Crowded environments reduce spatial memory in older but not younger adults

Abstract

Previous studies have reported an age-related decline in spatial abilities. However, little is known about whether the presence of other, task-irrelevant stimuli during learning further affects spatial cognition in older adults. Here we embedded virtual environments with moving crowds of virtual human pedestrians (Experiment 1) or objects (Experiment 2) whilst participants learned a route and landmarks embedded along that route. In subsequent test trials we presented clips from the learned route and measured spatial memory using three different tasks: a route direction task (i.e. whether the video clip shown was a repetition or retracing of the learned route); an intersection direction task; and a task involving identity of the next landmark encountered. In both experiments, spatial memory was tested in two separate sessions: first following learning of an empty maze environment and second using a different maze which was populated. Older adults performed worse than younger adults in all tasks. Moreover, the presence of crowds during learning resulted in a cost in performance to the spatial tasks relative to the ‘no crowds’ condition in older adults but not in younger adults. In contrast, crowd distractors did not affect performance on the landmark sequence task. There was no age-related cost on performance with object distractors. These results suggest that crowds of human pedestrians selectively capture older adults’ attention during learning. These findings offer further insights into how spatial memory is affected by the ageing process, particularly in scenarios which are representative of real-world situations.

Full text

Merriman et al.
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Crowded environments reduce spatial memory in older but not
younger adults
Niamh A. Merriman1, Jan Ondřej2, Alicia Rybicki1, Eugenie Roudaia1,
Carol O’Sullivan2, and Fiona N. Newell1*
1 School of Psychology and Institute of Neuroscience, Trinity College Dublin, Ireland;
2 Graphics, Vision and Visualisation Group, School of Computer Science and Statistics, Trinity College Dublin,
Ireland;
This is an Accepted Manuscript of an article published by Springer
Berlin Heidelberg in Psychological Research on 25/10/2016, available
online: https://doi.org/10.1007/s00426-016-0819-5
Cite this article as:
Merriman, N.A., Ondřej, J., Rybicki, A. et al. Psychological Research
(2018) 82: 407. https://doi.org/10.1007/s00426-016-0819-5

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Abstract
Previous studies have reported an age-related decline in spatial abilities. However, little is known about
whether the presence of other, task-irrelevant stimuli during learning further affects spatial cognition in older
adults. Here we embedded virtual environments with moving crowds of virtual human pedestrians (Experiment
1) or objects (Experiment 2) whilst participants learned a route and landmarks embedded along that route. In
subsequent test trials we presented clips from the learned route and measured spatial memory using three
different tasks: a route direction task (i.e. whether the clip shown was a repetition or retracing of the learned
route); an intersection direction task; and a task involving identity of the next landmark encountered. In both
experiments, spatial memory was tested in two separate sessions: first following learning of an empty maze
environment and second using a different maze which was populated. Older adults performed worse than
younger adults in all tasks. Moreover, the presence of crowds during learning resulted in a cost in performance
to the spatial tasks relative to the ‘no crowds’ condition in older adults but not in younger adults. In contrast,
crowd distractors did not affect performance on the landmark sequence task. There was no age-related cost on
performance with object distractors. These results suggest that crowds of human pedestrians selectively capture
older adults’ attention during learning. These findings offer further insights into how spatial memory is affected
by the ageing process, particularly in scenarios which are representative of real-world situations.
[242 words]

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Introduction
Older adults experience age-related decline across a number of cognitive functions (Madden, Whiting, &
Huettel, 2010; Park & Reuter-Lorenz, 2009) with evidence that spatial cognition is particularly affected
(Antonova et al., 2009; Driscoll et al., 2003; Konishi et al., 2013; Moffat, Zonderman, & Resnick, 2001).
Navigating through a large-scale environment, in which it is not possible to encode all the relevant spatial
information from a single viewpoint (Castelli, Latini Corazzini, & Geminiani, 2008), is a complex cognitive task
involving spatial memory and abstraction of the relationship between landmarks in the environment; but active
navigation also involves spatial attention and perceptual functions including obstacle avoidance, object
recognition and the perception of optic flow. However, little is known about how these functions contribute to
spatial cognition in older adults.
It is argued that successful navigation can be achieved by two separate spatial strategies. First, an
egocentric strategy, typically used for navigating a familiar route, involves the encoding of the spatiotemporal
sequence of features or landmarks in the environment relative to one’s own position in the environment and the
motion direction required to navigate to the next landmark (Hartley, Maguire, Spiers, & Burgess, 2003;
Wolbers, Weiller, & Büchel, 2004). Second, an allocentric spatial strategy, typically used when navigating a
new environment, involves a more global representation or ‘cognitive map’ of the environment in which
landmark locations are characterised by their spatial relationship to one another (see e.g. O’Keefe & Nadel,
1978; Tolman, 1948). Moreover, it has been argued that the ability to switch between these strategies, according
to the demands and complexity of environment, is a hallmark of successful navigation (Harris, Wiener, &
Wolbers, 2012; Rich & Shapiro, 2009).
Age-related deficits in the ability to successfully navigate have previously been reported (see Moffat,
2009 for review), with a reported decline in allocentric and, to a lesser extent, egocentric processing with age.
For example, older adults perform worse than younger adults in memory for the temporal order of landmarks,
and the direction taken at these landmarks (Head & Isom, 2010). Also, Wiener et al. (2012) reported worse
performance by older adults in tasks involving the discrimination of the learned direction taken through a route,
suggesting a greater age-related deficit in allocentric compared to egocentric processing. An important facet of
spatial learning is the ability to retrace one’s steps, that is, to navigate a recently travelled route in a new
environment from the end to the start (Foster & Wilson, 2006; Wiener et al., 2012). Findings from
electrophysiological recordings from place cells within the hippocampus of rats offer evidence as to the
allocentric processing involved in route retracing. Specifically, Foster & Wilson reported that when a rat stops at

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the end of a travelled path in a novel environment, the entire behavioural sequence of movement across place
cells is replayed in reverse order, beginning with those place cells corresponding to the animal’s current position
at the path end-point and ending with those place cells that were active at the beginning of travel (i.e. start-
point). Furthermore, these reverse-order replays occurred to a greater extent following navigation through a
novel compared to familiar environment. This result suggests that the retracing of a just-travelled route may
have a crucial role in supporting initial spatial learning in hippocampal-based tasks (Foster & Wilson, 2006).
Related functions have also been shown to decline with increasing age. For example, the ability to
accurately perceive optic flow in order to guide heading direction during walking has been shown to deteriorate
with age (Berard, Fung, McFadyen, & Lamontagne, 2009; Warren, Blackwell, & Morris, 1989). This ability
may be linked to a decline in motion perception per se in older adults (Arena, Hutchinson, & Shimozaki, 2012;
Roudaia, Bennett, Sekuler, & Pilz, 2009) which may lead, in turn, to a decline in the ability to navigate in the
presence of other moving stimuli. Visuospatial attention is another function which may be affected by ageing.
For example, older adults are often worse than younger adults at detecting and localising targets embedded
among distracting visual stimuli (e.g. Lustig, Hasher, & Tonev, 2006; McCarley, Yamani, Kramer, & Mounts,
2012). Furthermore, distracting stimuli have also been shown to negatively affect working memory performance
in older adults (Chao & Knight, 1997). The effect of distracting stimuli on performance by older adults may be
due to the reduced ability of older adults to inhibit the processing of irrelevant information during a task
requiring working memory (Hasher & Zacks, 1988). In line with this interpretation, the findings reported by
Gazzaley, Cooney, Rissman, & D’Esposito (2005) support the notion that the inability of older adults to inhibit,
or filter out, irrelevant visual information may be the result of impaired top-down modulation of attentional
control. In their experiment, younger and older adults were presented with separate images of scenes and faces.
In different conditions, participants were told to ignore the scenes and remember faces or vice versa. Gazzaley et
al. found that both younger and older adults showed increased activity in the parahippocampal cortex when
instructed to remember scenes, compared with a passive viewing control condition. However, when instructed to
ignore scenes, older adults did not show reduced activation of the parahippocampal cortex when scenes were
irrelevant to the task. Furthermore, Gazzaley et al. demonstrated that the inability of older adults to suppress
irrelevant information was associated with worse working memory performance.
The effect of distracting visual or auditory stimuli on a visual search task has also been examined under
more ecologically valid conditions. For example, McPhee, Scialfa, Dennis, Ho, & Caird (2004) reported that
older adults were worse at detecting a target traffic sign in visual clutter, (i.e. when the target was embedded

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among distracting visual information), and were more distracted by the presentation of auditory stimuli during
the visual search task than younger adults. Moreover, Trick, Toxopeus, & Wilson, (2010) demonstrated that
visual clutter is especially problematic for older adults in a simulated driving task. The authors manipulated the
density of oncoming traffic, the visual conditions, and wayfinding in a task in which participants were required
to memorise directions to a target location and to detect turn points and landmarks in the visual scene. Their
results indicated that reduced visibility, high traffic density and wayfinding all negatively impacted upon driving
speed and driving performance in older adults (Trick et al., 2010).
The effect of distracting stimuli on performance across younger and older adults may depend upon non-
domain specific factors such as the cognitive demands of the task to be completed (e.g. Clapp, Rubens, &
Gazzaley, 2010; Gazzaley, Sheridan, Cooney, & D’Esposito, 2007; Lavie & De Fockert, 2005; Lavie, Hirst, de
Fockert, & Viding, 2004; Postle, Desposito, & Corkin, 2005). For example, Lavie & De Fockert (2005)
manipulated the working memory load of a visual search task and reported that a salient (yet task-irrelevant)
singleton interfered more with the performance of younger adults when the task was completed under high
working memory than low working memory load conditions. Furthermore, Postle et al. (2005) reported that
younger adults’ performance during a spatial working memory task was more disrupted by motion distractors
(when tracking the movement of target circles within an array) during high, as opposed to low, working memory
load. These findings suggest that the higher the working memory load, the more interference by visual
distractors on the performance of younger adults, particularly in relation to spatial working memory (Repovš &
Baddeley, 2006).
Previous methodologies used for testing spatial cognition in older adults have ranged from the use of 2D
spatial patterns (such as trail-making task or visual search, e.g. Hommel, Li, & Li, 2004) to scenarios based on
real-world navigation (e.g. Rosenbaum, Winocur, Binns, & Moscovitch, 2012). While an age-related decline has
been demonstrated in both spatial navigation performance and in the ability to inhibit task-irrelevant visual
information during small scale search tasks, the impact of distracting visual information on spatial navigation in
large scales has received relatively little attention. While navigating a city or environment on foot for the first
time, crowds of pedestrians may be present in varying densities at different times and locations. During active
navigation, or wayfinding, visual attention must be allocated to potential obstacles, such as other pedestrians, as
well as to features of the environmental which may inform spatial decision making (Wiener, Hölscher, Büchner,
& Konieczny, 2012). To address this, some recent work has used virtual environments (VE) to mimic, in a
controlled manner, some of the specific cognitive aspects of spatial navigation. However, unlike in the real

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world, these virtual environments are typically designed to be empty of pedestrians or other dynamic objects. As
such, little is known about the effect of the presence of moving crowd distractors on the spatial abilities of
younger and older adults. Here, using a passive navigation task based on a virtual environment, we investigated
whether spatial cognition is further affected by the presence of crowds during learning in older relative to
younger adults.
For the purpose of the current study, we adopted the experimental paradigm previously described by
Wiener, Kmecova, & de Condappa (2012), to assess the effect of moving pedestrians during spatial learning on
subsequent spatial memory for routes taken through a novel environment. Moreover, we included distractor set
sizes of different crowd densities (high or low) which were composed of either virtual moving crowds of human
pedestrians (Experiment 1) or objects (Experiment 2). The performance measures used in the following study
were based on younger and older adults’ ability to make spatial decisions based on viewing a repeat or retrace of
a learned route in the environment.
The main aim of the current study was to investigate the effect of the presence of moving crowds of
pedestrians during spatial learning on subsequent egocentric and allocentric spatial processing in younger and
older adults (Experiment 1). On the basis of the findings reported earlier, we hypothesised that the presence of
dynamic crowd distractors would have a greater effect on the performance of older relative to younger adults.
Furthermore, we speculated that a cost of crowds on older adults’ performance would be specifically on
measures of allocentric processing as this requires additional cognitive effort (Byrne, Becker, & Burgess, 2007).
In Experiment 2 we examined whether any effect of dynamic crowds was related to processing human
characters specifically, or simply due to the presence of any dynamic distractors. To that end, we compared
spatial navigation performance between environments learned without distractors and those learned in which
dynamic objects with low and high density were presented (Experiment 2).
Experiment 1
This experiment examined whether the presence of dynamic virtual crowds during spatial learning would
affect participants’ subsequent performance on tasks assessing spatial memory. We measured performance in
younger and older adults using three types of tasks: the identification of the learned direction of the route; recall
of the direction taken at an intersection; and recall of the sequence of landmarks. The identification of a route as
a repetition of the learned route could be solved most efficiently using an egocentric spatial strategy by
associating a landmark with a specific turn (Head & Isom, 2010). On the other hand, the identification of a

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retracing of the learned route requires an allocentric strategy, in which the correct response could be derived
from assessing the spatial relationships between landmarks from a viewpoint-invariant representation of the
environment. Similarly, correct responses could also follow from a translocation of an egocentric representation
of the landmarks and associated direction taken through hippocampus-dependent mental transformation
processes such as perspective taking (e.g. Lambrey, Doeller, Berthoz, & Burgess, 2012; Vogeley & Fink, 2003).
Prior to the test, all participants learned a route through a novel environment which was either unpopulated or in
which crowds of moving human pedestrians were presented. Crowd distractors were present only during the
learning phase of the study and unpopulated environments were shown in the test trials.
As both egocentric and allocentric processing have been shown to deteriorate with age (see Moffat, 2009
for review) we expected that the overall performance by older adults would be worse than that of younger
adults. Furthermore, we expected to replicate the findings of Wiener et al., (2012) that older adults would be
especially poor on route retracing trials of these tasks. Moreover, consistent with the findings that moving
distractors disrupt spatial working memory in younger adults (Postle et al., 2005), and that distractor
interference is more likely to occur in older adults when the task is more cognitively demanding (Lavie, 1995;
Maylor & Lavie, 1998), we expected that spatial memory performance of younger and older adults would be
worse when crowds were presented during learning of new environments. We further speculated that this cost
would occur particularly when the test involved a retracing of the learned route, which is more cognitively
demanding.
Method
Participants
A total of 30 younger (20 female; M = 24.83, SD = 6.07, range 18-39) and 30 older
1
(M = 71.23, SD =
4.65, range 62-81; 19 female) participants volunteered to take part in the experiment. The younger participants
were recruited from the undergraduate and postgraduate student population of Trinity College Dublin and all
participated for nominal pay or course credit. All younger adults reported normal or corrected-to- normal vision
and normal hearing. The older participants were recruited through local ageing organisations and by advertising
through local media. All older participants were community dwelling. None of the older participants had a
history of psychiatric or neurological illness. The older participants underwent tests of global cognitive function,
assessed using the Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005), visual acuity (measured by
1
Although 33 older adults were initially recruited, data collected from three of the older adults were
subsequently removed from the analysis as they failed to perform above chance level on experimental measures
at session 1 (see Wiener et al., 2012).

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the ETDRS acuity chart), and contrast sensitivity (assessed using the Pelli-Robson Contrast Sensitivity Test)
prior to the main experiment. All had normal global cognitive function (MoCA; M = 27.53, SD = 2.01), normal
visual acuity (M = 0.09 logMAR, SD = 0.11) and contrast sensitivity (M = 1.94, SD = 0.06) for their age (see
Table 1 for more details).
All younger and older participants also provided a measure of self-reported spatial navigation ability
using the Santa Barbara Sense of Direction Scale (SBSOD) which is a validated measure of subjective
navigation abilities with a high degree of test-rest reliability (Hegarty, Richardson, Montello, Lovelace, &
Subbiah, 2002). Self-reported sense of direction has been shown to predict the performance of younger adults in
spatial memory tasks (Hegarty et al., 2002; Loomis et al., 1993; Sholl, Kenny, & DellaPorta, 2006; Wolbers &
Hegarty, 2010), with higher scores correlating with the ability to form and use a cognitive map (Arnold et al.,
2013). Furthermore, higher scores for younger adults on this measure have been shown to correlate with grey
matter volume in navigationally relevant cortical regions, such as the parahippocampal cortex (Wegman et al.,
2014).
The experiment was approved by the School of Psychology Research Ethics Committee, Trinity College
Dublin and conformed to the Declaration of Helsinki. Accordingly, all participants provided informed, written
consent prior to taking part in the experiment.
Stimuli and Apparatus
The experiment was programmed and responses were acquired using Presentation® software
(http://www.neurobs.com). The experiment was displayed on a HP L1710 17” LCD colour monitor presented on
a Dell Latitude E4300 laptop. The screen resolution was set to 1280 x 1024 pixels. The video stimuli subtended
a visual angle of approximately 32.67° horizontally and 20.75° vertically encompassing the entire stimuli
dimensions onscreen at a viewing distance of 57cm.
Virtual Environment
For the purpose of our experiment, we created two individual virtual environments, each comprising a
maze in which object landmarks were embedded (Maze A and B). Navigation along these routes through these
mazes was simulated using a proprietary engine based on Ogre 3D. Specifically, navigation was simulated using
a virtual camera which was positioned at typical eye height (i.e. 1.6m above the ground) and followed a
predefined path within each maze at a speed of 1.5m/s. This speed was chosen as it is representative of the
average normal walking speed of older adults (Berard, Fung, & Lamontagne, 2012; Schaefer, Schellenbach,
Lindenberger, & Woollacott, 2015). Each scene was rendered at 30fps and the engine exported uncompressed

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images at 720p resolution. These images were later converted to a video and compressed using the H.264 codec.
During the learning phase, each route was displayed for 107 seconds. Each route traversed 11 intersections that
were each identified by a single, unique landmark consisting of an image of an object presented on four sides of
a cube (side length: 0.5 m), suspended 2m above the floor (see Figures 1 and 2). Each image of an object
subtended an approximate visual angle of 2.8° horizontally and 2.7° vertically at a viewing distance of 57cm.
From a distance each landmark was initially obscured by fog and, as the camera approached, became visible
from 12.5 metres or from 8.3s, with a quadratic increase in light intensity. There were 22 landmarks in all,
randomly divided into two sets and each set was allocated to one maze environment, A or B (see Figure 1).
[INSERT FIG. 1 ABOUT HERE]
For each of the two routes, we created three different stimuli which were each rendered as video outputs:
the ‘no crowds’ stimulus; a ‘low density crowd’ stimulus; and a ‘high density crowd’ stimulus. Therefore, if a
participant was presented with Maze A during their first experimental session in which no distractors were
present, then in their second session they were presented with Maze B, populated with either high or low density
distractors according to the density condition to which they were assigned. The ‘no crowd’ stimulus consisted of
a route taken through an unpopulated maze. In the ‘low density crowd’ stimulus, 35 virtual human characters
were presented along the route whereas for the high density crowd, 70 human characters were presented along
the route. These characters were made up of 8 distinct female (height 1.75m) and 10 distinct male (height 1.8m)
virtual character meshes, with a variation of each character repeated between 1 and 5 times in the low density
conditions and between 4 and 8 times for the high density conditions. At a viewing distance of 57cm, male and
female characters subtended an approximate visual angle of 3.2° horizontally and 6.9° vertically, and 2.7°
horizontally and 6.6° vertically, respectively. In contrast to the speed of the virtual camera, virtual characters
moved through each maze at a speed of 1.3m/s, each following their own predefined path. This speed was
chosen in order to ensure that the virtual characters avoided colliding with other characters and to maintain a
consistent density of characters at any one time point along the route (see Figure 2). One virtual character was
depicted wearing a distinctive (cowboy) hat and appeared at a random location during the route and acted as a
‘catch’ distractor character. Participants were required to signal the presence of the ‘catch’ character by tapping
their hand on the table. As this experiment involved passive navigation, whereby the participant passively
viewed the learning environment, this catch distractor character was included as an indicator to the experimenter
that participants were paying attention to the entire scene including the route and the virtual characters, and not
just attending to the upper, central field of each scene thereby ignoring the distractors. Participants could easily

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find these catch distractors. However, on the rare occasions where the participant failed to signal the presence of
this catch distractor character, the experimenter brought this to the attention of the participant following the
presentation of the learning environment. The location and type of character wearing the hat varied across the
experimental sessions.
[INSERT FIG. 2 ABOUT HERE]
Test Stimuli
The test stimuli consisted of eighteen individual video clips, each with a duration of 13s. Each video
comprised a recording taken from the original (learned) route that traversed two intersections of the maze and
which stopped at the second intersection. Thus two intersections and two landmarks (one at each intersection)
were displayed during each test stimulus. For each stimulus, the learned route was presented either in the same
travel direction as that learned in the virtual maze environment (i.e. route repetition trial), or in the opposite
travel direction to that previously learned (i.e. route retracing trial). The test stimuli were created from empty
mazes only. Therefore distractor crowds were not presented during the test. Each test stimulus was unique in
that a video clip containing the same two landmarks was never repeated during a particular experimental
condition.
Design
The overall experimental design was based on mixed, factorial design with age group (younger, older)
and crowd density (low, high) as the between group factors and travel direction (repetition, retracing) and
distractor (no crowds present, crowds present) as the within group factors. Participants were pseudo-randomly
allocated to either the low or high crowd density condition, to ensure that younger and older participants were
matched for age, SBSOD, and that older adults were matched for sensory function and MoCA score across
conditions. This resulted in 15 younger and 15 older participants in each of the low and high crowd density
group.
There were two experimental sessions, which were presented in the same, fixed order across participants.
The first session tested spatial memory to a maze which was learned without the presence of crowds (baseline
performance) and the second session tested spatial memory to a maze which was learned in the presence of
either high or low density crowd distractors. This fixed presentation of session allowed us to replicate the
findings reported by Wiener et al. (2012) in the ‘no crowds’ condition and subsequently examine whether the
presence of crowds further disrupted performance

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Each of the two maze structures (see Figure 2) were randomly allocated to the ‘no crowds present’ and
‘crowds present’ experimental sessions for each participant. As such, half of the participants were presented
with Maze A during the first experimental session and with Maze B during the second session (and vice versa
for the other participants). Each experimental session included 6 blocks. Each block included a learning phase
followed by a test phase consisting of 18 test trials. During the learning phase, a video of a route through a
virtual maze environment (A or B) was presented and was immediately repeated (i.e. there were two
presentations of each route during learning). The test phase immediately followed the learning phase. Each test
phase included nine route repetition trials (each comprising a short segment from the learned route travelling in
the original learned direction) and nine route retracing trials (each comprising a short segment from the learned
route travelling in the opposite direction). These trials were presented in random order across participants in
each block. Thus there were 108 trials per experimental session (i.e. 6 blocks of 18 trials each). The presentation
of each test stimulus was immediately followed by the three tasks in the following fixed order: route direction,
intersection direction and landmark sequence tasks. For the route direction task, participants were instructed to
indicate, as quickly and as accurately as possible, the direction of travel of the test stimulus (repeat, retrace); for
the intersection direction task, participants were instructed to indicate the direction in which the learned route
proceeded at the end of each clip, relative to the presented travel direction of the test stimulus (which could be
repeat or retrace); for the landmark sequence task, participants were required to choose which of the three
presented landmarks they would expect to encounter next on the learned route, given the current travel direction
as presented in the test stimulus (i.e. repeat or retrace). The dependent variables for the route direction task were
both accuracy and response times. The dependent variable for the intersection direction and landmark sequence
tasks was accuracy only.
Procedure
Prior to testing, all participants completed the SBSOD questionnaire and all older participants only
underwent tests of global cognitive function, visual acuity, and contrast sensitivity.
In the main experiment, each participant was presented with the ‘no crowds present’ maze in their first
experimental session and then either the ‘high’ or ‘low’ density crowds condition in their second experimental
session, which took place one week later. Each session took between 60 to 90 minutes to complete. During the
second, ‘crowds present’ test session, each participant performed the learning phase with either a high or low
density of crowd distractors traversing the maze environment. In the ‘crowds present’ learning session, the
participant was required to tap on the table as soon as they noticed the ‘catch’ distractor. This character appeared

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approximately once over the course of each route through each maze. The testing procedure was the same across
both the ‘no crowds present’ and ‘crowds present’ experimental sessions.
Participants were seated approximately 57cm in front of the computer monitor. During the learning
phase, each participant passively viewed the route through the maze, and the route was repeated twice to ensure
learning occurred. Participants were instructed to remember the route shown, including the landmarks
encountered and the direction the route followed at each landmark location. The test phase immediately
followed learning and consisted of the presentation of the test stimulus followed by three different tasks, as
previously described. A test stimulus was preceded by a fixation cross for 500ms, followed by the test
instructions presented on a screen. The instructions for each task were presented in a white font against a black
background (see Figure 3).
[INSERT FIG. 3 ABOUT HERE]
For the route direction task, participants were required to press one of two assigned keys on the computer
keyboard to indicate travel direction: the “up arrow” indicated a repeated direction and “down arrow” indicated
a retraced direction (as shown in Figure 3a). Participants were instructed to respond as soon as they identified
the travel direction, even if this occurred during the presentation of the test stimulus. Response times were
recorded from the onset of the test video.
Immediately following the route direction task, participants were presented with the next instruction (see
Figure 3b) relating to the intersection direction task. Participants were instructed to press one of three
corresponding keys (i.e. “left arrow” for a left turn, “up arrow” for a maintained straight ahead course, and
“right arrow” for a right turn on a keyboard) to indicate the direction the route proceeded at the end of each clip.
When a repeated route was presented, participants had to decide in which direction to proceed next in order to
progress towards the end of the learned route. For a retraced route, participants had to decide the appropriate
next direction in order to return to the start location of the learned route.
The landmark sequence task was presented following a response made to the intersection task. This
consisted of the presentation of 3 landmarks (see Figure 3c) which remained on screen until a keyboard response
was made. Participants were required to press one of three assigned keys (“1” for the landmark to the left of the
screen, “2” for the landmark in the middle of the screen, “3” for the landmark to the right of the screen) on a
keyboard to indicate which landmark they expected to encounter next on the route, given the presented travel
direction.

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Results
Results from baseline performance
The results of the tests conducted prior to the main experiment, for younger and older participants
allocated to each of the low or high density crowd conditions, are presented in Table 1. Both the younger groups
[t(28) < 1] and the older groups [t(28) = 1.31 , p =0.20] were matched in age. As individuals are considered to
be variable in their ability to perceive spatial, egocentric self- or allocentric object-to-object distances (Norman,
Crabtree, Clayton, & Norman, 2005), we compared the performance of each of the younger and older groups
allocated to the crowd density conditions on measures of SBSOD to ensure there were no differences between
participants allocated to these crowd conditions. Younger adults were matched on their SBSOD score [t(28) < 1]
across the high and low crowd density conditions. Older adults were also matched in SBSOD score [t(28) < 1]
across these conditions. Finally, we found no difference in cognitive function (MoCA score [t(28) < 1]), visual
acuity [t(28) < 1], and contrast sensitivity [t(28) < 1] across older groups allocated to the different crowd density
conditions.
Table 1 Older and younger adults’ characteristics across the two crowd density experimental conditions to
which participants were pseudo-randomly assigned (mean performance is indicated with standard deviations in
parentheses).
High Crowd Density
Low Crowd Density
Older Adults
N = 15
N = 15
Age
70.13 (4.12)
72.33 (5.02)
SBSOD
4.72 (0.62)
4.70 (0.8)
MoCA
27.40 (2.26)
27.67 (1.80)
Visual Acuity
0.09 (0.13)
0.09 (0.10)
Contrast Sensitivity
1.94 (0.04)
1.93 (0.08)
Younger Adults
N = 15
N = 15
Age
25.73 (6.79)
23.93 (5.34)
SBSOD
3.92 (1.03)
4.23 (1.00)
Results from the main experiment

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We first wanted to ensure that there was no underlying difference in performance across participants
allocated to each of the crowd density conditions. To that end, we conducted a multivariate ANOVA on
performance across all tasks in the first experimental session, (i.e. without the presence of crowds during
learning), with crowd density condition (high or low) as the between group factor. This analysis revealed no
differences indicating that groups were matched on spatial performance prior to conducting the experimental
session in which crowds were then presented [F(6, 51) = 1.76, p = 0.13, p2 = 0.17].
The analyses of participants’ performance in the experimental session in which crowds were presented
during learning, revealed no performance difference in any of the tasks between the two crowd densities for
either the younger [all t(28) < 1] or older [all t(28) < 1] adults, nor any interactions between crowd density and
any other factor (all ps = n.s.). As a consequence, and for clarity, we decided to collapse the data from the crowd
density condition into one overall condition of ‘crowds present’. For all analyses, the alpha level was 0.05 and
Tukey HSD post hoc tests were used to explore significant effects across all analyses. Where planned
comparisons across age group and retracing trials were conducted, the alpha level was Bonferroni corrected to
0.025.
The following analyses were conducted to ascertain whether the presence of crowd distractors during
learning affected subsequent spatial memory performance in younger and older adults relative to their
performance in which no crowds were presented during learning. First, separate single-sample t-tests were
conducted to compare performance against chance level in each task. Then, separate, three-way, mixed
ANOVAs, with age group (younger, older) as the between group factor and distractor (‘no crowds present’ or
‘crowds present’) and travel direction presented (repetition, retracing) as the within group factors, were carried
out on performance accuracy to each of the three tasks. A mixed ANOVA was also carried out on the reaction
times to the trials in which correct responses were made to the route direction task. For each age group
separately (younger and older adults) and each travel direction, we considered a response time of more than 2.5
standard deviations above or below the mean response times of the respective group to each condition as outliers
and these were removed from further analyses (as such approximately 1.67% of the data were removed).
a) Route Direction Task
2
2
Note that only those responses to trials in which participants correctly identified the travel direction in the
route direction task were included in the subsequent analyses of the intersection direction and landmark
sequence tasks. The analyses on the full dataset showed similar trends of results.

Merriman et al.
15
Initial results confirmed that both younger and older adults performed significantly above chance level
(i.e. 50%) on the route direction task, including both the distractor conditions as well as the travel direction
conditions (all ps < 0.001). The percentage accuracy responses provided by both age groups in each of the
conditions are presented in Figure 4.
The results of the ANOVA revealed a main effect of age group [F(1, 58) = 51.22, p < 0.001, p2 = 0.47],
with worse accuracy performance for older (M = 76.39, SD = 10.04) than younger (M = 90.8, SD = 4.57) adults.
There was a main effect of travel direction [F(1, 58) = 35.76, p < 0.001, p2 = 0.38], with better performance for
route repetition trials (M = 87.16, SD = 10.59) compared to route retracing trials (M = 80.03, SD = 12.56).
Although there was no main effect of distractor [F(1, 58) = 1.18, p = 0.28, p2 = 0.02], there was a significant
interaction between age group and distractor [F(1, 58) = 5.85, p = 0.019, p2 = 0.09]. Post hoc analyses revealed
that older adults performed worse on this task when crowds were present than when no crowds were present
during learning (p = 0.024). In contrast, there was no difference in the performance of younger adults across
these distractor conditions (p = 0.33).
[INSERT FIG. 4 ABOUT HERE]
There was no evidence of an interaction between distractor and route direction [F(1, 58) = 2.52, p = 0.12,
p2 = 0.04], nor any 3-way interaction between age group, travel direction and distractor [F(1, 58) = 1.02, p =
0.32, p2 = 0.02]. The interaction between age group and travel direction approached significance [F(1, 58) =
3.97, p = 0.051, p2 = 0.06]. While performance was better for both age groups on the repetition trials (younger:
M = 93.18, SD = 3.58; older: M = 81.15, SD = 11.86) compared to the retracing trials (younger: M = 88.43, SD =
6; older: M = 71.64, SD = 11.8), there was a difference of 4.75% in performance of younger adults across the
two trial types, whereas older adults had a 9.5% drop in performance between the repeating and retracing trials.
This suggests there was a larger effect of travel direction in the older than younger group. Although this
interaction failed to reach significance, we did find an interaction between age and distractor condition, and
based on our prediction that distractors during learning may particularly affect performance on the difficult
retracing route, we decided to conduct a comparison across performance to the ‘no crowd’ and ‘crowd’
distractor conditions to the retracing condition only, for each age group. As shown in Figure 4, these planned,
post hoc comparisons revealed that older adults performed worse on the route retracing trials when crowds were
presented than when no crowds were presented during learning (t(29) = 2.41, p = 0.023). In contrast, the
performance of younger adults in the retrace trials did not differ across the distractor conditions of ‘no crowds’

Merriman et al.
16
or ‘crowds’ were presented during learning (t(29) < 1, p = 0.61). Moreover, performance was equivalent across
the distractor conditions in the repeat trials for each of the younger or older adult groups.
A similar analyses of the response times revealed a main effect of age group [F(1, 56) = 28.71, p < 0.001,
p2 = 0.34], with younger adults (M = 12.78, SD = 1.82) responding more quickly than older adults (M = 15.30,
SD = 1.63). There was a main effect of distractor [F(1, 56) = 12.74, p = 0.001, p2 = 0.19], with faster responses
in the ‘crowds’ (M = 13.57, SD = 2.21) than ‘no crowds’ conditions (M = 14.30, SD = 2.19). This was possibly
due the effects of practice in experiencing the same experimental paradigm on two different occasions. There
also was a main effect of travel direction [F(1, 56) = 7.51, p = 0.008, p2 = 0.12], with faster responses to route
repetition (M = 13.93, SD = 2.16) than route retracing (M = 14.15, SD = 2.18) trials. The interactions between
age group and distractor [F(1, 56) = 2.93, p = 0.093, p2 = 0.05], age group and direction [F(1, 56) < 1], and
between distractor and travel direction [F(1, 56) < 1] failed to reach significance. The 3-way interaction between
age group, distractor and travel direction also failed to reach significance [F(1, 56) = 2.76, p = 0.1, p2 = 0.05].
b) Intersection Direction Task
Initial analyses confirmed that both younger and older adults performed significantly above chance level
(i.e. 33%) on the intersection direction task, including both distractor conditions (for younger adults, all ps <
0.001; for older adults all ps < 0.05). A further examination of performance to the travel direction conditions
revealed that older adults performed significantly above chance on the route repetition trials (all ps < 0.001) but
their performance failed to differ from chance on the route retracing trials (i.e. their performance was (M =
35.43, SD = 17.55) for the ‘no crowds’ and (M = 29.44, SD = 14.58) for the ‘crowds’ conditions on this task.
The accuracy performance provided by both age groups in each of the conditions is presented in Figure
5. An analysis of accuracy performance on the intersection direction task revealed a main effect of age [F(1, 58)
= 94.47, p < 0.001, p2 = 0.62] with poorer performance by the older (M = 41.84, SD = 15.09) than younger (M
= 76.76, SD = 12.51) adults. There was a main effect of travel direction [F(1, 58) = 110.12, p < 0.001, p2 =
0.66], with better performance for route repetition (M = 66.81, SD = 21.23) than retracing trials (M = 51,79 SD
= 24.86). There was no effect of distractor [F(1, 58) = 1.26, p = 0.27, p2 = 0.02], but there was a significant
interaction between age group and distractor [F(1, 58) = 4.71, p = 0.034, p2 = 0.08]. Again, post hoc tests
revealed that older adults’ performance was worse when crowds were present during learning compared to
learning the route in an empty maze (p = 0.05). In contrast, there was no difference in the performance of
younger adults across these distractor conditions (p = 0.39).

Merriman et al.
17
[INSERT FIG. 5 ABOUT HERE]
There was a significant interaction between age group and travel direction [F(1, 58) = 6.98, p = 0.011,
p2 = 0.11], with better performance for both age groups on the repetition trials compared to the retracing trials.
There was a difference of approximately 11% in performance of younger adults across the two trial types, but a
difference of 19% in the performance of older adults suggesting there was a larger effect of travel direction in
the older group (all ps < 0.001). In examining performance to retracing trials in particular, planned comparisons
showed that although older adults performed slightly worse on these trial types during the ‘crowds’ compared to
the ‘no crowds’ distractor conditions, this difference failed to reach significance with a bonferroni corrected
alpha level of 0.025 [(t(29) = 2.23, p = 0.034]. The performance of younger adults did not differ across sessions,
nor was there a difference between the distractor conditions for the repeated direction in either age group. There
was no interaction between distractor and direction [F(1, 58) = 1.15, p = 0.29, p2 = 0.02], nor a 3-way
interaction between age group, distractor and route direction [F(1, 58) < 1].
c) Landmark Sequence Task
The performance of both younger and older adults was significantly greater than chance (33%) on the
landmark sequence task, including the distractor conditions and both the route repetition and retracing trials (all
ps < 0.001).
The accuracy performance on this task, by both age groups across conditions is shown in Figure 6. The
ANOVA analyses on accuracy revealed a main effect of age group [F(1, 58) = 67.35, p < 0.001, p2 = 0.54],
with older adults worse at remembering the order of the landmarks they encountered during learning (M =
53.92, SD = 16.66) than the younger adults (M = 83.33, SD = 10.38). There was a main effect of travel direction
[F(1, 58) = 73.35, p < 0.001, p2 = 0.56] with better performance for repetition (M = 72.7, SD = 19) than
retracing (M = 64.56, SD = 22.13) trials. There was no effect of distractor [F(1, 58) < 1] and the interaction
between age group and distractor failed to reach significance [F(1, 58) = 3.83, p = 0.055, p2 = 0.06]. However,
since this interaction was the most pertinent according to our predictions, we conducted post hoc analyses which
revealed no significant effect on older adults’ performance between the ‘crowds’ and ‘no crowds’ distractor
conditions (p = 0.066). The performance of younger adults also did not differ across these distractor conditions
(p = 0.45).
[INSERT FIG. 6 ABOUT HERE]

Merriman et al.
18
There was a significant interaction between age group and travel direction [F(1, 58) = 12.28, p = 0.001,
p2 = 0.18] There was a difference of approximately 5% in performance of younger adults across the two
direction types, whereas older adults had an approximate difference of 11% in performance. Post hoc analysis
revealed there was a larger effect of travel direction in the older (p < 0.001) than the younger group (p = 0.004).
Furthermore, as for the previous tasks, we conducted planned comparisons which revealed no effect on older
adults’ performance between the ‘crowds’ and ‘no crowds’ distractor conditions on route retracing trials [t(29) =
1.33, p = 0.20]. There was no interaction between distractor and travel direction [F(1, 58) < 1] or any 3-way
interaction between age group, distractor and travel direction [F(1, 58) < 1].
Discussion
We found that older participants performed worse than younger participants on all tasks. Furthermore,
we found that performance of both younger and older adult groups was worse on retracing trials compared to
repetition trials across all tasks. Although this finding was similar across both adult groups, post hoc tests and an
examination of mean performance across both trial types suggested that the difference in performance between
route repetition and retracing trials was greater in the older than the younger cohort. The results therefore
suggest that the difference in direction was largely driven by reduced performance to the retracing trials in the
older adults and indicates that older adults had impaired allocentric processing relative to egocentric processing
(Wiener et al., 2012; Head & Isom, 2010; Moffat, 2009). Moreover, the analysis of response times to the route
direction task indicated faster responses to route repetition compared to route retracing trials. Evidence for the
allocentric processing of spatial information which is required for route retracing has previously been shown in
animal studies (Foster & Wilson, 2006). The findings of Foster and Wilson suggest that the retracing of a
previously encountered route may have a crucial role in supporting hippocampal-dependent spatial learning. Our
results are therefore consistent with the idea that allocentric processing involved in route retracing is a more
complex operation than the egocentric processing of the route repetition trials as it requires additional cognitive
functions such as working memory in order to store and manipulate the number of landmarks to be remembered
(e.g. Byrne et al., 2007; Jensen & Lisman, 2005; Spiers, 2008).
For both the route direction and intersection tasks, the performance of older adults was consistently less
accurate when dynamic crowd distractors were present during learning compared to learning a route in the
empty maze. As there was no effect of crowd distractors on the performance of younger adults, these results
suggest that the presence of crowd distractors during spatial learning impacts differentially on spatial navigation
abilities with ageing. In contrast, the presence of crowd distractors did not affect older adults’ performance on

Merriman et al.
19
the landmark sequence task, at least not to the same magnitude as found in the other spatial tasks. It is unclear
why this was the case, although it might be argued that memory for sequences of landmarks does not rely on
spatial knowledge to the same extent as route direction or intersection direction tasks (e.g. Eichenbaum &
Cohen, 2014). Our results add some support to this assertion and suggest that participants may have used
temporal order, rather than spatial localisation, to recall landmark sequences. For example, the response times in
the route direction task suggest that participants of both age groups responded only after they encountered a
second landmark. The second landmark was visible from approximately 8s into the test stimulus and
subsequently younger and older adults made their decision as to whether the presented route was a repeat or
retracing of the learned travel direction. Reaction time data revealed that younger participants responded
approximately 4s and older adults approximately 7s after the second landmark became visible. Given this
timing, and in line with Wiener et al. (2012), this result suggests that participants used the temporal ordering of
landmarks to solve the route discrimination task. In other words, if participants were solving the task on the
basis of the turning direction shown at the first intersection encountered, their reaction times would have been
considerably faster. A second result also supports the idea of temporal processing of landmarks: both the
younger and older adults responded faster in the second relative to the first experimental session (i.e. when
crowds were present during learning than when the maze was empty). Therefore it is plausible that the temporal
coding of landmarks, as well as the nature of the landmark task itself (wherein the target landmark presented
with two foils from the route act as memory cues in themselves), resulted in less effortful processing in the
second session and possibly less reliance upon spatial strategies to recall the sequence of landmarks (e.g. Siegel
& White, 1975; van Asselen, Fritschy, & Postma, 2006). Consequently this task became somewhat easier when
experienced for the second time, albeit in a different maze, regardless of the presence of crowds. Although our
results suggest that spatial information was less relevant in the landmark sequence task, the finding that there
was no difference in performance on this task when crowds were present during learning, suggests that crowd
distractors specifically affect spatial processing during navigation.
Although we found that the presence of crowds during learning affected the performance of older adults
in our spatial tasks, the crowds had little effect overall on their performance across the route repetition and
retracing trials (apart from the route direction task in which we found a difference). In other words, although
travel direction resulted in worse performance to the retracing trials relative to the repeat trials for the older
adults in all tasks, this performance was not consistently affected by the presence of crowds during learning. The
effect of travel direction is consistent with those reported by Wiener et al. (2012) who argued that poor

Merriman et al.
20
performance in the retracing trials suggests a decline in allocentric processing with ageing. The performance of
the older adults to the intersection direction task suggests that they found it particularly difficult to process this
information in an allocentric manner: performance was significantly above chance for the route repetition trials,
suggesting preserved egocentric processing, but did not differ from chance in the route retracing trials.
As each participant experienced the same type of maze and testing stimuli for each of the three tasks
under different learning conditions across sessions, we could not rule out the contribution of practice effects to
our findings. Considering the performance of the older adults, we found a significant decline in performance in
the ‘crowds present’ (i.e. second) session of the route direction and intersection direction tasks suggesting that
the effect of crowd distractors during learning was strong enough to eliminate any benefits of practice on
performance. Furthermore, due to practice effects, we would expect to find improved performance in the
younger adult group across testing sessions (e.g. Benedict & Zgaljardic, 1998). In analyses not reported here
(see Merriman, 2015, unpublished thesis) we found that spatial memory performance improved across
experimental blocks within each testing session, suggesting that participants’ performance could improve with
repeated testing. However, there was no effect of distractor on performance accuracy in any of the tasks, nor
was there an effect of the presence of crowd distractors relative to no crowds present during learning on younger
adults’ performance on any of the three tasks. This suggests that there was either no effect of practice or that the
presence of crowd distractors during learning attenuated any practice effects which may be present.
The following experiment was carried out to ascertain whether the effects observed in Experiment 1 were
specific to the presence of human dynamic crowd distractors during spatial learning or whether the effects
extended to the presence of any type of dynamic distractors.
Experiment 2
We hypothesised that the presence of crowd distractors during spatial learning in Experiment 1disrupted
the subsequent spatial memory of older adults due to the automatic attentional capture of the biological motion.
As such in Experiment 2, it was important to ascertain whether the effect of the crowd distractors on older
adults’ performance was due to the automatic processing of human form stimuli specifically (Downing, Bray,
Rogers, & Childs, 2004) or whether this effect was due to age-related deficits in the perceptual processing of
moving distractors per se (Arena et al., 2012; Roudaia et al., 2009). Therefore, if the presence of generic,
homogeneous object distractors during spatial learning also impair subsequent spatial memory in older adults, it
may be determined that any type of distractor in motion is sufficient to impair the subsequent allocentric

Merriman et al.
21
processing of older adults. Specifically, we used the same maze environment as in Experiment 1 but replaced
the virtual human distractors with object distractors. Thus each object distractor followed the same predefined
path and travelled at the same speed as each virtual character in Experiment 1. The object distractors were
rendered at a height that was the average of the characters from Experiment 1 to ensure that the objects
subtended a similar visual angle on screen to that of the virtual characters. We used homogenous object
distractors in terms of colour and shape to ensure that any effects on performance was not due to the attentional
capture of different shapes of object distractors (e.g. Yantis & Hillstrom, 1994).
As in Experiment 1, the current spatial memory task placed high demands on cognitive processing and as
such should be subject to greater distractor interference from the presence of objects during learning than when
no objects were shown. Furthermore, as in Experiment 1, the current experiment also contained distracting
object stimuli in motion which was predicted to disrupt subsequent spatial memory performance as previously
reported (Postle et al., 2005).
Method
Participants
A total of 28 younger adults (21 female; M = 22.57, SD = 5.27, range 18-35) and 27
3
older adults (20
female; M = 70.37, SD = 4.59, range 65-81) volunteered to take part in the experiment. The younger and older
adults were recruited from the same respective populations as described in Experiment 1. None of the
participants took part in Experiment 1. All participants provided a measure of self-reported spatial navigation
ability using the SBSOD. Older adults (M = 4.61, SD = 1.15) rated their sense of direction significantly higher
[t(53) = 2.44 , p = 0.018] than younger adults (M = 3.9, SD = 1.03). All older adults had normal visual acuity,
measured by the ETDRS acuity chart (M = 0.07 logMAR, SD = 0.1), contrast sensitivity (M = 1.92, SD = 0.14,
assessed using the Pelli-Robson Contrast Sensitivity Test) and global cognitive function (MoCA; M = 26.89, SD
= 1.93) for their age (see Table 2 for details).
Stimuli and Apparatus
Virtual Environment and Test Stimuli
The testing environment for the ‘no objects present’ session (the first test session) was identical to that
used in Experiment 1. Each of these mazes were then recreated with the addition of high (70 objects) and low
(35 objects) density elongated 3D-shaped objects, for use during the learning phase of the maze in the ‘objects
3
The initial sample of older adults was 28, however data collected from one of the older adults were
subsequently removed as they failed to perform above chance level in the initial ‘no objects present’ condition
(see Wiener et al., 2012).

Merriman et al.
22
present’ session. An illustration of the object distractors is shown in Figure 2. The virtual objects (all the same
colour; RGB 120, 100, 150; height 1.7m) moved through each maze at a speed of 1.3m/s, each following their
own predefined path in order to avoid other virtual objects and to maintain a consistent density of objects
throughout the route. Each object subtended an approximate visual angle of 2.8° horizontally and 6.8° vertically,
with participants seated 57cm from the screen. An object with darker coloured stripes (RGB: 104, 87,130)
appeared at a random location during the video of the route through the maze and acted as a ‘catch’ distractor
object. The participant was required to inform the experimenter that they had seen this striped object by tapping
their hand on the table. If the participant failed to signal the presence of the striped object, the experimenter
brought this to their attention at the end of the particular learning phase as a reminder to signal its presence in
subsequent presentations of the learning environment. The location of the striped object varied across the
experimental session. The test stimuli were identical to those presented in Experiment 1.
Design and procedure
The overall experimental design was based on the same mixed, factorial design as described in
Experiment 1. Each participant completed two experimental sessions, the first session involved learning an
empty maze without the presence of object distractors and the second session involved either the presence of
high or low density object distractors during learning. Participants were pseudo-randomly allocated to the high
or low object density conditions, in order to ensure they were matched on the baseline characteristics outlined in
the Participant section. The dependent variables and the between and within group factors were the same as
described in Experiment 1. The procedure was the same as described in Experiment 1.
Results
In Table 2, the results are shown for the participant groups which were pseudo-randomly allocated to
each of the low or high density object conditions. Younger adults were matched in age and SBSOD score [all
t(26) < 1] across high and low object density conditions. Older adults were matched in age [t(25) < 1], SBSOD
score [t(25) = 1.1, p = 0.28], MoCA score [t(25) = 1.55, p = 0.13], visual acuity [t(25) = 1.21 , p = 0.24], and
contrast sensitivity [t(25) = 1.04 , p = 0.31] across object density conditions.
Table 2 Older and younger adults’ characteristics across object density conditions (with standard deviations in
parentheses).
High Object Density
Low Object Density
Older Adults
N = 13
N = 14

Merriman et al.
23
Age
70.85 (5.14)
69.93 (4.16)
SBSOD
4.87 (1.17)
4.38 (1.12)
MoCA
26.31 (1.84)
27.43 (1.91)
Visual Acuity
0.09 (0.11)
0.04 (0.10)
Contrast Sensitivity
1.89 (0.21)
1.95 (0.00)
Younger Adults
N = 14
N = 14
Age
22.43 (5.81)
22.71 (4.87)
SBSOD
3.82 (0.97)
3.97 (1.12)
We again wanted to ensure that there was no underlying difference in performance across participants
allocated to each of the object density conditions, as in Experiment 1. To that end, we conducted a multivariate
ANOVA on performance across all tasks in the first experimental session, (i.e. without the presence of objects
during learning), with object density condition (high or low) as the between group factor. This analysis revealed
no differences indicating that groups were matched on spatial performance prior to conducting the experimental
session in which objects were then presented [F(6, 46) = 1.56, p = 0.18, p2 = 0.17].
Analyses of participants’ performance in all three tasks revealed no effect of object densities on
performance of the younger adults on either trial type of the route direction and intersection direction task [all
t(26) < 1] or on repeat [t(26) < 1] or retracing trials of the landmark sequence task [t(26) = 1.06, p = 0.3]. For
older adults there was no effect of object density on repeat trials of the route direction [t(25) = 1.48, p = 0.15],
intersection direction [t(25) = 1.28, p = 0.21], or landmark sequence tasks [t(25) = 1.31, p = 0.2], or on retracing
trials of these three tasks [all t(25) < 1]. Moreover, no interactions were found between object density and any
other factor (all ps = n.s.). As a consequence, and for clarity, we decided to collapse the data from the object
density condition into one overall condition of ‘objects present’. For all analyses, the alpha level was 0.05.
Tukey Unequal N HSD post hoc tests were used to explore significant effects across all analyses.
A series of 2x2x2 mixed ANOVAs were conducted on performance accuracy on all tasks with age group
(younger, older) as the between group factor and the within group factors were distractor (no objects present or
objects present during learning) and travel direction (repetition, retracing). A similar, 3-way mixed ANOVA
was also carried out on the reaction time data to the correct responses during the route direction task only. For
each age group separately (younger and older adults) and each travel direction, response times of more than 2.5

Merriman et al.
24
standard deviations above or below the mean were considered as outliers and removed prior to further analyses
(approximately 3.75% of the data were removed).
a) Route Direction Task
4
The performance of both younger and older adults was significantly different from chance (50%) on the
route direction task as a whole and for both the route repetition and retracing trials analysed separately (all ps <
0.001).
The mixed ANOVA conducted on accuracy scores in the route direction task revealed a main effect of
age group [F(1, 53) = 47.63, p < 0.001, p2 = 0.47] with worse performance by older (M = 73.13, SD = 9.73)
than younger (M = 89.14, SD = 7.35) adults. There was a main effect of travel direction [F(1, 53) = 26.91, p <
0.001, p2 = 0.34] with better performance for route repetition trials (M = 85.84, SD = 10.33) compared to
retracing trials (M = 76.72, SD = 16.15). There was no effect of distractor [F(1, 53) = 2.9, p = 0.095, p2 = 0.05].
Furthermore, neither the interaction between distractor and travel direction [F(1, 53) = 3.02, p = 0.088, p2 =
0.05] nor the interaction between age group and distractor [F(1, 53) = 3, p = 0.089, p2 = 0.05] reached
significance. There was no 3-way interaction between age group, travel direction and distractor [F(1, 53) < 1].
There was a significant interaction between age group and travel direction [F(1, 53) = 4.61, p = 0.036,
p2 = 0.08], which is shown in Figure 7. Post hoc tests revealed that this interaction was driven by older adults
performing considerably worse on route retracing trials compared to route repetition trials (p < 0.001), whereas
the performance of younger adults was comparable across the two trial types (p = 0.14).
[INSERT FIG. 7 ABOUT HERE]
A further mixed ANOVA was used to assess differences in reaction times to the trials to which were
correctly responded on this task. There was a main effect of age group [F(1, 46) = 37.75, p < 0.001, p2 = 0.45],
with younger adults (M = 12.49, SD = 1.38) responding faster than the older adults (M = 15.60, SD = 2.03).
There was a main effect of travel direction [F(1, 46) = 9.37, p < 0.001, p2 = 0.17], with faster reaction times for
route repetition (M = 13.89, SD = 2.34) than route retracing trials (M = 14.18, SD = 2.37) and a main effect of
distractor [F(1, 46) = 25.85, p < 0.001, p2 = 0.36], with faster reaction times during the session with object
distractors present (M = 11.67, SD = 1.72) compared to ‘no objects present’ session (M = 13.22, SD = 1.48).
There were no significant interactions between age group and distractor [F(1, 46) < 1], age group and direction
4
Note that only trials in which participants correctly identified travel direction in the route direction task were
included in the analyses of the intersection direction and landmark sequence tasks. The analyses of the full
dataset showed similar trends of performance across groups.

Merriman et al.
25
[F(1, 46) < 1], nor between distractor and travel direction [F(1, 53) < 1]. The 3-way interaction between age
group, distractor and travel direction also failed to reach significance [F(1, 46) < 1].
b) Intersection Direction Task
Younger adults performed significantly above chance level (33%) on both route repetition and retracing
trials (all ps < 0.001). Overall performance by the older adults’ did not exceed chance levels on this task (p >
0.05). However, while older adults performed above chance on route repetition trials across the ‘no objects
present’ [t(26) = 2.7, p = 0.012] and ‘objects present’ sessions [t(26) = 2.51, p = 0.019], they performed
significantly below chance on the route retracing trials during both the ‘no objects present’ (M = 26.54, SD =
14.39) and ‘objects present’ sessions (M = 25.99, SD = 17.53) on this task. Figure 8 depicts the performance of
younger and older adults to route repetition and retracing trials in this task.
The mixed ANOVA conducted on accuracy performance in the intersection direction task revealed a
main effect of age group [F(1, 53) = 117.71, p < 0.001, p2 = 0.69] with older adults remembering the direction
taken less well (M = 34.58, SD = 15.47) than younger (M = 75.38, SD = 12.29) adults. There was a main effect
of travel direction [F(1, 53) = 86.63, p < 0.001, p2 = 0.62] with better performance for route repetition trials (M
= 62.63, SD = 24.89) compared to route retracing trials (M = 48.08, SD = 26.03). The interactions between age
group and travel direction [F(1, 53) = 1.71, p = 0.20, p2 = 0.03], and between distractor and travel direction
[F(1, 53) < 1] failed to reach significance. There was no evidence for a 3-way interaction between age group,
travel direction and distractor [F(1, 53) < 1].
There was a main effect of distractor [F(1, 53) = 4.99, p = 0.030, p2 = 0.09] with better performance
accuracy to the ‘objects present’ session (M = 57.37, SD = 27.52) compared to ‘no objects present’ session (M =
53.33, SD = 23.76). There was a significant interaction between age group and distractor [F(1, 53) = 4.9, p =
0.031, p2 = 0.09]. Post hoc analyses revealed no difference in performance of older adults across the ‘no
objects present’ and ‘objects present’ experimental sessions (p = 0.99), whereas younger adults performed better
in the ‘objects present’ session (i.e. the second session) in which objects were present during learning compared
to their baseline performance (p = 0.013). 
[INSERT FIG. 8]
c) Landmark Sequence Task
The performance of younger and older adults was significantly better than chance (33%) on the landmark
sequence task (all ps < 0.001). Younger adults performed above chance on both route repetition and retracing
trials when analysed separately (all ps < 0.001). Similarly, older adults’ performance was significantly greater

Merriman et al.
26
than chance on the route repetition trials across testing sessions (all ps < 0.001) and on the route retracing trials
in the ‘no objects present’ [t(26) = 2.76, p = 0.01] and ‘objects present’ sessions [t(26) = 2.36, p = 0.026].
The mixed ANOVA conducted on accuracy performance to the landmark sequence task revealed a main
effect of age [F(1, 53) = 58.59, p < 0.001, p2 = 0.53], with older adults performing worse at remembering the
order of the landmarks they encountered during learning (M = 49.09, SD = 16.26) than younger (M = 79.73, SD
= 13.33) adults. There was a main effect of travel direction [F(1, 53) = 47.87, p < 0.001, p2 = 0.48], with better
performance for route repetition trials (M = 68.99, SD = 19.94) than route retracing trials (M = 60.39, SD =
23.77). There was no effect of distractor [F(1, 53) < 1], and no significant interactions between age group and
distractor [F(1, 53) = 1.81, p = 0.18, p2 = 0.03] nor between distractor and travel direction [F(1, 53) < 1]. There
was a significant interaction between age group and travel direction [F(1, 53) = 12.2, p < 0.001, p2 = 0.19]
which is shown in Figure 9. Post hoc tests revealed that older adults performed worse on route retracing trials
compared to route repetition trials (p < 0.001), whereas younger adults performed comparably across trial types
(p = 0.08). The 3-way interaction between age group, travel direction and distractor failed to reach significance
also [F(1, 53) < 1].
[INSERT FIG. 9 here]
Discussion
Similar to the results of Experiment 1, and consistent with the results of previous studies (Moffat, 2009;
Wiener, Kmecova, et al., 2012), older adults’ accuracy performance was worse than that of younger adults
across both the route repetition and route retracing trials for the route direction, intersection direction and
landmark sequence tasks, again replicating previous findings that egocentric and allocentric spatial processing
decline with age. In particular, older adults’ performance was worse on retracing trials compared to repetition
trials across all tasks, suggesting a greater impairment in allocentric processing relative to egocentric processing
in this group. However, in contrast to Experiment 1, younger adults’ accuracy performance was comparable
across the route repetition and route retracing trials for both the route direction and landmark sequence tasks.
Similar to Experiment 1, and the findings of Wiener et al. (2012), older adults’ performance to retracing trials in
the intersection direction task did not differ from chance, while their performance to repetition trials in the same
task was significantly above chance levels, indicating that they could successfully recognise the direction during
the test phase of the task when the same direction as the learned route was presented.
Importantly, in contrast the results reported in Experiment 1, the presence of dynamic object distractors
during the learning phase had no effect on older adults’ performance in either repetition or retracing trials in any

Merriman et al.
27
of the three tasks. These results suggest that the detrimental effect of the crowd distractors in Experiment 1 may
have been due to moving human forms being more difficult for older adults to filter or suppress than generic
moving objects. Older adults’ performance was comparable across testing sessions, suggesting that the presence
of moving object distractors during learning did not hinder subsequent spatial memory in this group.
General Discussion
The aim of the present study was to investigate the effect of dynamic crowds during spatial learning on
subsequent spatial memory in both younger and older adults. Previous studies have reported that older adults are
impaired in using both egocentric and allocentric spatial strategies compared to younger adults, though they tend
to rely more on egocentric than allocentric strategies to navigate (Head & Isom, 2010; Moffat, 2009; Rodgers,
Sindone III, & Moffat, 2012). The results from Experiments 1 and 2 were largely consistent with these findings
in that older adults’ performance on both route repetition and route retracing trials was worse than that of
younger adults. Furthermore, the results of Experiment 1 suggest that, relative to younger adults, older adults
performed worse on both the route direction and intersection direction tasks when routes were learned in the
presence of human crowd distractors. This finding is interesting considering that performance may have
improved due to practice with experiencing the same paradigm twice, albeit under different learning conditions.
In a separate analysis, not reported here, we determined that the change in performance across sessions could
not be attributed to fatigue in the older adults, as performance significantly improved on all tasks within each
session, i.e. from the first three blocks to the last three blocks of trials (see Merriman, 2015). Instead, the result
that spatial performance declined in older adults when crowds were present during learning suggests that the
effect of crowd distractors was strong enough to eliminate any possible practice effects across sessions in this
cohort. In contrast, younger adults’ performance did not differ across the two testing sessions on all tasks in
Experiment 1.
There was no evidence for an effect of distractors in Experiment 2 in which we used objects rather than
human crowds. For example, whilst the presence of human distractors in Experiment 1 further reduced the
performance of the older adults in the route direction and intersection tasks, the presence of object distractors in
Experiment 2 had no such effect in any of the tasks. Furthermore, the performance of older adults was
comparable across testing sessions on all tasks in Experiment 2 unlike in Experiment 1, suggesting that moving
human characters may have captured the attention of older adults more so than moving object distractors. Both
human characters and object distractors shared similar properties in terms of distractor density, visual image size

Merriman et al.
28
and speed of movement through the (learned) environment (1.3m/s). This result therefore suggests that
performance on the spatial memory tasks may be differentially affected by the nature of the distractors, with
human forms demanding a greater allocation of attention than other object shapes.
In contrast to the results of the route direction and intersection trials in Experiment 1, there was no effect
of crowd distractors on the landmark sequence task. This difference in the effect of distractors across tasks may
suggest that the landmark sequence task was dependent on less spatial cognitive processes, such as temporal
order (Eichenbaum & Cohen, 2014). As previously discussed, reaction time performance to the route direction
task suggests that both age groups based their decision as to whether the test stimulus was a repeat or retrace of
the learned route by encoding of the order of landmarks during learning rather than directional information.
Furthermore, younger and older both groups responded more quickly to the route direction task when crowds
were present during learning than when the maze was empty, suggesting that they found it easier to encode the
order of the landmark objects. The older adults were less accurate than younger adults in the landmark sequence
task, which is consistent with Head & Isom (2010) who reported that older adults were worse than younger
adults at judging the temporal order of landmarks. Furthermore, other studies have suggested that while
landmark information is encoded first in the sequence of the development of route knowledge (Siegel & White,
1975), the temporal sequencing of landmarks may form the context for memory encoding and retrieval,
supported in part by activation in prefrontal regions of the brain (Dumas & Hartman, 2003; Fabiani & Friedman,
1997; Vakil, Weise, & Shmuel, 1997). Thus, one possible explanation why landmark sequencing is less subject
to interference by crowd distractors than other tasks is that it may depend on different cognitive resources than
more allocentric processing-dependent tasks (van Asselen et al., 2006).
The first spatial task tested in each experiment, i.e. the route direction task in which participants had to
discriminate between route repetition or retracing in the test stimulus, was designed to assess participants’
ability to recall the learned travel direction. Although both age groups’ performance was worse when judging
the retracing than the repeat direction, older adults performed relatively worse on the route retracing trials
compared to route repetition trials in both experiments, suggesting that spatial memory for route retracing was
particularly difficult for the older adults. This difference in performance across the route directions may provide
some insight into the nature of the spatial strategy adopted by the older adults to solve the task. Specifically, in
comparison to younger adults, the finding that older adults were relatively impaired at both route repetition and
retracing trials in both experiments is consistent with previous research suggesting age-related deficits in both
egocentric and allocentric spatial processing (Moffat, 2009).

Merriman et al.
29
Neuroimaging studies on the human brain have offered some insight into the neural mechanisms
underpinning spatial cognition in younger and older adults. For example, Wolbers & Büchel (2005) reported
that the initial learning of a novel environment activated the hippocampal region of the brain of younger adults.
Furthermore, the ability to retrace one’s steps, which is a task considered to be based on allocentric processing,
is also a process supported by the hippocampus (Foster & Wilson, 2006). Studies on older adults have, however,
reported a relatively reduced activation of the hippocampus during spatial learning (Antonova et al., 2009;
Moffat, Elkins, & Resnick, 2006). Furthermore, navigation tasks also activate the prefrontal cortex (PFC)
differentially in younger and older adults (Hartley et al., 2003; Maguire et al., 1998; Moffat et al., 2006;
Wolbers et al., 2004). In young adults, while activation of the hippocampus during spatial memory tasks is
accompanied by activation of PFC, the latter activation has been attributed to route planning, decision-making,
working memory and switching between navigational strategies (Spiers, 2008; Spiers & Barry, 2015). In older
adults, increased activation of the prefrontal cortex during spatial memory tasks, relative to that of younger
adults, may be ascribed to a compensatory shift in memory performance away from the medial temporal lobe,
including the hippocampal area, to more anterior frontal areas with age (Davis, Dennis, Daselaar, Fleck, &
Cabeza, 2008; Moffat et al., 2006). Thus, the age-related differences in performance to the route direction and
intersection direction tasks may be underpinned by reductions in hippocampal function with ageing. Despite the
well-documented effects of ageing on navigation ability, older adults’ self-reported sense of direction was much
higher [M = 4.71 and M = 4.61] than that reported by younger adults [M = 4.1 and M = 3.9] across Experiments
1 [t(58) = 2.77 , p = 0.007] and 2 [t(53) = 2.44 , p = 0.018] respectively using the SBSOD scale. However, while
self-reported sense of direction has been found to correlate with objective measures of spatial memory ability in
younger adults (see Wolbers & Hegarty, 2010), this has not been found to be the case with older adults as this
cohort tends to inflate their perceived sense of direction relative to their actual ability (Rosenbaum et al., 2012).
Furthermore, an inability to use a most advantageous spatial navigation strategy in older adults has been related
to less awareness of navigational difficulties in everyday life (Taillade et al., 2013a; Taillade et al., 2013b). This
may be explained by the finding that insight into one’s own cognitive functioning (i.e. metacognition) tends to
decrease with age (Isingrini et al., 2008).
Although the main effect of route direction found in both experiments suggested that all participants
found it more difficult to identify the retraced routes relative to the repeated routes, the presence of human
crowd distractors during spatial learning further reduced performance across both trial types in the older adult
group only. Hippocampal-based allocentric processing may be considered a more complex strategy than

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egocentric processing as it requires additional cognitive operations such as working memory in order to store
and manipulate the multiple landmarks to be remembered (Byrne et al., 2007; Jensen & Lisman, 2005; Spiers,
2008). Other studies have reported that the higher the working memory load, the more interference there is with
performance by task-irrelevant distractors (Lavie, 2010). Working memory performance has been shown to
deteriorate with age (Reuter-Lorenz & Sylvester, 2010) and those older adults with poor working memory
abilities are less able to filter out task-irrelevant stimuli (Gazzaley et al., 2005). Our result that older adults
performed worse on discriminating route direction in both the route direction and intersection direction tasks,
when crowd distractors were presented during learning, is consistent with this literature.
Some important differences between the set of human character and object distractors may have affected
performance. For example, while the set of human characters was heterogeneous (e.g. mixture of male and
female characters, mixture of colour and type of clothing), the set of object distractors was homogenous (i.e. of
uniform colour and shape). It is possible that the level of inter-object similarity between distractors may affect
the ability to attend to target objects in a scene (Duncan & Humphreys, 1989). Thus it is possible that the use of
homogeneous objects in Experiment 2 lead to ‘perceptual grouping’ of the object distractors and as such, their
presence was relatively easier to ignore during learning, particularly for the younger adults. On the other hand,
none of the distractor sets were similar to the target landmarks and, according to the model proposed by Duncan
& Humphreys (1989), there should have been little interference on spatial attention to the landmarks in the
presence of either human or object distractors. Nevertheless, it remains possible that object distractors could
affect performance under other circumstances, by changing their perceptual saliency or the degree to which they
capture attention (e.g. changes in the speed of object movement or changes in object size). Further research is
required to assess the limits by which moving objects can affect navigational performance in both young and
older adults.
Consistent with previous research, our results may reflect a privileged role for the perception of human
forms and biological motion. For example, some studies have found evidence that biological stimuli capture
attention more so than non-living objects (Downing et al., 2004; Pratt, Radulescu, Guo, & Abrams, 2010).
Furthermore, although older adults tend to perform worse in discriminating motion direction in random dot
patterns (see Bennett, Sekuler, & Sekuler, 2007; Roudaia et al., 2009), there is little evidence for age-related
effects on the discrimination of the direction of biological motion (Billino, Bremmer, & Gegenfurtner, 2008)
particularly if the display contained no other visual noise (Pilz, Bennett, & Sekuler, 2010). This suggests that the
specialised neural mechanisms supporting biological motion processing may be less affected by ageing. As

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31
such, with respect to the current study, any difference in performance due to the presence of crowd distractors
during learning is not likely to be the result of impaired biological motion processing in older adults. In contrast,
the capture of attention by, and the unimpaired perception of, moving human forms during learning by older
adults was probably sufficient to affect subsequent spatial memory performance.
In line with previous research (e.g., Head & Isom, 2010), older adults were also less accurate in the
intersection direction task than young adults. As with the route direction task, this effect was observed both for
route repetition trials as well as for route retracing trials for younger and older adults. However, while our
results suggest that older adults’ overall intersection direction task performance was better than chance, the
retracing process was particularly difficult for this cohort since their performance was sometimes no greater than
chance to the retracing trials of this task in both experiments, with and without distractors present during spatial
learning. As with the route direction task, route repetition trials involve the use of an egocentric spatial strategy,
whereas the route retracing trials require an allocentric strategy. Evidence for an allocentric processing of spatial
information which is required for route retracing has previously been shown in animal studies (Foster & Wilson,
2006). The work of Foster and Wilson on the electrophysiological recordings of the place cells of rats while
traversing a novel environment suggests that the retracing of a just-travelled route may have a crucial role in
supporting initial hippocampal-dependent spatial learning. The relatively poor performance by the older adults
to the retracing trials in this task is consistent with the results of neuroimaging studies which have reported a
reduced activation in the hippocampus of older adults during spatial learning (Antonova et al., 2009; Moffat et
al., 2006), and that this reduced activation correlated with poor performance on spatial tasks. Furthermore,
hippocampal volume has been shown to shrink between 1-2% annually in healthy older adults (Raz et al., 2005),
perhaps adding to age-related declines in spatial learning.
As with the route direction task, route retracing trials in the intersection direction task require the mental
manipulation of the order of the landmarks encountered in working memory as well as the added cognitive
dimension of judging in which direction the route should proceed according to the learned route relative to the
travel direction presented (repeat or retrace). As such, the more general age-related declines in working memory
and age-related difficulties in forming a cognitive map may have reduced performance on this task in the older
adults (Iaria, Palermo, Committeri, & Barton, 2009; Reuter-Lorenz & Sylvester, 2010). Working memory is an
essential component of spatial navigation as it involves the maintenance and manipulation of information that is
no longer available in the environment (Baddeley, 1986). A number of studies have reported age-related deficits
in working memory (e.g. Clapp, Rubens, Sabharwal, & Gazzaley, 2011; Gazzaley, Cooney, Rissman, &

Merriman et al.
32
D’Esposito, 2005; Gazzaley, Sheridan, Cooney, & D’Esposito, 2007; Reuter-Lorenz & Sylvester, 2010;
Salthouse, Babcock, & Shaw, 1991). In the context of the intersection direction task, older adults may have been
unable to maintain the multiple landmarks and associated turns in working memory due to the high cognitive
demands of the task. Furthermore, the performance of older adults to the trials in the intersection task was
particularly affected by the presence of human crowds during learning (Experiment 1), although not by the
presence of objects (Experiment 2). The presence of crowd distractors during learning, while participants were
attempting to encode and maintain the spatial information in memory, led to worse performance by older adults
on the intersection direction task as these distractors may have interrupted their working memory of the learning
environment. Future investigations of older adults’ spatial memory should aim to reduce cognitive load in terms
of landmarks and associated directions in this task.
With regard to working memory, a somewhat surprising result from both Experiments was that there was
no effect of crowd density on performance for either age group. This was unexpected as some previous accounts
suggest that spatial memory may be affected by the number of distractors present (see e.g. Lavie et al., 2004).
Furthermore, previous studies have suggested that older adults have reduced attentional capacity to filter task-
irrelevant information compared to younger adults, and this reduction in cognitive capacity was exasperated by
relatively poor working memory performance in this older cohort (Gazzaley et al., 2005). Considering that the
current study involved passive navigation, a ‘catch’ distractor was included in both experiments to which
participants were required to respond in order to ensure visual attention was allocated to the crowd distractors as
would occur in an active navigation task where crowds would have to be avoided. As the experimenter
monitored participants’ signalling of the ‘catch’ distractor character during the learning phase in the second
session of the experiments, it is unlikely that participants simply ignored the crowd distractors in order to build
up a spatial representation of the environment. A number of studies have demonstrated that when both younger
and older adults have sufficient time to process relevant stimulus features after practice on a cognitively
demanding task (Davidson, Zacks, & Williams, 2003; Dulaney & Rogers, 1994), interference effects are
significantly reduced (Kramer, Hahn, & Gopher, 1999). Our results indicate that the presence of crowd
distractors during learning in the second testing session, was sufficient to significantly impair older adults’
subsequent performance on spatial measures taken during in the route direction and intersection tasks, even
when they had experienced a similar learning environment without crowds during their first testing session.
Moreover, as both experiments involved passive navigation, each experiment contained the same number of
‘catch’ distractors to which the participant had to respond in order to control the amount of attention paid to both

Merriman et al.
33
crowd and object distractors. In an active navigation experiment, it would not be necessary to include ‘catch’
distractors as attention would inherently be allocated to all aspects of an environment, including pedestrians or
dynamic obstacles to avoid collusions as well as the environmental information necessary for spatial direction
judgement (Wiener et al., 2012).
There was a stark difference between the performance of younger adults on the intersection direction task
in Experiment 1 and those tested in Experiment 2. In Experiment 1, the presence of human crowd distractors
during learning reduced the younger adults’ performance compared to their performance when no crowds were
present during learning. In contrast, in Experiment 2, the younger adults’ performance was better to the session
in which objects were presented during learning than the session in which no objects were presented. Wiener et
al. (2013) reported that younger adults can successfully learn to adopt an allocentric spatial strategy over the
course of an experiment, whereas older adults could not (Wiener, de Condappa, Harris, & Wolbers, 2013). Our
results suggest that, at least in Experiment 2, younger adults had adopted the appropriate spatial strategies to
complete the intersection direction task successfully over the course of the first experimental session and
employed these strategies during the second experimental session. This may have reduced the working memory
load leading to less distraction from the objects. In contrast, there was no improvement across sessions in
Experiment 1, suggesting that crowd stimuli captured attention in younger adults in a similar way to older
adults.
To the best of our knowledge, this was the first study to examine the effect of human-character crowd
distractors on the spatial memory of younger and older adults. Given the effect of crowd distractors on older
adults’ memory for routes in a new environment, these findings offer insight into the potential consequences for
this age group who often report avoiding unfamiliar environments (Burns, 1999). Furthermore, these findings
may inform spatial learning interventions by training older adults in environments that include distracting
stimuli and thus may have real-life implications for ameliorating spatial memory deficits in older adults.

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34
Compliance with Ethical Standards:
Funding: This research was funded by the European Commission FP7 ‘VERVE’ Project, Grant No. 288914 and
by Science Foundation Ireland Principal Investigator grants (‘Metropolis’ project number 06/IN.1/I96 and
‘Socialising Agents’ project number 10/IN.1/13003) awarded to FNN and CO’S.
Conflict of Interest: The authors declare that they have no conflict of interest.
Ethical approval: All procedures performed in studies involving human participants were in accordance with the
ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration
and its later amendments or comparable ethical standards.
Informed consent: Informed, written consent was obtained from all individual participants included in the study
prior to testing.

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35
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Figures
Fig. 1 Schematic representation of the route through maze A (left) and B (right), each composed of 11
intersections between the start and end points.
Fig. 2 Examples of static images taken from the learned route from each of the three different
sessions: empty maze (top image); maze populated with virtual human crowds (middle image); maze
populated with objects (bottom image).

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Fig. 3 An example of stimuli instructions provided for each of the three tasks: a) the route direction
task; b) the intersection direction task; c) the landmark sequence task.

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Fig. 4 Mean percentage accuracy performance on the route repetition and retracing trials of the route
direction task for the two age groups in both the test sessions in which either ‘no crowds’ or ‘crowds’
were presented during learning. Error bars indicate ± 1 standard error of the mean. Chance
performance in this task was 50%.
Fig. 5 Mean percentage accuracy performance on the route repetition and retracing trials of the
intersection direction task for the two age groups during the ‘no crowds’ and ‘crowds’ sessions. Error
bars indicate ± 1 standard error of the mean.

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Fig. 6 Mean percentage accuracy performance on the route repetition and retracing trials of the
landmark sequence task for the two age groups in both the test sessions in which either ‘no crowds’ or
‘crowds’ were presented during learning. Error bars indicate ± 1 standard error of the mean. Chance
performance in this task was 33%.
Fig. 7 Mean percentage accuracy performance on the route repetition and retracing trials of the route
direction task for younger and older adults in Experiment 2. Error bars indicate ± 1 standard error of
the mean.

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Fig. 8 Mean percentage accuracy performance on the route repetition and retracing trials of the
intersection direction task for the two age groups during the session in which no objects were
presented during learning and at the session with objects presented during learning. Error bars indicate
± 1 standard error of the mean.
Fig. 9 Mean percentage accuracy performance on the route repetition and retracing trials of the
landmark sequence task for younger and older adults. Error bars indicate ± 1 standard error of the
mean.