figure 3. Also illustrated are data from a group of eight control
animals that received the same training except that the aversive
stimulation was delivered to the animals 5 min after leaving the
platform. In contrast to paired training, this unpaired training
supported no change in step latencies.
Spatial water maze
Summary data for animals 9–16 and data for animals 13 and 16
(“worst”and “best”aggregate learners, respectively) are provided
in Figure 4. The latency of animals to locate the platform de-
creased systematically across trials, as indicated by the group’s
mean performance. However, the performance of animal 13 was
unstable even on the latter trials.
Odor discrimination and choice
Figure 5 (top) illustrates the group performance of subjects 9 –16,
as well as the individual performance of subjects 13 and 16. Be-
cause many animals exhibit errorless performance by the fourth
training trial (and thus cannot be discriminated), the average
performance of individual animals on trials 2–3 was used for the
assignment of ranks for the purpose of analysis. Figure 5 (bot-
tom) illustrates the performance of a separate group of eight
Figure 1. A Lashley III maze was constructed of black Plexiglas. The alleys were 58 ⫻6 cm,
and the walls were 16 cm high. The animal was placed in the start compartment and allowed to
traverse the maze to obtain a food pellet located in the goal box.
Figure 2. Top, Latency across trials to find food in the Lashley maze. Bottom, Errors (turns in
wrong direction, retracing) across trials.
Matzel et al. •General Learning Abilities J.Neurosci., July 23, 2003 •23(16):6423– 6433 • 6427
animals trained concurrently with a variant of the procedure fol-
lowed in training subjects 9–16. In this alternate procedure, the
location of food was switched from the cup marked by mint odor
to the cup marked by almond odor after the completion of trial 3.
These animals’subsequent performance on trial 4 was signifi-
cantly impaired, indicating that the target odor (i.e., odor dis-
crimination) was a critical determinant of the animals’improved
performance across training trials.
Associative fear conditioning
Little variability was observed in the animals’latency to complete
the 25 licks that preceded the onset of the tone CS (with latencies
ranging from 4 to 7 sec). However, considerable variability be-
tween animals was observed in their completion of 25 licks in the
presence of the tone, and it is this latency that serves as our index
of learned fear. The mean latency of animals 9 –16 to complete 25
licks in the presence of the tone is illustrated in Figure 6, as are the
latencies of subjects 13 (which exhibited the worst aggregate
learning performance) and 16 (the best aggregate learning per-
formance). A group of eight additional animals were similarly
trained, except that the tone and shock were explicitly unpaired
(6 min ISI), and these animals (also illustrated in Fig. 6) exhibited
appreciably faster lick rates during the tone CS during testing.
Thus the suppression exhibited by animals trained with paired
presentations of the tone and shock can be surmised to reflect the
formation of a learned association that was dependent on the
contiguous occurrence of the tone and shock.
Individual differences and the expression of general
learning abilities
Above were summarized data obtained from subjects 9 –16 (illus-
trated in Figs. 2–6) tested in the five tasks that comprise our
learning battery. Here, summary analyses of behavioral data from
56 animals (including subjects 9–16) are described. For qualita-
tive purposes, the rank of each animal relative to its peers can
illuminate individual differences in learning on each task, as well
as differences between animals in their abilities across tasks. For
ranking, the performance of animals on each task was assessed at
Figure 3. Latency to step from platform after training relative to pretraining in the passive
avoidance task.
Figure 4. Latency to find a hidden platform in the water maze across six training trials.
Figure 5. Top, Errors to retrieve food across four trials during odor discrimination. Bottom,
Control subjects for whom the target odor was switched from mint to almond before the fourth
training trial.
6428 •J. Neurosci., July 23, 2003 •23(16):6423– 6433 Matzel et al. •General Learning Abilities
a point in training that did not typically support asymptotic
learning, i.e., each animal’s rate of acquisition served to deter-
mine its rank (as described previously). Animals 9 –16 (described
in Figs. 2–6) will be used to illustrate the utility of such rankings.
Table 2A provides the individual performance scores used to
calculate ranks, and Table 2B provides the rank of these animals
on each of the five learning tasks. These ranks were then averaged
to provide an index of each animal’s overall performance. When
two or more animals performed similarly on a task (i.e., commit-
ted the same number of errors on the relevant test trials), those
animals were assigned the mean rank based on the ranks spanned
by those animals. Individuals’rank by task and average ranks
across tasks are illustrated in Figure 7.
As can be discerned from Table 2 and Figure 7, individual
animals express distinct general learning abilities. The shaded
rows in Table 2 highlight those animals with the highest and
lowest average ranks in this sample. The aggregate performance
of each animal relative to its peers is best discerned from the
animals’mean rank across tasks. If animals’performance on each
task was independent (i.e., subject to no general influence), then
performance on each task would reflect only the influence of
task-specific abilities, and average ranks would be expected
(probabilistically) to accumulate around the unbiased median
(i.e., at a value of 4.5). In contrast, average performances were
widely distributed. The distribution of average ranks for the en-
tire sample of 56 subjects is illustrated in Figure 8.
Quantitative analyses of the raw performance scores obtained
from the sample of 56 animals will now be described. A matrix of
correlations between individuals’performance on every combi-
nation of learning tasks is presented in the top left portion of
Table 3. From this matrix it is possible to estimate the degree to
which animals’performance on any given task is indicative of
their performance on other tasks. [For this matrix and subse-
quent factor analysis, all performance measures are entered such
that lower values indicate better learning. Thus fewer errors in the
Lashley maze, fewer errors in odor discrimination, and shorter
latencies to locate the hidden platform in the water maze are
indicative of better learning (and are entered in their nominal
form). In the fear conditioning and passive avoidance tasks,
higher nominal performance scores are indicative of better learn-
ing. In these two instances, performance scores are converted to
negative numbers so that lower values represent better learning
on all tasks. This has no statistical impact on the analyses (i.e., the
magnitude of correlations or factor loadings), but the consistent
directionality of the performance measures simplifies the de-
scription and illustration of the correlations and subsequent fac-
tor analyses.] In the top left portion of Table 3, it can be seen that
all of the possible pair-wise correlations between learning perfor-
mance variables are positive, i.e., reflect some common source of
variance. Such observations in the human test literature are taken
as evidence for a conserved influence on general cognitive abili-
ties (for review, see Plomin, 1999; Sternberg, 1997).
Performance in both the Lashley maze and the passive avoid-
ance task were similarly and most highly predictive of perfor-
mance in other tasks in the battery. These latter correlations are
quite illuminating, given the diametric demands of these two
tasks (i.e., activity vs passivity, appetitive vs aversive control, path
integration vs egocentric localization). These observations miti-
gate against an explanation of an animal’s aggregate performance
that supposes some inherent commonality in the performance
demands of the tasks that comprise this battery.
A principal component method of factor analysis was con-
ducted on the performance scores of the 56 animals in the test
sample. To maximize sensitivity to any general factors, no factor
rotation was performed. Only a single factor (eigen value ⫽1.92)
was extracted from this data set, and that factor accounted for
38% of the total variance in performance across all tests. The
loadings of each learning task on this factor are provided in Table
4. Loadings of the individual learning tasks in this factor confirm
the above interpretation of the correlation matrix (Table 3), i.e.,
performance on the Lashley maze and passive avoidance tasks
load strongly, whereas the loading of fear conditioning and the
water maze are relatively weaker. It is impossible to discern from
such an analysis what properties of a task
account for its loading weight. Neverthe-
less, the consistency and weight of the in-
dividual task loadings strongly suggest that
this factor is indicative of a general influ-
ence on learning that transcends idiosyn-
cratic task demands. In this regard, it is of
interest that the “gfactor”that is proposed
to influence diverse tests of human intelli-
gence accounts for (by various estimates)
25–50% of the variability in performance
across individuals (Sternberg, 1997; Plo-
min, 1999).
Table 2. Performance of individuals on each of five learning tasks
A. Individuals’ performance scores B. Individuals’ relative ranks
Ss LM PA WM OD FC Ss LM PA WM OD FC Mean rank
9 6.0 .77 4.3 2.5 28 9 4.5 6 1 6 4 4.9
10 14.0 .60 12.6 1.5 109 10 8 8 2 3.5 2 4.7
11 6.0 1.82 37.7 1.0 15 11 4.5 4 6 1.5 6.5 4.5
12 4.0 3.72 17.5 2.0 4 12 3 2 3 5 8 4.2
13
a
12.5 .76 46.6 6.0 15 13
a
7 7 8 7.5 6.5 7.2
14 3.0 7.70 18.0 1.5 89 14 2 1 4 3.5 3 2.7
15 8.5 1.36 44.0 6.0 25 15 6 5 7 7.5 5 6.1
16
a
2.5 2.38 30.8 1.0 268 16
a
1 3 5 1.5 1 2.3
LM, Lashley maze; PA, passive avoidance; WM, water maze; OD, odor discrimination; FC, fear conditioning.
a
Group summary data and individual data for subjects (Ss) 13 and 16 are plotted in Figures 2– 6.
Figure 6. Latency (seconds) to complete 50 licks in the presence of a tone after paired or
unpaired presentations of the tone with shock.
Matzel et al. •General Learning Abilities J.Neurosci., July 23, 2003 •23(16):6423– 6433 • 6429
Reliability of ranks as an index of animals’ learning abilities
We next determined the degree to which animals’ranks were a
reliable index of relative learning abilities. To address this con-
cern, a group of eight animals were trained and tested on the
learning battery described above and subsequently on a second
series of learning tasks. Each of the tasks in the second battery
required new learning, although the nature of the tasks and the
underlying processes were nominally identical to those that com-
prised the first series of tests. With data obtained from animals
tested in each of the two batteries it was possible to assess the
degree of consistency of individual animals’ranks on each of two
analogous tasks, as well as the degree to which individuals’aggre-
gate performances (i.e., average ranks) were correlated across the
two series of tests.
After completion of the initial battery, animals began a second
series of tests. Modifications of the tasks were as follows. (1) The
black Lashley III maze was replaced with a white maze that re-
quired a different route to efficiently retrieve the food reinforcer.
(2) For passive avoidance, animals were trained in a distinct con-
text and the safe platform was white (cf. black). Furthermore, an
odor (28 gm Vick’s VapoRub) was added to the chamber to dis-
tinguish it from the chamber that had been used previously. (3)
In the water maze, the spatial cues were replaced by a new set of
geometric shapes located at different coordinates, the escape plat-
form was moved to a different quadrant of the maze, and start
locations were changed. (4) For odor discrimination, three new
odors [i.e., rum, anise, coconut (target)] were used as discrimi-
native cues, and the pattern of start locations were changed. (5)
New training and test contexts were used for fear conditioning,
and a flashing light (250 msec on/250 msec off) located in the top
center of each box served as the CS.
The results of testing on the initial battery of tasks were similar
in nature to those described previously for subjects 9–16 (Table
2, Fig. 7). As summarized in Table 5, the average rank (aggregate
performance) of individual animals varied widely on the initial
test battery, with average ranks ranging from 2.3 to 6.0. The ranks
of these animals on the two sets of individual learning tasks are
also provided in Table 5. Comparing the performance of animals
on individual tasks, the correlations between their ranks ranged
from r⫽0.2 (water maze) to r⫽0.75 (Lashley maze), suggesting
that the tasks were variously reliable in their depiction of the
“true”performance/ability of any individual animal. Even so, the
correlation of the average ranks of individual animals, i.e., the
estimate of general learning ability, was significant. Thus al-
though the performance of each animal varied (to different de-
grees) across the successive batteries of tests on any single task,
the overall estimation of an animal’s performance relative to its
peers was a reliable estimation of individuals’general learning
ability.
Relationship of native behaviors and characteristics to
general learning abilities
In addition to being tested on five learning tasks, these 56 animals
were monitored in a walled open field (segmented into a grid of
6⫻6 square quadrants). Four performance measures were ob-
tained in the field, including running speed (during bursts of
straight running), overall activity (total quadrant entries), entries
into open relative to closed quadrants of the field (a behavior
often equated with novelty seeking) (Kabbaj et al., 2000), and the
number of excreted bolli (a putative measure of “emotionality”).
In addition, animals’body weights at the onset of testing were
recorded. All of these measures, in combination with perfor-
mance on learning tasks, were subjected to separate analyses.
Figure7. Top,Eachbar represents an individual’s relative rank(1⫽best performer) on each
learning task in this sample (n⫽8). Bottom, The average of each individual’s ranks on the five
learning tasks (⫾SEM).
Figure 8. A total of 56 animals were tested in five replications, and animals’ average ranks
across learning task were computed relative to the other animals in its replication. Plotted is the
distribution of average ranks (indicative of general learning ability) of all 56 animals.
6430 •J. Neurosci., July 23, 2003 •23(16):6423– 6433 Matzel et al. •General Learning Abilities
Pair-wise correlations between each of these five variables as well
as between these variables and animals’performance on the five
learning tasks are provided in Table 3.
It can be seen in Table 3 that animals’body weights were
unsystematically and nonsignificantly related to other perfor-
mance indices, including those obtained in the five learning tasks.
Likewise, running speed in the open field was not correlated with
performance on any of the five learning tasks. Total quadrant
entries (an index of overall activity) in the open field were unsys-
tematically related to performance on learning tasks, although
more activity was positively correlated with better performance
in the Lashley maze and fear conditioning tasks. Not surprisingly,
running speed and overall activity in the open field were strongly
related. Most interestingly, the propensity of animals to explore
the open quadrants of the field (i.e., the ratio of entries into open
relative to closed quadrants) was directly related to performance
on all learning tasks except fear conditioning, i.e., an increase in
the proportion of time spent in open areas was associated with
more efficient learning (i.e., lower performance scores are indic-
ative of better learning) on four of five tasks. Importantly, the
propensity of animals to enter the open areas of the field was
unrelated to both overall activity or running speed in the field
(r⫽0.07, 0.03, respectively). This latter result differentiates the
impact of movement from influences more obviously related to
exploratory/motivational tendencies. Defecation (number of
bolli) in the open field was not significantly correlated either with
measure of exploration (overall activity or entries into open
quadrants) or with any of the learning measures. Because defeca-
tion is often interpreted to reflect emotionality (e.g., fear), this
result suggests that variations in emotionality do not influence
animals’exploratory behaviors and cannot account for differ-
ences between animals in their overall learning performance.
A principal component method of factor analysis extracted
three factors to account for these nine variables. Variable loadings
on these factors are provided in Table 6. Here we will interpret
only the primary factor. Factor 1 accounted for 25% of the total
variance, and each of the five learning tasks loads consistently on
this factor, suggesting its homology to that factor extracted from
performance only on learning tasks (Table 4). Again, running
speed, defecation, and body weights loaded weakly on this factor.
However, animals’propensity to explore the open areas of the
open field also loaded heavily, suggesting that this exploratory
tendency is co-regulated with general learning ability, is influ-
enced by general learning ability, or is a determinant of general
learning ability.
Discussion
In a sample of 56 outbred CD-1 mice, we observed a pattern of
results that indicate that individual mice express varying degrees
of general learning ability. These results address questions that
are at the forefront of research on human cognitive abilities but
have been mostly ignored in research efforts with animal subjects.
Analysis of animals’performance on five distinct learning
tasks extracted a single factor that accounted for 38% of the vari-
ance between individuals across all tasks. It is interesting to note
that a general influence on human intelligence test performance
(i.e., the gfactor) has been variously estimated to account for
between 25 and 50% of the variance between individuals (Jensen,
1998; Plomin, 1999; Sternberg, 2000). It is well established that
general intelligence abilities (i.e., like those characterized in a
standardized IQ test) are co-regulated with or directly impinge
on learning, such that indices of learning and intelligence are
highly correlated (Kolligian and Sternberg, 1987; Carroll, 1993;
for review, see Jensen, 1998). The psychometric and conceptual
analogy between intelligence and learning, as well as the degree of
explanatory value of the general influence on learning that we
find, suggests that the battery of tests described here may be sen-
sitive to a factor analogous to human g. This conclusion must be
considered with great caution, however, particularly given the
relatively limited number of tests that comprise the present bat-
tery and the unlikelihood that they adequately represent all learn-
ing abilities. Although the present data indicate the existence of a
general learning factor in mice, the proportion of variance in
learning accounted for by this factor may not accurately represent
its true impact on learning abilities (Jensen and Weng, 1994).
We observed that individuals’entries into the open areas of
the open field (relative to entries in areas adjacent to the field’s
walls) was significantly correlated with performance on four of
five learning tasks and loaded heavily in that factor, which ac-
counted for general learning abilities. The propensity to explore
the open quadrants of a field is often interpreted as an index of an
animal’s proclivity for novelty seeking and may reflect the degree
to which an animal experiences stress in the unfamiliar open
Table 3. Correlations (rvalues) of individuals’ (nⴝ56) performance across tasks
PA LM OD FC WM OF % open OF activity OF speed OF bolli Body weight
PA 0.47*** 0.24 0.23 0.21 ⫺0.37** ⫺0.13 ⫺0.03 0.14 ⫺0.26
LM 0.47*** 0.29* 0.14 0.10 ⫺0.35** ⫺0.30* ⫺0.15 0.08 ⫺0.17
OD 0.24 0.29* 0.21 0.22 ⫺0.30* ⫺0.15 0.09 ⫺0.03 0.04
FC 0.23 0.14 0.21 0.09 ⫺0.03 ⫺0.28* ⫺0.21 0.01 ⫺0.22
WM 0.21 0.10 0.22 0.09 ⫺0.28* 0.06 ⫺0.09 0.07 0.12
OF % open ⫺0.37** ⫺0.35** ⫺0.30* ⫺0.03 ⫺0.28* 0.07 0.03 ⫺0.08 ⫺0.07
OF activity ⫺0.13 ⫺0.30* ⫺0.15 ⫺0.28* 0.06 0.07 0.55*** ⫺0.02 ⫺0.08
OF speed ⫺0.03 ⫺0.15 0.09 ⫺0.21 ⫺0.09 0.03 0.55*** ⫺0.01 ⫺0.11
OF bolli 0.14 0.08 ⫺0.03 0.01 0.07 ⫺0.08 ⫺0.02 ⫺0.01 0.05
Body weight ⫺0.26 ⫺0.17 0.04 ⫺0.22 0.12 ⫺0.07 ⫺0.08 ⫺0.11 0.05
n⫽56; *p⬍0.05; **p⬍0.01; ***p⬍0.001. OF, Open field; LM, Lashley maze; PA, passive avoidance; OD, odor discrimination; WM, water maze; FC, fear conditioning.
Table 4. Unrotated factor loadings; principal component extraction
Variable Factor 1
Passive avoidance .76
Lashley maze .71
Odor discrimination .64
Fear conditioning .48
Water maze .45
Eigen value 1.92
Proportion of total variance .38
Matzel et al. •General Learning Abilities J.Neurosci., July 23, 2003 •23(16):6423– 6433 • 6431
environment (Anderson, 1993; Kabbaj et
al., 2000). The relationship of novelty
seeking and indices of maze reasoning has
been observed previously in laboratory
rats (Anderson, 1993). It is notable that
among human infants, the degree of pref-
erence for novelty is positively correlated
with later performance on standardized IQ
test batteries (Bornstein and Sigman,
1986; Vietze and Coates, 1986), an obser-
vation which further suggests that the gen-
eral learning factor that we observe in this
population of mice might be analogous to
the gfactor described in humans. Al-
though the nature of this relationship between novelty seeking
and learning/intelligence is unknown, it is possible that animals
more engaged by novelty are more likely to recognize (or attend
to) those environmental relationships on which learning de-
pends. Related to this, animals that are prone to novelty seeking
may be less susceptible to the experience or physiological conse-
quences of stress, which in many instances can impede learning
(for review, see Shors, 1998). The data reported here do not allow
us to distinguish between these (or other) possibilities.
A general influence on cognitive abilities has been described as
one of the most stable human quantitative traits (Plomin, 1999),
and the elucidation of its brain substrates could have tremendous
functional significance. It is thus surprising that so little work has
been done to establish the existence of this trait in laboratory
animals. An exception is a battery of mixed complex and simple
tasks (ranging from complex mazes to avoidance learning) con-
structed by Thorndike (1935) and assessed with laboratory rats.
In this study, positive pair-wise correlations were observed in the
performance of animals across all tasks, a pattern of results re-
ported more recently by Anderson (1993). Similarly, Locurto and
Scanton (1998) have reported that the performances of individ-
ual mice across six distinct spatial navigation tasks were strongly
correlated, although the processing requirements of the six tasks
may not be sufficiently distinct to conclude that performance was
influenced by a general (as opposed to domain-specific) factor.
Only one relevant analysis has been attempted with laboratory
mice, in which Galsworthy et al. (2002) subjected heterogeneous
stock mice to a battery of tests that assessed learning (including in
the spatial water maze), memory, and native exploratory behav-
iors. Galsworthy et al. (2002) reported that ⬃30% of the variance
between tasks was accounted for by a single factor. Although
comparable in magnitude to the general factor found to influence
performance in our battery of tests (in which 38% of the variance
was accounted for by a single factor), the relatively weaker factor
strength reported by Galsworthy et al. (2002) might reflect their
explicit intent to include a strong memory component (and other
presumed cognitive influences) in their battery of tests. Nonethe-
less, across species and test batteries, converging evidence is
emerging from which to infer the existence in laboratory animals
of a general influence on cognitive abilities that transcends sen-
sory, motor, and motivational demands, as well as neuroana-
tomical learning systems and “domains”of abilities. With an
approach like that reported here it will be possible to separate the
impact of a manipulation (e.g., a transgene or pharmacological
intervention) on specific learning systems from its impact on
general learning abilities, a prerequisite for delineating the under-
lying basis for individual differences in learning and intelligence.
References
Anderson B (1993) Evidence in the rat for a general factor that underlies
cognitive performance and that relates to brain size: intelligence? Neuro-
sci Lett 153:98–102.
Bolles RC (1969) Avoidance and escape learning: simultaneous acquisition
of different responses. J Comp Physiol Psychol 68:355–358.
Bornstein M, Sigman M (1986) Continuity in mental development from
infancy. Child Dev 57:251–274.
Carroll JB (1993) ⌯uman cognitive abilities. New York: Cambridge UP.
Galsworthy MJ, Paya-Cano JL, Monleo´n S, Plomin R (2002) Evidence for
general cognitive ability (g) in heterogeneous stock mice and an analysis
of potential confounds. Genes Brain Behav 1:88–95.
Gilbert PE, Kesner RP, Lee I (2001) Dissociating hippocampal subregions:
double dissociation between dentate gyrus and CA1. Hippocampus
11:626–636.
Gray JR, Chabris CF, Braver TS (2003) Neural mechanisms of general fluid
intelligence. Nat Neurosci 6:316–322.
Hampson RE, Rogers G, Lynch G, Deadwyler SA (1998) Facilitative effects
of the ampakine CX516 on short-term memory in rats: enhancement of
delayed-nonmatch-to-sample performance. J Neurosci 18:2740–2747.
Jensen AR (1998) The gfactor: the science of mental ability (human evolu-
tion, behavior, and intelligence). New York: Praeger.
Jensen AR, Weng L-J (1994) What is a good g? Intelligence 18:231–258.
Kabbaj M, Devine DP, Savage VR, Akil H (2000) Neurobiological correlates
of individual differences in novelty-seeking behavior in the rat: differen-
tial expression of stress-related molecules. J Neurosci 20:6983–6988.
Kolligian Jr J, Sternberg RJ (1987) Intelligence, information processing, and
specific learning disabilities: a triarchic synthesis. J Learn Dis 20:8–17.
Lavond DG, Kim JJ, Thompson RF (1993) Mammalian brain substrates of
aversive classical conditioning. Annu Rev Psychol 44:317–342.
LeDoux JE (2000) Emotion circuits in the brain. Annu Rev Neurosci
23:155–184.
Locurto C, Scanlon C (1998) Individual differences and a spatial learning
factor in two strains of mice. J Comp Psychol 112:344–352.
Mackintosh NJ (1998) IQ and human intelligence. Oxford: Oxford UP.
Matzel LD, Gandhi CC (2000) The tractable contribution of synapses and
their component molecules to individual differences in learning. Behav
Brain Res 18:200–214.
Morris RGM (1974) Pavlovian conditioned inhibition of fear during
shuttlebox avoidance behavior. Learn Motiv 5:424–447.
Table 5. Reliability of ranks
Ss LM
1
LM
2
PA
1
PA
2
WM
1
WM
2
OD
1
OD
2
FC
1
FC
2
Mean rank
1
Mean rank
2
1 5 7 3 5 7 2 7 8 7.5 7 5.9 5.8
2 4 2 6 8 8 7 1.5 4 4 8 4.7 5.8
3 3 4 4 1 1 3 3.5 6 5 1 3.3 3
4 8 5.5 8 6 6 5 5 6 3 4.5 6 5.4
5 6 5.5 5 4 5 4 8 6 6 6 6 5.1
6 1 1 2 2 2 1 1.5 2 7.5 2 2.8 1.6
7 7 8 7 7 3 8 6 2 2 4.5 5 5.9
8 2 3 1 3 4 6 3.5 2 1 3 2.3 3.4
r⫽0.79* r⫽0.68 r⫽0.24 r⫽0.52 r⫽0.09 r⫽0.82**
*p⬍0.05; **p⬍0.02. LM, Lashley maze; PA, passive avoidance; FC, fear conditioning; WM, water maze; OD, odor discrimination; 1, Standard tasks; 2, task
variants.
Table 6. Unrotated factor loadings; principal component extraction
Variable Factor 1 Factor 2 Factor 3
Lashley maze 0.72 0.07 ⫺0.13
Passive avoidance 0.71 0.28 ⫺0.24
Water maze 0.38 0.29 0.45
Odor discrimination 0.54 0.27 0.14
Fear conditioning 0.47 ⫺0.27 ⫺0.36
Open field (% in open) ⫺0.59 ⫺0.36 ⫺0.32
Open field (total activity) ⫺0.50 0.71 ⫺0.07
Open field (running speed) ⫺0.35 0.76 ⫺0.19
Open field (# bolli) 0.15 0.10 0.17
Body weight 0.15 ⫺0.17 0.85
Eigen value 2.43 1.57 1.33
Proportion of variance .25 .16 .13
6432 •J. Neurosci., July 23, 2003 •23(16):6423– 6433 Matzel et al. •General Learning Abilities
Morris RGM (1981) Spatial localization does not require the presence of
local cues. Learn Motiv 12:239–260.
Plomin R (1999) Genetics and general cognitive ability. Nature [Suppl]
402:C25–C29.
Plomin R (2001) The genetics of gin human and mouse. Nat Rev Neurosci
2:136–141.
Plomin R, Spinath FM (2002) Genetics and general cognitive ability (g).
Trends Cogn Sci 6:169–176.
Sara SJ, Roullet P, Przybyslawski J (2001) Consolidation of memory for
odor-reward association: B-adrenergic receptor involvement in the late
phase. Learn Memory 6:88–96.
Shors TJ (1998) Stress and sex effects on associative learning: for better or
for worse. Neuroscientist 4:353–364.
Shors TJ, Servatius RJ, Thompson RF, Rogers G, Lynch G (1995) Enhanced
glutamatergic neurotransmission facilitates classical conditioning in the
freely moving rat. Neurosci Lett 186:153–156.
Squire LR, Zola-Morgan S (1991) The medial temporal lobe memory sys-
tem. Science 253:1380–1386.
Staubli U, Rogers G, Lynch G (1994) Facilitation of glutamate receptors
enhances memory. Proc Natl Acad Sci USA 91:777–781.
Sternberg RJ (1997) Intelligence and life-long learning. Am Psychol
52:1134–1139.
Sternberg RJ (2000) Cognition. The holy grail of general intelligence. Sci-
ence 289:399–401.
Sternberg RJ, Kaufman JC (1998) Human abilities. Annu Rev Psychol
49:479–502.
Tang YP, Shimizu E, Dube GR, Rampon C, Kerchner GA, Zhuo M, Liu G,
Tsien JZ (1999) Genetic enhancement of learning and memory in mice.
Nature 401:63–69.
Thorndike RL (1935) Organization of behavior in the albino rat. Psychol
Monographs 17:1–70.
Vietze P, Coates D (1986) Information-processing approaches to early
identification of mental retardation. In: Mental retardation: research, ed-
ucation, and technology transfer (Wisniewski H, Snyder D, eds), pp 266 –
276. New York: New York Academy of Sciences.
Matzel et al. •General Learning Abilities J.Neurosci., July 23, 2003 •23(16):6423– 6433 • 6433
