Separating Patterns of Space and Time: Differences in Memory for Event Duration
and Sequential Order
by
Iva Kristl Brunec
A thesis submitted in conformity with the requirements for the degree of Master of Arts
Department of Psychology University of Toronto
© Copyright by Iva Kristl Brunec 2015
ii
Separating Patterns of Space and Time:
Differences in Memory for Event Duration and Sequential Order
Iva Kristl Brunec
Master of Arts
Department of Psychology
University of Toronto
2015
Abstract
Our memories are temporally organized. However, little is known about how the duration of
individual events is encoded in episodic memory. We used a virtual reality navigation paradigm
where periods of navigation were interspersed with pauses of different durations. In Experiment
1, we show that the active experience of the passage of time selectively enhanced memory for
duration, with no effect on memory for the temporal sequence of events. In Experiment 2, we
show that active navigation interspersed with passive waiting did not result in a similar
enhancement of temporal memory. Crucially, in both studies participants were able to reliably
distinguish the durations of subjectively re-experienced events, but not familiar ones. This effect
was unique to duration. We posit that time is represented in episodic memory in a manner akin to
space, and that the hippocampally-supported ability to recollect an event enables the
reinstantiation of its temporal and spatial context.
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Table of Contents
List of Figures ................................................................................................................................. v
1 Introduction ................................................................................................................................ 1
2 Experiment 1 .............................................................................................................................. 4
2.1 Methods ............................................................................................................................... 4
2.1.1 Participants .............................................................................................................. 4
2.1.2 Experimental Design and Procedure ....................................................................... 4
2.2 Results ................................................................................................................................. 8
2.2.1 Recognition Memory .............................................................................................. 8
2.2.2 Temporal Discrimination ........................................................................................ 8
2.2.3 Distance Discrimination ........................................................................................ 11
2.2.4 Duration and Sequence Order ............................................................................... 14
2.2.5 Time Estimation .................................................................................................... 16
2.3 Discussion ......................................................................................................................... 17
3 Experiment 2 ............................................................................................................................ 18
3.1 Methods ............................................................................................................................. 18
3.1.1 Participants ............................................................................................................ 18
3.1.2 Experimental Design and Procedure ..................................................................... 18
3.2 Results ............................................................................................................................... 19
3.2.1 Recognition Memory ............................................................................................ 19
3.2.2 Temporal Discrimination ...................................................................................... 19
3.2.3 Distance Discrimination ........................................................................................ 20
3.2.4 Re-experience vs. Know ....................................................................................... 22
3.2.5 Duration and Sequence Order ............................................................................... 26
3.2.6 Time Estimation .................................................................................................... 28
iv
3.3 Discussion ......................................................................................................................... 29
4 General Discussion ................................................................................................................... 30
4.1 Active Experience of the Passage of Time ....................................................................... 31
4.2 Hippocampus and Time .................................................................................................... 32
4.3 Limitations and Future Directions .................................................................................... 34
4.4 Conclusions ....................................................................................................................... 35
References ..................................................................................................................................... 36
v
List of Figures
Figure 1: (A) First-person view screenshots from several intersections along the route; (B) Map
of the entire route. ........................................................................................................................... 5
Figure 2: (A) Performance on the temporal discrimination task (B) Performance on the temporal
discrimination task in each of the conditions split according to their self-reported memory
quality (recollection vs. familiarity) ............................................................................................. 10
Figure 3: (A) Performance on the distance discrimination task (B) Distance discrimination
performance in both conditions split according to participants’ self-reported memory quality.. 13
Figure 4: (A) Performance on sequential position and duration magnitude ordering tasks in the
passive condition. (B) Performance on sequential position and duration magnitude ordering
tasks in the active condition .......................................................................................................... 15
Figure 5: Time estimation in the active and passive conditions in Experiment 1 ......................... 16
Figure 6: Temporal and spatial discrimination performance in Experiment 2 ............................ 21
Figure 7: Performance in the active and passive conditions split according to participants’ self-
reported memory quality ............................................................................................................... 24
Figure 8: Sequence and duration magnitude ordering task performance. (A) Passive condition
(B) Active condition ...................................................................................................................... 27
Figure 9: Time estimation in the active and passive conditions in Experiment 2. ........................ 28
1
1 Introduction
Episodic memory represents sequentially unfolding events, each associated with a particular
temporal and spatial context (Conway, 2009; Moscovitch, Nadel, Winocur, Gilboa, &
Rosenbaum, 2006). Time is a crucial aspect of episodic memory and has been postulated to be
closely linked to the conscious re-experience of episodic memories (Nyberg, Kim, Habib,
Levine, & Tulving, 2010; Tulving, 1985, 2002). Knowing not only the spatial location of a
particular event but also how long ago it happened and how long it lasted may be evolutionarily
advantageous (Howard & Eichenbaum, 2013). For example, recalling how long it takes to reach
home from a specific location in the world is important in order to optimize animal or human
behaviour and the preservation of resources.
Time and space are inextricably linked in everyday navigation, motivating the idea that the same
network of neural substrates underlies the encoding of both modalities into episodic memory.
The role of the hippocampus in episodic memory and spatial navigation is well established
(Burgess, Maguire, & O’Keefe, 2002; Maguire et al., 2000). Research in rodents suggests that
this ability is supported by hippocampal place cells and grid cells, thought to code the structure
of a particular space and an animal’s position in it (Hafting, Fyhn, Molden, Moser, & Moser,
2005; O’Keefe & Dostrovsky, 1971; O’Keefe & Nadel, 1978). More recently, ‘time cells’ have
also been reported in rodent hippocampi, reflecting the passage of time during a temporal gap in
navigation (Jacobs, Allen, Nguyen, & Fortin, 2013; Kraus, Robinson, White, Eichenbaum, &
Hasselmo, 2013; MacDonald, Lepage, Eden, & Eichenbaum, 2011; Pastalkova, Itskov,
Amarasingham, & Buzsáki, 2008; for a review see Eichenbaum, 2014).
Prior research provides strong evidence to suggest that temporal information could be encoded
into sequences of events, like an index to later be used in the disambiguation of individual events
which comprise a longer sequence. One line of evidence supporting this view is the finding that
the hippocampus tracks the temporal sequence of events (Davachi & DuBrow, 2015; Liang Tien
Hsieh, Gruber, Jenkins, & Ranganath, 2014; Tubridy & Davachi, 2011) and the duration of
different events within a sequence (Barnett, O’Neil, Watson, & Lee, 2014). Furthermore,
hippocampal activity patterns differentiated between the temporal contexts in distinct sequences
(Liang Tien Hsieh et al., 2014), suggesting that information about time is used to distinguish
between otherwise similar events. In line with this capacity to pattern separate individual events,
2
the hippocampus was also found to be crucial in the disambiguation or separation of overlapping
sequences (Fortin, Agster, & Eichenbaum, 2002; Lehn et al., 2009).
Theories of memory draw a distinction between remembering an event, accompanied by a rich
set of contextual information such as the temporal and spatial context, and knowing that an event
had happened, based on familiarity with a presented cue, but bereft of its context at acquisition
(Hudon, Belleville, & Gauthier, 2009; Skinner & Fernandes, 2007). Specifically in the context of
spatial navigation and episodic memory, it was shown that only remembering elicited
hippocampal activation whereas knowing was associated with a network of regions in the medial
temporal lobes that did not include the hippocampus (Bowles et al., 2007; Diana, Yonelinas, &
Ranganath, 2010; Koen & Yonelinas, 2014; Ranganath et al., 2004). Crucially, temporal context
only appeared to be reinstated and to elicit hippocampal activation when subjects reported items
as remembered but not familiar (Sadeh, Moran, & Goshen-Gottstein, 2014).
This fits with the well-described hippocampal capacity for creating unique, non-overlapping
memory representations via the mechanism of pattern separation (Bakker, Kirwan, Miller, &
Stark, 2008; Kirwan & Stark, 2007). Based on this literature on spatial pattern separation which
shows performance improves as a function of the distance between stimuli and of the diminished
overlap that entails (Holden, Hoebel, Loftis, & Gilbert, 2012; Holden, Toner, Pirogovsky,
Kirwan, & Gilbert, 2013), we hypothesized that as the difference between two durations
increased, participants would become better at discriminating time. Furthermore, we predicted
that accuracy at discriminating event duration would be significantly better when participants
would report re-experiencing an event, rather than knowing that it occurred. Such self-reported
re-experiencing suggests more extensive processing of contextual information and the
incorporation of temporal information into this contextual representation.
In order to explore and connect the ideas presented here, we created a virtual reality spatial
navigation task similar to the rodent time-cell paradigm in which participants navigated along a
pre-set route with specific temporal gaps between segments of the journey. Instead of recording
changes in neural activation on-line, however, we were interested in how event duration is
encoded and retrieved from memory. Learning in the present task was made as incidental as
possible to allow us to explore whether temporal information is incorporated into memories
without explicitly attending to the time elapsed. We aimed to characterize the way humans
3
encode information about event duration and how such information is sequenced, as well as how
it may interact with spatial information.
An important aspect to consider in spatial navigation is the degree to which an individual
interacts with the environment. More specifically, active navigation and route following were
found to rely on different neural substrates (Hartley, Maguire, Spiers, & Burgess, 2003; Spiers &
Maguire, 2008). Furthermore, route memory was found to be enhanced when participants were
required to actively navigate, in contrast to being led through the route passively (Plancher,
Barra, Orriols, & Piolino, 2012). This finding was reported in other domains, such as verbal
memory, in terms of the production effect, referring to superior memory subsequent to
performing a task such as reading a word out loud, rather than just scanning the text (MacLeod,
Gopie, Hourihan, Neary, & Ozubko, 2010; Ozubko, Hourihan, & MacLeod, 2012).
In the present experiment, we explored two possibilities. In Experiment 1, we made the
experience of the passage of time during stops either active or passive, but controlled
participants’ movement through space so that they were led along the route at a set pace. In
Experiment 2, in contrast, participants were required to navigate along the route actively but wait
for the entire duration of each stop passively. This manipulation allowed us to investigate
whether any active interaction with the environment enhances memory non-selectively (i.e., both
duration and temporal order in both tasks), whether there is no difference between active and
passive navigation in any condition, or whether memory for duration may be selectively
enhanced when only that aspect of navigation is made active.
Based on the converging evidence presented here, we hypothesized that information about the
duration of a particular event might be encoded in a manner akin to location, such that the
duration of individual events in a sequence would be integrated into the contextual representation
of the sequence. No previous study has attempted to investigate the link between memory for a
temporal sequence of events and their duration despite converging evidence suggesting common
neural mechanisms.
4
2 Experiment 1
2.1 Methods
2.1.1 Participants
48 participants took part in the study. 24 took part in the active time passage condition (17
female, average age 21.3 years, SD = 3.7) and 23 in the passive condition (15 female, average
age 19.3 years, SD = 1.4). All were recruited from the University of Toronto, and received either
partial course credit or monetary compensation for their time. All participants were fluent in
English, right-handed, with no history of neurological or psychiatric disorder, and unfamiliar
with the city of Chicago. All participants provided their informed consent and were either paid
$10 per hour for their participation or received partial course credit. This study was approved by
the ethics committee at the University of Toronto.
2.1.2 Experimental Design and Procedure
Virtual environment navigation
A virtual rendering of the city of Chicago was used in this study. The navigation software was
written in MATLAB v7.5.0.342 and used the PsychToolbox v3.0.10 (Brainard, 1997). The
software used first-person images from Google Street View to allow navigation through a virtual
Chicago. Load times for images were approximately 70 ms and after each image was presented
for 100 ms, it was crossfaded gradually with the next image over a period of 200 ms to provide
the illusion of fluid motion.
Participants were taken along a route in a first-person, street-level perspective through a virtual
Chicago (Fig. 1a). Because the participants were not familiar with the city prior to the
experiment, none of the effects can be attributed to, or confounded by, remote spatial memory. A
route through the city was designed so that there were equal numbers of left and right turns and
that the travel time between intersections was roughly equal (Fig. 1b).
5
Figure 1: (A) First-person view screenshots from several intersections along the route; (B) Map of the
entire route.
Passive condition. The participants in the passive condition were taken along the route as
passengers and did not have to navigate actively so as to closely control the timing of the task.
The car stopped at 8 intersections along the route, of which 4 were followed by a left turn and 4,
by a right turn. The range of durations was 1.5-12 seconds with a 1.5 second interval between
each duration. The same route was repeated 7 times by each participant in an attempt to
maximize their learning and simulate repetitive training in rodent studies (MacDonald, Cheng, &
Meck, 2012; MacDonald et al., 2011; Pastalkova et al., 2008). Each individual intersection was
associated with the same duration on all 7 repetitions the route, but the durations across
intersections were randomized for each individual participant so that effects of spatial salience
could not be assumed to contribute to the performance on the temporal memory task.
Participants were told to pay close attention to the route and were not prompted to pay attention
to the time spent waiting at each intersection. They were told to imagine that they were
passengers in a car and that their only task was to remember as many details about the route and
the environment as possible. They were told that the car would stop at certain intersections as if
waiting at a red traffic light, which would be indicated by red horizontal bars at the top and the
bottom of the screen, and that these intersections would be the same on every repetition. On
occasion (2 intersections per repetition, starting with the 2nd repetition), they had to indicate by
using the arrow keys, the direction in which the journey should proceed. Navigation on each
route lasted approximately 4.5 minutes. While the timing of each intersection was closely
constrained, there were slight variations in duration of the trials when participants had to respond
6
with the direction following the stop. After reaching the end of the route after each repetition,
participants were allowed to initiate the next trial at their own pace; some took a brief break prior
to each repetition. None of the participants, however, took more than 35 minutes to complete the
task.
Active condition. The experimental design and procedure for the active condition were largely
the same as that for the passive condition. The key difference was that participants were
instructed to hold down the space bar on the keyboard for the entire duration of each stop. If they
waited for longer than 500 milliseconds to press the key or released it pre-emptively (before the
interval elapsed), a warning appeared on the screen, instructing them to press the key. Similarly,
if they waited for longer than 500 milliseconds to release the space bar after the red bars had
disappeared from the screen, a text box appeared on the screen, alerting them to release the space
bar so the journey could continue. This was designed with the intention to motivate participants
to be as accurate as possible so as to control the duration of each stop.
Behavioural testing and analysis
After completing all 7 repetitions of the route, participants completed 5 memory tasks: (1)
recognition memory, (2) temporal discrimination, (3) spatial discrimination, (4) duration and
sequence ordering, and (5) time estimation tasks.
Recognition memory task. First, they were asked to complete a recognition memory task in
which they were shown images of all 8 intersections at which they had stopped and 8 lures in a
randomized order, yielding 16 trials in the task. The lures were images of intersections from the
same part of Chicago which the participants had never seen before. For each image, they were
asked to respond whether they could re-experience (R) waiting in that intersection, whether they
knew (K) the intersection, or whether it was a new (N) intersection. Following each of these
decisions, they were asked to also provide their confidence that the intersection was old (if they
responded with R or K) or new on a scale from 1-6.
Temporal discrimination task. In the temporal discrimination task, each intersection at which
the participants had waited was paired with every other one and shown pairwise in a randomized
order, generating 28 trials in total. The side of the screen on which each intersection was
displayed was randomized on each trial. Participants were asked to indicate using the arrow keys
7
at which of the two intersections they had waited longer. Following this decision, they were
again asked to provide a confidence rating on a scale from 1-6, where 1 would indicate
completely guessing and 6 would indicate completely sure.
Spatial discrimination task. In the spatial discrimination task, images of all intersections that
the participants had waited at were again paired and displayed side by side in a newly
randomized order, generating 28 trials in total. Here, participants were asked to indicate which of
the two images was closer to the end of the route. As in the previous task, they were asked to
provide a confidence rating on a scale from 1-6.
The distance and temporal discrimination task were presented in counterbalanced order but were
always preceded by the recognition memory task to prevent exposing participants to the old
intersections.
Duration and sequence ordering tasks. Following these tasks, participants were shown images
of all 8 intersections at which they had waited and were asked to complete two ordering tasks:
the duration and the sequence ordering task. In the sequence ordering task, they were asked to
drag the images into the correct sequential order. In the duration ordering task, they were asked
to order the images according to their duration, from the intersection where they waited the
shortest amount of time to the one where they waited the longest. The order of these tasks was
counterbalanced across participants.
Time estimation task. Finally, participants were shown images of all 8 intersections at which
they had waited in a randomized order and asked to estimate the time they spent waiting at each
of them by typing the number of seconds and then providing a confidence rating between 1 and 6
for each estimate.
8
2.2 Results
Prior to any analyses, all responses in the distance and duration discrimination tasks that were
marked as ‘guesses’ were removed. We also removed all responses with RTs more than 2 SDs
from each participant’s mean. Finally, all responses falsely endorsed as ‘new’ in the recognition
memory task were removed from both the duration and distance discrimination tasks. In the
active condition, these measures excluded 14.1% of all responses in the distance discrimination
task and 11.0% of all responses in the duration discrimination task. In the passive condition,
these measures excluded 13.5% of all responses in the distance discrimination task and 15.8% of
all responses in the duration discrimination task. The data from two participants in the active
condition were excluded from analyses in the duration discrimination task because their mean
proportion correct was more than 2 SDs below the mean and significantly below chance,
suggesting the task was completed incorrectly. Their data from the distance discrimination task
were included in the analyses.
2.2.1 Recognition Memory
On the recognition memory task in the active condition, 4.6% of all responses were false alarms
(lures incorrectly recognized as old) and 1.0% of all responses were misses (old items incorrectly
recognized as new). Of the correct old responses, 79.9% were re-experience responses and
20.1% were know responses. In the passive condition, 1.6% were false alarms, 5.4% were
misses, and of the correct old responses, 77.5% were reported as re-experience and 22.5% as
know.
2.2.2 Temporal Discrimination
In the active condition, the overall proportion correct on the temporal discrimination task
collapsed across all durations and distances was 0.72 (SD = 0.14) and in the passive condition it
was 0.62 (SD = 0.17). Two one-sample t-tests indicated that performance in both the active
(t(21) = 7.26, p < .001) and the passive (t(22) = 3.33, p = .003) conditions was significantly
better than chance (0.50). A further independent samples t-test indicated that performance was
significantly better in the active condition relative to the passive condition: t(43) = 2.15, p = .037
9
(Figure 2a). This suggests that being actively involved while experiencing the passage of time
significantly contributes to memory for temporal duration.
For each duration comparison, the absolute difference between the durations of the two images
was calculated and the mean performance in each temporal difference bin for each participant
was entered into a regression analysis. This analysis showed that the difference between the
durations of two intersections was a significant predictor of performance in both the active (F(1,
152) = 16.67, p < .001) and the passive task (F(1, 152) = 8.77, p = .004).
We then analyzed participants’ temporal discrimination performance relative to their recognition
memory data (Figure 2b). Trials were divided according to whether participants reported
recollection or familiarity for each of the intersections in each pair. This allowed us to divide
trials into two categories: where both intersections were reported as re-experienced (both-R) or
where either one or both were reported as familiar (some-K). There were not enough trials to
allow us to divide trials into three categories (R-R, R-K, and K-K) in the present experimental
design.
In the passive condition, the mean proportion correct on the temporal discrimination task in the
both-R category was 0.65 (SD = 0.21) and in the some-K category, 0.61 (SD = 0.19). A one-way
ANOVA indicated that there was no significant difference in performance in these two
categories (F(1, 35) < 1, p = .561). The average reported confidence was found to be a significant
covariate: F(1, 35) = 19.19, p < 0.001).
In the active condition, the mean proportion correct on the temporal discrimination task in the
both-R category was 0.76 (SD = 0.11) and in the some-K category, 0.63 (SD = 0.17). A one-way
ANOVA indicated that performance in the both-R category was significantly better than in the
some-K category (F(1, 33) = 5.56, p = .024); confidence, however, was not a significant
covariate: F(1, 33) = 1.06, p = .310. This suggests that any difference observed in performance in
the quality of self-reported subjective memory can be attributed to confidence in the passive, but
not active condition.
10
Figure 2: (A) Performance on the temporal discrimination task as a function of the temporal
separation between the two intersections compared (chance = 0.5). Active interaction with the
environment during the wait increases participants’ ability to discriminate between the two durations at
all degrees of temporal separation. (B) Performance on the temporal discrimination task in each of the
11
conditions split according to their self-reported memory quality (recollection vs. familiarity). Active
interaction with the environment significantly increases rates of recollection, but not familiarity.
An independent samples t-test indicated that temporal discrimination performance for trials that
had been judged as remembered was significantly better in the active condition than in the
passive condition (t(42) = 2.14, p = .038), but there was no significant difference in performance
between conditions in the some-K category (t(28) = .36, p = .725). This suggests that being
active while experiencing the passage of time selectively enhances duration discrimination
performance relying on recollection. However, one critical limitation of this finding is the low
number of cases in the some-K category, limiting our ability to draw conclusions due to lower
power. We address and resolve this limitation in Experiment 2.
2.2.3 Distance Discrimination
We performed the same set of analyses on data on distance discrimination. We calculated the
spatial separation between each pair of intersections (range 1-7). In the active condition, the
overall proportion correct on the distance discrimination task collapsed across all distance
separations was 0.95 (SD = 0.05) and in the passive condition it was 0.92 (SD = 0.12). An
independent samples t-test indicated that there was no significant difference in performance
between the active and passive condition: t(45) = 1.30, p = .199 (Figure 3a). One possibility for
this result is the observed ceiling effect in the distance discrimination task. To overcome this, we
ran Welch’s non-parametric t-test after performing a median split and only using the lower half
of the data. The means in the bottom half of the data in active and passive conditions,
respectively, were .91 (SD = .04) and .85 (SD = .15). Again, we found no significant difference
between the two conditions (although it must be noted that the sample is very small): t(11) =
1.28, p = .23.
For each distance comparison, the number of road segments travelled between the two
intersections compared on a given trial was calculated and the mean performance for each degree
of spatial separation for each participant was entered into a regression analysis. As for temporal
duration, this analysis showed that the distance between the intersections was a significant
predictor of performance in both the active (F(1, 165) = 32.95, p < .001) and the passive
condition (F(1, 158) = 7.96, p = .005).
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We then again divided trials into two memory categories: both-R and some-K (Figure 3b). In the
active condition, the mean proportion correct in the both-R category was 0.95 (SD = 0.08) and
0.93 (SD = 0.09) in the some-K category. In the passive condition, the mean proportion correct
in the both-R category was 0.94 (SD = 0.12) and in the some-K category, 0.88 (SD = 0.15).
There was no significant difference in performance in these categories in the passive condition
(F(1, 34) = 1.12, p = .298), where the reported confidence was a significant covariate (F(1, 34) =
23.08, p < .001). The same pattern emerged in the active condition, with no significant main
effect of the subjective memory category (F(1, 33) = .06, p = .812), and confidence as a
significant covariate: F(1, 33) = 9.07, p = .005. When comparing across acitive and passive
conditions, there was no significant difference in performance in either the all-R category (t(41)
= .155, p = .878) or the some-K category (t(28) = 1.22, p = .234).
13
Figure 3: (A) Performance on the distance discrimination task as a function of the number of road
segments travelled between the two intersections compared. There is no significant difference between
the two conditions in this task. (B) Distance discrimination performance in both conditions split
14
according to participants’ self-reported memory quality. There were no significant differences in this
task between the two conditions.
2.2.4 Duration and Sequence Order
Next, we investigated participants’ performance on the two ordering tasks (duration and
sequential position). For the sake of clarity, we collapsed participants’ responses into four bins,
each containing the two durations or locations that were closest together in time or space (e.g.,
Bin 1 would therefore contain participants’ performance correct at ordering 2-4 seconds or
intersections 1-2 on the duration and distance order tasks, respectively).
In the passive condition, we found primacy and recency effects when participants were required
to order the intersections according to their sequential position, but performance was
significantly better than chance (25%) in all four bins (all bins p < .001). On the duration
ordering task, participants only performed significantly better than chance in the first and fourth
bin (p = .01 and p = .005, respectively) (Figure 4a). The shortest and longest durations appear to
be most salient, enabling participants to arrange them more accurately than intermediate ones.
This finding that the shortest durations were ordered with similar accuracy to the longest ones
excludes the interpretation that participants were simply more accurate at longer durations
because they were exposed to those intersections for longer.
We observed a similar pattern of results in the active condition (Figure 4b). However, a greater
benefit can now be observed for the shorter durations on the ordering task. In the distance
ordering task, performance in all bins was significantly above chance (all p < .001), and on the
temporal ordering task, bins 1 and 4 were again significantly above chance (p < .001 and p =
.006, respectively), mirroring the finding from the passive condition.
15
Figure 4: (A) Performance on sequential position and duration magnitude ordering tasks in the
passive condition. While the first and last two intersections were placed in their sequential positions
most accurately, performance in all bins is significantly above chance. On the duration ordering task,
only the shortest and the longest durations were placed in their corresponding magnitude position with
16
accuracy above chance. (B) Performance on sequential position and duration magnitude ordering
tasks in the active condition. The results from the active condition closely resemble those in the passive
condition, including the significance levels.
2.2.5 Time Estimation
Finally, we investigated participants’ explicit ability to estimate the elapsed temporal intervals
(Figure 5). We calculated the bias in participants’ temporal estimates by subtracting the actual
wait time from their estimated wait time. The average overestimation (bias) in the active
condition was 4.50 seconds (SD = 3.34), whereas the average underestimation in the passive
condition was 2.80 seconds (SD = 1.15). There was a significant difference in the bias between
the active and passive conditions (t(45) = 9.88 p < .001).
Figure 5: Participants’ estimates of waiting times at each intersection. Subjects in the active condition
appeared to overestimate waiting times, while those in the passive condition showed little variance
across the range of durations estimated.
17
2.3 Discussion
Combined, these results suggest that the ability to retrieve an event’s duration relies on the
capacity to recollect its contextual details. In contrast, the ability to recollect a particular event is
not critical for the capacity to distinguish between temporal order positions of events in a
sequence. Furthermore, being active while experiencing an empty temporal gap during
navigation increased rates of recollection and significantly contributed selectively only to
performance in the duration, but not distance discrimination task. Crucially, there was no
difference in performance on the distance discrimination task between the two conditions,
highlighting the notion that only memory for duration was influenced by this manipulation.
The strength of these conclusions is limited by the smaller number of some-K cases relative to
both-R. Furthermore, as performance on the distance discrimination task is much better than on
the duration discrimination task, the lack of difference observed in the distance discrimination
task may be affected by ceiling effects. In Experiment 2, we increased the number of trials in the
attempt to explore these trends further.
These findings support the prediction that re-experiencing an event would result in greater
temporal discrimination accuracy. Furthermore, our results are consistent with the hypothesis
that actively engaging with the task during the passage of different temporal intervals increases
the ability to discriminate their durations. However, one possibility is that any kind of active
engagement during the task engages attention to a greater degree than passive route following.
We address this possibility in Experiment 2, where participants’ experience of the empty
temporal gaps was passive (no activity), but they had to actively navigate sections of the route
between individual stops. If the finding in Experiment 1 resulted from such a general process, we
should find the same pattern of results in Experiment 2. In contrast, if active engagement with
the environment during the stops is crucial for the memory of their duration, we should find no
difference on the temporal discrimination task between the active and passive navigation
conditions in Experiment 2.
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3 Experiment 2
3.1 Methods
3.1.1 Participants
A new group of 46 participants took part in the study. Half were assigned to the active condition
(13 female, average age 19.5 years, SD = 2.45) and half were assigned to the passive condition
(17 female, average age 19.0 years, SD = 2.02). All were recruited from the University of
Toronto, and received either partial course credit or monetary compensation for their time. All
participants were fluent in English, right-handed, with no history of neurological or psychiatric
disorder, and unfamiliar with the city of Chicago. All participants provided their informed
consent and were either paid $10 per hour for their participation or received partial course credit.
This study was approved by the ethics committee at the University of Toronto.
3.1.2 Experimental Design and Procedure
The experimental design of the passive condition was largely the same as in Experiment 1. The
number of stops along the route was increased to 12: 4 were followed by a left turn, 4 by a right
turn, and 4 by continuing the journey straight ahead. The range of durations used remained very
similar – 1-12 seconds, but this time with a 1-second interval between each duration. Again, the
intersections with which the durations were associated were randomized for each participant.
As outlined above, the main change in this experiment was the nature of active navigation. Here,
subjects navigated actively by pressing arrow keys while travelling along the route. We ensured
that they could not get lost or otherwise leave the route by constraining their movement to the
route. They could not make turns at points other than the predetermined intersections and could
not turn around and return in the direction from which they came. In order to indicate to the
subjects in which directions they could and could not proceed, an indicator was displayed at the
bottom of the screen which remained green for as long as the subjects could continue in the same
direction and turned dark red when participants reached a turning point or attempted to navigate
off-route.
19
Whenever the subjects reached the intersections where they had to wait, red horizontal bars
identical to those in Experiment 1 appeared on the screen and the participants could not override
this wait. In the instructions, we emphasized to subjects that they must continue their journey as
soon as the red bars disappear so they should pay attention to them. The procedure for the
passive condition remained the same. The route in the active and passive condition was the same.
Subjects were again asked to respond which direction the car should continue in at 2
intersections per trial, regardless of whether they took part in the active or passive condition.
3.2 Results
Prior to all analyses, the same measures were taken as in Experiment 1. All responses marked as
‘guesses’ on the duration and distance discrimination tasks and all responses with RTs more than
2 SDs away from each participant’s mean were removed from subsequent analyses, as well as all
responses on the distance and duration discrimination tasks to images incorrectly endorsed as
‘new’ in the recognition memory task. This removed 9.75% of all trials on the duration
discrimination and 12.8% on the distance discrimination task in the active condition and 6.7% of
all trials on the duration discrimination and 7.9% on the distance discrimination task in the
passive condition.
3.2.1 Recognition Memory
In the active condition recognition memory task, 8.2% of all responses were false alarms and
1.6% of all responses were errors. Of the correct old responses, 67.0% were re-experience
responses and 33.0% were know responses. In the passive condition recognition memory task,
need to fill in.
In the passive condition recognition memory task, 0.6% of all responses were errors and 8.9% of
all responses were false alarms. Of the correct old responses, 76.2% were re-experience
responses and the remaining 23.8% were know responses.
3.2.2 Temporal Discrimination
In the active condition, the overall proportion correct on the temporal discrimination task
collapsed across all durations and distances was 0.57 (SD = 0.09) and in the passive condition it
20
was 0.60 (SD = 0.09). Two one-sample t-tests indicated that performance in both the active
(t(22) = 3.75, p = .001) and the passive (t(22) = 5.26, p < .001) conditions was significantly
better than chance (0.50). However, there was no significant difference in performance between
the active and the passive condition: t(44) = 1.17, p = .248 (Figure 6a). This suggests that active
navigation did not significantly contribute to the encoding of the duration of empty temporal
intervals embedded within the route.
Two linear regression analyses showed that performance increased as a function of the degree of
temporal separation. The difference between the two durations compared was a significant
predictor of performance on both the passive (F(1, 10) = 34.61, p < .001) and the active
condition (F(1, 10) = 22.62, p = .001).
3.2.3 Distance Discrimination
In the active condition, the overall proportion correct on the distance discrimination task
collapsed across all distance separations was 0.82 (SD = 0.10) and in the passive condition it was
0.84 (SD = 0.12). An independent samples t-test indicated that there was no significant
difference in performance between the active and passive condition: t(44) = .462, p = .646
(Figure 6b). The number of segments travelled between the two intersections being compared
was a significant predictor of performance in both the active (F(1, 10) = 15.55, p = .003) and the
passive condition (F(1, 10) = 32.16, p < .001).
We then correlated each participant’s performance on the distance and duration discrimination
task to explore the idea that performance on both tasks might rely on the same underlying
mechanism. We found no significant correlation between the two measures in the passive
condition (r = -.132, p = .549) and a marginally significant negative correlation in the active
condition (r = -.405, p = .055). This suggests that performance on the two tasks was largely
independent and was not consistent within participants.
21
Figure 6: Temporal and spatial discrimination performance in Experiment 2. (A) In contrast to
Experiment 1, there was no significant difference in performance on the temporal discrimination task
between the active and passive conditions, suggesting that any kind of active interaction with the
environment is not sufficient for the increase in temporal discrimination. (B) Spatial discrimination
22
task results closely resemble those from Experiment 1; performance is likely lower across the board
because of the increased difficulty resulting from more target locations to remember.
3.2.4 Re-experience vs. Know
We again categorized participants’ responses in the temporal and distance discrimination task
according to whether the intersections compared were previously characterized as R or K.
However, the design of this second experiment allowed us to divide these responses into three
possible R-K categories: R-R (both re-experienced), R-K (one re-experienced and one known),
and K-K (both known).
In the active condition temporal discrimination task, the overall proportion correct in the R-R
case was 0.57 (SD = 0.15), in the R-K case it was 0.58 (SD = 0.12), and 0.52 (SD = 0.27) in the
K-K case (Figure 7a). The mean proportion correct was calculated for each R-K category for
each participant and entered into a one-way ANOVA, with participants’ confidence in their
response on the recognition memory task as a covariate. There was no significant main effect of
the R-K category (F(2, 58) = .329, p = .721), nor was confidence a significant covariate (F(1, 58)
= .477, p = .493). However, this might also result from the overall low performance in this
condition. Comparing performance in each of these categories against chance in a one-sample t-
test revealed that both R-R and R-K categories were significantly above chance (t(20) = 2.15, p =
.04 , and t(18) = 3.0, p = .008 , respectively), but the K-K category was not significantly different
from chance (t(17) = .372, p = .714).
In the passive condition temporal discrimination task, the overall proportion correct in the R-R
case was 0.66 (SD = 0.13), 0.60 (SD = 0.12) in the R-K case, and 0.44 (SD = 0.26) in the K-K
case (Figure 7a). A one-way ANOVA was again carried out. This time, there was a significant
main effect of the R-K category (F(2, 53) = 3.82, p = .029), and participants’ confidence was
also a significant covariate (F(1, 53) = 5.87, p = .019). However, as in the active navigation
condition, performance in the K-K category was not significantly above chance (t(12) = .80, p =
.439), in contrast to the R-R (t(22) = 5.45, p < .001) and the R-K conditions (t(16) = 3.49, p =
.003).
In the active condition distance discrimination task, the overall proportion correct in the R-R
category was 0.86 (SD = 0.14), in the R-K case it was 0.82 (SD = 0.09), and in the K-K case, it
23
was 0.77 (SD = 0.27). In the passive condition distance discrimination task, the overall
proportion correct in the R-R category was 0.84 (SD = 0.14), in the R-K case it was 0.79 (SD =
0.15), and in the K-K category, it was 0.78 (SD = 0.29) (Figure 7b). A one-way ANOVA
indicated that there was no significant main effect of the R-K category in either the active (F(2,
57) = .048, p = .953) or the passive condition (F(2, 53) = .108, p = .898). Confidence, however,
was a significant covariate in the active condition (F(1, 57) = 21.04, p < .001) and a marginally
significant covariate in the passive condition (F(1, 53) = 4.14, p = 0.05).
24
Figure 7: Performance in the active and passive conditions split according to participants’ self-
reported memory quality: either both intersections were reported as re-experienced, one of them was
reported as re-experienced and the other as familiar, or both were reported as familiar. (A) In both
conditions, performance on the temporal discrimination task was significantly greater than chance if
25
at least one of the intersections was reported as re-experienced, and did not differ from chance when
both intersections were reported as familiar. The benefit of ‘re-experiencing’ the wait was, in contrast
to Experiment 1, more pronounced in the passive condition. (B) In the distance discrimination task, no
difference was observed between the two conditions or between any of the R-K category levels.
It appears, therefore, that the subjective sense of the richness of recollection (R vs. K) is only a
significant factor in duration discrimination, with no significant effect on the ability to
distinguish between serial order positions of individual locations along a route. The likely reason
we did not observe a main effect of the R-K category in the active condition is that performance
is generally low and variability is high. However, the observed discrepancy in the overall
performance rates might preclude the observation of nuanced effects – performance in the
distance discrimination task was significantly better than the duration discrimination task. In
order to explore this effect further, we identified conditions with comparable performance in
both tasks. Here, only data from the passive condition are included, where a significant main
effect was found in the overall ANOVA.
In the following analysis, we included the first three ‘bins’ of distance comparisons for each
participant (range 1-3 segments between the two intersections being compared), where the
overall proportion correct was 0.73 (SD = 0.25), totalling 605 cases. As a point of comparison of
roughly equal performance, we included the last four ‘bins’ of temporal duration comparisons
(range 7-11 second difference between the two intersections being compared), where the overall
proportion correct was 0.71 (SD = 0.27), totalling 315 cases. The discrepancy in the number of
cases is inevitable due to the design of the study (i.e., the comparisons with a smaller degree of
spatial/temporal separation were far more frequent).
In the bins included from the temporal comparison task, 65.7% were R-R cases, 26.0% were R-K
cases, and 7.3% were K-K cases. In the bins included in this analysis in the distance comparison
task, 61.5% were R-R responses, 27.1% were R-K cases, and 11.4% were K-K cases.
For the temporal comparison, the average proportion correct in the R-R case was 0.75 (SD =
0.21), in the R-K case it was 0.82 (SD = 0.18) and in the K-K case it was 0.36 (SD = 0.37). A
one-way ANOVA indicated that there was a significant main effect of the R-K category: F(2, 44)
= 9.54, p < .001. Participants’ confidence in their responses was not a significant covariate: F(1,
44) = 1.25, p = .271. Further pairwise t-tests indicated that there was no significant difference
26
between the R-R and the R-K categories (t(15) = 1.26, p = .23), however, performance in the K-
K condition was significantly worse than both the R-K condition (t(5) = 3.36, p = .02) and the R-
R condition (t(5) = 2.99, p = .03). While these results are constrained by the small sample size in
the K-K condition, they do suggest that the pattern of results holds up and that the recollection of
at least one of the intersections is necessary for the ability to retrieve and compare their temporal
duration.
For the distance comparison, the average proportion correct in the R-R case was 0.77 (SD =
0.25), in the R-K case it was 0.69 (SD = 0.20), and in the K-K case it was 0.70 (SD = 0.31). A
one-way ANOVA indicated that there was no significant main effect of the R-K category: F(2,
52) = .322, p = .726. Participants’ confidence in their responses was a significant covariate: F(1,
52) = 6.09, p = .017. This suggests, in contrast to the temporal discrimination task, that
recollection is not necessary for the ability to retrieve and compare the sequential positions of
spatial locations, even when matching performance to the temporal discrimination task.
3.2.5 Duration and Sequence Order
Next, we investigated participants’ performance on the two ordering tasks. As in Experiment 1,
we collapsed participants’ responses into four bins, this time each containing three durations or
locations (Bin 1 would therefore contain participants’ performance correct at ordering 1-3
seconds or intersections 1-3 on the duration and distance order tasks, respectively).
In the passive condition, we found that participants showed a strong primacy effect in the
distance ordering task, but performance was significantly better than chance (25%) in all four
bins (all p < .001). On the duration ordering task, participants only performed significantly better
than chance in the first and fourth bin (p = .01 and p = .002, respectively) (Figure 8a). These data
are consistent with the idea that greatest differences in duration are the most easily detectable as
both the shortest and the longest intervals are arranged most accurately.
In the active condition, we see a virtually identical pattern (Figure 8b). However, a greater
benefit can now be observed for the longer durations on the ordering task. In the distance
ordering task, performance in all bins was significantly above chance (all p < .001); and on the
temporal ordering task, only Bin 4 was significantly above chance (p < .001), while Bin 1 was no
longer significant (p = .08).
27
Figure 8: Sequence and duration magnitude ordering task performance. (A) In the passive condition,
we observe the typical primacy and recency effects in the sequential ordering task. In the duration
ordering task, very short and very long durations appear to be more salient and are ordered more
accurately (> chance) than the durations in the middle two bins. (B) In the active condition, the results
from the sequential ordering task are highly similar to the passive condition. In contrast, very short
28
distances appear to be less salient, likely due to the demands of navigating a novel route, and
diminishing attention to the waiting periods as a result.
3.2.6 Time Estimation
Finally, in order to explore whether participants may have relied on counting during the empty
temporal intervals and then used this as a mnemonic device during the temporal discrimination
task, we analyzed their time estimation performance (Figure 9). We found that actual wait times
were not a significant predictor of participants’ estimates in the passive condition (F(1, 10) =
2.71, p = .130) or in the active condition (F(1, 10) = 1.51, p = .248).
Figure 9: Time estimation in the active and passive conditions in Experiment 2. There was no
significant difference between the two conditions, and no significant correlation between the estimates
and actual waiting times.
29
3.3 Discussion
In Experiment 2, we found no difference between active and passive navigation, when the stops
along the route were passive. This suggests that the enhancement for temporal memory that we
report in Experiment 1 is specific to time and does not generalize to temporal order/sequence
judgments, and that attentional factors alone cannot explain the improvement in performance.
However, broadly consistent with Experiment 1, we show that the subjective report of ‘re-
experiencing’ a particular event enabled participants to distinguish between durations of
individual events significantly better than those with which they were only familiar.
Below, we discuss the two experiments more broadly and link our findings to current theoretical
views of memory and hippocampal function.
30
4 General Discussion
The experiments reported here show, using an incidental encoding paradigm, for the first time,
how information about the duration of individual events in a sequence is encoded in episodic
memory and how this differs from temporal order memory. We show that participants’ ability to
distinguish between sequential positions and event durations increased as a function of spatial
and temporal separation, respectively. This is consistent with the literature on hippocampal
pattern separation, which suggests that more dissimilar items will be distinguished more readily
(Bakker et al., 2008; Holden et al., 2012).
Importantly, our findings suggest that subjects were only able to reliably distinguish between
memories for two durations when they reported re-experiencing both events with their associated
spatiotemporal contexts. This was not the case in the distance discrimination task, where
performance was approximately equal regardless of their subjective sense of recollection. Even
after matching performance on the spatial and temporal discrimination tasks, the same overall
pattern persisted.
There are two possible explanations for the observed pattern of data. One possibility is that
temporal information is only reliably incorporated into the context of an episodic memory when
the memory is perceptually rich and vivid. Alternatively, the sense of recollection might rely on
the ability to retrieve temporal duration information in addition to temporal order information.
We show that findings on delayed recall in the temporal order literature also apply to memory for
duration. For example, Sadeh et al. (2014) showed that only items reported as recollected, and
not familiar ones, showed a temporal contiguity effect or a reactivation of surrounding items.
This suggests that only recollected or ‘re-experienced’ items are placed on the accurate position
on the mental timeline, and that this effect is supported by the hippocampus. The finding of
superior temporal discrimination is in line with research on pattern separation, where two highly
similar stimuli are only reliably distinguished when the hippocampus is intact (Ally, Hussey, Ko,
& Molitor, 2013; Kesner, 2013; Sahay, Wilson, & Hen, 2011). Combined with the robust finding
that recollection is uniquely supported by the hippocampus, the present study can be
conceptualized as the first study on ‘temporal pattern separation’ as only the durations of re-
experienced intersections were reliably separated.
31
While this study only provides behavioural evidence to support this view, there is strong
evidence to support the distinction between recollection and familiarity as being supported by the
hippocampus and extrahippocampal structures, respectively (Eichenbaum, Yonelinas, &
Ranganath, 2007; Montaldi & Mayes, 2010; Yonelinas, 2002). In contrast, no significant
difference between recollection and familiarity was observed in the distance discrimination task.
This suggests that information about the sequential order of events may be retrieved based on
familiarity alone; however, one important limitation in reaching this conclusion is that
performance on the distance discrimination task was significantly better overall and thus this
distinction was not reflected in the data.
One possible trivial explanation for the improvement in performance corresponding to the degree
of temporal separation is that subjects were simply exposed to intersections with longer temporal
intervals for more time, making it possible for them to rely on their knowledge only for those
specific intersections when making temporal comparisons. However, the temporal ordering task
suggests that this effect is not simply due to a longer exposure to one of the intersections;
instead, it may be driven by salience as both the very short and the very long durations are
equally well remembered. This has adaptive value, if one of the key functions of a useful
memory system is to encode and retrieve temporal relationships for various repeated events in an
environment (Howard & Eichenbaum, 2013). That participants were unable to estimate the time
they spent waiting at each location suggests that they did not rely on an external mnemonic
technique to encode time, such as counting.
4.1 Active Experience of the Passage of Time
Recollection rates were also increased when the empty temporal interval involved active input
from the subjects (button press). While this manipulation also numerically improved distance
discrimination, the difference between the active and passive conditions was not significant. This
suggests that motor/perceptual feedback enables the formation of stronger temporal memory
traces, highlighting the importance of active interaction with the environment in the encoding of
temporal aspects of new routes. It is important to note, however, that even when the wait was
passive, performance on the duration discrimination task was significantly above chance. This is
consistent with the time cell literature which suggests that time cells are not merely indexing
32
idiothetic cues and that firing patterns in head-fixed rats closely resemble those of the animals
who navigated (MacDonald, Carrow, Place, & Eichenbaum, 2013; MacDonald et al., 2011).
An alternative possible explanation for this finding is related to the production effect, which is
that performing a task enhances later memory (MacLeod et al., 2010; Ozubko et al., 2012). Were
this the only factor explaining the improvement in performance, we should also see concordant
improvement in performance in the active navigation task (Experiment 2), but this is not the
case. Instead, it appears that this benefit is modality specific such that actively experiencing the
passage of empty temporal intervals selectively enhances the memory for their duration, but
active navigation between these intervals does not contribute to superior memory for time. It
must be noted, however, that participants’ memory for the time spent navigating was not tested,
and so it may be that active navigation would have improved their memory in that respect.
Future studies in this area should further explore this somewhat surprising finding which
suggests that increased attention to the whole route (Experiment 2, active navigation) may not
contribute to the encoding of all aspects of the navigational experience.
All participants in the present study were healthy younger adults. The benefit of active
navigation may not be evident until performance is affected by aging-related neurocognitive
changes. For example, studies on healthy older participants and those with amnestic mild
cognitive impairment (aMCI) and Alzheimer’s disease (AD) suggest that only healthy
participants show benefits in memory following active navigation, distinguishing them from
those with aMCI and AD (Plancher, Tirard, Gyselinck, Nicolas, & Piolino, 2012). Furthermore,
this study showed that participants’ performance on this virtual reality navigation task was more
closely related to their everyday memory deficits than conventional memory tests, highlighting
the importance of using such rich, naturalistic stimuli in assessing cognitive function.
4.2 Hippocampus and Time
Several previous studies have explored time and memory. However, many of these studies have
focused on very short timescales which primarily recruit cortical networks (Block, Hancock, &
Zakay, 2010; Koch et al., 2008; Lewis & Miall, 2006; Wiener & Thompson, 2015), limiting the
applicability of findings to theories of medial temporal lobe function. Alternatively, subjects
were explicitly instructed to encode temporal information (Casini & Macar, 1997; El Haj,
33
Moroni, Samson, Fasotti, & Allain, 2013; Taatgen & van Rijn, 2011; Wencil, Coslett, Aguirre, &
Chatterjee, 2010), making it difficult to generalize and apply these findings to real world
settings.
Importantly, the present research fits with the ideas presented in the theoretical framework
recently advanced by Howard and Eichenbaum (2013) which differentiates between ordinal and
temporal representation of a sequence of events. The former discards information about the
duration of individual events and merely enables access to the sequential position of individual
events. The latter, however, retains the entire timeline, incorporating the durations of individual
events, and allows us to extract ordinal level information. While there is established evidence for
hippocampal involvement in representing ordinal information, there is also a growing body of
empirical work suggesting a crucial role for hippocampal oscillations in encoding, and possibly
creating, the temporal representation. For example, Pastalkova et al. (2008) argue that sequential
firing of cell assemblies in the hippocampus enables the reinstatement of a particular
spatiotemporal position on an internal timeline, or a ‘jump back in time’, accompanied by a
perceptually vivid sense of re-experiencing.
Our data certainly support this view – we show that knowledge about the duration of an event is
only reliably retrieved when participants reported the ability to subjectively re-experience it. This
finding fits with the idea that recollection is the recovery of a particular state associated with a
unique spatiotemporal context (Howard & Eichenbaum, 2013). Our data provide behavioural
evidence to support the distinction between the temporal and ordinal representation – the former
can only be reliably accessed when the spatiotemporal context is fully re-experienced, whereas
this is not a prerequisite for retrieving ordinal level representation (sequential position of a
location).
The finding that discrimination between sequential positions was nonetheless better when events
were reported as re-experienced is still congruent with this view. While the capacity to re-
experience two events is not critical for the ability to distinguish between their serial positions,
the addition of a temporal representation from which ordinal information can be extracted is
likely to enhance the quality of memory and subjective confidence. As a result, it is also not
surprising that subjective confidence in the decision is a significant covariate; indeed, it has been
34
argued that neurons in the temporal lobe code for confidence (Rutishauser et al., 2015), which
may interact with subjective perceptual vividness.
Based on the existing literature, there are two prominent possibilities for how time may be
integrated into our episodic memories. One possibility is that the proposed hippocampal ‘time
cells’ track the passage of time and locally integrate this information in addition to the code for
location and space and ordinal information (Eichenbaum, 2014; Howard & Eichenbaum, 2013).
A second option is that a separate temporal signal is received by the hippocampus from another
cortical or subcortical structure (Howard & Eichenbaum, 2013; Meck, Church, & Matell, 2013).
Strong candidate structures appear to be the striatum (Adler et al., 2012; Adler, Finkes, Katabi,
Prut, & Bergman, 2013; Hsieh & Ranganath, 2015; Meck, Penney, & Pouthas, 2008), parietal
cortex (Danckert et al., 2007; Davis, Christie, & Rorden, 2009; Vicario, Martino, & Koch, 2013;
Wiener, Turkeltaub, & Coslett, 2010), and the insula (Wittmann, Simmons, Aron, & Paulus,
2010), with the PFC-posterior cortical connectivity mediating conscious access to this temporal
information (Hayashi et al., 2013; Kragel & Polyn, 2013; Pouthas et al., 2005; Rubia et al.,
1998). The striatum in particular has been suggested to code for a conjunctive representation of
an event and its temporal properties in a manner akin to time cells (Adler et al., 2012; Howard &
Eichenbaum, 2013). Exploring striatal-hippocampal interactions in interval timing would provide
key evidence for the resolution of this argument (Hsieh & Ranganath, 2015).
4.3 Limitations and Future Directions
The main limitation of the present study is that performance was not equal on the temporal and
distance discrimination tasks. While we were able to match performance on subsets of data, this
resulted in unequal sample sizes. Increasing the degree of temporal separation and potentially
reducing the number of stops would likely improve performance on the temporal discrimination
task and therefore make the two tasks more directly comparable. Further, we found no
correlation between participants’ performance on the temporal and distance discrimination tasks,
despite speculations that the two should involve highly similar processes. One possibility is that
participants relied on different strategies. Alternatively, they may truly be qualitatively different.
Again, improving performance on the temporal discrimination task so that performance would be
above chance in all bins of temporal separation would likely enable us to disentangle this finding
further.
35
Another somewhat surprising finding is the absence of a difference in performance in the active
and passive conditions in Experiment 2. While we have speculated on the possible reasons for
this finding (see above), it is also possible that participants simply found navigation to be too
difficult and the route was still not fully learned by the time of testing, thus precluding them from
incorporating temporal information into this representation. While this opens interesting new
avenues of interpretation, future research should employ a simpler and more easily learned route
so that these possibilities can be reconciled.
An important avenue for future work is to determine whether the hippocampus reliably
distinguishes between memories for duration and whether the distinction between remember and
know also applies to information about time, the same way it does to contextual information in
other modalities. Furthermore, the role of aging in the encoding and retrieval of information
about time in episodic memory should be researched further with the aim of identifying possible
deficits and linking them to the existing knowledge of neurodegeneration.
4.4 Conclusions
The two experiments reported here show, for the first time, how information about duration is
encoded into episodic memory in a rich virtual reality navigation paradigm. We show that the
pattern of results describing memory for time closely resembles previously reported findings on
temporal order memory and that the re-experience of a past event appears to be crucial in
enabling the retrieval of temporal information. Further, we highlight the importance of idiothetic
cues and perceptual feedback in the encoding of time, as active interaction with the environment
resulted in significantly better ability to discriminate between temporal intervals. However, we
show that this effect cannot be explained through a purely attentional account, as active
navigation interspersed by passive waiting did not enhance memory for the time spent waiting.
In sum, we show that the reinstatement of the spatiotemporal context of an event is crucial for
the retrieval of temporal information, consistent with current theories of hippocampal function.
This opens important new avenues for both research and theoretical work and provides new
insight into our understanding of the nature of human episodic memory.
36
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