Journal of Cognitive Psychology
ISSN: 2044-5911 (Print) 2044-592X (Online) Journal homepage: http://www.tandfonline.com/loi/pecp21
Understanding the underlying mechanism of
the spacing effect in verbal learning: a case for
encoding variability and study-phase retrieval
Geoffrey B. Maddox
To cite this article: Geoffrey B. Maddox (2016): Understanding the underlying mechanism of
the spacing effect in verbal learning: a case for encoding variability and study-phase retrieval,
Journal of Cognitive Psychology, DOI: 10.1080/20445911.2016.1181637
To link to this article: http://dx.doi.org/10.1080/20445911.2016.1181637
Published online: 26 May 2016.
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Download by: [Rhodes College], [Geoffrey Maddox]
Date: 27 May 2016, At: 09:37
JOURNAL OF COGNITIVE PSYCHOLOGY, 2016
http://dx.doi.org/10.1080/20445911.2016.1181637
Understanding the underlying mechanism of the spacing effect in verbal
learning: a case for encoding variability and study-phase retrieval
Geoffrey B. Maddox
Downloaded by [Rhodes College], [Geoffrey Maddox] at 09:37 27 May 2016
Department of Psychology, Rhodes College, Memphis, TN, USA
ABSTRACT
ARTICLE HISTORY
The spacing effect refers to the mnemonic benefit of spacing repeated study events
across time compared to massing (i.e. cramming) repeated study events. Due to the
robustness of this finding, substantial research has been devoted to uncovering the
spacing effect’s underlying mechanism. Specification of such a mechanism has been
guided by consistent findings across methodologies and several experimental
manipulations that serve as boundary conditions. Past reviews of the spacing effect
literature have generally considered subsets of these factors but never an exhaustive
set. Thus, the current review considers more comprehensively six consistent findings in
the extant spacing effect literature pertaining to human memory to better discriminate
among the previously proposed theories. Review of the literature provides substantial
evidence indicating the need for an encoding variability mechanism [e.g. Glenberg,
A. M. (1976). Monotonic and nonmonotonic lag effects in paired-associate and
recognition memory paradigms. Journal of Verbal Learning and Verbal Behavior, 15, 1–
16] in addition to a reminding mechanism [e.g. Benjamin, A. S., & Tullis, J. G. (2010).
What makes distributed practice effective? Cognitive Psychology, 61, 228–247].
Received 29 December 2015
Accepted 18 April 2016
Introduction
The spacing effect was first reported over 100 years
ago (Ebbinghaus, 1885) and refers to the long-term
memory benefit produced by spaced study events
compared to consecutive study events (massed
study). The benefit of spaced study is further modulated by the specific interval (i.e. lag) that separates
presentations of an item (i.e. the lag effect, see
Cepeda, Pashler, Vul, Wixted, & Rohrer, 2006;
Cepeda, Vul, Rohrer, Wixted, & Pashler, 2008). Critically, the mnemonic benefit of spaced study has
been observed in different animal species (e.g.
Scharf et al., 2002; Tully, Preat, Boyton, & Del
Vecchio, 1994), across the human lifespan (e.g.
Balota, Duchek, & Paullin, 1989; Simone, Bell, &
Cepeda, 2013), and with numerous experimental
manipulations (see Crowder, 1976; Dempster, 1996;
Hintzman, 1974 for reviews). Because of its robustness, the spacing effect has the potential to be
applied across a variety of contexts as a way of
improving learning and memory. In fact, the
spacing effect has been observed with educationally
relevant verbal materials (e.g. Dunlosky, Rawson,
CONTACT Geoffrey B. Maddox
[email protected]
© 2016 Informa UK Limited, trading as Taylor & Francis Group
KEYWORDS
Spacing effect; distributed
practice; study-phase
retrieval; encoding variability
Marsh, Nathan, & Willingham, 2013) as well as
math and science-based materials (e.g. KüpperTetzel, 2014; Rohrer, 2012; Rohrer, Dedrick, &
Burgess, 2014; Rohrer, Dedrick, & Stershic, 2014).
Moreover, it has been observed in the classroom
(for a review see Carpenter, Cepeda, Rohrer, Kang,
& Pashler, 2012; Dempster, 1988) and with memory
impaired populations (e.g. Camp, Foss, Stevens, &
O’Hanlon, 1996; Green, Weston, Wiseheart, & Rosenbaum, 2014; Schacter, Rich, & Stamp, 1985).
As a result of its rich history, spacing effect
research has been subject to numerous reviews
which have been useful in developing and evaluating
theoretical mechanisms proposed to account for the
phenomenon. The most recent reviews have generally converged on a combined mechanism which
incorporates two separate theories, namely encoding
variability (e.g. Glenberg, 1976; Melton, 1970) and
study-phase retrieval (e.g. Thios & D’Agostino,
1976). In contrast, a meta-analysis reported by Benjamin and Tullis (2010) suggests that this combined
mechanism is not as parsimonious as an account
which relies on a single reminding mechanism.
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G. B. MADDOX
Moreover, Benjamin and Tullis’ meta-analysis
suggests that encoding variability faces substantial
difficulty in accounting for all of the reliable findings
in the spacing effect literature when considered in its
original instantiation (Stimulus Sampling Theory;
Estes, 1955a, 1955b). Although meta-analyses are
useful for identifying consistent results in extant literature (e.g. Benjamin & Tullis, 2010; Cepeda et al.,
2006), the inherent limitation to the meta-analytic
approach is that the selected search and inclusion criteria can limit the scope of the review. In turn, consistent findings and trends in the literature beyond the
scope of the meta-analysis may not be considered
when forming theoretical conclusions. Provided the
strong evidence reported by Benjamin and Tullis in
support of a parsimonious reminding account and
the otherwise converging conclusions from other
reviews regarding the need for a dual study-phase
retrieval and encoding variability mechanism to
account for the spacing effect (e.g. Delaney, Verkoeijen, & Spirgel, 2010; Greene, 1989), a clear way of
adjudicating between these accounts is needed.
One approach for adjudicating between the two
leading accounts of the spacing effect is to consider
more fully the reliable finding that intentionally
manipulating encoding conditions across presentations of an item often reduces the size of the spacing
effect. This finding has not been considered in
several recent reviews (e.g. Benjamin & Tullis, 2010;
Cepeda et al., 2006); thus, the inclusion of this consistency in the current review provides an important
extension. To foreshadow the conclusions, the
reduced spacing effect observed under conditions of
intentional, experimenter-introduced encoding variability can be accounted for by a combined studyphase retrieval and encoding variability mechanism
but not by the reminding mechanism. Moreover, the
current review expands on past reviews that have
not fully considered the various instantiations of
encoding variability (e.g. Delaney et al., 2010; Gerbier
& Toppino, 2015). A more comprehensive examination
of encoding variability is critical given Benjamin and
Tullis’ argument against the combined mechanism.
Moreover, it is necessary to reassess encoding variability as a viable account of the spacing effect provided
the historical significance of encoding variability in
accounting for the benefits of spaced retrieval (e.g.
Crowder, 1976; Melton, 1967, 1970) and the role of
contextual encoding in numerous memory models
(e.g. Anderson & Bower, 1972; Howard & Kahana,
2002; Murdock, 1997; Polyn, Norman, & Kahana,
2009; Raaijmakers, 2003; Raaijmakers & Shiffrin, 1981).
In sum, the current review aims to accomplish
two objectives to better evaluate the mechanisms
proposed to underlie the spacing effect. First, the
current review will examine critical findings in the
spacing effect literature that have been absent in
prior reviews. Specifically, attention will be given
to reviewing the influence of experimenter-introduced encoding variability on long-term retention
of massed items relative to spaced items, which in
turn will help adjudicate between the two leading
accounts of the spacing effect. Second, consideration of various instantiations of the encoding variability account (e.g. McFarland, Rhodes, & Frey, 1979)
that extend beyond the basic assumptions of the
Stimulus Sampling Theory (Estes, 1955a, 1955b)
will be provided as a means of assessing the viability
of an encoding variability mechanism to explain the
long-term memory (LTM) benefit of spaced study.
After addressing these two aims, the current
review will consider the extent to which the critical
findings that have been absent from previous
reviews can be accommodated by the reminding
account (e.g. Benjamin & Tullis, 2010) and the combined study-phase retrieval and encoding variability
mechanism (e.g. Greene, 1989).
To achieve these goals, the first section briefly
reviews the consistent findings and boundary conditions that a sufficient theory of the spacing effect
must accommodate and provides a more thorough
discussion of consistent findings that have been
absent from prior reviews. The second section
assesses past and present theoretical accounts of
the spacing effect with particular attention given
to an assessment of the reminding (Benjamin &
Tullis, 2010) and the combined study-phase retrieval
and encoding variability accounts (e.g. Greene, 1989;
Raaijmakers, 2003).
Empirical consistencies and boundary
conditions of the spacing effect
The history of spacing effect research has yielded
several consistent findings and construed boundary
conditions. These conditions are presented in Table 1
and are discussed briefly in the current review.
Non-monotonic function relating lag and LTM
performance
Cepeda et al. (2006) reported a meta-analysis that
confirmed earlier reports (e.g. Glenberg, 1976) of a
non-monotonic, inverted U-shaped relationship
JOURNAL OF COGNITIVE PSYCHOLOGY
3
Table 1. Summary of consistent findings and boundary conditions.
Critical finding
Non-monotonic function
Trace dependence
Intentional and incidental learning
conditions
Pure vs. mixed condition lists
Lag × retention interval
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Experimenter-introduced encoding
variability
General finding
Memory performance initially increases as lag increases but then decreases as lag continues to increase.
Memory performance for repeated items is often greater than the predicted memory performance for two
unique items separated by the same lag.
The spacing effect is larger under intentional learning than incidental learning conditions. The optimal lag is
longer in the former condition compared to the latter condition.
The size of the spacing effect is smaller when using pure condition lists compared to mixed condition lists.
The optimal amount of spacing (lag) between repetitions increases as the retention interval increases. This is
observed in the Spacing × RI interaction and in the comparison of the spacing effect when spacing lists of
items vs. single items.
When contextual elements (e.g. semantic meaning, stimulus background) are experimentally manipulated at
encoding, the spacing effect is often reduced due to an increase in massed performance.
between lag and memory performance. As seen in
Figure 1, memory performance increases until an
optimal lag, after which performance declines as
lag continues to increase. I will refer to this finding
as the non-monotonic performance function.
items. For example, repetition of an item may lead
to elaboration of a single memory trace rather
than the creation of multiple independent traces.
Intentional versus incidental encoding
Trace dependence and superadditivity
A recent meta-analysis of 735 spaced study comparisons (Benjamin & Tullis, 2010; see also Ross & Landauer, 1978) suggested that the probability of
recalling an item repeated after a specific lag is
greater than the probability of recalling one of two
different items separated by the same number of
intervening items (i.e. spaced study typically produces greater performance than would be predicted
if each presentation of the repeated item were
encoded and stored independent of the other).
This finding violates statistical independence (i.e. P
(A∩B) = P(A)*P(B)) and suggests that there is dependence between the memory traces of repeated
Figure 1. The non-monotonic performance function reflects
the increasing and then decreasing size of the spacing effect
as lag between repetitions increases. The point at which
performance is highest reflects the inflection point of the
function and the optimal lag given the materials and task
demands of the given paradigm.
Although past research suggests that the spacing
effect is obtained under incidental and intentional
encoding conditions, the effect is typically larger
when material is intentionally learned compared to
incidentally learned in free recall (e.g. Shaughnessy,
1976), cued recall (Challis, 1993), and recognition performance (Russo, Parkin, Taylor, & Wilks, 1998). For
example, Shaughnessy (1976, Experiment 2) had participants rate words using two different scales across
spaced and massed repetitions (i.e. perceived frequency and imageability) or study the items intentionally before completing a free recall test. As seen
in Figure 2, performance in the spaced-incidental
condition was comparable to performance in the
massed-intentional condition, which speaks to the
mnemonic benefit of spacing. More noteworthy is
the observation that the spacing effect was larger
Figure 2. Mean recall performance as a function of encoding and spacing conditions. Figure adapted with permission
from Shaughnessy (1976, Table 3).
Downloaded by [Rhodes College], [Geoffrey Maddox] at 09:37 27 May 2016
4
G. B. MADDOX
for items studied intentionally (M = .15) than items
learned incidentally (M = .09). Similarly, Russo et al.
(1998) examined recognition memory for massed
and spaced items under intentional (Experiment 1a)
and incidental (Experiment 1b) learning conditions
in which participants provided pleasantness and imageability ratings. Although a comparison of the
spacing effect size across learning conditions was
not directly tested, Russo et al. observed a spacing
effect under both encoding conditions, and again,
the effect was larger under intentional (M = .12)
than under incidental learning (M = .07).
Given the non-monotonic function between lag
and the spacing effect, it may be that the optimal
lag is different between incidental and intentional
learning conditions, and in turn, the magnitude of
the spacing effect may be equivalent across learning
conditions when compared at each condition’s
optimal lag. To test this hypothesis, Verkoeijen,
Rikers, and Schmidt (2005) repeated words at multiple lags (lags 0, 2, 5, 8, 14, and 20 items). Recall
results revealed that the optimal lag under intentional encoding (lag 14) was longer than under incidental learning (lag 8), but the magnitude of
the spacing effect was still larger for intentional
(M = .18) than for incidental (M = .11) encoding conditions at their respective optimal lags.
Taken together, past research suggests that the
spacing effect is obtained under incidental encoding
(e.g. Challis, 1993; Russo et al., 1998; Shaughnessy,
1976) but is reduced relative to the size of the
spacing effect obtained under intentional learning
conditions. Moreover, the difference in effect size
observed between incidental and intentional learning conditions is obtained when differences in
optimal lag across learning conditions are accommodated (Verkoeijen et al., 2005).
One of the earliest comparisons of spacing effect
size across pure and mixed conditions lists was
reported by Greeno (1970) in a study which revealed
a larger spacing effect for mixed lists than pure lists.
Greeno attributed this finding to time-sharing that
occurred during the second presentation of massed
items in mixed condition lists but not in pure condition lists. Specifically, he suggested that massed
items in mixed condition lists were only attended to
for a portion of their second presentation and the
remaining time was spent rehearsing spaced items.
Such time-sharing was not proposed to occur on
the second presentation of spaced items. Moreover,
massed items in pure condition lists were suggested
to benefit from spacing in terms of cumulative
rehearsal. For example, in a list of massed items
(e.g. dog, dog, chair, chair, tree, tree), the participant
may covertly rehearse the items in a spaced form
(e.g. the participant may first repeat “dog”, then
may alternate between “dog” and “chair”, and then
could cycle through multiple items, “dog, chair, tree,
dog, chair, tree”). To examine further the spacing
effect using pure condition lists, Hall (1992) presented
participants with four lists of repeated words. Two
lists contained massed items and two lists consisted
of spaced items. The lists for each condition were presented consecutively to avoid contamination effects,
and the order of conditions was counterbalanced
across participants. Results revealed equivalent performance for massed and spaced items (M = .59 in
both conditions). However, subsequent studies (e.g.
Kahana & Howard, 2005; Toppino & Schneider,
1999) failed to replicate Hall’s finding, and instead,
results were consistent with Greeno’s (1970) original
finding which indicated that comparison of pure
spaced and massed lists of items can yield a
spacing effect.
Mixed versus pure condition lists
Lag by retention interval interaction
The paradigm most frequently used in spacing effect
studies involves massed and spaced repetitions of
items interleaved within the same list during encoding (i.e. mixed condition lists). Alternatively, massed
and spaced conditions can be manipulated
between lists such that the participant studies one
list of massed items and a second list of spaced
items (i.e. pure condition lists). Generally, spacing
effects are more robust when using a within-list
manipulation compared to a between-list manipulation (e.g. Delaney & Knowles, 2005; Delaney & Verkoeijen, 2009).
The interaction between lag and retention interval
reflects increases in the optimal lag as the retention
interval between acquisition and final test increases.
For example, Glenberg (1976) utilised a continuous
paired associated task in which word pairs were
repeated following one of several lags (0, 1, 4, 8, 20,
and 40) and were then tested after varying retention
intervals (i.e. 2, 8, 32, or 64 trials following the word
pair’s second presentation). Importantly, Figure 3 displays the critical finding that the lag which maximised
performance shifted across retention interval conditions such that the optimal lag increased as the RI
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JOURNAL OF COGNITIVE PSYCHOLOGY
Figure 3. Mean recall performance in a continuous pairedassociate task as a function of lag and retention interval.
Figure adapted with permission from Glenberg (1976,
Figure 1).
increased. More recently, a meta-analysis (Cepeda
et al., 2006) indicated that the lag which maximised
performance following retention intervals less than
one minute was shorter than the lag which maximised performance following retention intervals
greater than a month (lags between 11 and 29 s
and lags between 2 and 28 days, respectively). Critically, the Lag x Retention Interval interaction has
been observed on the order of seconds (e.g. Glenberg, 1976, 1977), minutes (e.g. Glenberg, 1979),
and days (Glenberg & Lehmann, 1980; Pyc, Balota,
McDermott, Tully, & Roediger, 2014).
It is also important to note that Glenberg’s (1976)
results indicated a benefit in cued recall following a
short retention interval for word pairs that were
massed (i.e. Lag 0) over word pairs separated by
short lags (see Figure 3). Although longer lags
yielded the predicted benefit in final test performance over massing, the benefit of massing over
short lags observed in the short RI conditions replicated results originally reported by Peterson,
Wampler, Kirkpatrick, and Saltzman (1963). In its
most extreme form, the Lag × Retention Interval
interaction refers to the crossover Spacing Condition × Retention Interval interaction that has
been observed across numerous paradigms using
different types of stimuli (e.g. Bahrick, Bahrick,
Bahrick, & Bahrick, 1993; Balota et al., 1989; Bloom
& Shuell, 1981; Peterson et al., 1963; Rawson &
Kintsch, 2005; Spieler & Balota, 1996). The interaction
reflects better performance for massed items over
spaced items following a short retention interval
but superior performance for spaced items over
massed items following a long retention interval.
This interaction is particularly interesting, because
5
massed and spaced items should be equally activated and retrievable following an item’s second
presentation when the retention interval is limited
to a very short time span. Thus, one would not
expect a benefit of massed over spaced repetition,
and instead, performance should be equivalent
across massed and spaced study conditions.
Another set of studies relevant to the discussion
of the Lag × Retention Interval interaction involves
the manipulation of spacing for lists of items rather
than individual items. Most notably, Underwood
(1961) reported results from a series of experiments
that examined the influence of spacing on lists of
word pair associates in which he classified massed
study as repetition of lists that occurred within 2–
8 s of initial presentation and spaced study as repetition of lists that occurred with at least 15 s
between presentations. This series of experiments
yielded mixed results and multiple failures to
obtain a list-level spacing effect which led Underwood to the conclusion that the lag between repetitions and the intervening interference must be
jointly considered. A shorter lag may be required
when interference of intervening material is high
compared to low, and in extreme circumstances of
interference Underwood suggested that spacing
repetitions of material may be entirely ineffective.
Of course, this conclusion makes sense given that
repetition of lists of items naturally incorporates
spacing between repetitions of the individual
items, and as a result, spacing lists exaggerates the
spacing between the individual items. Thus,
massed repetitions of lists may be more likely to
hit the “sweet spot” or optimal lag for the individual
items, whereas spaced repetition of lists is more
likely to incorporate too much spacing between repetitions of the individual items (i.e. a lag that is
longer than optimal as displayed in Figure 1).
Experimenter-introduced variability across
repetitions
A number of studies have directly manipulated the
way in which massed and spaced items were processed across repetitions to examine the viability
of Estes’ (1955a, 1955b) Stimulus Sampling Theory
as an account of the spacing effect. Critically, this
account assumes that spacing repetitions of an
item across time will yield more variable encoding
than massed repetitions. Variable encoding is
assumed to occur with spaced study, because
elements in the environment change across time
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6
G. B. MADDOX
which yields different associations between context
and the studied item. In contrast, there is little
change in the environment across repetitions of an
item that occur in massed form, and as a result,
the associations between context and the studied
items are reduced compared to items studied in
spaced form. Importantly, this level of variable
encoding is assumed to occur in addition to any
intentional manipulation of processing across repetitions introduced by the experimenter. For
example, the experimenter could have the participant make the same judgment on each presentation
of an item (e.g. Will the object fit into a shoebox?) or
different judgments between the first presentation
(e.g. Will the object fit into a shoebox?) and the
second presentation (e.g. Is this object animate?
see Bui, Maddox, Zou, & Hale, 2014). Although
encoding variability (Glenberg, 1976; Melton, 1970)
and Estes’ Stimulus Sampling Theory will be given
substantial consideration in the subsequent Theories
section, there are several findings observed in
studies designed to examine these accounts that
are worth noting in advance.
One particularly compelling study (Gartman &
Johnson, 1972, Experiment 3) attempted to manipulate the semantic meaning activated for a given
series of homographs by using similar or distinct
preceding list items across repetitions. Participants
studied four lists of words, which were separated
by immediate free recall. Each list consisted of six
homographs repeated after short (2 items), moderate (8–10 items), or long (16–18 items) lags. Preceding the presentation of each homograph were two
items used to bias the same or different homograph
meaning (e.g. leg neck foot followed by arm hand
foot vs. inch meter foot). Finally, there were six nonhomograph control words, which were repeated
after the same lags. Free recall results revealed a
typical lag effect for the control condition but no
lag effect for either homograph condition. Performance for different meaning homographs was significantly higher than the control condition, whereas
performance in the same meaning condition was
equivalent with the control condition for items
repeated after a short lag and significantly lower
than the control condition after moderate and
long lags. Although provocative, this pattern of
data has not been replicated (including Gartman &
Johnson’s (1972) Experiment 1).
In contrast with the Gartman and Johnson (1972)
results, there are two typical findings reported in
studies of experimenter-introduced encoding
variability (cf., Janiszewski, Noel, & Sawyer, 2003).
First, massed items benefit from intentional, experimenter-introduced variability in semantic encoding
compared to constant semantic encoding across
repetitions (e.g. Hintzman, Summers, & Block, 1975;
Verkoeijen, Rikers, & Schmidt, 2004) such that
items are biased with different meanings or the
same meaning, respectively. Second, spaced items
benefit from consistency in semantic meaning
across repetitions compared to variability in semantic meaning across repetitions (e.g. Hintzman et al.,
1975; Johnston & Uhl, 1976; Slamecka & Barlow,
1979). For example, Hintzman et al. (1975, Experiment 2) presented participants with 80 word pairs
in which the target was always a homograph, and
the cue word was used to bias its semantic
meaning during encoding. Word pairs were
divided into four conditions: (1) the cue–target
pairing remained the same across repetitions
(same cue condition; e.g. flower-bulb and flowerbulb), (2) the cue changed but biased the same
meaning of the homograph (same meaning condition; e.g. flower-bulb and tulip-bulb), (3) the cue
changed but biased the alternative meaning of the
homograph (different meaning condition; e.g.
flower-bulb and light-bulb), and (4) homographs
were randomly paired together to provide a single
presentation lag comparison (random condition; e.
g. airplane-jet and wood-log). Recognition test
results revealed a significant spacing effect in the
same cue condition and a small, “reverse” spacing
effect in the different meaning condition (i.e.
massed items were remembered better than
spaced items). No effects were obtained in the
same meaning or random conditions. This evidence
suggests that intentional variability only benefits
massed items (i.e. the “reverse” spacing effect
observed in the different meaning condition). In a
similar study, Slamecka and Barlow (1979, Experiment 3) compared cued recall performance for
twice presented homographs (lag 24) in the same
cue, same meaning, and different meaning conditions used by Hintzman et al. A cued recall test
was administered immediately following the encoding phase. Results revealed significantly better performance for homographs that were studied with
the same cue (M = .67) compared with the other
two conditions (M = .51 for both conditions). Thus,
in some instances, variable encoding for spaced
items actually reduces performance relative to
spaced items that are processed in similar ways
across repetitions.
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JOURNAL OF COGNITIVE PSYCHOLOGY
Similar patterns of results have been observed
across other types of stimuli and with different
manipulations of variability. For example, the
surface level structure of longer passages can be
repeated verbatim or paraphrased across repetitions
as a means for examining the influence of variable
encoding and spacing on LTM (Glover & Corkill,
1987; Krug, Davis, & Glover, 1990). Specifically,
Krug et al. reported a benefit for spaced repetitions
of an essay compared to massed repetitions of the
essay (Experiment 1). Moreover, a selective increase
in performance was observed when the second
presentation of the essay was a paraphrased
version of the original form relative to a verbatim
repetition in the massed condition but not the
spaced condition (Experiment 2). This resulted in a
reduction of the spacing effect when shifting from
verbatim repetitions (M spacing effect = 12 idea
units) to paraphrased repetitions (M spacing effect
= 7.2 idea units). Thus, results generally suggest
that manipulation of semantic meaning and
surface level structure of verbal materials enhances
LTM performance for massed repetitions and has
no effect or a deleterious effect on LTM performance
for spaced repetitions.
Finally, the pattern of results obtained in studies
incorporating intentional, experimenter-introduced
variability of verbal materials across repetitions has
been observed in studies that manipulated the
physical environment in which study sessions and
final testing occurred (e.g. Glenberg, 1979; Smith,
Glenberg, & Bjork, 1978) and in studies that manipulated the background on which repeated items
appeared (e.g. Verkoeijen et al., 2004). For
example, Verkoeijen et al. (2004) observed a
smaller spacing effect (M = .05) when the background on which an item appeared changed
across presentations (e.g. an item appeared on a
green background during its first presentation and
a red background on its second presentation) than
when it remained constant across presentations
(M = .18; e.g. a repeated word appeared on a green
background for both presentations). Again, the
observed difference in spacing magnitude reflected
a specific benefit of variable encoding for massed
items that was not observed for spaced items.
Thus, there is substantial evidence that intentional
variability in semantic meaning, surface structure,
and other elements of the environment can influence performance for massed and spaced repetitions which consequently modulates the
magnitude of the spacing effect.
7
In sum, there are six criteria by which mechanisms proposed to account for the spacing effect
will be evaluated. The six criteria are summarised
in Table 1 and will be revisited following the introduction and review of the mechanistic accounts of
the effect.
Proposed theories to account for the
spacing effect
This section of the paper introduces and assesses
the extent to which prior theories of the spacing
effect can accommodate the consistent behavioural
findings and boundary conditions outlined in the
preceding section. It is important to note in
advance that single mechanisms can account for
portions of the findings included in Table 1 but
cannot account for these findings in their entirety.
After briefly reviewing these mechanisms, attention
will focus on a more detailed discussion of the
reminding and encoding variability accounts given
the aims of the current review and emphasis on
these mechanisms in current research. Each
account of the spacing effect is listed in Table 2
and assessed against the consistent findings and
boundary conditions discussed in the previous
section.
Deficient processing
Deficient processing accounts assume that the
quantity or the quality of processing of an item’s
second presentation is reduced for massed relative
to spaced items. Various accounts highlight the
degree to which deficient processing may be controlled by the participant (e.g. Rundus, 1971) or is
automatic in nature (e.g. Challis, 1993; Cuddy &
Jacoby, 1982).
Controlled deficient processing
The underlying assumption of the controlled
deficient processing mechanism is that participants
choose to study massed items less than spaced
items. For example, Rundus (1971, Experiment 3;
see also Delaney & Verkoeijen, 2009) recorded
each participant’s spoken rehearsal throughout the
duration of a word learning experiment and compared the number of rehearsals completed for
massed and spaced items. As seen in Figure 4, the
total number of rehearsals per item tracked free
recall performance in a way that generally increased
across lag conditions lending support to an account
8
G. B. MADDOX
Table 2. Evaluation of proposed mechanisms as a function of consistent findings and boundary conditions such that an X
reflects a mechanism’s ability to accommodate the consistent finding in the literature.
Consistent finding
Mechanism
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Deficient processing –
controlled
Deficient processing –
automatic
Differential rehearsal
Study-phase retrieval
Reminding
Encoding variability
Encoding variability +
study-phase retrieval
Non-monotonic
function
X
X
Trace
dependence
Incidental vs.
intentional
encoding
Pure vs. mixed
condition lists
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
of the spacing effect based on reduced quantity of
processing in the massed condition. Converging evidence came from a study (Zimmerman, 1975; see
also Shaughnessy, Zimmerman, & Underwood,
1972, Experiment 3) in which participants controlled
their study time for massed and spaced items.
Results indicated that participants allotted increasing amounts of total time studied to once presented,
twice presented massed, twice presented short lag,
and twice presented long lag items, respectively.
Beyond the discrepancy in quantity of rehearsal
across spacing and lag conditions reported by
Rundus (1971), Verkoeijen and Delaney (2008)
argued for greater consideration of objective
versus functional spacing. Considered in turn, objective spacing refers to the experimenter-determined
lag between presentations of an item, whereas functional spacing refers to the spacing incorporated
between repetitions of an item in a participant’s
covert rehearsal. Verkoeijen and Delaney (2008;
Figure 4. Mean recall performance and mean number of
rehearsals as a function of lag. “Once” reflects items presented one time. Figure adapted with permission from
Rundus (1971).
Lag × RI
interaction
Experimenter-introduced
encoding variability
X
X
X
X
X
Experiment 2) examined differences in objective
versus functional spacing in pure condition lists
and hypothesised based on previous work
(Delaney & Knowles, 2005; Verkoeijen & Delaney,
2008, Experiment 1) that various experimental
manipulations would differentially influence functional spacing (e.g. a fast presentation rate would
limit spacing between repetitions of massed items
in covert rehearsal, whereas a slower presentation
rate would allow for longer spacing intervals
between covert repetitions of massed items).
Specifically, Verkoeijen and Delaney hypothesised
that a spacing effect in pure condition lists would
be obtained whenever objective, experimenterdetermined spacing was greater than the functional
spacing that occurred for massed items in the participant’s covert rehearsal. To test this prediction,
performance in a pure massed list was compared
with performance in a pure spaced list that included
short (M = 2.16 items) and long lags (M = 7 items).
Verkoeijen and Delaney argued that short lag
items in the pure spaced list would be rehearsed
less than long lag items if participants perceived
that short lag items were better learned than long
lag items. They also assumed massed items would
be functionally spaced in the participant’s covert
rehearsal. Consequently, the functional spacing of
massed items was predicted to be greater than the
objective spacing of short lag items but less than
the objective spacing of long lag items in the pure
spaced list. This pattern of objective and functional
spacing was predicted to yield an interaction
between lag and spacing condition reflecting
similar memory performance for massed items and
items repeated after short lags whereas memory
performance for items repeated after long lags
would be greater than memory for massed items.
In fact, this is what their results revealed.
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In sum, evidence suggests that rehearsal exaggerates the spacing effect in mixed condition lists (e.g.
Delaney & Knowles, 2005; Delaney & Verkoeijen,
2009; Verkoeijen & Delaney, 2008) provided that
spaced items are often rehearsed to the detriment
of massed items. There are several reasons for why
this may occur. First, the massed trace may decay
quickly after its second presentation and cannot
be retrieved for cumulative rehearsal after presentation of several subsequent words. Second, it may
be that massed repetitions produce a strong
feeling of knowing that results in the decision not
to rehearse those items (e.g. Kornell & Bjork, 2008).
Third, as noted by Verkoeijen and Delaney (2008),
methodological parameters may influence covert
rehearsal strategies. Of course, it could be some
combination of all of these factors.
There is clear evidence that suggests participants
study massed and spaced items differently, and a
controlled deficient processing account can accommodate some of the findings discussed in the previous section. Indeed, a controlled deficient
processing account can accommodate the findings
related to list structure (i.e. pure vs. mixed lists)
and feelings of knowing that may drive differences
in rehearsal strategies for spaced and massed
items (e.g. Verkoeijen & Delaney, 2008). Additionally,
if change in how an item is processed across presentations recruits full attentional processing on an
item’s second presentation, then this account can
also accommodate the effect of intentional, experimenter-introduced encoding variability. Nonetheless, a controlled deficient processing account
cannot accommodate all of the extant literature provided that the spacing effect has been observed in
children who do not rehearse (Toppino, FearnowKenney, Kiepert, & Teremula, 2009) and in situations
in which material was incidentally encoded (e.g.
Challis, 1993; Greene, 1989). Moreover, it is not
readily apparent how a controlled deficient processing account can accommodate the non-monotonic
function relating lag to final test performance,
because one would predict a function in which
final test performance continues to increase or
approaches asymptote as lag continues to increase.
For similar reasons, this account cannot accommodate the Lag × RI interaction. Finally, as an independent mechanism, it is unclear how the deficient
processing account can address the consistent
finding of superadditivity, because one would
expect that repetitions separated by a long lag (i.e.
repeated items that are fully processed on each
9
instance) would yield similar performance as two
unique items separated by the same lag. However,
it may be that deficient processing operates automatically, which will be considered next.
Automatic deficient processing
In terms of an automatic deficient processing
account, there have been a number of instantiations
which have received mixed support. Two accounts
have suggested that the quantity of processing is
reduced for massed items. First, Greeno (1967)
suggested that a repeated item will receive less processing if it currently resides in short-term memory
on its second presentation (i.e. massed items) compared to items not in short-term memory (i.e.
spaced items). Second, Challis (1993) suggested
that a reduction in the quantity of processing
occurs for massed items, because the first presentation primes its second presentation, whereas the
detrimental effect of priming is reduced or eliminated for spaced repetitions. At minimum, it is
unclear how either of these accounts can accommodate the non-monotonic function, because each
predicts that LTM performance will asymptote as
lag exceeds working memory capacity or once
priming is minimised.
In contrast with accounts that emphasise a
reduction in the quantity of processing, Jacoby
(1978; see also Cuddy & Jacoby, 1982) suggested
that there is qualitatively different processing on
an item’s second presentation as a result of
spacing. Specifically, Jacoby (1978) presented participants with a series of cue–target word pairs in
which the cue was always presented and the
target was either read or constructed from a provided word fragment. Pairs were repeated across
four lags (lags 0, 10, 20, and 40 items), and results
revealed a monotonically increasing function
across lags suggesting that short-term memory
and priming are not the only factors responsible
for spacing effects. Instead, Jacoby argued that the
increasing amount of effort required to remember
the earlier presented solution as lag increases
between repetitions may influence the magnitude
of the spacing effect at final recall. It is important
to note that the automatic deficient processing
account has also been supported by differences in
secondary task performance across massed and
spaced conditions (e.g. Johnston & Uhl, 1976, Experiment 1) such that secondary task reaction time
decreased across repetitions of massed items but
not spaced items. These results suggest that
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10
G. B. MADDOX
attention towards the primary learning task was
reduced on repetition trials in the massed condition
such that greater attention could be devoted to the
secondary tone task.
Notably, the automatic deficient processing
account can accommodate the differences observed
across intentional and incidental learning conditions. Specifically, one may expect to see a
shorter optimal lag under incidental encoding than
under intentional learning if difficulty remembering
an item’s first presentation is modulated by quality
of encoding on its first presentation. Moreover, the
automatic deficient processing account is consistent
with the typical finding related to experimenterintroduced encoding variability. Specifically, one
would expect that intentional variability in processing should lead to more processing on a massed
item’s second presentation. This would occur if the
context cues at the second presentation made
retrieval of the first presentation more difficult compared to situations in which context cues overlapped entirely. Nonetheless, the automatic
deficient processing account has difficulty accommodating the non-monotnic function and the
Lag × RI interaction for the reasons described in
the preceding section for a controlled deficient processing account. Notably, retrieval difficulty should
continue to increase or asymptote as the time
between repetitions increases. Thus, as a single
mechanism account, deficient processing based on
differences in retrieval effort between massed and
spaced conditions cannot readily handle the ubiquitous non-monotonic performance function. Thus,
despite the ways in which the automatic deficient
processing account can accommodate certain consistent findings, it fails to accommodate all of the
consistent findings.
Study-phase retrieval
Broadly, the study-phase retrieval account suggests
that studying a given item when initially acquiring
to-be-learned material may trigger retrieval of an
earlier presentation of the same item or other
related items. In this sense, the learner is retrieving
previously studied information throughout the
study-phase itself. With respect to the spacing and
lag effects, this account was originally proposed as
a simple contingency that items must be recognised
as repetitions during study to produce a spacing
effect on final test performance (e.g. Hintzman
et al., 1975; Madigan, 1969; Thios & D’Agostino,
1976) and has been recently revisited as a viable
theoretical mechanism (e.g. Appleton-Knapp, Bjork,
& Wickens, 2005; Greene, 1989). Early evidence in
support of the study-phase retrieval account was
reported by Madigan (1969) in a study which
required participants to recall and provide frequency
judgments for lists of words separated by various
lags during acquisition. The noteworthy finding
was an absence of spacing and lag effects for
repeated items identified by participants as only
having been studied once. These results suggest
that if the participants did not remember two occurrences of the item then retrieval of the item’s first
presentation failed, and as a result, no spacing
effect was obtained. Subsequent studies provided
additional support for the study-phase retrieval
account (e.g. Bellezza, Winkler, & Andrasik, 1975;
Melton, 1967; Thios & D’Agostino, 1976). However,
these studies must be re-evaluated due to a
shared confound. These studies compared performance across lags for items that were and were not
successfully recognised (or retrieved) on their
second presentation during the acquisition phase.
Reduced performance for these items on the final
test may truly capture failed study-phase retrieval
or may simply reflect an item difficulty effect.
Namely, if an item is too difficult to recognise
during the acquisition phase it is unlikely to be
recalled at final retrieval. Thus, reduction in the magnitude (or complete elimination) of the spacing
effect at final retrieval for items not recognised on
their second presentation during acquisition may
be due to their difficulty and not to a failure of
study-phase retrieval.
One way to avoid confounding study-phase
retrieval with item difficulty is to use non-repeated
items and manipulate the extent to which participants search their memories for related items. For
example, Jacoby (1974, Experiment 2) presented
participants with a list of words and asked whether
the currently presented item belonged to the
same semantic category as previously studied
items based on a certain “retrospective window”.
Specifically, participants were asked to identify
whether the item presented on the screen belonged
to the same semantic category as the item on the
preceding trial (1-back) versus any previously
studied item (n-back). This allowed Jacoby to
examine the role of mental contiguity (associating
list items that are not necessarily physically contiguous) in memory performance under incidental
encoding. Items either were the only category
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member presented or had another category
member presented during acquisition separated
by various lags (0, 1, 3, or 7 items). Cued recall
results revealed decreasing performance across
lags in the 1-back condition and relatively stable performance that increased slightly across lags in the nback condition. As expected, recall was significantly
higher for items recognised than items not recognised at encoding as having come from the same
category as an earlier presented item (M = .73 and
M = .45, respectively). The results from Jacoby’s
(1974) study provide evidence in support of the
study-phase retrieval mechanism without the confound of item difficulty discussed above.
With regard to its viability in accounting for the
spacing effect, the study-phase retrieval mechanism
can account for several of the consistent findings
reported in Table 1. First, to the extent that retrieval
of an item’s first presentation at the time of its
second presentation is essential for final test performance, one could assume that the resulting traces
would be dependent on one another. Second,
regarding the consistent differences observed
across incidental and intentional learning conditions,
the study-phase retrieval account can accommodate
the longer optimal lag and larger spacing effect
under intentional learning conditions than incidental
learning conditions if the quality of original encoding
modulates study-phase retrieval success. Third, if
retrieval difficulty on an item’s second presentation
modulates the way in which items are subsequently
rehearsed, then the study-phase retrieval mechanism
can account for the consistent finding in rehearsal
differences. Finally, intentionally introducing encoding variability across presentations of an item could
reduce the probability of retrieval success on the
second presentation of spaced items. However, it is
not immediately clear how changing the processing
across presentations of a massed item would
enhance memory based on a study-phase retrieval
account.
It is also important to consider that the studyphase retrieval mechanism can account for the
decreasing portion of the non-monotonic performance function, but it has difficulty in accounting for
the increasing portion of the non-monotonic performance function. Study-phase retrieval should be
more successful when items are massed than
when spaced. As a result, final test performance
should be relatively stable for items separated by a
range of short lags and should then decrease for
lags long enough to produce failure of study-
11
phase retrieval. For similar reasons, it cannot easily
accommodate the Lag × RI interaction. To accommodate this finding, the study-phase retrieval
theory must make additional considerations. One
viable assumption may be that the difficulty of
retrieval associated with longer lags may lead to
increased final test performance (e.g. Jacoby,
1978). Such an assumption is supported by evidence
that retrieval of an item acts as a memory modifier
(Bjork, 1975; also see Roediger & Karpicke, 2006)
and that successfully retrieved material is strengthened in memory to a greater extent when retrieval
is difficult than when it is easy (Bjork, 1994; also
see Schmidt & Bjork, 1992). Indeed, this assumption
is incorporated into a more recent instantiation of
study-phase retrieval referred to as the reminding
account (Benjamin & Tullis, 2010) which will be considered next.
Reminding account
Benjamin and Tullis (2010; see also Hintzman, 2004,
2010) recently proposed a reminding account which
is similar in nature to the original study-phase retrieval theory. Their theory assumes that items are forgotten over time based on the power-law of
forgetting, and when presented, items have some
capacity to spontaneously cue retrieval (i.e.
remind) of an earlier item. The capacity to spontaneously cue retrieval of an earlier item is high
when the item is a repetition, moderate when the
currently presented item is an associate of the
earlier item, and low when the currently presented
item has low (or no) association with the earlier presented item. Finally, memory is influenced by the
relative difficulty of retrieval such that difficult-toretrieve items will be strengthened more than
easy-to-retrieve items (see Bjork, 1975, 1994; Roediger & Karpicke, 2006 for discussions on the mnemonic benefits of testing).
A particularly nice component of the reminding
theory is the degree to which it is mathematically
specified. Even in simple form, Benjamin and Tullis
(2010) demonstrated that this model can accommodate situations in which superadditivity is and is not
obtained by manipulating forgetting rate and
memory performance for single items. Additionally,
the reminding account is able to accommodate
the non-monotonic performance curve as a result
of the forgetting rate parameter included in the
model such that the conflicting relationship
between forgetting rate and potentiating value of
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G. B. MADDOX
memory creates a non-monotonic performance
function. Moreover, if the difficulty of reminding
serves to modulate subsequent rehearsal of items,
then this mechanism successfully accommodates
the consistent finding of differential rehearsal such
that difficult-to-retrieve (i.e. spaced) items will be
rehearsed more than easy-to-retrieve (i.e. massed)
items. Finally, differences observed in the comparison of intentional and incidental learning conditions
can be accommodated by the reminding mechanism in a way similar to the study-phase retrieval
mechanism.
Despite its benefits, the reminding account faces
difficulty explaining the consistent finding that
experimenter-introduced
encoding
variability
reduces the spacing effect through enhanced performance in the massed condition compared to conditions in which encoding is not intentionally varied
across repetitions by the experimenter (e.g. Gartman
& Johnson, 1972; Johnston & Uhl, 1976; Verkoeijen
et al., 2004). Specifically, an item’s repetition
should have high reminding potential and high
success when it is massed (Benjamin & Tullis,
2010), despite biasing an alternative meaning or
appearing with a different background. Because forgetting should be minimal in this condition, the
potentiating value of the repetition should be low.
However, results show that massed items that are
variably encoded via some intentional manipulation
are often better remembered than massed items
without the experimenter-introduced encoding
variability manipulation (e.g. Verkoeijen et al.,
2004). Of course, it is possible that despite minimal
forgetting across variably encoded massed repetitions, retrieval of the item’s original meaning
and environmental context on its first presentation
may require more effort than retrieval of an item’s
first presentation when encoding contexts are constant across repetitions. Although this is a possibility,
the difficulty of retrieval is unlikely to differ across
conditions of intentional, experimenter-introduced
variability versus consistency when presentations
are massed. Instead, change in contexts is likely to
yield increased processing relative to the constantcontext condition as noted in the discussion of the
deficient processing mechanism.
Despite the difficulty that the reminding account
has in explaining the Spacing × RI interaction, this
account can accommodate the decrease in performance for variably encoded spaced repetitions compared to similarly encoded spaced repetitions if
one assumes that reminding is more likely to fail
when environmental context changes across time.
In these instances, reminding is too difficult to
occur successfully, and no benefit in LTM would be
expected.
A second drawback of the reminding account is
that it cannot fully account for the crossover interaction between spacing and RI in which performance is enhanced for massed over spaced
repetitions following a short RI but performance is
enhanced for spaced over massed repetitions following a long RI (e.g. Peterson et al., 1963). Although
Benjamin and Tullis (2010) provide evidence that the
reminding mechanism can accommodate changes
in optimal lag as the retention interval increases, it
is unclear how reminding can address the extreme
form of this interaction that compares spaced
versus massed study events. Specifically, when the
final test occurs shortly after the second presentations of massed and spaced items, one would
expect those items to be equally active and accessible in memory. Moreover, one would expect that
retrieval of the item’s earlier presentation would be
more desirably difficult in the spaced condition
than in the massed condition based on the reminding account (Benjamin & Tullis, 2010). Thus, the
reminding account would predict equivalent performance across conditions or a benefit of spaced
over massed study for a short RI. This prediction is
clearly in the opposite direction of the pattern that
is typically observed.
Given the difficulty faced by the reminding
account in accounting for the effects of encoding
variability on memory for massed repetitions as
well as its difficulty in accounting for the Lag × RI
crossover interaction, it is important to evaluate
the encoding variability mechanism as a viable
account of the spacing effect.
Encoding variability
Another theoretical explanation for the spacing
effect is encoding variability (see Crowder, 1976 for
a review), which posits an item is more likely to be
encoded in different ways when repetitions are
spaced than when they are massed (e.g. Bower,
1972; Martin, 1972). Specifically, encoding variability
invokes a mechanism rooted in Estes’ (1955a, 1955b)
Stimulus Sampling Theory which assumes that contextual elements naturally fluctuate over time
between available and unavailable states of awareness and that the set of elements available at a
given time will be encoded with the item. As seen
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in Figure 5, when two repetitions occur close in time,
the set of available contextual elements is likely to
remain unchanged, whereas the inclusion of
spacing between repetitions will likely lead to differences in the elements available on each presentation. The base assumption with this mechanism
is that increased encoding variability (due to
increased spacing) will lead to an increased
number of retrieval routes and probability of retrieval success at later test (e.g. Melton, 1967, 1970).
However, there have been numerous instantiations
of encoding variability proposed after the original
mechanism was introduced, and these instantiations
mostly differ on two issues. First, early versions of
this framework assumed that multiple presentations
of an item produce independent memory traces,
whereas subsequent instantiations allowed for the
formation of dependent traces or a single, elaborate
trace. Second, encoding variability accounts differ in
what is assumed to vary (e.g. semantic meaning vs.
physical context). These issues will be considered
separately.
With respect to the first issue of trace independence versus dependence across presentations of
an item, original instantiations of the encoding variability account assumed that the memory traces produced by the repeated presentations of an item
would approach independence as lag increased.
This assumption was initially supported by a monotonically increasing performance function reported
by Melton (1970; Madigan, 1969). Of course, subsequent research suggested that the benefit of
spacing reflected dependent rather than independent traces (e.g. Benjamin & Tullis, 2010). This issue
can be accommodated if one considers the situation
in which the stimulus representation remains relatively intact across presentations while contextual
elements vary. Indeed, McFarland et al. (1979)
argued that the spacing effect is obtained when
one dimension of an item (i.e. semantic meaning)
remains constant across repetitions but the features
of the semantic meaning are sampled separately
with each presentation. In this sense, the constant
sampling of some dimension of the stimulus
allows for trace dependence while variability in
other dimensions allows the trace to be more
richly developed and elaborated (see Anderson &
Bower, 1972, 1974 for more discussion of contextual
variability and its influence on retrieval). Thus, it is
necessary to consider the factors that may vary
and subsequently contribute to the benefit of
spaced study.
13
To date, the most comprehensive account of
encoding variability was provided by Glenberg
(1979) in his component-levels theory of the
spacing effect. This theory incorporated earlier theories of encoding variability and included dimensions of contextual, structural, and item level
variability. Glenberg’s theory assumed that an
encoded item was represented by a multi-component trace consisting of contextual, structural,
and descriptive components that ranged from
general information (e.g. the physical context of
the experiment) to item-specific information (e.g.
semantic meaning of the stimulus). Glenberg
posited that contextual information (e.g. testing
room) is automatically encoded and later updated
at the second repetition of an item. In contrast, structural components (e.g. inter-item associations) are
controlled by the participant and reflect the encoding strategies that arise from the local context, such
as recently presented items (i.e. preceding items
may lead to category chunking or single item associations). Finally, descriptive components consist of
lexical and semantic features of the item that also
depend on encoding processes (e.g. levels of processing; Craik & Lockhart, 1972) and local context (e.g.
items may prime the interpretation of subsequent
items). The inclusion of automatic and participantcontrolled encoding of contextual elements provides a distinction that helps explain the consistent
finding of a larger lag effect under intentional
encoding than under incidental encoding. Specifically, encoding variability is assumed to enhance
memory through automatic and intentional processing of contextual elements under intentional learning conditions but only through automatic
processing of contextual elements under incidental
learning conditions. It is critical to note that this
comprehensive theory can accommodate a single
trace with variable contextual information and
does not require the creation of multiple, independent traces. Thus, dismissing the encoding variability
mechanism given evidence of superadditivity (e.g.
Benjamin & Tullis, 2010; Gerbier & Toppino, 2015)
is unwarranted when initial assumptions of Estes’
(1955a, 1955b) Stimulus Sampling Theory have
been modified as in Glenberg’s theory. Thus, in considering the most basic assumption that increased
spacing leads to increased encoding variability,
dependent traces or a single elaborated trace can
be accommodated if it is assumed that some dimension of the item is repeatedly sampled across presentations and other dimensions are variably
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14
G. B. MADDOX
Figure 5. Encoding variability posits that contextual elements fluctuate across time, and those elements available at time 1
(e.g. Context A) are likely to be different than those available at a later time (e.g. Context B). When items are massed (e.g. M1,
M2), they are likely to be encoded in a similar way due to overlap in the available contextual elements (e.g. Context A). In
contrast, when items are spaced (e.g. S1, S2), they are likely to be encoded with multiple elements or retrieval routes (e.g.
Contexts A and B). Finally, the degree of overlap between the elements available at encoding and those available at final test
(e.g. Context X) will depend in part on whether the retention interval between the item’s second presentation and final test is
short (top pane) or long in duration (bottom panel). In turn, the overlap between these elements will contribute to the probability of successful retrieval across massed and spaced conditions. Figure adapted from Crowder’s (1976, Figure 9.6).
encoded with that constant dimension to produce a
more elaborate trace.
In addition to considering how dependent traces
may be established, it is important to consider how
the creation of dependent traces may fail. Such an
event may occur when the probability of sampling
at least one dimension of the item across both presentations approaches zero. For example, too much
time may elapse between repetitions of an item
and repeated sampling fails due to overall forgetting
of the item’s first presentation. Moreover, independent traces may be established if there is minimal
overlap in contexts across repetitions of an item
resulting from intentional variability and the natural
fluctuation in contextual elements across time due
to the lag separating presentations. For example, if
an alternative meaning of the word is biased (e.g.
intentional variability) and it occurs in a substantially
different list context (e.g. a long lag is included
between repetitions of an item) on its second presentation, a trace representing that new concept may be
created rather than the original trace being activated
a second time. Thus, systematically varying
contextual elements across item repetitions may
influence structural and organisational processing
that affects later memory positively for massed
items and adversely for spaced items depending on
the constraints and manipulations included in a
given experiment. Similarly, this may modulate
rehearsal of massed and spaced items in addition
to the information that is rehearsed with each item
(e.g. variable semantic meanings) that may later
facilitate retrieval. In contrast, when the repetition is
spaced and the first trace is available, experimenterintroduced variability in semantic meaning is more
likely to produce updating and elaboration of the
initial trace than it is to lay down a separate trace.
Although there are clear ways in which various
instantiations of encoding variability theory can
accommodate superadditivity in final test performance and the influence of experimenter-introduced
encoding variability on LTM, the inherent limitation
of this mechanism is that it does not predict a nomonotonic function relating lag to final test performance. Namely, one would predict that variability
continues to increase across time, and as a result,
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the function relating lag and final test performance
should monotonically increase or approach asymptote with increases in lag. This is clearly inconsistent
with the ubiquitous finding described above (see
Table 1). For similar reasons, the encoding variability
account does not account for shifts in optimal lag as
retention interval continues to increase (i.e. the
Lag × RI interaction). However, the encoding variability account does accommodate the extreme
version of this interaction in which massing leads
to better performance on an immediate memory
test and spacing leads to superior performance on
a delayed memory test (e.g. Peterson et al., 1963).
Massing repeated study events is expected to lead
to fewer, but stronger, environmental cues
encoded with the to-be-remembered material,
whereas spacing repeated events study will lead to
a greater number of unique environmental cues
that may not be as strongly associated with the tobe-remembered environment. In turn, when the
final test occurs shortly following the encoding
phase, the environmental cues at test are assumed
to be similar to the environmental cues encoded
during acquisition; therefore, fewer but more
strongly associated cues from encoding will yield
higher performance than more varied, weaker
environmental cues encoded during acquisition
(see the top panel of Figure 5; cf. Tulving &
Thomson, 1973). In contrast, when the final test is
delayed following the learning phase and it is
unclear what environmental cues will be available
during that test period, having encoded more variable cues during the learning phase will provide
more retrieval routes at the time of final test and
will yield higher overall performance than having a
limited set of strong retrieval cues that may or
may not be available at the time of delayed testing
(see the bottom panel of Figure 5). Although encoding variability can accommodate the Spacing Condition × RI interaction, its ability to account for the
downward turn of the non-monotonic function
within the broader Lag × RI interaction is severely
limited. One mechanism proposed to address this
limitation of encoding variability incorporates a
study-phase retrieval component, and this combined mechanism will be considered next.
Encoding variability and study-phase
retrieval
The combined encoding variability and study-phase
retrieval mechanism was initially proposed by
15
Greene (1989) and can account for many of the consistent findings in the spacing effect literature (see
Table 2). Notably, the combination of these two
mechanisms provides a way of accounting for the
increasing portion of the non-monotonic function
relating lag to final test performance (i.e. encoding
variability) as well as the decreasing portion of the
non-monotonic function (i.e. study-phase retrieval).
Specifically, encoding variability leads to increased
final test performance up to the point at which
study-phase retrieval fails. Beyond its ability to
account for the non-monotonic function, the encoding variability and study-phase retrieval dual mechanism is consistent with results from a quantitative
model that can accommodate many of the results
discussed above (SAM; Raaijmakers, 2003; see also
Siegel & Kahana, 2014). Thus, evidence in support
of this mechanism as a viable account of the
spacing effect will be considered next.
With respect to the dual mechanism’s ability to
account for existing findings, Raaijmakers (2003)
examined this dual mechanism using the Search of
Associative Memory (SAM; Raaijmakers & Shiffrin,
1981) theory which models how items are retrieved
from memory. Specifically, SAM assumes a memory
trace contains item, associative, and contextual
information that is determined at encoding. As is
assumed with the encoding variability theory,
these elements fluctuate between available and
unavailable states (Mensink & Raaijmakers, 1988,
1989), and as a result, retrieval of a given trace is
dependent on the associative strength between
the cues encoded during learning and the available
retrieval cues.
To account for spacing and repetition effects,
Raaijmakers (2003) assumed that an item is initially
stored in the short-term store (STS) at which point
a new trace is formed in LTM. At an item’s second
presentation, there are three possible situations
which may occur: (1) the item is still in the STS and
is correctly remembered, (2) the item is not in the
STS and is correctly remembered, or (3) the item is
not in the STS and is not correctly remembered. In
the first situation, no new information (e.g. associative or contextual information) would be added to
the trace in LTM, because the item is still in the
STS. This assumption is likely too strict, because
the second presentation of an item should be
entered separately into the STS. As a result, any contextual change (albeit minimal) will be included with
that second entry. In the second situation, new information available at the second presentation would
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G. B. MADDOX
be added to the trace retrieved from LTM, because
the item had been moved out of the STS. Finally,
in the third situation, a new LTM trace would be
created. With these additional assumptions, Raaijmakers demonstrated the model’s ability to fit
three existing data sets (Glenberg, 1976, Experiment
1; Rumelhart, 1967; Young, 1971) including the nonmonotonic performance function and the crossover
interaction between lag and retention interval discussed earlier in the Boundary Conditions section
(see Table 1).
Additional support for the dual mechanism
account is obtained when findings from the encoding variability literature are reconsidered (e.g.
Appleton-Knapp et al., 2005; Hintzman et al.,
1975; Slamecka & Barlow, 1979). In these studies,
memory for massed items was better when encoding variability was intentionally manipulated, but
memory for spaced items was better without the
addition of intentional variability. Verkoeijen et al.
(2004) argued that experimenter-introduced
encoding variability had a beneficial effect when
the original presentation could be remembered
(e.g. massed items) but had a detrimental effect
when it led to a failure of study-phase retrieval (e.
g. spaced items).
Finally, a third way of examining this theory was
presented by Verkoeijen et al. (2005). Participants
learned a list of words that were repeated at
varying lags (0, 2, 5, 8, 14, and 20) under intentional
or incidental encoding conditions. If performance is
determined by a combined encoding variability and
study-phase retrieval mechanism, one would expect
the inflection point on the non-monotonic curve to
occur earlier in the incidental learning condition
than in the intentional learning condition. Namely,
if incidental encoding produces a weaker or less
elaborate trace that cannot be recalled after longer
delays, it will lead to an earlier inflection point
when compared against intentional encoding.
Indeed, the results revealed an inflection point following a longer lag in the intentional encoding condition compared to the incidental encoding
condition (Lag 14 vs. 8, respectively).
In sum, the dual mechanism account incorporating encoding variability and study-phase retrieval
has been examined in the form of a quantitative
model (SAM; Raaijmakers, 2003) which produced
good fits for existing data sets (including the nonmonotonic performance function and Spacing Condition × Retention Interval crossover interaction).
Additionally, reinterpretation of results from earlier
encoding variability studies that found a benefit
for massed items, but not spaced items, when variability was intentionally manipulated suggests that
too much variability may contribute to study-phase
retrieval failure which, as a consequence, limits the
benefit of spacing. Moreover, this dual mechanism
can account for superadditivity and the consistent
effects related to differential rehearsal as outlined
in the encoding variability mechanism section.
Finally, research has examined the dual mechanism
account by comparing free recall for items repeated
after various lags when studied intentionally or incidentally (Verkoeijen et al., 2005) and found an earlier
inflection point under incidental encoding compared with intentional encoding, which may reflect
a relationship between trace strength and the
optimal lag (Raaijmakers, 2003; Toppino & Bloom,
2002). Although there is evidence from a number
of approaches that supports the study-phase retrieval and encoding variability combined theory,
others (e.g. Benjamin & Tullis, 2010) have argued
that a drawback of this dual mechanism is a lack
of parsimony. The critical question that will be considered next is whether a single process theory (i.e.
reminding) can account for a range of results
similar to that of the study-phase retrieval and
encoding variability combined mechanism.
Summary and evaluation of spacing effect
theories
The major theories that have been proposed to
account for the spacing effect have been introduced
along with the ways in which they succeed and fail
in accommodating the consistent findings and
boundary conditions shown in Table 1. As noted in
the introduction to the Mechanisms section, single
mechanism accounts may contribute in some
capacity to the spacing effect but cannot accommodate all of the critical findings. Thus, the subsequent
summary and evaluation will emphasise the reminding account and combined study-phase retrieval
and encoding variability account.
The reminding account offers a simpler approach
to explaining the benefits of spaced repetition than
does the combined encoding variably and studyphase retrieval mechanism (as noted by Benjamin
& Tullis, 2010). However, the simplicity of the mechanism must be considered jointly with its ability to
account for the consistent findings in the literature.
As displayed in Table 2, both mechanisms can
accommodate a majority of the consistent findings
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JOURNAL OF COGNITIVE PSYCHOLOGY
and boundary conditions of the spacing effect, but
there are two critical differences between these
mechanisms in their abilities to account for the ubiquitous Lag × RI finding and the influence of experimenter-introduced encoding variability on the LTM
benefits of massed and spaced study. These two
differences will be considered in turn.
With respect to the Lag × RI interaction, an adequate mechanism must be able to accommodate
the shift in optimal lag with increasing retention
interval. Moreover, the broad notion of a Lag × RI
interaction is inclusive of the Spacing Condition ×
RI crossover interaction which reflects the finding
that massing is better than spacing on an immediate
test but spacing is better than massing on a delayed
test. Whereas the shift in optimal lag as RI increases
may reflect either the balance between encoding
variability and forgetting (e.g. the combined mechanism) or retrieval difficulty and retrieval success
(e.g. the reminding account), the Spacing × RI crossover interaction can only be accommodated by the
combined mechanism.
With consideration for the influence of intentional, experimenter-introduced encoding variability
on memory for massed and spaced repetitions, the
combined study-phase retrieval and encoding variability mechanism can accommodate increased performance for variably encoded massed items
relative to situations in which these items are
encoded in consistent form across presentations.
Moreover, the combined mechanism can accommodate the finding of reduced or similar performance
for variably encoded spaced items compared to consistently encoded items. In contrast, the reminding
account can accommodate the latter finding but
faces substantial difficulty in accommodating the
finding of enhanced performance for variably
encoded massed items, because it predicts that
there should be no difference in performance for
this class of items relative to massed items that did
not experience intentional, experimenter-introduced encoding variability. Taken together, the
dual study-phase retrieval and encoding variability
mechanism can account for the consistent findings
and boundary conditions in the spacing effect literature, whereas the reminding account is challenged
in accommodating two of the consistent findings.
General conclusions and future directions
The goals of the current paper were to provide a
representative review of the spacing effect literature,
17
to identify the consistent findings and boundary
conditions related to the spacing effect, and to
better specify the theoretical mechanism underlying
the spacing effect. Moreover, the current review
aimed to discuss consistent findings that have not
been simultaneously considered in previous
reviews and to incorporate an additional consistent
finding that has generally been unconsidered (i.e.
the influence of experimenter-introduced encoding
variability). The consistent findings and the boundary conditions identified in the literature provided
the criteria for assessing theoretical mechanisms
proposed to underlie the spacing effect (see Table 1),
and this assessment provided evidence that the
spacing effect can be accounted for with a combined
encoding variability and study-phase retrieval mechanism (see Table 2).
Critically, the current review of the extant spacing
effect literature suggests that there is a clear need
for an encoding variability mechanism above and
beyond a reminding mechanism (e.g. Benjamin &
Tullis, 2010) to account for the effects of experimenter-controlled encoding variability across
massed and spaced repetitions of an item and the
Spacing × Retention Interval interaction. Of course,
one may question how and why variable encoding
enhances LTM. Initial accounts suggested that variable encoding led to a greater number of retrieval
routes that could be used at final test (e.g. Melton,
1967, 1970). However, intentional variability may
also influence study time allocation for massed
and spaced items and may lead participants to pay
fuller attention to a massed item’s second presentation compared to the attention given to the
second presentation when presented in the same
context (cf. Zimmerman, 1975). Moreover, it is
important to consider the types of variability that
will yield benefits in LTM performance. Although
there is evidence that variability in the background
colour or picture on which an item is presented
and the semantic meaning of an item can influence
LTM performance, there are other variables that may
or may not influence performance (e.g. item vs. relational processing, Huff & Bodner, 2014). In considering these additional factors, it is likely that some
factors will enhance performance and will be
encoded with the memory trace (e.g. change in
semantic meaning of a word across repetitions),
whereas other factors will influence performance
(e.g. attention and full encoding of an item) but
will not be encoded as part of the memory trace
(e.g. change of the background colour when such
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18
G. B. MADDOX
a dimension is irrelevant to the primary task). Moreover, additional factors may not be remembered nor
exert influence on LTM performance.
In considering the role of reminding or studyphase retrieval, Hintzman (2004, 2010) proposed a
recursive remindings account which suggests the
participant may consciously recognise such remindings and can use those instances as a way of increasing recollection at later retrieval (see also Wahlheim,
Maddox, & Jacoby, 2014). Such use of successful
remindings may produce elaboration of the
memory trace in a way similar to encoding variability.
Moreover, it is also important to address a finding
Crowder (1976) regarded as consistent across spacing
effect studies. Specifically, Crowder emphasised
results reported by Hintzman, Block, and Summers
(1973) which suggested that the spacing effect is
localised to the second presentation of an item
such that updating or additional processing of the
item on its second presentation produces the mnemonic benefit of spacing. In their study, Hintzman
et al. presented participants with word pairs one
time or two times (lags 0, 1, 5, and 15 items) in
which both presentations of a word pair were
visual, both were auditory, or one presentation was
visual and the other auditory (with the order of
modality equated). At final test, participants were
asked to report the order of modalities in which
items were presented. In instances in which participants did not identify the correctly ordered combination of modalities, responses were more likely to
be the modality of the second presentation when
repetitions were spaced but the modality of the first
presentation when repetitions were massed. This
finding is consistent with additional evidence
suggesting that the spacing effect results from differences in processing of an item’s second presentation
when it is massed relative to spaced (e.g. Greeno,
1970; Johnston & Uhl, 1976; Rundus, 1971; Shaughnessy et al., 1972). However, recent evidence suggests
that there is a unique enhancement in long-term
recall and cued recall performance for the first item
of two associates presented separately within a list
context (e.g. king later followed by queen) that is
not observed for the second item (Tullis, Benjamin,
& Ross, 2014; see also Hintzman, 2004, 2010). Thus,
more research is needed to better understand the
ways in which massed and spaced repetitions modulate how the item is stored in memory.
It is critical to note the current review examined
studies that separated repetitions of material with
other intervening material. Often the intervening
material was similar in nature (e.g. repetitions of
words separated by other to-be-remembered words
or repetitions of word pairs separated by intervening
to-be-remembered word pair) which in some
instances may lead to high interference and in
other instances may lead to low interference across
to-be-remembered
materials.
An alternative
approach to separating repetitions of material is to
utilise unfilled time intervals or intervals filled with
unrelated activity (e.g. Rawson & Kintsch, 2005).
When these approaches are directly compared,
results indicate that interleaving material may be critical for enhancing inductive learning above and
beyond utilising temporal spacing in which interleaving does not occur (Kang & Pashler, 2012). This evidence has been used in support of a discriminativecontrast hypothesis in which memory is enhanced
through a two-step process (e.g. Birnbaum, Kornell,
Bjork, & Bjork, 2013; Kornell, Klein, & Rawson, 2015).
In the first step, the to-be-remembered material
must be successfully retrieved. In the second step,
useful retrieval cues will be strengthened and less
efficient retrieval cues will be weakened. The dual
mechanism combining study-phase retrieval and
encoding variability is consistent with the discriminative-contrast hypothesis such that this hypothesis
posits a necessary role for retrieval or reminding of
an item’s earlier presentation. Moreover, the encoding variability mechanism posits shifts in context
across spaced repetitions that would facilitate evaluation of a retrieval cue’s relative utility for a later
test. In turn, retrieval routes associated with contextual cues can be strengthened, modified, or combined across repetitions (see Wahlheim et al., 2014
for a discussion of preserving unique contextual
elements across repetitions of an item in a recursive
trace). Although the benefit of repeated study
appears to be maximised through interleaving of
material over introducing temporal spacing, utilising
temporal spacing may facilitate the discrimination
step when contexts are intentionally varied in a substantial or meaningful way. Of course, future studies
may wish to address this possibility.
It is also important to note that although the
current review emphasised past spacing effect
studies that have examined the spacing effect in
verbal learning, similar consistent findings have
been observed in other domains. For example,
Arthur et al. (2010) examined short versus long interval spacing of practice sessions for individuals
acquiring complex decision making skills and the
requisite psychomotor skills for executing a
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JOURNAL OF COGNITIVE PSYCHOLOGY
computer game (i.e. Fleet Command). Training sessions were distributed across one or two weeks,
and results revealed increased immediate performance and enhanced retention for training sessions
spaced across a longer period of time compared to
the condition in which all training sessions occurred
within one week. Similarly, an examination of the
influence of spacing on music performance revealed
a benefit (i.e. increased performance accuracy) when
practice sessions were separated by a long 24 hour
lag compared to a shorter 6 hour lag (Simmons,
2012). However, each of these studies suffered
from some methodological limitations and must
be considered with caution. Finally, a meta-analysis
of spacing effects on task performance (Donovan &
Radosevich, 1999) indicated that although there
was an overall spacing effect across studies, the
benefit of spaced practice was more pronounced
for simple tasks than for complex tasks (e.g. crank
turning vs. air traffic controller simulation). Interestingly, as the complexity of the task increased, the
optimal interval between repetitions also increased.
However, future work will be needed to more precisely manipulate the lag effect to rely less categorically on distinctions between 1 minute, 1–10
minutes, 1 day, and more than 1 day. Indeed, utilising a method similar to that reported by Cepeda
et al. (2008) will provide stronger support of the findings resulting from this meta-analysis.
Finally, in considering the way in which spaced
and massed repetitions of an item influence how
an item is stored in memory, it is important to
examine the biological underpinnings of the
spacing effect. Indeed, one early account of the
spacing effect, consolidation theory (e.g. Hintzman,
1969; Landauer, 1969), invoked Hebbian (Hebb,
1949) learning to explain why spaced repetitions
yielded improved memory compared to massed
presentations. Although full consideration of neuropsychological research on the spacing effect is
beyond the scope of the current review, it is important to note that accumulating evidence indicates
that spacing and massing study events differentially
influence long-term potentiation (LTP) induction in
fruit flies (e.g. Tully et al., 1994), mice (e.g. Woo,
Duffy, Abel, & Nguyen, 2003), and honeybees
(Deisig, Sandoz, Giurfa, & Lachnit, 2007). Moreover,
research examining the influence of spacing on consolidation in human memory suggests differential
effects on multiple biological processes (e.g.
Hupbach, Gomez, Hardt, & Nadel, 2007; Litman &
Davachi, 2008). Finally, the neural underpinnings of
19
the spacing effect are likely to differ when examining mnemonic benefits of spacing for verbal
materials and procedural memory. Thus, future
studies should examine further the ways in which
the study-phase retrieval and encoding variability
mechanism can account for the benefits of spacing
in verbal and non-verbal domains.
In closing, the current review simultaneously considered consistent findings from the extant spacing
effect literature that had not previously been considered in concurrent fashion, and in turn this
allowed for further specification of the theoretical
mechanism underlying the spacing effect. Moreover,
the current review highlights several important
implications for the application of spaced retrieval.
First, the study-phase retrieval component of the
underlying mechanism suggests that individual
and group differences will be necessary considerations when designing spaced study schedules (e.
g. young adults vs. healthy older adults vs. patient
populations). Indeed, initial evidence suggests that
this is the case (e.g. Bui, Maddox, & Balota, 2013;
Wahlheim et al., 2014). Second, given various time
constraints in learning new material, encoding
material from a variety of perspectives may be
optimal when learning time is limited and massed
presentation is required, whereas intentionally
varying encoding may actually be detrimental
when learning is spaced. These applied situations
and the limitations of the literature noted above
suggest that there is still substantial opportunity to
parameterise and examine the spacing effect.
Acknowledgements
I would like to thank David Balota, Mary Pyc and Ashley
Bangert for their helpful comments at various stages of
this work.
Disclosure statement
No potential conflict of interest was reported by the
author.
Funding
This work was supported by National Institute on Aging
[grant number AG00030].
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