The external-internal loop of interference: Two types of attention and their influence on the learning abilities of mice

Abstract

Attention is a component of the working memory system, and as such, is responsible for protecting task-relevant information from interference. Cognitive performance (particularly outside of the laboratory) is often plagued by interference, and the source of this interference, either external or internal, might influence the expression of individual differences in attentional ability. By definition, external attention (also described as “selective attention”) protects working memory against sensorial distractors of all kinds, while internal attention (also called “inhibition”) protects working memory against emotional impulses, irrelevant information from memory, and automatically-generated responses. At present, it is unclear if these two types of attention are expressed independently in non-human animals, and how they might differentially impact performance on other cognitive processes, such as learning. By using a diverse battery of four attention tests (with varying levels of internal and external sources of interference), here we aimed both to explore this issue, and to obtain a robust and general (less task-specific) measure of attention in mice. Exploratory factor analyses revealed two factors (external and internal attention) that in total, accounted for 73% of the variance in attentional performance. Confirmatory factor analyses found an excellent fit with the data of the model of attention that assumed an external and internal distinction (with a resulting correlation of 0.43). In contrast, a model of attention that assumed one source of variance (i.e., “general attention”) exhibited a poor fit with the data. Regarding the relationship between attention and learning, higher resistance against external sources of interference promoted better new learning, but tended to impair performance when cognitive flexibility was required, such as during the reversal of a previously instantiated response. The present results suggest that there can be (at least) two types of attention that contribute to the common variance in attentional performance in mice, and that external and internal attentions might have opposing influences on the rate at which animals learn.

Full text

The external–internal loop of interference: Two types of attention
and their influence on the learning abilities of mice
q
Bruno Sauce, Christopher Wass, Andrew Smith, Stephanie Kwan, Louis D. Matzel
⇑
Department of Psychology, Program in Behavioral and Systems Neuroscience, Rutgers University, Piscataway, NJ 08854, United States
article info
Article history:
Received 8 April 2014
Revised 10 October 2014
Accepted 20 October 2014
Available online 24 October 2014
Keywords:
Attention
External attention
Internal attention
Working memory
Learning
Individual differences
Mice
abstract
Attention is a component of the working memory system, and is responsible for protecting task-relevant
information from interference. Cognitive performance (particularly outside of the laboratory) is often pla-
gued by interference, and the source of this interference, either external or internal, might influence the
expression of individual differences in attentional ability. By definition, external attention (also described
as ‘‘selective attention’’) protects working memory against sensorial distractors of all kinds, while
internal attention (also called ‘‘inhibition’’) protects working memory against emotional impulses, irrel-
evant information from memory, and automatically-generated responses. At present, it is unclear if these
two types of attention are expressed independently in non-human animals, and how they might differ-
entially impact performance on other cognitive processes, such as learning. By using a diverse battery of
four attention tests (with varying levels of internal and external sources of interference), here we aimed
both to explore this issue, and to obtain a robust and general (less task-specific) measure of attention in
mice. Exploratory factor analyses revealed two factors (external and internal attention) that in total,
accounted for 73% of the variance in attentional performance. Confirmatory factor analyses found an
excellent fit with the data of the model of attention that assumed an external and internal distinction
(with a resulting correlation of 0.43). In contrast, a model of attention that assumed one source of vari-
ance (i.e., ‘‘general attention’’) exhibited a poor fit with the data. Regarding the relationship between
attention and learning, higher resistance against external sources of interference promoted better new
learning, but tended to impair performance when cognitive flexibility was required, such as during the
reversal of a previously instantiated response. The present results suggest that there can be (at least)
two types of attention that contribute to the common variance in attentional performance in mice,
and that external and internal attentions might have opposing influences on the rate at which animals
learn.
Ó2014 Elsevier Inc. All rights reserved.
1. Introduction
Top-down attention is a component of the working memory
system that protects task-relevant information from interference
(Engle, 2002). Because goal-directed activities are often sur-
rounded by interference, working memory is critically dependent
on attention for efficient processing/execution (with ‘‘attention’’
being sometimes referred to as ‘‘executive control’’; Jarrold &
Towse, 2006). Variations in attention in humans are a defining fea-
ture of disorders like ADHD, schizophrenia, and autism (Allen &
Courchesne, 2001; Barch, 2005; Tsal, Shalev, & Mevorach, 2005),
and the efficacy of attention is believed to have an influence on
human intelligence (Conway, Kane, & Engle, 2003).
Humans that perform well in one particular cognitive task (such
as reading comprehension) tend to perform well in tasks that
explicitly reflect other intellectual domains (like quantitative rea-
soning). This positive manifold (i.e., the pattern of positive inter-
correlations between domains) is believed to exist due to
different intellectual domains either emerging together into (Van
der Maas et al., 2006) or being under the hierarchical influence of
(Mackintosh, 2011) a single ‘‘general’’ factor. This factor, originally
referred to as ‘‘g’’ (Spearman, 1904), accounts for as much as 50% of
an individual’s performance across sets of diverse cognitive tasks,
(Carroll, 1993; Jensen, 1998). The attentional component of work-
ing memory is widely asserted to impact performance across all (or
most) cognitive domains, and thus may be critical to variations in
‘‘g’’ (Dempster & Corkill, 1999; Jarosz & Wiley, 2012).
http://dx.doi.org/10.1016/j.nlm.2014.10.005
1074-7427/Ó2014 Elsevier Inc. All rights reserved.
q
This work was supported by Grants from the National Institute of Aging
(AG022698) and the Office of Naval Research (N000141210873) to L.D.M.
⇑
Corresponding author. Fax: +1 732 445 2263.
E-mail address: matzel@rci.rutgers.edu (L.D. Matzel).
Neurobiology of Learning and Memory 116 (2014) 181–192
Contents lists available at ScienceDirect
Neurobiology of Learning and Memory
journal homepage: www.elsevier.com/locate/ynlme

Previously, we have assessed the relationship between ‘‘intelli-
gence’’ and attention using genetically heterogeneous mice as sub-
jects. Using non-human animals to study cognition provide many
methodological advantages, and might allow future lines of genetic
and neurophysiological investigation that are impractical with
humans (e.g., Kolata et al., 2010; Wass et al., 2013). Mice’s perfor-
mance across diverse learning tasks is positively correlated, and a
single factor accounts for 25–48% of the variance in performance
across as many as eight tasks (reviewed in Kolata, Light, &
Matzel, 2008). This factor correlates with reasoning skills – sug-
gesting that the performance in our learning tests captures other
cognitive abilities, too (Wass et al., 2012). Interested in studying
attention, we developed a version of the Stroop Test for mice
(see review in Matzel & Kolata, 2010), since the original Stroop Test
has been the gold-standard of attentional measures in humans (for
a review, see MacLeod, 1991). We found that a mouse’s perfor-
mance in the Mouse Stroop Test strongly and consistently pre-
dicted its general learning ability (Kolata, Light, Grossman, Hale,
& Matzel, 2007). However, as a single test, performance on the
mouse Stroop task might reflect non-attentional variables, such
as foraging capacity, sensory acuity, and motivation, all of which
could confound the effect of attention per se. Even more impor-
tantly, attention can be divided into several subtypes, and these
might be differently related to intelligence (Conway et al., 2005;
Mirsky, Anthony, Duncan, Ahearn, & Kellam, 1991; Schweizer,
Moosbrugger, & Goldhammer, 2005).
The distinction between internal and external attention is of
critical functional significance (Mysore & Knudsen, 2013; Nee &
Jonides, 2008; Nee et al., 2012). ‘‘External attention’’ protects
working memory against external sources of interference (senso-
rial distractors of all kinds), and is commonly referred to as ‘‘selec-
tive attention’’ and ‘‘perceptual attention’’ (examples in Kane &
Engle, 2002; Lavie & Fox, 2000; Tiitinen et al., 1993). ‘‘Internal
attention’’, on the other hand, protects working memory against
internal sources of interference (e.g., emotional impulses, irrele-
vant information from memory, and automatically-generated
responses), and is commonly alluded to the literature as represent-
ing ‘‘inhibition’’, ‘‘self-control’’, and ‘‘reflective attention’’ (exam-
ples in Johnson, Reeder, Raye, & Mitchell, 2002; Kuntsi,
Oosterlaan, & Stevenson, 2001; Lister et al., 2013; Tangney,
Baumeister, & Boone, 2004). Due to those properties, internal and
external attention (or the attention against external and internal
sources of interference) are believed to be at the core of the inter-
face between perception, working memory, and long term memory
(Chun & Johnson, 2011). At present, it is unclear if or how these
two subtypes of attention differentially impact an animal’s perfor-
mance on non-attentional tests, such as those that assess learning.
Here, we aimed to obtain a robust (and less task-specific) mea-
sure of attention in mice by using diverse attention tests. In addi-
tion, we used our attention battery to explore the attentional
property of source of interference, as well as its relationship to
general learning performance of mice. The four tests in our ‘‘mouse
attention battery’’ involve unique combinations of many non-
attentional properties, such as learning, navigation, retrieval, and
emotional reaction. Because of this, the common variance obtained
from the battery should represent that which the tests share in
common: attention. The interference imposed on the animals dur-
ing the execution of the tasks originates from internal and external
sources (in varying degrees), and thus differentially tax external
and internal attentional systems.
For the current study, we focused on the correlational approach
in Psychology, using procedures dealing with differences between
individuals, not groups. While the experimental approach can eas-
ily reveal the causes underlying a simple trait, the correlational
approach is better suited to identify causes of variation behind
complex traits – determining how much a set of variables
contribute to observed differences between individuals (for the
importance of using the correlation approach in Psychology, see
Cronbach, 1957; Kline, 2011. For its importance in studies on
learning and behavior, see Sauce & Matzel, 2013). First, we con-
ducted exploratory factor analyses to examine the patterns of the
mice’s performances in the four distinct tests of attention. Then,
we used confirmatory factor analysis to test if the theoretical
model with a single factor (‘‘attention’’) as the unique systematic
source of variance was able to explain intercorrelations between
animals’ performance on the four attentional tests. Then, we
assessed a model with two factors of source of interference (where
two tests with mostly internal sources of interference were com-
pared to two tests with mostly external sources of interference).
Finally, we explored the relationship of attention with learning
abilities (using learning data derived from the preparatory steps
for the attention tests).
2. Materials and methods
2.1. Animals and environment
We used 26 outbred CD-1 male mice (Harlan Sprague Dawley
Inc., Indianapolis, IN) that weighed 25–30 g and were approxi-
mately 70 days of age upon arrival in our laboratory (Animals were
group housed among siblings prior to weaning at 21 days of age,
and singly housed thereafter.) They were singly housed in clear
shoebox cages (28.5 17.5 12 cm) inside a temperature-con-
trolled colony room with a 12:12 h light–dark cycle. To reduce
the effect of individual differences in stress due to handling and
removal from the home cage, we removed each animal from its
cage and handled it daily for 60 s during the two weeks before
the start of behavioral testing. (Handling consisted of holding a
mouse on the palm of an experimenter’s hand, and systematically
walking it around the laboratory.) The mice were young adults
(approximately 90 days of age) at the start of testing.
2.2. Attention battery
The attention battery consisted of four different tests: Mouse
Stroop Test, T-Maze Reversal, Coupled Latent Inhibition, and Dual
Radial Arm Maze (administered in this order). To reduce confound-
ing effects (such as differential stress responses) on the variation in
attentional performance, we administered the tests in the same
order for all animals (a standard practice when assessing individ-
ual differences). This insures that we are sensitive to innate differ-
ences between individuals, rather that differences that might arise
were animals to be exposed to tests in different orders. We per-
formed two interleaved runs of the attention battery following
the scheme in Fig. 1, with 13 mice assigned to each run. For the
Mouse Stroop Test, T-Maze Reversal, and Dual Radial Arm Maze,
we food deprived the mice by giving them only 90 min of access
to food daily, beginning on the day prior to training. This protocol
leads to an average loss of 5% of the ad libitum body weight.
2.2.1. Mouse Stroop Test
When administered to humans, the Stroop Test presents sub-
jects with color-words printed in inks of various colors, and
requires them to report the ink color of the word. Subjects show
slowed responses and more errors when there is interference from
a word naming a different color, such as the word ‘red’ printed in
blue color. In an analog of this test adapted for mice, mice were
first required to associate meaning to odor and visual cues, analo-
gous to the way the human Stroop Test requires subjects to know
how to identify words and colors. For this, we trained the mice in
two tasks: odor discrimination and visual discrimination.
182 B. Sauce et al. /Neurobiology of Learning and Memory 116 (2014) 181–192

In the odor discrimination task, mice had to use a specific odor
cue to find food. The task was administered in a square box of black
Plexiglas, called here ‘‘odor box’’ (45.5 cm 45.5 cm surface and
20 cm walls), under dim light (10 Lux). Three of the box’s four cor-
ners always contained cups (that could hold food), and the fourth
corner served as a start location. The cups were held in place by
vertical structures with small holes creating patterns, and each of
these structures could be illuminated from the rear by light bulbs
(10 W). These illuminated patterns of holes function as visual cues
for the visual discrimination task (see below), but their lights were
off during the odor discrimination task. Each cup had a metal mesh
separating the bottom from the top, and the bottom contained cot-
ton with 20
l
L of flavor extract; one with mint, one with lemon,
and one with almond (McCormick flavor extracts). All of the cups
had three food pellets (chocolate-flavored crisp rice, 30 mg), but
only one cup (marked with the target cue: mint) had one accessible
piece of food (above the mesh), while the other two cups had three
inaccessible pieces (below the mesh). This controlled for differen-
tial degrees of food odor at each cup. On each trial, the corner with
a particular odor (and its associated food cup) was systematically
re-arranged to avoid any association of the target odor (mint) with
specific spatial cues. Thus in this task, the animal’s goal was to
locate the accessible food using the odor of mint as the discrimina-
tive stimulus.
In the visual discrimination task, mice had to use a specific
visual cue to find the food. The cotton in the cups did not have fla-
vor extracts, but in this task the lights bulbs illuminated the pat-
terns of holes (a 14 14 cm grid comprised of 0.5 cm holes). One
pattern of holes formed an X (the target cue), one a circle, and
one a rectangle. The visual discrimination was performed in a dif-
ferent box (called here ‘‘visual box’’) than the odor discrimination.
The visual box had the same dimensions, but with vertical white
stripes (2 cm wide, with 2 cm between each stripe) on the walls
of the box. This difference was important during the critical phase
of the Mouse Stroop Test to allow mice to distinguish between the
odor box (‘‘mint = food’’) and the visual box (‘‘X = food’’). All other
features of the visual task were as described for the odor task.
The day before starting the first task (odor discrimination), each
mouse was acclimated to an empty odor box for 20 min. We then
administered three days of odor discrimination training and four
days of visual discrimination training, with four trials per day
and an 8 min intertrial interval in both tasks. These training
parameters have previously been shown to support asymptotic
(near errorless) performance in each task (Kolata et al., 2007).
After training separately in the odor and visual discrimination
tasks, we then conducted the Mouse Stroop Test. It consisted of a
complex discrimination task that requires mice to ignore interfer-
ence from one of the learned target cues and maintain attention on
the context-appropriate cue. The context (the two different train-
ing boxes) determined if the relevant cue (the one that marks
the available food) is either visual (X) or odor (MINT). For this
purpose, we conducted the tests in the visual box with both the
previously trained visual cues (with X as the target), and odor cues
(with MINT as the task-relevant distractor) present. (During these
tests, MINT and X were never presented at the same location.) We
conducted similar tests in the odor box, with, in this instance,
MINT being the target cue, and the visual X serving as the task-rel-
evant distractor. A mouse was judged to have committed an error
whenever it put its nose inside/above a non-target cup (i.e., one
marked by a non-target cue). Since mice naturally rely more on
odor cues than visual cues for foraging, the odor distractors in
the visual box typically induce more errors (i.e., are more difficult
to ignore), and, hence, those results are better suited to detect indi-
vidual differences in attention. For this reason, we used the average
number of errors in the visual box of the Mouse Stroop Test as a
measure of attention (better attention = fewer errors).
2.2.1.1. Mouse Stroop Test: design in relation to the source of
interference. Regarding the two different types of interference, it
is possible that our mouse version of the Stroop Test, similarly to
the human version, taxes both internal and external sources. As
described above, the critical test trials in the human Stroop Test
(also called ‘‘incongruent trials’’) have color and word providing
conflicting information. This is a clear case of external interference.
However, if test trials are preceded by congruent trials (in which
color and word match), there is an internal source of interference
originating from the repetition of the tendency to read the word
(Cohen, Dunbar, & McClelland, 1990; Kane & Engle, 2003). Simi-
larly, in mice, trials in the odor box (odor as the target) during
the Mouse Stroop Test might create an error-prone expectation
in mice that food will always be on the naturally salient odor cue
instead of the visual cue. Hence, in addition to external interfer-
ence, in the Mouse Stroop Test the animal also needs to ignore
internal interference elicited by the native tendency for odors to
guide searches for food. However, there were only six test trials
in total and they were intercalated, so the error-prone expectation
from trials with odor as target never accumulated (unlike in the
case of the many congruent trials in a row during the human
Stroop Test). For this reason, although the Mouse Stroop Test prob-
ably taxed both external and internal attentions, we believe the
predominant source of interference originated externally.
2.2.2. T-Maze Reversal
For the T-Maze Reversal task, we first trained the mice in a rein-
forced alternation task where they must alternate their foraging
(for a food reward) between two arms. Then, reversal training
began, wherein food was always located in the same arm. This
reversal training required animals to ignore the previously learned
response and maintain attention to the new task requirements. In
other words, the animal must resist a source of interference that
originates internally (i.e., the prior learning).
Fig. 1. The timeline of procedures for the four attention tests for a single run of animals: Mouse Stroop Test, T-Maze Reversal, Coupled Latent Inhibition, and Dual Radial Arm
Maze. The procedures inside the boxes are: acclimation (Acc), odor discrimination (OD), visual discrimination (VD), Stroop Test (Stroop), reinforced alternation (Alt), Reversal
of Alternation (Reversal), Fear Conditioning with Tone (FC Tone), Exposure to Light (Exp Light), Fear Conditioning with Light (FC Light), Radial Arm Maze with the black maze
(Black), Radial Arm Maze with the grey maze (Grey), Radial Arm Maze with the black maze in the morning and grey maze in the afternoon (Black ?Grey), Dual Radial Arm
Maze (DRAM). Numbers indicate duration in days. Space between rectangles indicates the resting time for one run of animals, during which time the other run of animals was
tested. Animals were 90 days old (young adult) when the procedures started.
B. Sauce et al. /Neurobiology of Learning and Memory 116 (2014) 181–192 183

The apparatus consisted of a start arm (9.5 cm wide 31 cm
long) that intersected at its extremity with two choice arms
(39.5 cm long 9.5 cm wide, each), forming a ‘‘T’’ shape that has
10 cm black acrylic glass walls. The initial part of the start arm,
the start area (13.5 cm long), had a vertical door that was remotely
operated. At the entry of each choice arm, there was another remo-
tely operated, vertical door. To help animals distinguish between
arms, one of the arms’ walls had vertical white stripes, and the
other had horizontal white stripes.
On the first day of the reinforced alternation task, animals were
acclimated to the apparatus by giving them four forced choices. A
mouse was held in the start area for 30 s, and then allowed to pass
through, with only the door leading to the right arm opened. After
the food was eaten, we returned the mouse to the start area for a
20-s intertrial interval. We then repeated this procedure with the
right door closed and the left door opened instead. After the food
was eaten, this sequence restarted for a total of four forced-choice
trials. On the day following acclimation, we administered training
trials in which mice chose between either open arms. On the first
trial, food was available in both arms, and the animal was able to
make one free choice. On the second trial, food was only available
in the arm not chosen on the first trial. If an incorrect choice was
made, we allowed the animal to 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 and waited 20 s for the following
trial, now with food only in the opposite choice arm. Accordingly,
the mice had to alternate their choices to most efficiently obtain
food. We administered two days of training with 12 trials per
day. From our previous experience, this level of training is suffi-
cient to support a high level of efficacy.
One day after the completion of training, the T-Maze Reversal
test of attention began. The first four trials were forced alternations
(where the animals can only alternate), administered in the same
manner as the first four trials of the acclimation day. This was to
better instantiate the animals’ tendency to alternate. After this,
12 reversal trials were administered. For this testing, the food
was in the same choice arm every trial, which could be either
always left or always right depending on a free choice that each
animal made at the start of Trial 5. (In this manner, animals could
self-select a side for their first choice. Hence, ‘‘side biases’’ should
not have differentially impacted the group.) To perform well in this
test, a mouse has to learn the new contingency, and thus perfor-
mance during this phase of testing is a function of both new learn-
ing and their capacity to attend exclusively to the new contingency
(and ignore the previous alternation contingency). To specifically
assess animals’ ability to reverse (i.e., ignore a previously learned
response rule, or resist this source of internal interference), we
divided the results from the reversal by the average number of cor-
rect choices during the same period (trials 5–12, out of 24) of the
reinforced alternation task. Hence, this measure reflected an ani-
mal’s reversal performance relative to its respective normal perfor-
mance (and thus should be relatively independent of learning
ability per se). If we had only considered animals’ performance dur-
ing reversal training (irrespective of their performance during
alternation training), an animal that was slow to learn could be
evaluated as performing ‘‘worse’’ than an animal that learns more
quickly, even though the slow learning animal might have higher
attentional abilities. (Such an analysis regimen is crucial, because
although learning and attentional abilities tend to co-vary, they
are not a single process.) We used this as a measure of mice’s
attention (better attention = higher values).
2.2.2.1. T-Maze Reversal: design in relation to the source of interfer-
ence. The type of interference imposed on the animal during T-Maze
Reversal training arises entirely from the mice’s learned tendency to
alternate arms, so we considered it as taxing internal attention.
2.2.3. Coupled Latent Inhibition
In latent inhibition, an animal is repetitively exposed to a stim-
ulus (that will later serve as a CS) that has no explicit meaning (i.e.,
it is presented alone). Subsequently, it is difficult for that animal to
learn to associate that stimulus with a second stimulus (i.e., asso-
ciative learning is impaired). Latent inhibition is often regarded as
an instance where the animal comes to ignore irrelevant stimuli,
and thus is considered to be dependent on variations in attention
(Kaplan & Lubow, 2011; Lubow, 1973). Here, we used a Coupled
Latent Inhibition procedure that, in principle, could assess varia-
tion in attention independently of variation in learning. First, we
conducted a fear conditioning task (tone-shock pairings) to deter-
mine each mouse’s learning rate in the absence of interference (i.e.,
no prior exposure to the CS). Then, we conducted a fear condition-
ing task to determine their rate of learning of a light-shock associ-
ation after extensive latent inhibition trials with non-reinforced
exposure to the light (i.e., interference from prior experience). In
this later case, the animals had to overcome the habit of ignoring
the light, and maintain attention to the new relevance of this stim-
ulus (i.e., its relationship to the shock). Here, the measure of the
effects of latent inhibition (interference) was reflected as the dif-
ference between the rate of learning of the tone-shock and light-
shock associations. (It should be noted that owing to our interest
in individual differences, here it would not have been prudent to
counterbalance the CSs used in this test. To overcome this possible
complication, we chose parameters of the tone and light condition-
ing regimens based on prior research in which we determined val-
ues that supported similar rates of acquisition to each of these
stimuli.)
During the initial fear conditioning (tone-shock) task, we placed
the animals in a box (26 26 21 cm; length width height)
contained within a sound and light-attenuating chamber. The
box was brightly lit (100 Lux) with clear Plexiglas walls, and had
a stainless steel grid floor (spacing of 5 mm) that could deliver a
short (500 ms) 1 mA scrambled foot shock. A camera recorded ani-
mals inside the box for later assessment of freezing during the tone
that predicted the onset of shock (see below). Animals were accli-
mated to the box for 30 min one day before the start of presenta-
tion of any explicit stimuli. During a trial, a mouse was
presented with a tone (CS1, emitting at 60 dBs above background)
for 20 s (cycles of 0.7 s ON and 0.3 s OFF), which terminates with
foot shock (US). The mice received eight trials (in a single day) with
a 4-min intertrial interval. With successive pairings, animals usu-
ally exhibit increased freezing during the CS, a response indicative
of fear and defined here as head and body immobilization with the
four paws on the floor. We calculated the time of CS1 freezing by
measuring the time spent freezing during the 20 s of tone, and sub-
tracting the time spent freezing during 20 s before the tone (the
latter a measure of context freezing). Negative numbers were
assigned a value of ‘‘0’’ since they represent an absence of learning.
During the latent inhibition phase of this test, we used the same
box and chamber, but now with a different smell (thyme powder
added to the inside of the chamber), with illumination coming only
from outside the chamber (5 Lux inside the chamber), and with
walls covered by vertical black stripes (3 cm wide, with 3 cm
between stripes). We did this to minimize any fear associated with
the previous training context. Animals were acclimated to the box
for 30 min one day before the first presentation of any explicit
stimuli. Then, they were exposed to 20 s (cycles of 0.7 s ON and
0.3 s OFF) of flashes of unfiltered light (CS2, emitting 100 Lux)
without any shocks for 48 trials (eight per day for six days), with
a 4-min intertrial interval. These presentations of CS2 without
shock presumably led animals to ignore (habituate to) the CS2.
(Prior work had determined that this pattern of presentation of
CS results in subsequent retardation of a light-shock association,
i.e., induced latent inhibition.) After all mice had been pre-exposed
184 B. Sauce et al. /Neurobiology of Learning and Memory 116 (2014) 181–192

to CS2, we began to pair CS2 with foot shock (i.e., fear condition-
ing). This training proceeded in the same manner as for the tone-
shock pairings. We calculated time of CS2 (light) freezing in the
same way described for CS1 (tone).
Having determined freezing during CS1 and CS2, we then sub-
tracted, for each animal, the average value during acquisition of
fear to CS1(in our case, trials 2–4) from the average value during
the same period(trials 2–4) of acquisition of fear to CS2 (which
had undergone nominal latent inhibition training). The result
was the value for Coupled Latent Inhibition, which is a measure
of attention. Since both of the fear conditioning tasks were depen-
dent on animals’ basic learning ability, this subtraction procedure
provided a measure of the impact of latent inhibition that was
independent of an animal’s characteristic learning ability (e.g. a
slow animal during fear conditioning was compared to itself dur-
ing latent inhibition). Relative to the performance in fear condi-
tioning before latent inhibition, a lower performance in fear
conditioning after latent inhibition leads to negative values in
the Coupled Latent Inhibition measurement. In other words, a good
performance in attention requires values after latent inhibition to
be close to (or higher than) the performance in fear conditioning
before latent inhibition (better attention = higher values).
2.2.3.1. Coupled Latent Inhibition: design in relation to the source of
interference. Interference in Coupled Latent Inhibition arose from
predominantly internal sources because mice had to actively inhi-
bit the learned tendency to ignore CS2, and this was the only con-
sistent distractor. It is important to note that in order for the
distractor’s strength be the same for all animals, the level of habit-
uation (c.f., latent inhibition) to CS2 at the start of CS2-US pairing
must be comparable across all animals. Without a comparable
level of latent inhibition across all animals, our data would be dif-
ficult to interpret. For instance, Sörqvist, Nöstl, and Halin (2012)
found that greater working memory in humans is correlated with
greater habituation rate, and Light, Grossman, Kolata, Wass, and
Matzel (2011) found that higher general learning abilities in CD-
1 mice are correlated with higher habituation rates. Consequently,
a mouse with higher attention would have habituated more to the
CS2, and, consequently, would be slower to learn the CS2-US asso-
ciation (due to a stronger tendency to ignore CS2). Such a pattern
would result in our underestimation of the attentional ability of
animals of high general intelligence. Here, we minimized this com-
plication by exposing animals to a very high number of latent inhi-
bition trials, which in prior work in our laboratory was sufficient to
asymptotically habituate all mice.
2.2.4. Dual Radial Arm Maze
In the Dual Radial Arm Maze, we assessed animals’ ability to
operate simultaneously on two related sets of guidance cues. We
first trained the mice in two different (visually distinct) eight-
arm radial mazes located in a single testing room (which thus
shared extra-maze visual cues). In order to efficiently find food,
each maze requires an animal to use spatial cues (distributed
around the maze) to guide its search and/or to maintain a memory
of arms that have been visited within a trial. After reaching asymp-
totic (near errorless) performance in each maze, the attentional
phase of testing began. During this phase, animals had to alternate
choices between the two mazes. The two mazes were located in a
single room, so the spatial cues were common to both mazes. Since
animals must maintain a memory of the cues segregated according
to the appropriate reference maze, the test required animals to
maintain attention to the spatial cues relevant to the maze in
which it was currently in, and ignore interference from the cues
appropriate for the other maze.
Of the two mazes, one was made of black and the other of grey
Plexiglas. They both consisted of a central area (15 cm of diameter)
with eight arms evenly radiating out (40 cm long and 4.5 cm wide).
The end of each arm had a depression containing a piece of food
(14 mg of Dustless Precision pellets, Bio-Serv). The black maze
had walls of clear acrylic glass enclosing the central area with a
remotely operated door for each arm. Also, the black maze had
walls of clear acrylic glass covering one third of one side of each
arm. The grey maze, in contrast, did not have any walls. The two
mazes were located next to each other in a room with a variety
of visual cues (including architectural details, light strings, and
geometric shapes affixed to the walls).
Before training, mice were acclimated to the black maze. During
the first day of acclimation, all doors were closed. We placed the ani-
mals in one arm at a time; changing arms after a mouse ate the food
piece and spent at least 90 s in the arm. During the second day of
acclimation, we placed the mice in the center of the maze with
one door opened at a time; switching the opened door after a mouse
ate the food at the end of that arm. Due to its similarity with the black
maze, mice were not acclimated to the grey maze.
For training in the black maze, we placed an animal in the cen-
tral area with all doors closed and all arms baited. Then, all doors
were simultaneously opened until an animal made a choice,
counted as hind paws passing 1/4 of an arm’s length. Upon choos-
ing, all of the remaining doors were closed until the subject ate the
food at the end of that arm. The process then restarted with the
animal re-entering the central area through the arm’s door. A trial
continued until the animal ate all eight pieces of food. For the grey
maze, a mouse, after being placed in the central area, was allowed
to move freely until it retrieved the last of the eight pieces of food.
Again, choices were counted as hind paws passing 1/4 of an arm’s
length. If a mouse chose an arm with food but did not eat it, we
treated it as neither a correct choice nor an error. We administered
four trials (one per day) for the black maze and, subsequently, four
trials (one per day) for the grey maze. With this phase of training
over, we started training the mice in both mazes during a single
day: black maze in the morning and grey maze in the afternoon
(four hours after the black maze). This second phase continued
for 10 days.
After all animals were performing well at both mazes (a total of
14 trials in each maze, which, from our past experience, supports
near errorless performance in each maze), we started the Dual
Radial Arm Maze attention test. We placed an animal in the central
area of the black maze, and the usual procedure was followed.
However, after three correct choices, we removed the mouse from
the black maze and placed it in the central area of the grey maze.
The animal was then allowed to navigate through the grey maze
until it made a total of three correct choices. Following this, we
removed the mouse from the grey maze and placed it in the central
area of the black maze. These transfers happened again after three
more correct choices in the black, three more in the grey, the two
final correct choices in the black, and concluded with the two final
corrects in the grey maze. This ‘‘dual maze’’ testing continued for
three trials (one per day).
We recorded the number of errors that an animal made, and
used the average of the three trials (each a sum of errors in the
black and grey mazes) during the Dual Radial Arm Maze test as a
measure of their attention (better attention = fewer errors).
2.2.4.1. Dual Radial Arm Maze: design in relation to the source of
interference. In the Dual Radial Arm Maze, there was only one test
per day, so any internal interference from memory/habit was min-
imized (see Roberts, 1984 for review). On the other hand, because
many of the visual cues guiding the animals overlapped between
mazes, the performance in this task was heavily influenced by
external sources of interference.
B. Sauce et al. /Neurobiology of Learning and Memory 116 (2014) 181–192 185

2.3. Blood glucose levels
Since three of the tests in our attention battery were dependent
on food as a reinforcer, we were concerned that differences in ani-
mals’ glucose responses to food deprivation could be a major factor
in determining the variation in their performance (independent of
attention per se). (Of course, because animals had a typical loss of
5% of their ad libitum body weight before each test, they should
all be motivated. However, this could only affect variation in per-
formance if animals are differentially motivated.) To examine this
potential interaction, we measured blood glucose levels of each
animal twice after the attentional tests were over. We extracted
the blood using tail incision and used a blood glucose meter (One-
Touch Ultra). For the first extraction, mice were food deprived for
one day, while, for the second extraction, they had normal access
to food. For the analyses, the relative value (in %) of blood glucoses
levels during deprivation were compared to the value during ad
libitum. Blood glucose levels are believed to be inversely related
to appetite: hypoglycemia can inhibit the release of the hormone
leptin, leading to hunger (Mueller et al., 1998). Hence, this mea-
surement allowed us to indirectly infer individual differences in
the mice’s motivation to obtain food during the attentional tests.
2.4. Statistical analyses
For the separate evaluation of each test of the attention battery,
we performed two-tailed t-tests and ANOVAs on SPSS 21. We then
categorized each mouse’s attention ability based on its perfor-
mance across the battery. For this, we performed an unrotated
exploratory factor analysis (‘‘principal axis factoring’’) on SPSS 21
of the scores from the four different tests. The analysis identifies
the relationship of variables, and suggests underlying factors that
explain (linearly fits) the variation of all observed variables. This
results in new (latent) variables with high repeatability, since they
aggregate similar measures with minimal error. Since these result-
ing factors from an exploratory factor analysis are nonhypothe-
sized (have no assumptions about their structure), they are
useful to show each measured variable’s contribution (or ‘‘load-
ing’’) before testing hypothetical models in confirmatory factor
analysis (Gerbing & Hamilton, 1996). Here, we considered only fac-
tors with eigenvalues greater than 1 (a widely accepted standard).
To further explore the meaning of each test, we applied a Direct
Oblimin rotation on the factor analysis. This technique makes each
factor have either large or small loadings on any particular variable
by maximizing their variance. Therefore, the Direct Oblimin rota-
tion simplifies the interpretation of different causes for a set of
variables because each original variable tends to be associated with
one of the factors. In both unrotated and rotated exploratory factor
analyses, we considered a factor loading above 0.3 as an indication
that a test is meaningfully loading onto the respective factor
(Tabachnick & Fidell, 2001).
After the exploratory factory analyses, we then tested the two
attention models: (1) the one factor model based on the assump-
tion that a single factor explained the variance across all attentional
tests; (2) the two factor model for source of interference assuming
that T-Maze Reversal and Coupled Latent Inhibition related to
one factor interpreted as internal attention, and the Mouse Stroop
Test and Dual Radial Arm Maze related to another factor inter-
preted as external attention. For this, we used confirmatory factor
analyses based on maximum likelihood estimation in AMOS 21.
This particular estimation for a confirmatory factor analysis has
advantages in cases with relatively small samples (like ours), with
fitness indexes working better with maximum likelihood than with
other statistical estimation procedures (Chou & Bentler, 1995). We
assessed model fit by using three absolute indices (Model Chi-
Square, RMSEA, and SRMR) that describe how well the model
represents the observed data, where lower values mean better fit
(hence, they are also referred to as tests of ‘‘badness-of-fit’’)
(Kline, 2011). For the Model Chi-Square (
v
2M
), the null hypothesis
is the model itself, so failing to reject it (i.e., a small Model Chi-
Square) indicates a good fit (with alpha here set at 0.05) (Kline,
2011). Following a similar reasoning, RMSEA values of 0.06 and
below are considered good (while values greater than .10 are con-
sidered a poor fit), as well as SRMR values of 0.09 and below (Hu &
Bentler, 1999). In addition to these three absolute indices, we also
assessed model fit with two incremental indices (CFI and TLI) that
describe how well the model fits in comparison a to a baseline
model where all variables are uncorrelated and without latent
variables, and where higher values mean better fit (‘‘goodness-of-
fit’’ in the literal sense) (Kline, 2011). CFI and TLI indicate an ade-
quate model fit at values of 0.95 or above (Hu & Bentler, 1999).
We chose these tests due to their statistical relevance and frequent
use (Hooper, Coughlan, & Mullen, 2008; Kline, 2011). For hierarchi-
cal comparisons between the one factor (general) model and the
two factor (source of interference) model, we performed the Chi-
Square Difference Test (
v
2D
), where the null hypothesis represents
no differences between the models.
In the process of training animals for their ultimate tests of
attention, animals underwent acquisition of five basic learning
tasks (odor discrimination, visual discrimination, reinforced alter-
nation, fear conditioning, and Radial Arm Maze). Consequently,
the rate of acquisition on each of these tasks can be used to con-
struct an aggregate measure of animals’ general learning perfor-
mance (see Kolata et al., 2008, for details of this procedure). For
this purpose, acquisition scores were entered into an unrotated
exploratory factor analysis, and a factor score (based on the pri-
mary, or ‘‘general learning’’ factor) was computed for each animal.
A factor score is analogous to an average z score based on an ani-
mal’s performance on each individual test, weighted by the degree
to which that test loads on the primary factor. Thus factor scores
serve to rank each animal on the variable captured by the primary
factor (in this case, general learning ability).
3. Results
First, we describe the training phases and summarized results
from each test of the attention battery for all 26 animals. We then
describe the results of our factor analysis, which revealed the two
different, and meaningful, latent variables. Then, we describe the
results of the confirmatory factor analyses which estimated the
goodness-of-fit for a model with a single latent factor (‘‘attention’’)
explaining the performance across tests of attention, as well as for
a model of external and internal attention as distinct (although
correlated) latent factors. Lastly, we examined the relationship of
variation in attention to individual animal’s general learning
performance.
3.1. Individual tests of the attention battery
3.1.1. Mouse Stroop Test
The mean performance of animals during the training for odor
discrimination and visual discrimination is presented in Fig. 2.At
the end of both tasks, mice were performing better than the two
errors expected by random chance (although errors by chance
could easily exceed two if choices were repeatedly directed toward
a non-target food cup). Animals started odor discrimination train-
ing making on average 17.54 (±1.20) errors to find food, and by the
12th trial errors had fallen to 0.92 (±0.23), a significant decrease
across trials (t
25
= 13.45, p< 0.0001) with a large effect on perfor-
mance (Cohen’s d= 3.13). For visual discrimination, however, mice
began training with a low number of errors, and thus did not
186 B. Sauce et al. /Neurobiology of Learning and Memory 116 (2014) 181–192

improve from trials 1–16, (t
25
= 1.72, p> 0.05). This likely reflected
the fact that animals were previously trained on a task (odor dis-
crimination) which had very similar demands, and thus in this
task, animals could focus exclusively on learning the relatively
simple visual discrimination.
The right portion in Fig. 2 illustrates the group mean perfor-
mance in the visual box of the Mouse Stroop Test (i.e., in the pres-
ence of odor distractors). The group mean number of errors during
the first of these tests was significantly higher than the average
performance on the last day (Trials 13–16) of visual discrimination
training (t
25
= 2.43, p< 0.05), revealing the negative impact of the
addition of the odor distractors.
3.1.2. T-Maze Reversal
Fig. 3A illustrates the relative frequency of correct responses for
the group of animals as a function of trials. Animals started the task
correctly choosing approximately half of the time, as expected by
chance. On the 23rd trial, all animals made a correct choice, a sig-
nificant difference from the first trial (W=105, p< 0.001). This
indicates that animals had reached a high level of performance
prior to the reversal task. The results for the T-Maze Reversal are
seen in Fig. 3B. During this reversal phase (in which reinforcement
was always located on the same side), animals performance fell
well below chance levels, indicating a propensity to continue to
alternate choices. However, by the 12th trial, animals’ performance
had improved to the pre-reversal level.
3.1.3. Coupled Latent Inhibition
We observed rapid learning of the tone-shock association
absent prior exposure to the tone CS (i.e., without prior latent inhi-
bition training; Fig. 4A), with the mean time of freezing for CS1
(tone predicting shock, and calculated by the time freezing during
the CS1 minus the time freezing before the CS1) increasing more
than seven times across trials (i.e., at the start of Trial 6). Even with
a drop in performance during the final two pairings (Trials 7 and 8),
mice’s performance on the last trial (Trial 8) was still significantly
different than on the first trial (t
22
= 3.82, p< 0.001), indicative of
the formation of a tone-shock association (Cohen’s d= 0.83).
The bottom curve in Fig. 4A illustrates the acquisition of fear to
a light (that was initially presented unpaired) when paired with
shock. Mice were slow to acquire a fear response during light-
shock pairings following extensive exposure to the light in the
absence of shock (i.e., latent inhibition). Acquisition of fear to the
light was slower than the acquisition of fear to the tone across
all trials (repeated measure ANOVA task trial, F
1,401
= 93.00,
p< 0.0001). However, even after latent inhibition training, animals
ultimately acquired the light-shock association, with the mean
time of freezing for CS2 (light predicting shock, and calculated by
the time freezing during the CS2 minus the time freezing before
the CS2) rising 3 s at its maximum value, during Trial 7. There
was a significant difference from the first and last trials
(t
25
= 4.62, p< 0.0001), indicative of successful (although weak)
acquisition (Cohen’s d= 1.21). (It is worth re-iterating that the slow
acquisition of fear in response to the light relative to the tone is not
likely the result of innate differences in the ability of these stimuli
to support learning. The parameters for these stimuli were specif-
ically chosen based on prior work which determined that these
stimuli would support similar rates of learning when paired with
shock. Instead, the slow learning during light-shock pairings is pre-
sumed to reflect the extensive prior experience that the animals
had with the unreinforced light.)
The performance of mice for Coupled Latent Inhibition is a mea-
sure of the performance on fear conditioning after latent inhibition
minus the performance on fear conditioning with no prior latent
inhibition. A very high attentional capacity should lead to higher
values (close to zero). A value of zero here means that the animal
learned about CS2 (after latent inhibition) at the same rate as CS1.
3.1.4. Dual Radial Arm Maze
During training in both the black and grey Radial Arm Mazes,
mice gradually improved their performance (Fig. 5), making signif-
icantly fewer errors during the last trial (Trial 14) compared to the
first (t
25
= 2.81, p< 0.01 in the black maze, and t
25
= 5.20, p< 0.0001
in the grey maze), corresponding to moderate (Cohen’s d= 0.57)
and large (Cohen’s d= 0.87) effect sizes, respectively. In addition,
Fig. 5 illustrates the performance of all animals in the Dual Radial
Arm Maze phase of testing. As illustrated, errors increased in this
more complex phase of Radial Arm Maze testing (where animals
were simultaneously performing in two mazes that shared an
Fig. 2. Average number of errors for the 26 mice to retrieve food across 12 trials of
odor discrimination training, across 16 trials of visual discrimination training, and
across three trials in the Mouse Stroop Test (visual discrimination box in the
presence of odor distracters). Brackets indicate standard error of the mean.
Fig. 3. A. Average frequency of correct choices for 26 mice across 12 trials of
reinforced alternation training. B. Average frequency of correct choices for 26 mice
across 12 trials of T-Maze Reversal. Dashed lines indicate a level of random
responding (i.e., 50% correct choices). Brackets indicate standard error of the mean.
B. Sauce et al. /Neurobiology of Learning and Memory 116 (2014) 181–192 187

overlapping set of visual guidance cues). For subsequent analyses,
fewer errors in this phase indicated better attention.
3.2. Exploratory factor analyses of the attention tests
Table 1 shows the descriptive statistics of all 26 animals on each
of the four attention tests that contributed to the factor analysis (as
well as on the learning tasks described further below). The values
entered for each test are as follows: (1) Mouse Stroop Test = aver-
age number of errors in the visual box in the presence of task-rel-
evant odor distractors (Trials, 1–3), (2) T-Maze Reversal = number
of correct choices during the reversal phase divided by the number
of correct choices during the alternation learning phase (Trials 5–
12), (3) Coupled Latent Inhibition = CS2 (light) time of freezing dur-
ing light-shock pairings (following latent inhibition training)
minus CS1 (tone) time of freezing during tone-shock pairing
(hence, the existence of negative numbers; Trials 2–4), and (4)
Dual Radial Arm Maze = average number of errors for the sum of
black and grey mazes during trials in which mice alternated
choices between the two mazes (Trials, 1–3).
Table 1 also contains skewness and kurtosis measures of the
data distribution. For skewness, values between 2 and 2 are con-
sidered to be inside the acceptable range for a normal distribution
(Gardner, 2001). Note that SPSS uses a formula that estimates the
Fig. 4. A. Average time of ‘‘CS freezing’’ (time spent freezing during the 20 s of tone minus the time spent freezing during 20 s before the tone) for 26 mice across 8 trials of
fear conditioning (upper curve) and fear conditioning to the light after pre-exposure (i.e., latent inhibition) to the light (bottom curve). Brackets indicate standard error of the
mean.
Fig. 5. Average number of errors for 26 mice across 14 trials of black and grey maze training, and across 3 trials of Dual Radial Arm Maze. Brackets indicate standard error of
the mean.
Table 1
Mean, standard deviation, range, and measures of skewness and kurtosis of the performance of the 26 mice in the attention tests and the learning tests.
Test Mean Standard deviation Range Skewness
*
Kurtosis
*
Attention

Mouse Stroop Test 1.91 1.17 4.7 to 0.3 1.13 0.87
T-Maze Reversal 1.14 0.46 0.5 to 2.5 1.19 1.59
Coupled Latent Inhibition 2.89 3.48 10.4 to 2.7 0.35 0.47
Dual Radial Arm Maze 5.00 3.21 10.8 to 0.3 0.36 0.90
Learning
à
Odor discrimination 2.66 1.59 7.6 to 0.4 1.48 2.97
Reinforced alternation 0.59 0.15 0.3 to 0.9 0.04 0.11
Fear conditioning 4.93 2.6 0.5 to 11.1 0.57 0.1
Radial Arm Maze 8.83 2.2 13.2 to 4.4 0.33 0.08

Four tests of attention. Mouse Stroop Test (number of errors), T-Maze Reversal (correct choices in reversal per correct choices in normal learning), Coupled Latent Inhibition
(seconds of CS freezing during latent inhibition minus seconds of CS freezing during fear conditioning), and Dual Radial Arm Maze (number of errors).
à
Four tests of learning. Odor Discrimination (number of errors), reinforced alternation (correct choices), fear conditioning (seconds of CS freezing), and Radial Arm Maze
(number of errors). Each of the four learning tests was part of an attention test.
*
For skewness, values between 2 and 2 are considered to be inside the acceptable range for normality. For kurtosis, values between 3 and 3 are considered to be inside
the acceptable range for normality.
188 B. Sauce et al. /Neurobiology of Learning and Memory 116 (2014) 181–192

skewness in the population (Z
g1
), and, hence, its acceptable range
differs from the more common formula (g1) of 1to1(Gardner,
2001). For kurtosis, values between 3 and 3 are considered to
be inside the acceptable range for a normal distribution (Sheskin,
2003). Hence, dependent measures on all attention tests were well
within the range of normalcy and had an adequate distribution for
the exploratory factor analyses and the confirmatory factor analy-
ses that followed.
Table 2 presents the results of the unrotated exploratory factor
analysis of animals’ performance on the four attentional tests.
Regarding the first factor, the Mouse Stroop Test and the Dual
Radial Arm Maze (each impacted by external (E) sources of inter-
ference) had a considerable loading (>0.5), while the T-Maze
Reversal had a modest loading, and the Coupled Latent Inhibition
did not load at all. A common influence on attentional performance
accounted for approximately 44% of the total variation in test
scores between mice. For the second factor, which accounted for
29% of total variation, Coupled Latent Inhibition had a considerable
loading (>0.5), T-Maze Reversal had a modest loading (where both
tasks are impacted primarily by internal (I) sources of interfer-
ence), and neither the Mouse Stroop Test nor the Dual Radial
Arm Maze loaded.
The Direct Oblimin rotation revealed the same overall pattern
found in the unrotated factor analysis, and more clearly separated
two distinct factors underlying overall performance across the four
tests (Table 3). The Mouse Stroop Test and the Dual Radial Arm
Maze loaded strongly on the first factor, while T-Maze Reversal
and Coupled Latent Inhibition loaded on the second factor
(although the T-Maze Reversal also had a slight loading on the first
factor, with a value above the threshold of 0.3). Like the unrotated
exploratory factor analysis (Table 2), these results suggest two dif-
ferent influences in our attention battery that match with our
expectation of a differential influence of sources of interference
(external and internal). Through the confirmatory factor analysis,
we were able to further verify this claim.
3.3. Confirmatory factor analyses of proposed models
The confirmatory factor analyses are described separately for
each model: ‘‘single attention’’ and ‘‘external/internal attention’’.
The model with only a single factor of attention (all tests
together under a single potential cause) had a good fit based on
three indices (
v
2M
2) = 3.04, p= 0.219, failing to reject the null
hypothesis of model and observed data being the same;
CFI = 0.97, where values above .95 represent a good fit;
SRMR = 0.08, where values below 0.09 represent a good fit), and
a relatively poor fit in the other two (TLI = 0.90, where values above
.95 represent a good fit; RMSEA = 0.07, where values below 0.06
represent a good fit). This suggests that one factor is not sufficient
to capture the common variance among the four attentional tests.
The two factor model of external/internal attention (T-Maze
Reversal and Coupled Latent Inhibition with the factor interpreted
as internal attention, and Mouse Stroop Test and Dual Radial Arm
Maze with the factor interpreted as external attention) had a very
good fit to the data (as suggested from the unrotated and rotated
exploratory factor analyses). The Model Chi-Square failed to reject
the null hypothesis of model and observed data being the same,
v
2M
(1) = 0.41, p= 0.522. The RMSEA and the SRMR values were
<0.001 and 0.08, respectively, suggesting a very good fit, and, the
CFI and the TLI values were 1 and 1.27, respectively, further sug-
gesting that this model explains well the performance of the mice
in the attention battery. The parameters estimated for this model
(Fig. 6) shows similarly high loading of Mouse Stroop Test and Dual
Radial Arm Maze on External Attention, and a very high loading of
T-Maze Reversal and a moderate loading of Coupled Latent Inhibi-
tion on the hypothesized internal attention factor. The correlation
between external and internal attention was 0.43.
Even though having a very good fit, the ‘‘external/internal atten-
tion’’ model, though, was not significantly different than the ‘‘sin-
gle attention’’ model,
v
2D
(1) = 2.63, p= 0.105. Since the ‘‘single
attention’’ model did not fit well in two of the four indices, it might
be the case that the external/internal attention model is indeed
superior, but we may have failed to detect a significant difference
to the single factor model due to our low sample size.
3.4. The influence of blood glucose
We observed a significant reduction in the mice’s blood glucose
levels under food deprivation (t
25
= 11.47, p< 0.001). The percent-
age of blood glucose after food deprivation relative to ad libitum
levels was, on average, 65.39, with SD = 15.22, and did correlate
with the ad libitum levels (r
24
=0.23, p> 0.05). This variation in
the loss of blood glucose did not load with either of the two result-
ing factors of an exploratory factor analysis containing the four
Table 2
Factor loadings and variance explained by the first two factors extracted from the four
attention tests. (E) and (I) indicate tasks influenced by primarily (E)xternal and
(I)nternal sources of interference.
Attention test Factor 1 Factor 2
Mouse Stroop Test (E) .64 .03
T-Maze Reversal (I) .41 .42
Coupled Latent Inhibition (I) .02 .58
Dual Radial Arm Maze (E) .87 .16
Eigenvalue 1.76 1.18
Proportion of total variance 43.9% 29.4%
Table 3
Factor loadings and variance explained by the first two rotated factors (Direct Oblimin
rotation) from the four attention tests. (E) and (I) indicate tasks influenced by
primarily (E)xternal and (I)nternal sources of interference.
Attention test Factor 1 Factor 2
Mouse Stroop Test (E) .65 .09
T-Maze Reversal (I) .38 .50
Coupled Latent Inhibition (I) .06 .56
Dual Radial Arm Maze (E) .87 .01
Fig. 6. Confirmatory factor analysis of the two factor model with internal and
external attentions.
v
2M
(1) = 0.41, p= 0.522, RMSEA < 0.001, SRMR = 0.08, CFI = 1,
TLI = 1.27. Parameters are standardized.
B. Sauce et al. /Neurobiology of Learning and Memory 116 (2014) 181–192 189

attention tests (Table 4). Also, the variation in the loss of blood glu-
cose did not correlate with either the first factor (r
24
= 0.04,
p> 0.05) or the second factor (r
24
=0.21, p> 0.05) extracted from
the attention battery.
3.5. Relationship of attention and learning
Rate of acquisition could be determined for four of the five tests
of learning that were administered here. (Animals exhibited near-
asymptotic levels of performance on the first trial of visual discrim-
ination training, and thus acquisition rates were not considered.) A
factor analysis was performed on the acquisition rates of individual
animals on these four learning tasks (descriptive statistics is
described in Table 1): odor discrimination (Trials 2–6), reinforced
alternation (Trials 5–12), Fear Conditioning (Trials 2–4), and Radial
Arm Maze (Trials 2–6). A single factor accounted for 37% of the var-
iance across animals, indicative of a general influence on learning
across all tasks. (This level of explanatory value is comparable to
most of our previous work with larger batteries of learning tasks
and larger sample sizes, e.g., Kolata et al., 2008; Matzel et al.,
2011; Matzel, Grossman, Light, Townsend, & Kolata, 2008.) We
then derived factor scores for each animal from this analysis.
Scores ranged from 2.99 to 1.10, where higher values indicate
better aggregate performance across all tasks. Scores had a skew-
ness of 1.6 and a kurtosis of 2.9; thus these values are inside
the acceptable range of normality. We entered these learning
scores into a factor analysis with the performance of the animals
on each of the four tests of attention. The results of this analysis
are presented in Table 5. On the first factor, all tests but the Cou-
pled Latent Inhibition had a moderate to strong positive loading
together with the learning factor scores (the latter of which is
indicative of general learning ability). For the second factor, the
Mouse Stroop Test and Dual Radial Arm Maze did not load, while
performance on the T-Maze Reversal and Coupled Latent Inhibition
had positive loadings contrasted with the learning factor’s negative
loading, suggesting that they are inversely related. The implica-
tions of these results are addressed more fully below.
4. Discussion
Using outbred CD-1 mice, we observed considerable differences
in individual performance across four distinct tests of attention.
Contrary to our expectation, results here suggest that there is weak
(if any) evidence that the described ‘‘attention battery’’ can be used
as a robust measure of ‘‘general’’ attention. On the other hand, our
results here suggest that the (often ignored!) distinction between
internal and external sources of interference could have an impor-
tant role in how an individual utilizes attention. Furthermore, we
found that these internal and external attentional systems can
potentially explain some of the variation in learning abilities. The
following examination of results from the exploratory and confir-
matory factor analyses makes the implications of our findings
more clear.
Considering the design of all tests of the attention battery
together (as described in Section 2.2), half the tests had primarily
external sources of interference (odor cues of Mouse Stroop Test
and overlapping spatial cues of Dual Radial Arm Maze), while the
remaining two tests had primarily internal sources of interference
(prior learned alternation during T-Maze Reversal, and the habitu-
ated CS during latent inhibition training). Regarding our expecta-
tions that the source of interference (external and internal)
might influence variation in attention, the results of the unrotated
factor analysis and the Direct Oblimin rotation agreed with each
test’s reliance on these different attentional directions. Perfor-
mances in the Mouse Stroop Test and the Dual Radial Arm Maze
were the strongest predictors of the first rotated factor, and both
had external distractors as their main source of interference. In
comparison, the T-Maze Reversal and the Coupled Latent Inhibition
loaded strongly with the second factor of the Direct Oblimin rota-
tion, and both required attention exclusively (or nearly so) against
internal sources of interference (the previously learned response/
stimulus). The results from the confirmatory factor analysis further
suggest a distinction between internal and external influences on
attention, with all indices exhibiting a very good fit with a model
that assumed such a distinction. Internal attention and external
attention are indeed believed to be distinct processes that help bal-
ance the levels between abstractions (e.g., recalls from memory
and the projection of cognitive maps) and of sensorial input
(Johnson, 2006), with different tasks and environmental demands
possibly requiring different levels of resistance to internal and
external sources of interference. Furthermore, those two systems
engage some distinct brain regions in humans, with lateral regions
of the PFC being preferentially activated when attention is directed
to internal representations (and preferentially connected to
regions in the prefrontal and parietal cortex), whereas the medial
PFC is activated when attention is directed to sensory events
(and connected to the basal ganglia, thalamus, and sensory associ-
ation cortices) (Henseler, Krüger, Dechent, & Gruber, 2011).
Despite the differences, however, internal and external attention
do (as one would expect) have very similar cognitive properties
(for a review, see Chun & Johnson, 2011) and many of their under-
lying brain regions overlap in humans (Nobre et al., 2004). Our
obtained correlation of 0.43 between internal and external atten-
tion might reflect some of these overlapping properties.
Given that three of the four tests in the attention battery used
dependent measures that were reliant on food deprivation (Mouse
Stroop Test, T-Maze Reversal, and Dual Radial Arm Maze), one
might suppose that the common variance between them (indi-
cated by the loading in the first factor of both unrotated and
rotated analyses) arises from the animals’ motivation to obtain
food. However, this conclusion is unlikely given our results. Blood
glucose levels (see Table 4) did not load either onto the first factor
(with the three appetitive tests), or onto the second factor (with
the appetitive test T-Maze Reversal, and the aversive test Coupled
Latent Inhibition). Furthermore, as described above (Section 2.2),
Table 4
Factor loadings and variance explained by the tests of attention and the loss in blood
glucose levels (% from ad libitum levels).
Variable Factor 1 Factor 2
Mouse Stroop Test .71 .11
T-Maze Reversal .32 .32
Coupled Latent Inhibition .22 .96
Dual Radial Arm Maze .78 .09
Loss of blood glucose .14 .25
Eigenvalue 1.77 1.37
Proportion of total variance 35.3% 27.4%
Table 5
Factor loadings and variance explained by the tests of attention and general learning
performance (learning factor). (E) and (I) indicate tasks influenced by primarily
(E)xternal and (I)nternal sources of interference.
Variable Factor 1 Factor 2
Mouse Stroop Test (E) .72 .04
T-Maze Reversal (I) .40 .60
Coupled Latent Inhibition (I) .06 .44
Dual Radial Arm Maze (E) .78 .05
Learning factor .38 .36
Eigenvalue 1.91 1.33
Proportion of total variance 38.3% 26.5%
190 B. Sauce et al. /Neurobiology of Learning and Memory 116 (2014) 181–192

the food deprivation procedure consisted of 90 min of free access
to food, meaning that individual differences in motivation to
obtain food were already limited by this long window of opportu-
nity to gather food. Hence, our results provide evidence that the
deprivation (and subsequent decrease in blood glucose levels,
and hunger) was not a major source of the co-variation in perfor-
mance across tests. It is important to remark that there could still
be other explanations behind the variation in the attention battery
here that are not related to attention. Task persistence, general
level of motivation, or even personality could be traits representing
the factors we obtained. We certainly do not believe that attention
is the only trait explaining the common variance in the attention
battery. However, from the explicit design/rationale of the tests,
we believe it is likely and more parsimonious to infer that atten-
tion is playing a major role in establishing the observed factor
structure.
The learning factor (general learning ability) had a moderate,
positive correlation with the external attention factor (the first
attention factor) and a moderate, negative correlation with the
internal attention factor (the second attention factor). We can then
ask: why is high attention to internal sources of interference
related to poor learning? All of the learning data here were derived
from tasks that were new to the mice. However, the two internal
attention tests (T-Maze Reversal and Coupled Latent Inhibition)
had changes in contingencies that were previously well known to
the animal; while the two external attention tests (Mouse Stroop
Test and Dual Radial Arm Maze) did not. This point suggests that
tests of attention that are highly dependent on internal sources
of interference (thus reflecting internal attention) may correlate
with poor performance in standard learning tasks, while tests of
external attention may correlate with good performance in stan-
dard learning tasks, but correlate with poor performance in learn-
ing tasks with subtle changes in conditions (like in reversals).
We hope that results here can assist in creating better measure-
ments of attention in laboratory animals, and more importantly, a
better appreciation of the different properties of attention, and
their relation to general cognitive abilities (i.e., ‘‘intelligence’’) in
non-human animals. In sum, the present work adds some insight
to our understanding of variations in cognitive performance, and
provides a more complete foundation for studies of the neurobio-
logical basis of attention in non-human animals.
Acknowledgments
This work was supported by Grants from the National Institute
of Aging (R01AG029289), the Busch Foundation, and the Office of
Naval Research (N000141210873).
References
Allen, G., & Courchesne, E. (2001). Attention function and dysfunction in autism.
Frontiers in Bioscience: A Journal and Virtual Library, 6(7), D105–D119. http://
dx.doi.org/10.1093/intimm/dxs032.
Barch, D. M. (2005). The cognitive neuroscience of schizophrenia. Annual Review of
Clinical Psychology, 1, 321–353. http://dx.doi.org/10.1146/
annurev.clinpsy.1.102803.143959.
Carroll, J. B. (1993). Human cognitive abilities: A survey of factor analytic studies.
Cambridge: Cambridge University Press.
Chou, C.-P., & Bentler, P. M. (1995). Estimates and test in structural equation
modeling. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and
applications. Sage.
Chun, M. M., & Johnson, M. K. (2011). Memory: Enduring traces of perceptual and
reflective attention. Neuron, 72(4), 520–535. http://dx.doi.org/10.1016/
j.neuron.2011.10.026.
Cohen, J. D., Dunbar, K., & McClelland, J. L. (1990). On the control of automatic
processes: A parallel distributed processing account of the Stroop effect.
Psychological Review, 97(3), 332–361. http://dx.doi.org/10.1037/0033-
295X.97.3.332.
Conway, A. R., Kane, M. J., Bunting, M. F., Hambrick, D. Z., Wilhelm, O., & Engle, R. W.
(2005). Working memory span tasks: A methodological review and user’s guide.
Psychonomic Bulletin and Review, 12(5), 769–786. http://dx.doi.org/10.3758/
BF03196772.
Conway, A. R., Kane, M. J., & Engle, R. W. (2003). Working memory capacity and its
relation to general intelligence. Trends in Cognitive Sciences, 7(12), 547–552.
http://dx.doi.org/10.1016/j.tics.2003.10.005.
Cronbach, L. J. (1957). The two disciplines of scientific psychology. American
Psychologist, 12(11), 671–684. http://dx.doi.org/10.1037/h0043943.
Dempster, F. N., & Corkill, A. J. (1999). Individual differences in susceptibility to
interference and general cognitive ability. Acta Psychologica, 101(2–3), 395–416.
http://dx.doi.org/10.1016/S0001-6918(99)00013-X.
Engle, R. W. (2002). Working memory capacity as executive attention. Current
Directions in Psychological Science, 11(1), 19–23. http://dx.doi.org/10.1111/
1467-8721.00160.
Gardner, R. (2001). Psychological statistics using SPSS for windows. Upper Saddle
River, New Jersey: Prentice Hall.
Gerbing, D. W., & Hamilton, J. G. (1996). Viability of exploratory factor analysis as a
precursor to confirmatory factor analysis. Structural Equation Modeling: A
Multidisciplinary Journal, 3(1), 62–72. http://dx.doi.org/10.1080/
10705519609540030.
Henseler, I., Krüger, S., Dechent, P., & Gruber, O. (2011). A gateway system in rostral
PFC? Evidence from biasing attention to perceptual information and internal
representations. NeuroImage, 56(3), 1666–1676. http://dx.doi.org/10.1016/
j.neuroimage.2011.02.056.
Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling:
Guidelines for determining model fit. Electronic Journal of Business Research
Methods, 6(1), 53–60.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure
analysis: Conventional criteria versus new alternatives. Structural Equation
Modeling: A Multidisciplinary Journal, 6(1), 1–55. http://dx.doi.org/10.1080/
10705519909540118.
Jarosz, A. F., & Wiley, J. (2012). Why does working memory capacity predict RAPM
performance? A possible role of distraction. Intelligence, 11. doi:10.1016/
j.intell.2012.06.001.
Jarrold, C., & Towse, J. N. (2006). Individual differences in working memory.
Neuroscience, 139(1), 39–50. http://dx.doi.org/10.1016/
j.neuroscience.2005.07.002.
Jensen, A. R. (1998). The g factor: The science of mental ability (human evolution,
behavior, and intelligence). New York: Praeger.
Johnson, M. K. (2006). Memory and reality. The American Psychologist, 61(8),
760–771. http://dx.doi.org/10.1037/0003-066X.61.8.760.
Johnson, M. K., Reeder, J. a., Raye, C. L., & Mitchell, K. J. (2002). Second thoughts
versus second looks: An age-related deficit in reflectively refreshing just-
activated information. Psychological Science, 13(1), 64–67. doi:10.1111/1467-
9280.00411.
Kane, M. J., & Engle, R. W. (2002). The role of prefrontal cortex in working-memory
capacity, executive attention, and general fluid intelligence: An individual-
differences perspective. Psychonomic Bulletin and Review, 9(4), 637–671. http://
dx.doi.org/10.3758/BF03196323.
Kane, M. J., & Engle, R. W. (2003). Working-memory capacity and the control of
attention: The contributions of goal neglect, response competition, and task set
to Stroop interference. Journal of Experimental Psychology: General, 132(1),
47–70. http://dx.doi.org/10.1037/0096-3445.132.1.47.
Kaplan, O., & Lubow, R. E. (2011). Ignoring irrelevant stimuli in latent inhibition and
Stroop paradigms: The effects of schizotypy and gender. Psychiatry Research,
186(1), 40–45. http://dx.doi.org/10.1016/j.psychres.2010.07.025.
Kline, R. B. (2011). Principles and practice of structural equation modeling. Guilford
press.
Kolata, S., Light, K., Grossman, H. C., Hale, G., & Matzel, L. D. (2007). Selective
attention is a primary determinant of the relationship between working
memory and general learning ability in outbred mice. Learning and Memory
(Cold Spring Harbor, N.Y.), 14(1), 22–28. doi:10.1101/lm.408507.
Kolata, S., Light, K., & Matzel, L. D. (2008). Domain-specific and domain-general
learning factors are expressed in genetically heterogeneous CD-1 mice.
Intelligence, 36(6), 619–629. http://dx.doi.org/10.1016/j.intell.2007.12.001.
Kolata, S., Light, K., Wass, C. D., Colas-Zelin, D., Roy, D., & Matzel, L. D. (2010). A
dopaminergic gene cluster in the prefrontal cortex predicts performance
indicative of general intelligence in genetically heterogeneous mice. PLoS ONE,
5(11), e14036. http://dx.doi.org/10.1371/journal.pone.0014036.
Kuntsi, J., Oosterlaan, J., & Stevenson, J. (2001). Psychological mechanisms in
hyperactivity: I response inhibition deficit, working memory impairment, delay
aversion, or something else? Journal of Child Psychology and Psychiatry, 42(2),
199–210. http://dx.doi.org/10.1111/1469-7610.00711.
Lavie, N., & Fox, E. (2000). The role of perceptual load in negative priming. Journal of
Experimental Psychology: Human Perception and Performance, 26(3), 1038–1052.
http://dx.doi.org/10.1037/0096-1523.26.3.1038.
Light, K., Grossman, H., Kolata, S., Wass, C., & Matzel, L. D. (2011). General learning
ability regulates exploration through its influence on rate of habituation.
Behavioural Brain Research, 223(2), 297–309. http://dx.doi.org/10.1016/
j.bbr.2011.04.050.
Lister, R., Mukamel, E. a., Nery, J. R., Urich, M., Puddifoot, C. a., Johnson, N. D., et al.
(2013). Global epigenomic reconfiguration during mammalian brain
development. Science (New York, N.Y.). doi:10.1126/science.1237905.
Lubow, R. E. (1973). Latent inhibition. Psychological Bulletin, 79(6), 398–407. http://
dx.doi.org/10.1037/h0034425.
Mackintosh, N. (2011). IQ and human intelligence. Oxford: Oxford University Press
(p. 456).
B. Sauce et al. /Neurobiology of Learning and Memory 116 (2014) 181–192 191

MacLeod, C. M. (1991). Half a century of research on the Stroop effect: An
integrative review. Psychological Bulletin, 109(2), 163–203. http://dx.doi.org/
10.1037/0033-2909.109.2.163.
Matzel, L. D., Grossman, H., Light, K., Townsend, D., & Kolata, S. (2008). Age-related
declines in general cognitive abilities of Balb/C mice are associated with
disparities in working memory, body weight, and general activity. Learning and
Memory (Cold Spring Harbor, N.Y.), 15(10), 733–746. doi:10.1101/lm.954808.
Matzel, L. D., & Kolata, S. (2010). Selective attention, working memory, and animal
intelligence. Neuroscience and Biobehavioral Reviews, 34(1), 23–30. http://
dx.doi.org/10.1016/j.neubiorev.2009.07.002.
Matzel, L. D., Light, K., Wass, C., Colas-Zelin, D., Denman-Brice, A., Waddel, A. C.,
et al. (2011). Longitudinal attentional engagement rescues mice from age-
related cognitive declines and cognitive inflexibility. Learning and Memory (Cold
Spring Harbor, N.Y.), 18(5), 345–356. doi:10.1101/lm.2034711.
Mirsky, a F., Anthony, B. J., Duncan, C. C., Ahearn, M. B., & Kellam, S. G. (1991).
Analysis of the elements of attention: A neuropsychological approach.
Neuropsychology Review, 2(2), 109–145. http://www.ncbi.nlm.nih.gov/
pubmed/1844706.
Mueller, W. M., Gregoire, F. M., Stanhope, K. L., Mobbs, C. V., Mizuno, T. M., Warden,
C. H., et al. (1998). Evidence that glucose metabolism regulates leptin secretion
from cultured rat adipocytes. Endocrinology, 139(2), 551–558. http://dx.doi.org/
10.1210/endo.139.2.5716.
Mysore, S. P., & Knudsen, E. I. (2013). A shared inhibitory circuit for both exogenous
and endogenous control of stimulus selection. Nature Neuroscience, 16(4),
473–478. http://dx.doi.org/10.1038/nn.3352.
Nee, D. E., Brown, J. W., Askren, M. K., Berman, M. G., Demiralp, E., Krawitz, A., &
Jonides, J. (2012). A meta-analysis of executive components of working
memory. Cerebral Cortex (New York, N.Y.: 1991) (pp. 1–19), (Barch 2005).
doi:10.1093/cercor/bhs007.
Nee, D. E., & Jonides, J. (2008). Dissociable interference-control processes in
perception and memory. Psychological Science, 19(5), 490–500. http://
dx.doi.org/10.1111/j.1467-9280.2008.02114.x.
Nobre, a. C., Coull, J. T., Maquet, P., Frith, C. D., Vandenberghe, R., & Mesulam, M. M.
(2004). Orienting attention to locations in perceptual versus mental
representations. Journal of Cognitive Neuroscience, 16(3), 363–373.
doi:10.1162/089892904322926700.
Roberts, W. A. (1984). Some issues in animal spatial memory. In H. S. T. H. L. Roitblat
& T. G. Bever (Eds.), Animal cognition (pp. 425–441). New York, NY: Psychology
Press.
Sauce, B., & Matzel, L. D. (2013). The causes of variation in learning and behavior:
Why individual differences matter. Frontiers in Psychology, 4(July), 395. http://
dx.doi.org/10.3389/fpsyg.2013.00395.
Schweizer, K., Moosbrugger, H., & Goldhammer, F. (2005). The structure of the
relationship between attention and intelligence. Intelligence, 33(6), 589–611.
http://dx.doi.org/10.1016/j.intell.2005.07.001.
Sheskin, D. J. (2003). Handbook of parametric and nonparametric statistical procedures
(3rd ed.). CRC Press.
Sörqvist, P., Nöstl, A., & Halin, N. (2012). Working memory capacity modulates
habituation rate: Evidence from a cross-modal auditory distraction paradigm.
Psychonomic Bulletin and Review, 19(2), 245–250. http://dx.doi.org/10.3758/
s13423-011-0203-9.
Spearman, C. (1904). General intelligence, objectively determined and measured.
The American Journal of Psychology, 15(2), 201–292.
Tabachnick, B. G., & Fidell, L. S. (2001). Principal components and factor analysis. In
B. G. Tabachnick & L. S. Fidell (Eds.), Using multivariate statistics (4th ed.,
pp. 582–652). Needham: Allyn and Bacon.
Tangney, J. P., Baumeister, R. F., & Boone, A. L. (2004). High self-control predicts
good adjustment, less pathology, better grades, and interpersonal success.
Journal of Personality, 72(2), 271–324. http://dx.doi.org/10.1111/j.0022-
3506.2004.00263.x.
Tiitinen, H., Sinkkonen, J., Reinikainen, K., Alho, K., Lavikainen, J., & Näätänen, R.
(1993). Selective attention enhances the auditory 40-Hz transient response in
humans. Nature, 364(6432), 59–60. http://dx.doi.org/10.1038/364059a0.
Tsal, Y., Shalev, L., & Mevorach, C. (2005). The diversity of attention deficits in
ADHD: The prevalence of four cognitive factors in ADHD versus controls. Journal
of Learning Disabilities, 38(2), 142–157. http://dx.doi.org/10.1177/
00222194050380020401.
Van der Maas, H. L. J., Dolan, C. V., Grasman, R. P. P. P., Wicherts, J. M., Huizenga, H.
M., & Raijmakers, M. E. J. (2006). A dynamical model of general intelligence: The
positive manifold of intelligence by mutualism, 113 (4), 842–861.
Wass, C., Denman-Brice, A., Rios, C., Light, K. R., Kolata, S., Smith, A. M., et al. (2012).
Covariation of learning and ‘‘reasoning’’ abilities in mice: Evolutionary
conservation of the operations of intelligence. Journal of Experimental
Psychology. Animal Behavior Processes, 38(2), 109–124. http://dx.doi.org/
10.1037/a0027355.
Wass, C., Pizzo, A., Sauce, B., Kawasumi, Y., Sturzoiu, T., Ree, F., et al. (2013).
Dopamine D1 sensitivity in the prefrontal cortex predicts general cognitive
abilities and is modulated by working memory training. Learning and Memory
(Cold Spring Harbor, N.Y.), 20(11), 617–627. http://dx.doi.org/10.1101/
lm.031971.113.
192 B. Sauce et al. /Neurobiology of Learning and Memory 116 (2014) 181–192