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Analysis of Learning Deficits in Aged Rats on the W-Track Continuous Spatial Alternation Task

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Young and aged animals were tested on a spatial alternation task that consisted of two interleaved components: (1) an “outbound” or alternation component (working memory) and (2) an “inbound” component, requiring the animal to remember to return to a central location in space (spatial memory). In the present study, aged rats made more outbound errors throughout testing, resulting in significantly more days to reach learning criterion, as compared to young rats. Furthermore, while all animals were able to learn the hippocampus-dependent inbound component of the task, most aged animals remained just above chance on the outbound component, even after extended testing days. Aged rats may be more impaired on the outbound part of the task because it requires cooperation of both the hippocampus and mPFC, each of which is compromised with age. In addition to presenting these results, we compare one commonly used analysis (repeated measures ANOVA) and two less common hierarchical modeling techniques (hierarchical generalized linear model and state-space random effects model) to determine the best method for comparing population learning over time. We found that hierarchical modeling is the most appropriate for this task and that a state-space model better captures the behavioral responses.
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Analysis of learning deficits in aged rats on the W-track
continuous spatial alternation task
Adele J. Kapellusch1,2, Adam W. Lester1,2, Benjamin A. Schwartz1,2, Anne C. Smith1,2, and
Carol A. Barnes1,2,3
1Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ
2Division of Neural System, Memory and Aging, University of Arizona, Tucson, AZ
3Departments of Psychology, Neurology and Neuroscience, University of Arizona, Tucson, AZ
Abstract
Young and aged animals were tested on a spatial alternation task that consisted of two interleaved
components (1) an “outbound” or alternation component (working-memory) and (2) an “inbound”
component, requiring the animal to remember to return to a central location in space (spatial
memory). In the present study, aged rats made more outbound errors throughout testing, resulting
in significantly more days to reach learning criterion, as compared to young rats. Furthermore,
while all animals were able to learn the hippocampus-dependent inbound component of the task,
most aged animals remained just above chance on the outbound component, even after extended
testing days. Aged rats may be more impaired on the outbound part of the task because it requires
cooperation of both the hippocampus and mPFC, each of which is compromised with age. In
addition to presenting these results, we compare one commonly used analysis (repeated measures
ANOVA) and two less common hierarchical modeling techniques (hierarchical generalized linear
model and state-space random effects model) to determine the best method for comparing
population learning over time. We found that hierarchical modeling is the most appropriate for this
task and that a state-space model better captures the behavioral responses.
Keywords
learning; memory; aging; prefrontal cortex; hippocampus; spatial working memory; alternation
task; state-space methods; hierarchical models
INTRODUCTION
The hippocampus and the medial prefrontal cortex (mPFC) are part of a functional system
involved in memory-guided decision making, a cognitive process particularly vulnerable to
age-related decline in human and animal models of aging. It is known that the hippocampus
encodes episodic memories including spatial memory e.g., (M. Smith & Milner, 1981;
Morris, Garrud, Rawlins, & O’Keefe, 1982), while the PFC supports working memory
function e.g., (Petrides & Milner, 1982; Granon & Poucet, 1995; Delatour & Gisquest-
Verrier, 1999). These two brain structures are directly connected via a unidirectional
projection from the ventral hippocampus to the mPFC (Ferino, Thierry, & Glowinski, 1987;
Jay, Glowinski, & Thierry, 1989; Swanson, 1981). Hippocampal input to the cells in the
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mPFC likely provides spatial context to promote learning on spatial memory tasks (Jung,
Qin, McNaughton, & Barnes, 1998) and damage or inhibition to the hippocampal-prefrontal
cortical circuit can result in significantly impaired learning on spatial alternation tasks
(Wang & Cai, 2006).
The W-track continuous spatial alternation task (Frank, Brown, & Wilson, 2000), in which
rats learn to alternate arm visits in order to receive a food reward, has been demonstrated to
be a good test of both hippocampus and prefrontal cortex function. There are two interleaved
components of this task: an “outbound” or alternation component (working memory) and an
“inbound” component, requiring the animal to remember to return to the center arm (spatial
memory). The inbound component is primarily dependent on the hippocampus. The
outbound component, in contrast, likely utilizes both the PFC to maintain a working
memory of the previously visited arm, as well as the hippocampus to localize the currently
rewarded position in absolute space. Previous work has shown that rats with lesions to the
hippocampus are impaired in learning all components of the task and show a pattern of
perseverative inbound errors during initial learning (Kim & Frank, 2009). Although
hippocampal lesions result in slower learning rates, animals are still able to reach learning
criterion with time, suggesting adaptive compensation among parallel cognitive networks.
Our aims in this paper are two-fold. The first aim is to examine the behavior of young and
aged animals performing the W-track continuous spatial alternation task to compare
potential age-related changes in hippocampus and PFC function. There is abundant evidence
that age-associated changes in cognition are accompanied by deficits in spatial memory e.g.,
(Barnes, 1979; Birren, 1962; Goodrick, 1972; Markowska et al., 1989; Gallagher & Rapp,
1997) and in working memory e.g., (Frick, Baxter, Markowska, Olton, & Price, 1995; Ando
& Ohashi, 1991; Bimonte, Nelson, & Granholm, 2003). The impact of brain aging has not
been examined on this task with respect to learning rates that demand the interaction of both
memory systems. We hypothesize that aged rats will be slower to acquire this task and may
show behavioral characteristics similar to that of rats with lesions to the hippocampus (Kim
& Frank, 2009).
The second aim is to compare different analytical approaches for modeling learning and
detecting performance differences between two age groups. The raw behavioral responses
are modeled using four methods: a state-space model applied to each individual’s behavior, a
repeated measures ANOVA, a hierarchical generalized linear model, and a hierarchical state-
space model. The first approach makes use of state-space smoothing to estimate the most
likely learning curve for each animal (A. Smith et al., 2004). The second approach is a
standard, off-the-shelf analysis-of-variance (ANOVA) to compare population learning rates.
Approaches 2 and 3 make use of hierarchical generalized linear models (Gelman & Hill,
2006) and hierarchical state-space models (A. Smith, Stefani, Moghaddam, & Brown, 2005),
respectively. Hierarchical, or multi-level models, are advantageous because they
simultaneously account for individual performance while at the same time providing an
estimate of group performance.
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METHODS
Subjects
Two experiments were performed with the same protocol over two different testing period
lengths. In the first experiment, 16 young (9 month) and 12 aged (23 month) male Fischer
344 rats were tested over 14 consecutive days. Because most aged animals were unable to
acquire the working memory-dependent component of the task in these 14 sessions, a
second experiment was conducted to examine behavior over an extended testing period. In
this follow-up experiment, 6 additional young and 6 additional aged rats were tested over a
longer testing period of 21 consecutive days.
The experiments described here follow guidelines established by the National Institutes of
Health and were approved by the Institutional Animal Care and Use Committee at the
University of Arizona. Rats were obtained from the National Institute of Aging’s colony at
Charles River and were housed in separate cages and maintained on a reverse 12hr:12hr
light/dark cycle. Each rat was restricted to a regulated diet to maintain their weight at 85%
of their original body weight in order to motivate the animals to run for food reward.
Animals had
ad libitum
access to water.
Testing Apparatus and Data Collection
The behavioral apparatus (Figure 1 (a)) consists of an 150cm × 90cm acrylic track with three
identical arms, each with a small food dish at the end. Infrared sensors along the track
monitor the animals position and signal food reward delivery upon a correct arm visit using
an Arduino microcontroller and a miniature inert liquid valve. Figure 1 (b-e) shows a
schematic of the apparatus. Liquid food reward consisted of diluted vanilla nutrition shake
(
Ensure
) and approximately 0.3 mL was administered upon every correct arm visit.
Timestamps, correct/incorrect decisions, and position data were collected through a custom
desktop application written in C#.
Behavioral Paradigm
Animals were first “pre-trained” to run for food rewards on a 150cm long linear track with a
food dish at the end of each arm. The rat was required to walk the entire length of the track
to receive a reward (this was considered a
lap
), and no reward was given for consecutive
repeat visits to the same food dish. Rats were trained twice a day for 20 minutes until they
were able to complete 30 laps in 20 minutes, at which point testing on the W-track began.
In each testing session, the animal had one hour to freely explore the track and to collect as
many food rewards as they were able to retrieve. At the start of a session, the rat was first
placed on the center arm, facing the direction of the food dish at the end of the arm (Figure
1b). After running to the end of the center arm and receiving a reward, the rat must then visit
one of the outside arms (Figure 1c), return to the center (Figure 1d), and then visit the
opposite outside arm (Figure 1e). The rat continues alternating between the outside arms,
always returning to the center arm in-between each outbound trial. If, at any point, the
animal makes an incorrect decision, they must return back to the center arm and resume the
pattern.
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Data Analysis
Individual Performance: State-Space Model.—Individual performance was measured
using a state-space model applied to each animal’s performance (A. Smith et al., 2004). On
each day,
k
, the observation for the
j
th animal is the number of correct responses,
nj,k
, in the
mj,k
trials performed during the one hour session. The individual performance for each rat is
estimated using the state-space model described in detail in (A. Smith et al., 2004). Briefly,
we assume each animal has an underlying cognitive state that is governed by a random walk.
The model assumes the observations are binomial and computes the most likely cognitive
state that fits the data via the Expectation-Maximization algorithm. From the cognitive state,
it is then possible to compute a probability of a correct response for each animal.
A “learning session” (the session where the animal successfully acquired the task) was
considered to be a session where the lower 90% confidence bound of the performance was
above chance and remained above chance for the remaining trials in the testing period.
Young and old animals’ learning session estimates were compared using the non-parametric
Mann-Whitney U-test.
Group Performance: Repeated Measures ANOVA.—The first analysis method used
to compare young and aged group performance is a repeated measures ANOVA. This is a
commonly used approach that compares the differences between groups on measurements
gathered sequentially. It tests the null hypothesis that our sample means come from the same
population mean. The ANOVA analysis requires calculation of the variation across days,
SStime
the variation within group,
SSwithin
and the variation across subjects,
SSsubjects
. Each
subject is treated as a level of an independent factor (i.e. each observation is independent and
identically distributed, IID) and assumes the test variables follow a multivariate normal
distribution.
We compute the sum of squares over time,
SStime
, from
SStime =
k= 1
K
Jk(x
kx
)2(1)
where
Jk
is the total number of subjects the
k
th day, x
k is the mean score of the
k
th day,
1
KΣk= 1
Knj,k
mj,k, and x
is the grand mean 1
KΣk= 1
Knj,k
mj,k and x
=1
JΣj= 1
J. The sum of squares for
within subjects,
SSwithin
, is computed from:
SSwithin =
young
(xjyoung x
young)2+
old
(xjold x
old)2.(2)
Similarly, sum of squares for between subjects,
SSsubjects
, is
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SSsubjects =K(x
jx
)2.(3)
The sum of squares error,
SSerror
, is defined as
SSerror =SSwithin SSsubjects .(4)
And the resulting F statistic for
K
days and
J
animals is computed:
F=SStime
(K− 1) ×(J− 1)(K− 1)
SSerror
(5)
Group Performance: Hierarchical Generalized Linear Model (GLM) and
Hierarchical State-Space Model.—We present two alternative methods here: a
hierarchical generalized linear model (model 1, GLM) and a hierarchical state-space model
(model 2, SS).
Group performance can be well-characterized using a random effects or hierarchical
modeling approach since this allows, via the exchangeability assumption, an accurate
estimate of group variability. For our analysis we fit each group (young and aged) with a
hierarchical GLM and hierarchical SS model, both described below. A Bayesian approach is
used to estimate the model using the Python package PYMC3 (
Python Software and
Foundation. Python Language Reference (version 2.7) Available at http://www.python.org
,
2010; Salvatier, Wiecki, & Fonnesbeck, 2016).
The observation model can be expressed as the binomial probability density function:
Pr(nj,kpj,k,xj,k) =
mj,k
nj,k
pj,k
nj,k(1 − pj,k)1 − nj,k(6)
where the probability of a correct response,
pj,k
, relates to the state with a logistic function:
pj,k=exp(xk+βj)
1 + exp(xk+βj).(7)
The parameter β
j
is unique to each animal and is assumed to be drawn from a normal
distribution with zero mean and variance σβ
2:
βj=N(0, σβ
2) . (8)
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The variance σβ
2 describes the spread of the individuals about the group mean. For model 1
(GLM), we define the state by the linear equation:
xj,k=αjk+ ∊k(9)
where α
j
is a constant and ϵ
k
is Gaussian random noise with zero mean and variance σ
2. For
model 2 (SS) we define the state by the random walk:
xj,k=xj,k− 1 + ∊k.(10)
Priors for both models are as follows:
x0~𝒩(0, 0.01), (11)
σ~𝒰(0.01, 10.0) (12)
and
σb= ~𝒰(0.01, 1.0) (13)
Convergence was assessed by visual inspection of the time series of 1000 Monte Carlo
estimates after an initial burn-in of 500 iterations was discarded.
Performance Comparison for Bayesian Models.—We use the Watanabe-Akaike
Criterion (WAIC) (Watanabe, 2010) to compare model fits and predictive accuracy. The
WAIC is a measure of the pointwise out-of-sample prediction accuracy from a fitted
Bayesian model using the log-likelihood evaluated at the posterior simulations of the
parameter values (Vehtari, Gelman, & Gabry, 2017). It is defined by
WAIC = − 2(lpd pwaic)(14)
where lpd is the computed log pointwise predictive density given by
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lpd =
j= 1
J
k= 1
k
log 1
SS
s= 1p(nj,kxj,k
(s),x0
(s),σ
2(s),σβ
2(s))p(xj,k
(s)xj,k− 1
(s))p(βj
(s)σβ
2(s))p
(σβ
2(s))p(σ
2(s))p(x0
(s))
(15)
and pwaic is the estimated effective number of parameters given by
pwaic =
j= 1
J
k= 1
k
Vs= 1
Slog p(nj,kxj,k
(s),x0
(s),σ
2(s),σβ
2(s))p(xj,k
(s)xj,k− 1
(s))p(βj
(s)σβ
2(s))p(σβ
2(s)
)p(σ
2(s))p(x0
(s)) .
(16)
Here, superscript
s
indicates the
sth
draw from the estimated posterior distributions and
Vs= 1
S is the variance of the samples with respect to the mean.
RESULTS
Individual Performance Analysis
Figures 2 (a) and 2 (b) show the modes of the learning curves from a state-space analysis of
each individual animal for inbound and outbound decisions, respectively, across the 14-day
and 21-day testing protocols. With the exception of four old animals performing the shorter
protocol, all animals’ performance tends to improve across time. It is also evident that for
both young (green lines) and aged animals (purple lines), the inbound component (Figure
2a) of the task is learned more quickly than is the outbound component (Fig 2b). Figure 3
provides a summary of the learning trials computed from the lower 90% confidence bounds
of the state-space model fits to each animal’s performance data for the two different length
experiments considered separately.
Shorter 14 Day Testing Protocol.—For the inbound learning trials in the 14 day testing
protocol the young animals learned significantly sooner than the old animals (Figure 3a,
Mann-Whitney-U-test
p
= 0.03,
U
= 56.5; mean,
μyoung
= 3.0, standard deviation,
σyoung
=
1.17; mean,
μold
= 3.92, standard deviation,
σold
= 1.38). For the outbound component of the
task, young animals learned approximately halfway through the testing period, whereas 10
of the 12 aged animals never learned this component of the task (Figure 3b). Assuming the
learning trial for the animals that did not learn in 14 trials was 15, we find again that the two
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age groups were significantly different (Figure 3a, Mann-Whitney-U-test
p
< 0.001,
U
=
15.0; YOUNG: μ
young
= 8.71, σ
young
= 2.42; OLD: μ
old
= 14.2, σ
old
= 2.59]
Since learning in the outbound component was not observed in the aged animals, a second
experiment was conducted with an extended testing period of 21 sessions.
Longer 21 day testing protocol.—The group that was tested over 21 sessions had
similar inbound learning sessions as the first cohort though in this case the between-age
group difference was not significant (Figure 3b, Mann-Whitney-U-test
p
= 0.5,
U
= 17.5;
μ
young
= 3.33, σ
young
=1.03; μ
old
= 4.33, σ
old
= 3.14).
For the outbound group, all animals learned during the extended testing paradigm. The
learning session for the outbound component is significantly later for older animals than
young (Mann-Whitney-U-test
p
= 0.0099,
U
= 3.0, μ
young
= 8.33, σ
young
= 1.97; aged: μ
old
=
16.0, σ
old
= 4.65).
Group Performance Analysis
In order to accurately infer group statistics, three different analysis approaches were used:
(1) repeated measures ANOVA, (2) hierarchical generalized linear model (GLM), and(3)
hierarchical state-space.
Repeated Measures ANOVA.—The average proportion of correct responses per session
computed from the raw behavioral performance data is shown in Figure 4. The animals in
the young group, on average, perform better than did those in the aged group (green lines >
purple lines) and this age difference appears to be more pronounced in the outbound trials.
The error bars are larger from session 15 onwards because the estimate is based on fewer
observations. There is a tendency for both groups to perform slightly below chance at the
beginning of the experiment on the inbound trials. We hypothesize that this is a result of pre-
training on a linear track, as, during pre-training, animals are conditioned to run from one
end of an arm to the other end without making any turns in the middle of their path. This
may explain why both groups tend to make more inbound errors at the beginning of the
testing period.
We looked for age effects using a two-way ANOVA for repeated measures, a conventional
method used in population learning studies. For the group with 14 testing sessions, younger
animals performed better than old animals in the outbound part of the task (F(1,27) = 36.36,
p
< 0.0001) but not for the inbound component (F(1,27) = 2.74,
p
= 0.11). This was also true
of the group that underwent extended testing (Inbound: F(1,12) = 1.53,
p
= 0.24 Outbound:
F(1,12) = 31.44,
p
< 0.0001)
Hierarchical GLM and Hierarchical State-Space Random Effects Models.—The
second two approaches use hierarchical modeling. First, a hierarchical GLM was fit to the
data (Figure 5) for both the 14-session and extended testing procedures. The young animals’
performance significantly exceeded the aged animals’ performance for the outbound part of
the task, but was not significantly different for the inbound part of the task. The GLM also
shows a greater separation of the groups, when compared to the mean performance data
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(Figure 4). It is clear that there is a separation of performance between the young and aged
animals, but the learning curves do not look comparable to the raw data (Figure 4).
Second, a hierarchical state-space random effects model was fit to the data (Figure 6).
Comparing the group learning curves with the GLM estimates, we see a general overall
trend for the young to perform better than the aged do (green > purple). However, the state-
space model has greater curvature, especially for the inbound trials which are learned more
quickly.
To assess which of the two hierarchical models best represents the data we looked at their
respective WAICs (Table 1). In all cases, the WAIC of the hierarchical state-space model is
lower than that of the hierarchical GLM, indicating better fit or representation of the data.
It is clear from Figure 6 that young animals learn the working memory-dependent part of
this task significantly (outbound) faster and their performance remains above the
performance of old animals throughout testing, even with additional testing sessions.
DISCUSSION
There were two primary novel findings that emerged from these experiments. First, aged rats
made more errors throughout testing on the W-track spatial alternation task, requiring more
days to reach learning criterion. In addition, aged animals remain below young rats’
performance on the outbound (working memory) component of the task, even with extended
testing. The inbound component was not, however, substantially affected by age after
learning criterion was met. Aged rats may be more impaired on the outbound part of the task
because it required cooperation between the hippocampus and the mPFC, each of which is
compromised with age, and when required to work together for task solution, may
compound the performance deficit.
The deficits observed in aged rats on this task suggest impaired hippocampal-prefrontal
interactions. There are at least two possible sources of these deficits, including altered
synchrony across oscillatory networks in the hippocampus and the PFC (Jones & Wilson,
2005), impaired reactivation of behavioral experiences via awake or sleep sharp wave ripple
events(Tang, Shin, Frank, & Jadhav, 2017), or a combination of these. To that end, future
directions of this work should include performing simultaneous dual-region ensemble
recordings from these two structures. A comparison of young and aged animals with both
behavior and electrophysiology will provide a deeper insight into how age impacts network
dynamics between these two regions. Additionally, these experiments may identify the
source of wide variability in performance within both age groups, and may be particularly
interesting in animals that demonstrate atypical behavioral performance for their age.
The second main finding of this study was that hierarchical state-space modeling appears to
be the most effective analysis method for comparing young and old rats’ behavior on the W-
track spatial alternation task. Although a state-space model analysis fit to each animal’s
behavioral performance is sufficient for analyzing an individual’s learning over time and
provides us with an idea of the distribution of performance, it does not allow session-by-
session comparison of the two groups. To initially compare the learning of young and aged
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animals on this task, we performed a repeated measures ANOVA test, which is commonly
used in experiments that measure an observation over time. While the repeated measures
ANOVA did provide support for overall age differences on the task performance, this
statistic was not helpful in identifying the subtle features of the learning differences between
age groups.
To achieve a more fine grained evaluation of behavior differences, hierarchical modeling
seemed well-suited because such models assume that observations from individuals come
from the same underlying distribution, allowing us to detect individual differences without
ignoring their inherent similarities. For that reason, we fit a hierarchal GLM and a
hierarchical state-space model to the data and compared their fit to the raw behavioral data.
Our assessment of the hierarchical GLM analysis is that it is too rigid to capture the
curvature of the actual learning process. In practice, the hierarchical state-space model
appears to be better suited to describe animal learning rates, as it is better able to capture the
dynamics of the behavioral responses over time.
In summary, older rats made more errors overall and took longer to reach learning criterion
on the W-track spatial alternation task than did younger rats. When the main component
parts of the task were viewed independently, the old rats were the most impaired on the
outbound, spatial working memory aspect of the task - the component that requires an
interaction between prefrontal cortex and the hippocampal systems. The analytic tool that
was most sensitive to these age differences was the hierarchical state-space model, which
should prove extremely useful for analysis of complex behavioral data.
ACKNOWLEDGEMENTS
We would like to thank Jessica Brewster, Olivia Pietz, and Revathi Pillai with the assistance in the collection of
data, as well as Michelle Albert, Luann Snyder, and Michael Montgomery for administrative support. We would
also like to acknowledge software consultant, Daniel Kapellusch, for guidance in the creation of the custom-made
W-Track desktop application for data collection.
This work was supported by the McKnight Brain Research Foundation and National Institutes of Health Grant R01
AG050548.
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Figure 1. The W-Track Spatial Alternation Task.
TOP: (a) Schematic of the W-track apparatus. The 150cm × 90cm track has three identical
arms. Liquid food rewards are dispensed into a food dish at the end of each arm. BOTTOM:
Sequential illustration of correct performance on the W-track continuous spatial alternation
task, demonstrating one possible sequence of arm visits that would result in a correct choice
and a reward at every arm. (b) The animal starts at the center arm, facing the direction of the
food dish at the end of the arm. An outbound decision (c and e) is considered to be any trial
in which the rat departed from the center arm, while an inbound decision (d) is considered to
be any trial in which the rat departed from either outside arm.
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Figure 2. Individual learning analysis through state-space analysis.
Each animal’s proportion of correct decisions were fit to a state-space model and the
resulting learning curves for the inbound (a) and outbound (b) component of the task are
plotted here for both the animals that completed 14 sessions and animals that completed 21
sessions. Young animals are plotted in green and old are in purple.
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Figure 3. Summary of Learning Sessions.
(a) Learning sessions for animals that were tested for 14 sessions. Ten of the 12 aged
animals were unable to learn the outbound component of the task in 14 sessions. (b)
Learning sessions for animals that were tested for 21 sessions. All animals were able to learn
the outbound component of the task with this extended testing paradigm.
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Figure 4. Mean task performance.
Average group performance with 95% confidence intervals on the inbound (a) and
outbound(b) components of the task for all animals tested. The extended testing period for
the 12 animals is shaded in blue. Mean performance was used for the repeated measures
ANOVA.
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Figure 5. Hierarchical GLM analysis.
Results of the hierarchical GLM fit to data for inbound decisions (a) and outbound decisions
(b).
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Figure 6. Hierarchical state-space analysis.
Results of the hierarchical state-space random effects model fit to data for inbound decisions
(a) and outbound decisions (b).
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Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Kapellusch et al. Page 18
Table 1
WAIC values for hierarchical models using 14-session-data (LEFT) and 21-session-data (RIGHT). In all cases,
the Hierarchical State-Space model has a better fit than does the hierarchical GLM.
Age Trial Type Model WAIC Age Trial Type Model WAIC
Young
Inbound Hierarchical State Space 162.81
Young
Inbound Hierarchical State-Space 301.54
GLM 345.16 GLM 430.90
Outbound Hierarchical State-Space 101.84 Outbound Hierarchical State-Space 147.01
GLM 150.31 GLM 159.30
Aged
Inbound Hierarchical State-Space 216.42
Aged
Inbound Hierarchical State-Space 283.74
GLM 464.63 GLM 449.07
Outbound Hierarchical State-Space 89.33 Outbound Hierarchical State Space 166.60
GLM 146.80 GLM 169.72
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... When dealing with many aging captive nonhuman primate populations, we must consider the effects of their cognitive abilities, motivation, and participation in experiments that vary across the 262 RAWLINGS, LEGARE, BROSNAN, AND VALE life span because these will ultimately have consequences on the conclusions we draw. Whether animals attempt and persist at tasks, for example, can decline with age (Barbary macaques, Macaca sylvanus; Rathke & Fischer, 2020), whereas perseveration with known solutions or strategies can increase (rhesus macaques; Lai et al., 1995; e.g., on aging and cognitive decline in other species, see Chapagain et al. [2020] for dogs, Kapellusch et al. [2018] for rats, and Kwapis et al. [2020] for mice). Openness, linked with cognitive performance, also changes over the chimpanzee life span-with males in particular decreasing over adulthood (Rawlings et al., 2020). ...
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