Psychological Research
DOI 10.1007/s00426-016-0811-0
ORIGINAL ARTICLE
Unitization of route knowledge
Yaakov Hoffman1 · Amotz Perlman2 · Ben Orr-Urtreger3 · Joseph Tzelgov4,5,6 ·
Emmanuel M. Pothos7 · Darren J. Edwards8
Received: 12 March 2016 / Accepted: 20 September 2016
© Springer-Verlag Berlin Heidelberg 2016
Abstract There are many theories that explain how route
knowledge is acquired. We examined here if the sequence of
elements that are part of a route can become integrated into a
single unit, to the extent that the processing of individual
transitions may only be relevant in the context of this entire
unit. In Experiments 1 and 2, participants learned a route for
ten blocks. Subsequently, at test they were intermittently
exposed to the same training route along with a novel route
which contained partial overlap with the original training
route. Results show that the very same stimulus, appearing
in the very same location, requiring the very same response
A. Perlman and Y. Hoffman contributed equally to this publication
(order of authorship for these authors was determined by coin toss).
& Yaakov Hoffman
hoffmay@biu.ac.il
1
Interdisciplinary Department of Social Sciences, Bar-Ilan
University, 52900 Ramat Gan, Israel
2
Department of Management, Bar Ilan University, Ramat Gan,
Israel
3
Department of Psychology, Ben-Gurion University of the
Negev, Beersheba, Israel
4
Department of Psychology, Zlotowski Center for
Neuroscience, Ben-Gurion University of the Negev,
Beersheba, Israel
5
Department of Brain and Cognitive Sciences, Zlotowski
Center for Neuroscience, Ben-Gurion University of the
Negev, Beersheba, Israel
6
Department of Psychology, Achva Academic College,
Arugot, Israel
7
Department of Psychology, City University London, London,
UK
8
Department of Interprofessional Health Studies, Swansea
University, Swansea, UK
(e.g., left turn), was responded to significantly faster in the
context of the original training route than in the novel route.
In Experiment 3, we employed a modified paradigm containing landmarks and two matched routes which were both
substantially longer and contained a greater degree of
overlap than the routes in Experiments 1 and 2. Results were
replicated, namely, the same overlapping route segment,
common to both routes, was performed significantly slower
when appearing in the context of a novel than the original
route. Furthermore, the difference between the overlapping
segments was similar to the difference observed for the nonoverlapping segments, i.e., an old route segment in the
context of a novel route was processed as if it were an
entirely novel segment. We discuss the results in relation to
binding, chunking, and transfer effects, as well as potential
practical implications.
Introduction
Imagine the following scenario: a driver is driving down
the same fixed route for the past week, month or year. Due
to road works, the route is diverted, so that for the next
several turns she must travel a new route which partially
overlaps with the original route; e.g., from BLOCK1 three
to BLOCK six the novel route is exactly the same as the
original route. While we expect that the driver will not
show the same level of proficiency on the new parts of the
novel route, what about the old parts of the novel route,
those overlapping with segments of the old route? Will
1
BLOCK (upper-case) refers to a segment connecting two intersections, whilst block (lower-case) refers to a group of experimental
trials).
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Psychological Research
these overlapping route sections, which are the same for
both the old familiar route and the novel route, be treated
with the same proficiency? This possibility is intuitive, yet
research into chunking reveals an alternative, striking
possibility. Namely once a (route) sequence has been sufficiently learned and its representation is unitized, the
individual elements comprising it cease to play a role in
performance (e.g., see Perlman, Hoffman, Tzelgov, Pothos
& Edwards, 2016). Accordingly, specific route information, e.g., “turn right at this corner”, may only exist in the
context of a given familiar route and cease to exist from a
performance perspective when the very same information
(e.g., making the same right turn at the same corner) is
presented in the context of a different route.
This question, in addition to having theoretical value as
elaborated below, applies to a multitude of route learners,
who as opposed to, e.g., taxi drivers, typically follow a
fixed route when going from point A to point B, such as
mailmen, milkmen, lorry drivers, or GPS-guided driving.
In such cases, the development of a cognitive map, where
the entire geographic area is represented, is less feasible.
For example, the first author recently visited London,
where he asked a bus driver if he drives close to a certain
street, and the bus driver replied that he had “no idea”.
Fixed route learning may involve different processes at the
neural level as well. For example, London bus drivers
(fixed route) had smaller hippocampal volume than did taxi
drivers (non-fixed route), who were matched for mileage
and stress (Maguire, Woollett, & Spiers, 2006).
Typically, route learning is assumed to occur by nonunitized item-specific information. Paths, edges, districts,
landmarks, etc. have been suggested as important cues in
route learning (Epstein, & Vass, 2014, Gillner & Mallot,
1998; Kuipers, 1978, 2000; Kuipers, Tecuci, & Stankiewicz, 2003; Meilinger, 2008, Meilinger, Frankenstein, &
Bülthoff, 2014; Werner, Krieg- Brückner, & Herrmann,
2000). Additional information corresponds to directionbased strategies, which may rely on information about
angular direction at different locations to the final destination, has also been shown to play a role in route learning
(Bailenson, Shum, & Uttal, 2000; Fu, Bravo, & Roskos,
2015; Hochmair & Frank, 2002; Sakellaridi, Christova,
Christopoulos, Vialard, Peponis, & Georgopoulos, 2015).
Another plausible approach was suggested by Newell and
Simon (1972), whereby arriving at the intended location
involves incremental optimization, which is similar to cued
recall, whereby one step informs the next (Newell &
Simon, 1972). While these theories have substantial differences, they all focus on item-specific information, such
as a direction, an angle, or a previous step informing a later
response. Recently, the notion that route learning is more
complex than just simple stimulus response associations
and may actually be represented in a unitized manner has
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been noted (Strickrodt, O’Malley & Wiener, 2015; see also
Klippel, Tappe, & Habel, 2003; Richter, & Klippel, 2005).
Here, we extend this notion to empirically address if route
unitization, like other forms of unitization typical of the
sequence learning domain, renders item-specific information less relevant.
While in some of the reviewed studies, participants
were required to move through space, our task involved
following a moving dot across a map. Accordingly the
spatial learning processes may not be exactly the same.
Note, the current design is no less ecological as it is
typical of many current navigation applications (e.g.,
google maps). Further note that this design is compatible
with the current goal of addressing if the motor sequence
corresponding to spatial responses could be chunked into
a unitized representation.
Chunking, one of the most basic processes of the cognitive system (e.g., Boucher & Dienes, 2003; Goldstone,
2000; Knowlton, & Squire, 1996; Miller, 1956; Rosenbaum, Hindorff, & Munro, 1987; Rosenbaum, Kenny, &
Derr, 1983; Simon, & Barenfeld, 1969) relates to how
elementary units can be bound together in aggregate
chunks. In sequence learning (e.g., Cleeremans &
McClelland, 1991), for example, the notion of chunking is
central and refers to a situation where adjacent stimuli in a
fixed sequence may eventually be chunked into a single
unit. Chunking is a hierarchical process, where individual
items (e.g., A, B, C, and D) form sub units (e.g., AB, CD),
which go on to form a single chunk (ABCD) representing
an entire sequence (Perlman, Pothos, Edwards & Tzelgov,
2010). Chunking, as a process, necessitates a fixed order
(Perlman, et al., 2010, 2016). The underlying assumption in
chunking is that, as elementary units co-occur, larger units
build up. Such an analysis is motivated from associative
learning theory and has been embodied in important
research traditions, such as that of connectionism (e.g.,
Elman, 1990; Rumelhart, & McClelland, 1986).
When a fixed route is practiced repeatedly, we suggest
that it may be viewed as a sequence (e.g., Meilinger, 2008),
in the simple sense that individual route segments (e.g.,
turn right, go left, right and then right) form a sequence. In
effect, knowledge of this sequence constitutes route
knowledge (Meilinger, et al., 2014). Accordingly, as in
other sequence learning paradigms (e.g., Perlman, &
Tzelgov, 2006; see also Perlman, et al., 2016), after
learning is acquired, the emphasis may no longer be on
specific information, e.g., landmarks, individual turns or
cues, but rather on a process of sequential compression (or
chunking). The spatial information of represented route is
compressed into a single unit of information. For example,
consider a route that requires three turns to arrive at a
location. Initially, there are three parts of information to
process (i.e., turn right, turn left, turn right) whose
Psychological Research
processing may well be aided by landmarks. Once the
entire sequence is learned, the information is compressed
(or chunked) into a single unit of information (e.g., “route
X”). Showing that fixed-route learning follows the pattern
of typical sequence learning would indicate that it is an
instance of a broader sequence learning domain.
Moreover, addressing route learning as a chunking
process links such learning with other basic processes of
the cognitive system, such as development of expert
knowledge (e.g., Simon, & Barenfeld, 1969), category
learning (e.g., Goldstone, 2000; Knowlton, & Squire,
1996), working memory (e.g., Miller, 1956), motor control
(e.g., Rosenbaum, Hindorff, & Munro, 1987; Rosenbaum,
Kenny, & Derr, 1983) and control of complex and dynamic
situations (Vallacher, & Wegner, 1987). Such a link suggests that in route unitization, like other forms of chunking,
participants cease to make use of smaller units (Perlman,
et al., 2010) e.g., making a specific left turn following a
previous right turn. That is, after unitization develops, such
item-specific route information would no longer play a role
when traversing the route. Others maintain that representation of smaller units actually may disappear or decay (see
also, e.g., Giroux, & Rey, 2009; Perruchet, Vinter, Pacteau,
& Gallego, 2002; Perlman, et al., 2016; Pothos, & Wolff,
2006). In any case, these studies agree that item-specific
information no longer plays a key role in performance after
the learnt information becomes unitized into a single representation. Thus, even if full decay of item-specific
information does not ensue, for all intents and purposes
such information is not utilized in performance. Accordingly, our research question is: if once the entire fixed route
is learned, will the individual route parts become unitized,
thereby rendering the item-specific information irrelevant
to performance? Note, we do not suggest that mapping,
landmarks, angles, directions, and cued recall are not part
of a route mapping process; they most clearly are. Rather,
we ask whether a bias can arise in route recall, from the
extant theory on unitization, according to which the unitization of route knowledge can eventually supersede
representation of individual elements.
The present paradigm follows the theme outlined in the
aforementioned driving example. Namely, we measured
performance on an overlapping route segment, common to
both a previously learned route (which always appears in
red), and to a novel route (which always appears in blue).
In other words, both the original red training route and the
novel blue route contained the same overlapping route
section. Participants were randomly divided into two
groups; the sequential group who learned the entire route in
sequence and the random group who learned individual
route segments not in sequence. We consider whether
participants in the sequential group would show different
test performance for the very same overlapping route
segments when presented in different contexts, i.e., the
context of the old training route (red) and the new test route
(blue).
Accordingly, we expect the following: First, test performance on the red original route should be overall better in the
sequential-group than in the random-group. We further
predict an overall advantage for the same overlapping route
segment when performed in the context of the red route
versus the novel blue route. We also critically predict a twoway interaction between group (sequential vs. random) and
route (original vs. novel), so that only participants in the
sequential group will demonstrate better performance on
overlapping route segments performed in the context of the
red (original) route versus the blue (novel) route. This latter
interaction is compatible with a unitization account whereby
the individual elements cease to play a role.
While the aforementioned result pattern relates to
overlapping stimuli, results should be at least as robust in
the non-overlapping BLOCKs. We focus on the overlapping BLOCKs in Experiments 1 and 2 for two reasons:
First, only analysis of overlapping route segments can
address unitization. Second, the non-overlapping BLOCKs
could not be compared as they comprised different stimuli
and responses (although see Experiment 3).
Experiment 1
Method
Participants
Thirty students (7 males, mean age = 22.9) from introductory psychology courses at Ben Gurion University
participated in the experiment for course credit. All participants reported normal or corrected-to-normal vision.
Apparatus
The experiment was conducted using IBM compatible
Pentium III computers with 17″ monitors. The screen was
placed approximately 60 cm from the participants. Participants responded by keyboard press. The onset of a stimulus
started the timer; the stimulus changed location (intersection) as soon as the participant responded. Responses were
indicated by pressing the 1/0 keys (arrows pointing right/left
were taped onto these keys). Participants were asked to use
the index fingers of both hands for key presses.
Stimuli and procedure
The stimuli were all based on two routes, that we call the
red and the blue routes. Each route comprised BLOCKs,
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i.e., segments connecting two nearest intersections. An
‘intersection’ is defined as a map location where one may
make a turn. As shown in Fig. 1, there are seven BLOCKs
connected by eight intersections in each route. The routes
were depicted on a city map (Fig. 1) via a red line or blue
line. At all times, both red and blue lines, indicating the
two routes, were present, regardless of which route participants were responding to (this ensured saliency of the
overlap between the two routes). During training only the
red route was performed, but during test both the red and
blue routes were performed. Indicating the route segments
as BLOCKs 1–2, 2–3, 3–4, 4–5, 5–6, 6–7, and 7–8, we note
that two BLOCKs (4–5 and 5–6) were the same in both
routes. BLOCK 4–5 began at intersection 4 and extended to
intersection 5, and BLOCK 5–6 began at intersection 5 and
extended to intersection 6. Traversing this same overlapping route segment required exactly the same response to
the same stimuli at the same location.
Training: Two groups of participants (sequential- vs.
random-group) underwent extensive training on the red
Fig. 1 Upper panel the stimuli employed in Experiment 1, which
consisted of one learning route (red) and two test routes (the same red
route along with a novel blue route). Navigation takes place from the
upper part of the map downwards. Note that while only one route is
being performed at any given time, both routes are shown throughout
learning and test. Travel direction is indicated by the arrow. Lower
panel an example of the third learning trial in Experiment 1. After
participants have responded twice via arrow press, the smiley moves
to the third intersection. The correct response for this third trial would
be the left arrow (color figure online)
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route shown in Fig. 1. Both groups were instructed to
follow the entire red route by monitoring the movement of
a smiley and making the appropriate (left/right) response.
Following each response, the smiley would move to the
next intersection. In the sequential group, the smiley
appeared at the beginning of the route and participants had
to follow it by arrow-response across the entire route in
sequence. A Trial began with the smiley located at the
beginning of the training route (intersection 1, Fig. 1),
where the correct response would be pressing the left-arrow
key. A depiction of the third trial is shown in Fig. 1, lower
panel, where after responding to the first two trials, the
smiley moves to the third intersection. The correct response
for this trial would be the left arrow which would move the
smiley to intersection four, and so on. Once the smiley
reached the end of the route, it returns to the beginning and
participants begun again. The direction of movement was
always from top to bottom.
In the random-group, participants were exposed during
training to individual BLOCKs along the red route, and
never performed the entire route in sequence. Namely, the
smiley appeared at random intersections across the route,
participants made a single response (turn), upon which the
smiley would randomly “jump” to another intersection. Some learning should occur even for the random
condition, as at the very least, participants should gradually
learn the correct responses to each intersection.
Training was comprised of 10 blocks,2 each consisting
of 105 trials (traversing 7 BLOCKs 15 times). The
response stimulus interval (RSI) separating each sequence
(of 7 responses) from each other was 1000 ms. During the
RSI, the map appeared with no smiley. Participants did not
receive feedback; the smiley jumped to the next intersection even after errors. Note that no feedback was necessary,
since the red line, indicating the route, was continuously
present on the screen and participants simply had to make
the correct responses, at different intersections (indicated
by the smiley).
Test: While participants did not perform the blue route
during training, it was present, and thus participants during
training saw that the overlapping route segment was
common to both the red and blue routes. After training,
participants in both groups proceeded to the same test,
where they had to intermittently perform the old route (red
training route) and a blue novel route in a random order.
Performance was guided by the smiley which moved in a
sequential manner from beginning to end. To reiterate, the
only difference between groups was at training, where the
random-group never performed the entire route in
sequence. There were ten test blocks; each block comprised
2
As explained above, block (lower-case) refers to a group of trials
and not route segment (BLOCKS).
Psychological Research
a red and blue route each appearing once yielding 140
trials. After each test route was performed, it was followed
by an RSI of 1000 ms. During both training and test trials,
participants had to respond, as fast as possible, by pressing
the corresponding arrow key to the appearing smiley.
The training results reveal improvement across training
trials [F(9, 252) = 54.69, MSE = 13,871, ἠ2p = 0.66,
p \ 0.001], from a mean of 612 ms to mean of 351 ms in
the experimental group and from a mean of 1008–699 ms
in the control group. These group differences were significant across all blocks [F(1, 28) = 335.29,
MSE = 217,918, ἠ2p = 0.92, p \ 0.001], even in the last
block [F(1, 28) = 321.00, MSE = 19,814, ἠ2p = 0.91,
p \ 0.001].
The key question of interest is whether a unitized representation developed, in a way that individual route
segments may no longer be relevant. To answer this
question, we focused on the differences between overlapping route segments performed in the context of the red and
blue routes. Both RT and error data for all test trials were
recorded. Comparable analyses were run on both of these
measures, yielding similar results, except that some RT
effects were not apparent in the error data. There was no
evidence of a speed-accuracy trade-off, thus, only RT data
from correct responses were included in the analysis and
are presented in detail. Average error rates were 3.3 % for
the sequential group and 2.4 % for the control group.
Test data: The median RT of each participant for each
overlapping intersection was calculated across blocks and
averaged across participants, see Fig. 2. These medians
were submitted to a two-way mixed analysis of variance
(ANOVA) with route (original/novel) and group (sequential/control) as between subject factors (Fig. 2).
Performance on the original red route was faster than on
the novel blue route [F(1, 28) = 8.402, MSE = 968,
ἠ2p = 0.23, p\0.01]. Overall performance of the sequential
group was marginally better than of the random group [F(1,
28) = 3.781, MSE = 5241, ἠ2p = 0.11, p \ 0.07]. The
critical Route by Group interaction was significant [F(1,
28) = 12.734, MSE = 968, ἠ2p = 0.31, p \ 0.01]. Planned
comparisons revealed a significant difference within the
sequential group, whereby latency was faster when participants responded to the overlapping BLOCKs appearing
in the context of the original (red) route than when
responding to these very same route segments appearing in
the context of the novel (blue) route [F(1, 28) = 20.799,
MSE = 967.82, ἠ2p = 0.42, p \ 0.001]. No such effect was
found in the random-group (F \ 1). Please see Fig. 2.
This main finding that RTs in the sequential group were
shorter for the very same overlapping intersections
310
300
Mean RT (ms)
Results and discussion
320
290
280
270
260
Original
Novel
250
Sequence
Control
Group
Fig. 2 Mean of median reaction times (RTs) for all test blocks of
trials for the original and novel routes, in each condition of
Experiment 1
(comprising BLOCKs 4–5 and 5–6) in the original route
than in the novel route provides evidence for the type of
unitization we hypothesized (see also Perlman, et al.,
2016). This result suggests that item-specific information,
which is necessary at least for initial performance, may be
superseded, when a unitized representation for the entire
sequence emerges (see also Vallacher, & Wegner, 1987).
It might be claimed that unitization was merely an
efficient strategy in Experiment 1, which resulted in
enhanced performance at test. Responding using individual
item-specific information for both the blue (novel) and red
(original) routes, would only benefit 2/7 responses (number
of overlapping stimuli), while responding on the basis of a
unitized representation would benefit the remaining 5/7
responses in the original red route (number of non-overlapping stimuli). Consequently, unitization rather than
being obligatory may merely be a preferred strategy
employed only when there is no (cognitive) reason to utilize item-specific information. Indeed, Perlman, et al.
(2010) showed that an increase in sequence overlap renders
unitization less likely. Yet Perlman et al.’s study concerned
the emergence of unitization and not the application of the
corresponding knowledge during test, after unitization was
(presumably) generated. Nevertheless, motivated by these
ideas, we can ask a similar question: assuming a unitized
representation has already emerged, is it the case that it will
be utilized when less efficient? Cognitively, as the overlap
between a learned sequence and a novel one increases,
perhaps it would make more sense to abandon a unitized
representation and revert to item-specific information. The
converse possibility is that, once a unitized representation
has emerged, its use is obligatory, regardless of its
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efficiency in subsequent application of the corresponding
knowledge (Perlman, & Tzelgov, 2006). In Experiment 2,
we examine if the same result pattern would be observed
when overlap increases to 3/7 (from 28 to 42 %).
Experiment 2
In Experiment 2, the degree of overlap between routes
increased to include intersections 4, 5, and 6 (BLOCKs 4–
5, 5–6, and 6–7, see Fig. 3). Observing the same pattern of
results as in Experiment 1 would suggest that a unitized
representation, once generated, will be employed in an
obligatory way, such that it is not possible to discard it,
even if it becomes less efficient to utilize.
Method
Thirty university students (7 males, mean age = 24.0) who
did not take part in Experiment 1, participated in Experiment 2. The map used in this experiment is presented in
Fig. 3. Aside from increased overlap this experiment was
identical to Experiment 1.
Results and discussion
320
310
300
Mean RT (ms)
Visual inspection of the mean latencies in the various
conditions presented in Fig. 4 show that the results of the
experiment are broadly similar to those of Experiment 1.
As before, to address how sequential vs. random (control)
training affects performance, we focused on test performance. Regarding training, we briefly mention that there
was evidence for improvement [F(9, 243) = 27.01,
MSE = 25,146, ἠ2p = 0.50, p \ 0.001], from a mean of
637 ms to a mean of 387 ms in the sequential group and
from a mean of 1148–726 ms in the control random group.
These differences between groups were also significant [F
(1, 27) = 82.03, MSE = 1,110,335, ἠ2p = 0.75, p \ 0.001]
even in the last block [F(1, 27) = 152.04, MSE = 39,262,
ἠ2p = 0.84, p \ 0.001].
Both RT and error data for all test trials were recorded.
Comparable analyses were run on both of these measures,
yielding similar results, except that some RT effects were
not apparent in the error data. As in Experiment 1, there
was no evidence of a speed-accuracy trade-off, thus, only
the RT data are presented in detail. Average error rates
were 4 % for both groups. Only RTs from correct responses
were included in the analysis.
As previously, for each participant, the median RT for
each overlapping intersection was calculated for all blocks
and averaged across participants, see Fig. 4. These medians
were submitted to a two-way mixed analysis of variance
(ANOVA) with route (original/novel) and group as a
between subjects factor (Fig. 4). As previously, we focus
on the interaction of group by route along with the
accompanying simple main effects. While the random
group should show no differences between performing the
overlap route segments in the context of the two routes
(original-red/novel-blue), the sequential group should show
better performance for the overlapping BLOCKs in the
context of the original route vs. the novel route.
The critical route with group interaction was significant
[F(1, 28) = 5.717, MSE = 437, ἠ2p = 0.16, p \ 0.05].
Planned comparisons reveal (see Fig. 4) a significant difference in the sequential group, where latency was faster
when participants responded to overlapping BLOCKs
appearing in the context of the original route than when
responding to these very same BLOCKs when they
appeared in the context of the novel (blue) route, [F(1,
290
280
270
260
Original
Novel
250
Sequence
Control
Group
Fig. 3 The stimuli employed in Experiment 2
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Fig. 4 Mean of median reaction times (RTs) for all test blocks of
trials for the original and novel routes, in each condition of
Experiment 2
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28) = 7.193, MSE = 4370, ἠ2p = 0.20, p \ 0.05]. No such
effect was found in the random group (F \ 1).
In both experiments, the sequential group responded
faster to the very same overlapping route segments when
they appeared in the context of the original route than when
they appeared in the context of the novel route. Such
results are consistent with unitization of route learning
occurring only in the sequential group. Showing this same
result pattern for increased overlap between routes where
there is less utility for a unitization strategy is consistent
with unitization being less of a strategy and more of an
obligatory process that occurs regardless of overlap degree
(Perlman, & Tzelgov, 2006). Once unitization develops,
item-specific information, such as specific individual
responses, may be less relevant to performance and execution of route knowledge (see also Perlman, et al., 2016).
In Experiments 1 and 2, a single route was employed at
training and two routes at test. Participants learned a
specific given red training route, and at test, performance of
the same overlapping route segment was examined in both
the context of this red route and in the context of a novel
blue route. It is possible (although unlikely) that results
may have been affected by this design. Also, having a
single training route and a single novel route at test rendered impossible the comparison between overlapping and
non-overlapping intersections. In Experiment 3, we ask
whether the impairment in performing the overlapping
route segment in a novel context is similar in magnitude to
perform a new route segment (non-overlapping). In other
words, is an old route segment appearing in a new context
treated it as if it were a new route segment? Accordingly,
Experiment 3 employed two counterbalanced routes, which
were shown to be equally difficult, enabling comparisons
between non-overlapping segments.
In addition, we rectified some limitations of the first two
experiments. First, perhaps unitization effects are limited to
relatively short routes comprising even shorter overlapping
segments. Unitization has been shown to be limited in
sequence learning paradigms to motor chunks involving
only four or five elements (Ganor-Stern, Plonsker, Perlman, & Tzelgov, 2013; Verwey, Shea, & Wright, 2015).
Thus, in Experiment 3, route length was increased to 15
intersections comprising an overlapping segment of six
BLOCKs. Second, in Experiments 1 and 2, there were no
landmarks that could cue participants. It can be claimed
that unitization in route learning would be less necessary in
the presence of landmarks, as learning can develop by
associating turns to landmarks (Epstein, & Vass, 2014).
Would evidence for unitization exist when landmarks are
able to cue participants to familiar route segments?
Demonstrating similar results even in the presence of
salient landmarks would render the current claims more
robust. Third, it might be claimed that showing the whole
route on the screen continuously (as in Experiments 1 and
2) facilitated unitization. In Experiment 3, route presentation is limited by participants’ progress. Finally, it could be
claimed that showing both routes across training, which on
the one hand is advantageous in rendering overlap more
salient, may on the other hand have been disadvantageous.
For example, it may have allowed for some passive
learning of the route intended as novel. In Experiment 3,
only the route that was performed was shown at any given
time.
Experiment 3
The goal of Experiment 3 was to rectify the aforementioned points and to examine if an old route segment that
appears in the context of a novel route is performed like a
new (non-overlapping) route segment. Like previous
experiments, unitization would be demonstrated by comparing performance for the same overlapping segments
from the original and novel routes. If item-specific information is relevant to performance, participants would by
definition recognize (at least show benefit for) the relatively long overlapping segment comprising the same
stimuli, locations, and response. Consequently, making the
same response sequence to the overlapping segment should
be similar, regardless of route, even if it is not consciously
recognized as overlapping. Yet if item-specific information
is less relevant after unitization, the overlapping route
segments should be responded to differently in the context
of the training route than in the context of the novel route,
this would demonstrate unitization of the training
sequence. If this difference is similar to that observed
between the non-overlapping segments of the original and
novel routes, it would suggest that an old route segment
presented in the context of a new route is akin to a novel
stimulus.
Method
Participants
Thirty students (5 males, mean age = 25) from introductory psychology courses at Bar Ilan University participated
in the experiment for course credit. All participants
reported normal or corrected-to-normal vision.
Stimuli and procedure
The experiment was programmed in C++, and conducted
with IBM compatible Pentium III computers and 17″
monitors. The screen was placed approximately 60 cm
from the participants. Participants responded using the
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computer mouse. The aim of the participant was to follow a
target along a route as shown in Fig. 5. Response times
were recorded.
Unlike Experiments 1 and 2, in this experiment the route
was not shown continuously during training, rather it was
incrementally drawn, as participants progressed through
task. Note, such a procedure is ecological, as in guided
route navigation applications (e.g., Waze), the depicted
route develops with one’s progress. The stimuli are shown
in Fig. 5a, b. The experiment was organized in 25 training
blocks and 5 test blocks. At training, participants received
one of two routes. Both routes comprised 15 intersections.
Each of the two different routes (see Fig. 5) contained a
common overlapping section comprising six BLOCKs.
Half the participants were trained on route A, and half were
Fig. 5 The stimuli employed in
Experiment 3 for routes 1
(a) and 2 (b). Note that the
overlapping segment in route A
(intersections 3–9) and route B
(intersections 6–12) is exactly
the same
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trained on route B. Each training block consisted of 15
trials. Participants were instructed to follow a pink routeline by moving the Microsoft mouse cursor to a circle at the
end of the depicted route segment (see Fig. 6). Upon a
response, the circle moved to the next intersection. Accordingly, participants were required to “touch” the mouse
cursor on the next circle, for the target to move on to the
following intersection. For example (see Fig. 6), when
moving from intersection 7–8, participants had to move the
mouse along the pink-line, when the cursor touches intersection 8, the pink-line begins moving to intersection 9.
Two test routes, an original route and a novel route were
presented intermittently in a random order. As in training,
at test as well, performance was guided by tracking the
route line with the mouse cursor (arrow, see Fig. 5).
Psychological Research
Fig. 6 Example of a learning
trial for route 1 (Experiment 3).
After responding to the seventh
learning intersection by moving
the mouse, the subject must
move the cursor to the eighth
intersection for the route to
continue to intersection 9
Results and discussion
We first checked that there were no overall differences in
route difficulty: there was no significant difference across
the learning trials between each of the two routes (F \ 1),
indicating that both routes were comparable. Likewise,
learning performance for both the overlapping segments of
these routes (F \ 1) and the non-overlapping segments
(F \ 1) was statistically the same (Fig. 7).
The mean RTs of each test block of responses were
submitted to a two-way analysis of variance (ANOVA)
with route (original-route/novel-route) and overlap (overlapping/non-overlapping) as within subject factors. The
overlap effect was significant [F(1, 29) = 21.077,
MSE = 1215, ἠ2p = 0.42, p \ 0.0001], indicating that the
overlapping route segment was performed significantly
faster than the non-overlapping route segment. As expected, the critical route effect was significant [F(1,
29) = 26.591, MSE = 829, ἠ2p = 0.47, p \ 0.0001], indicating that participants performed significantly faster on
the original route vs. the novel route. The Route by Overlap
interaction was not significant [F(1, 29) = 1.561,
490
480
470
Mean RT (ms)
Participants were required to respond as fast as possible.
Each route appeared five times during test.
As opposed to both previous experiments where unitization was demonstrated by a group by route interaction, in
Experiment 3 there was only one group. Accordingly,
unitization should be demonstrated by a Route main effect
(original vs. new), which should be significant even for the
overlapping segments. Namely, the overlapping route
segment in the context of a novel route should be performed significantly slower than in the context of the
original route.
460
450
440
430
Original
Novel
420
Non-Overlap
Overlap
Overlapping
Fig. 7 Mean of median reaction times (RTs across test trials and
blocks) for the original and novel routes in the overlap and nonoverlap conditions of Experiment 3
MSE = 1596, ἠ2p = 0.05, p = 0.22], indicating that the
observed performance advantage for the original vs. novel
route evident in the non-overlapping condition was not
statistically different than the same performance advantage
evident in the very same overlapping route segments.
Namely, performance of the non-overlapping route segment in the original route (443 ms) was faster than in the
novel route (479 ms). Likewise, performing the overlapping segment in the original route was faster (423 ms) than
performance of this very same segment in the novel route
(441 ms). Furthermore, these differences were similar; the
difference between overlapping segments performed in the
context of the old vs new route was similar to the
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difference observed between both routes in the non-overlapping route segments.
Unitization in the current experiment was demonstrated
even when the length of the entire route and its overlapping
segment was doubled. Demonstrating unitization for a
segment of six overlapping elements appears to extend
previous sequence learning findings where motor chunk
size was limited to four or five elements (Ganor-Stern,
et al., 2013; Verwey, Shea, & Wright, 2015). In addition,
unitization occurred even when landmarks were available
and could have been potentially used, and thus learning
could have relied on item-specific representations by
associating responses to landmarks (Epstein, & Vass,
2014). Note that landmark processing was not part of task
requirement (Strickrodt, et al., 2015), e.g., turn left at the
“big oak” Other studies however show that the mere
presence of constant landmarks is sufficient to allow their
processing in a manner beneficial to route learning (Foo,
Duchon, Warren, & Tarr, 2007). Thus, while it is likely
that landmark processing occurred, future research is
required to discern if the obtained results apply to situations where intentional and associational landmark
processing are part of task requirement. Finally, results
indicated that an old route segment in the context of a new
route is performed as if it were a completely new route
segment.
General discussion
It is typically the case that route learning is assumed to
depend on item-specific information such as angles,
direction or turns (Gillner, & Mallot, 1998; Kuipers,
1978, 2000; Werner, et al., 2000; Kuipers, et al., 2003;
Meilinger, 2008). Extending previous approaches (Strickrodt, et al., 2015), we have demonstrated here that, after
sufficient learning, a unitized representation for a fixed
route can emerge. That is, the sequence of elements comprising a route can be unitized. This unitized representation
of a route sequence may suffice to guide an individual
across the route, in an assumedly more efficient manner,
compared to a disjointed non-unitized representation.
Similarly, the motor behavior literature also indicates that
people have the capacity to control short sequences of
actions using chunks, whose elements can be treated collectively (Rhodes, Bullock, Verwey, Averbeck & Page,
2004; Sakai, Kitaguchi, & Hikosaka, 2003; Verwey,
1999, 2001; Verwey, Lammens, & van Honk, 2002, Verwey, & Wright, 2014). Additionally, in contrast to typical
sequence learning paradigms where motor chunks represented subsequences with up to four or five elements
(Ganor-Stern, et al., 2013; Verwey, Shea, & Wright, 2015),
Experiment 3 demonstrates unitization of a long sequence
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consisting of 15 segments. Accordingly, unitization may be
not as limited as previously supposed.
As mentioned, the current paradigm involving following
a dot across a map may involve different learning mechanisms than route learning in some of the aforementioned
route learning studies (e.g., Meilinger, et al., 2014), where
one actually moves through space. Thus, additional
research may be required before concluding that the current
results straightforwardly apply to all forms of route navigation. In any event, this paradigm is ecological because it
resembles navigation applications (Google-maps, Waze)
where one follows a moving dot across a map.
While a process of unitization is known to be relevant to
other sequence learning tasks (Perlman, et al., 2010), these
experiments extend earlier results to the domain of procedural route learning. These results suggest a new angle in
route learning and navigation, whereby route knowledge,
similar to information from other sequence learning paradigms, can be represented in a high density fashion, where
all item-specific information, e.g., a specific turn, is compressed into a single unit which cannot be readily
unpacked. Thus, as in other domains (Perlman, et al.,
2010, 2016), following unitization, the item-specific
information that preceded learning may cease to be
accessible or relevant. Consequently, individual elements
no longer appear familiar as indicated by the increased RT
for exactly the same turns in the overlapping segment when
appearing in the novel route relative to the training route.
This suggests that item-specific information of a given
response (turn) may exist only in the context of a given
route, and thereby would only be helpful when performing
the same exact route.
Unitization may be advantageous because it reduces the
amount of information necessary for representation, i.e.,
instead of maintaining a representation of seven or fifteen
individual turns, one may represent an entire route as a
single representation, e.g., “route x”, thereby reducing
cognitive load. As the route becomes unitized, it constitutes
a single object in working memory and thus presumably its
representation is less demanding, as opposed to representing seven or fifteen individual units of information.
Research by Bo and Seidler (2009) as well as Seidler, Bo,
& Anguera (2012) support the link between unitization and
working memory. Another advantage of unitization is that
the output, such as route navigation, may be performed
automatically; there is no need to consciously retrieve the
relevant information (see Ganor-Stern, et al., 2013 for such
an account).
Viewing route learning as an automatic execution of a
motor sequence does not belittle the importance of itemspecific information. On the contrary, we believe, in line
with other prominent models such as the ACT-R (Adaptive
Character of Thought-Rational, Anderson, & Matessa,
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1997) theory, that typical learning initially relies on itemspecific declarative information (“at the big oak tree turn
left”, “when you reach Macy’s turn right”). However, with
practice, a route sequence may gradually become unitized
and less reliant on item-specific information, as is the case
with other types of skill learning (see Anderson, &
Matessa, 1997). It is interesting to note that, even in the
superficially simple examples in the present experiments,
an algorithm that qualitatively reproduces our results
would not be straightforward. Such an algorithm would
still need to be context-dependent, that is, allow for the fact
that exactly the same stimuli may be responded to differently in different contexts (for illustration, in “Appendix 1”
we consider some simple examples of corresponding
algorithms).
Sequence learning may lead to sequence knowledge
consisting of associations between the stimuli (Mayr, 1996),
responses (Willingham, Wells, Farrell, & Stemwedel, 2000),
response–stimulus compounds (Ziessler, 1998) or stimulus–
response compounds (Schwarb, & Schumacher, 2010). The
present results demonstrate that these narrow associations
consisting of two elements are not driving current performance. Associations between two locations were not
relevant to performance, thus something more is needed to
explain learning. Accordingly, we would expect that drivers
who drive along a fixed route may perform their daily driving
in an automatic fashion, and so they would not be confused
by changing landmarks (e.g., removal of the oak tree, or
relocation of Macy’s). Clearly, there is a trade-off between
robustness of the route knowledge and inflexibility, in cases
when route variations are expected. Moreover, automaticity
of a unitized representation may also produce disadvantages
in performance, as changes in a sequence would be more
difficult, once a route is unitized. For instance, think of a
situation where one frequently travels from X to Y via route
A in 80 % of circumstances, then one day wishes to travel
from X to Y via route B, but wrongly takes the turn A instead.
As the route sequence (X to Y via A) is a single unit (or
decision), more cognitive resources may be needed to be
allocated for properly traveling from X to Y via B (this extra
processing is manifest through time latencies in classic
unitization studies).
In the current study, participants learned the route in a
guided manner, a manner of route learning that is highly
relevant for everyday life, due to the increasing popularity
of navigation aids based on the global positioning system
(GPS). Indeed while guided learning may produce fewer
errors than non-guided route learning, less spatial awareness may ensue as a result of guided route learning (Li,
Zhu, Zhang, Wu, & Zhang, 2013). The implications of the
current study pertain to fixed routes. Other disadvantages
have been documented for guided vs. non-guided walking
(Ishikawa, Fujiwara, Imai, & Okabe, 2008).
Along with the many advantages of a unitized route
representation, the current aforementioned possible disadvantage has implications for transfer. The rigidity of
unitized knowledge may generate a situation of nontransferable learning where knowledge about an overlapping route section in one context will not transfer to
another route. Such skill learning may remain specific, as
observed for example in sequence learning (Sanchez,
Yarnik, & Reber, 2014), perceptual tasks (e.g., Karni &
Sagi, 1991) or motor tasks (e.g., Pashler & Baylis, 1991).
Conversely, a non-unitized representation should transfer
from one context to another, as in the cases of pilots
benefiting from a simulation of a flight experience (Gopher,
Weil, & Bareket, 1994). Transfer of learning has been a
central theme in both cognitive psychology and practical
training courses. The current results can be viewed in this
light, as an absence of transfer, namely, knowledge of the
overlapping same turns did not transfer from the training
route to the novel route. The current lack of transfer may
differ from that in related situations, namely, as everything
about these overlapping stimuli in the present experiments
was identical (e.g., stimuli, location, and responses), still
the same stimuli were responded to faster in the context of
the familiar route versus the novel route. Perhaps transfer
would have occurred if the learning procedure varied, and
thus item-specific information would not have been tied
exclusively to a given context, e.g., a given route (Perlman,
et al., 2016; see also Green, & Bavelier, 2008, for a similar
claim). Accordingly if from the beginning of training, turns
along a given route are equally traveled in the context of
other routes, transfer may be more likely. More research is
needed to investigate this interesting topic.
Compliance with ethical standards
Funding This study was not funded.
Conflict of interest The authors declare that they have no conflict of
interest.
Ethics All procedures performed in the reported studies were in
accordance with the institutional ethical committee and with the 1964
Helsinki declaration and its later amendments or comparable ethical
standards.
Informed consent Informed consent was obtained from all individual participants included in the study.
Appendix 1
Simple algorithms for modeling sequence learning: We ask
what kind of simple algorithms could, in principle, describe
performance in our experiments and, specifically, the key
finding that the overlapping stimuli were responded to
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differently in the context of a practiced sequence than in
isolation. These algorithms are clearly not cognitive models, but still they may be useful in that they illustrate the
algorithmic complexity of the obtained results. For example, imagine one needs to program the order of operations
for a robot from 1 to n. This can be done in several ways,
A–D.
D:
If x=1 then 2
Else if x<n then x+1
A:
Else if x=n end
If 1 than 2
If 2 then 3
If 3 than 4
In this (A) situation after 1, 2 has to appear. Even when
a robot performs Action 2 after (say) 6 rather than after the
Action 1, it knows to proceed to Action 3. In B, if 2
appears, then 3 may not necessarily appear, rather, only if 1
and 2 appear in sequence will 3 follow.
B:
If 1 than 2
If 1 & 2 than 3
If 1 & 2 & 3 than 4
C:
References
If 1 then 2
If 2 then 3
If 3 then 4
End if
End if
End if
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In C and D situations, Action 3 must appear after Action 2
that follows Action 1. In Situation C for example, when the
robot performs Action 2 after (say) Action 6 rather than after
the first action, it does not know that it has to continue to
Action 3. If the robot in situation D performs Action 6 and
then 3, it will correctly infer Action 4. Yet even in such a case
the robot does not seem able to reproduce the obtained
behavioral results, as the overlapping segment is performed
differently in the original and novel routes. The very same
route sequence is performed differently by the cognitive
system according to the route context it appears in.
One of the possibilities that arise from this study is that
during training, there is a transition from declarative
memory of separate connections between the locations
from 1 to n, that is as in A, to procedural and automatic
execution where Action 1 leads to Action 2 which leads to
Action 3 which leads to 4 as in B, C and D. If one preforms
the route in an automatic manner as a unit, but at some
point transfers to a different route that partly overlaps with
the old route, performance must revert again to declarative
memory of separate connections between the locations.
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