ArticlePDF Available

The Hebb repetition effect in simple and complex memory span

Authors:

Abstract and Figures

The Hebb repetition effect refers to the finding that immediate serial recall is improved over trials for memory lists that are surreptitiously repeated across trials, relative to new lists. We show in four experiments that the Hebb repetition effect is also observed with a complex-span task, in which encoding or retrieval of list items alternates with an unrelated processing task. The interruption of encoding or retrieval by the processing task did not reduce the size of the Hebb effect, demonstrating that incidental long-term learning forms integrated representations of lists, excluding the interleaved processing events. Contrary to the assumption that complex-span performance relies more on long-term memory than standard immediate serial recall (simple span), the Hebb effect was not larger in complex-span than in simple-span performance. The Hebb effect in complex span was also not modulated by the opportunity for refreshing list items, questioning a role of refreshing for the acquisition of the long-term memory representations underlying the effect.
Content may be subject to copyright.
The Hebb repetition effect in simple and complex memory span
Klaus Oberauer &Timothy Jones &Stephan Lewandowsky
#Psychonomic Society, Inc. 2015
Abstract The Hebb repetition effect refers to the finding that
immediate serial recall is improved over trials for memory lists
that are surreptitiously repeated across trials, relative to new
lists. We show in four experiments that the Hebb repetition
effect is also observed with a complex-span task, in which
encoding or retrieval of list items alternates with an unrelated
processing task. The interruption of encoding or retrieval by
the processing task did not reduce the size of the Hebb effect,
demonstrating that incidental long-term learning forms inte-
grated representations of lists, excluding the interleaved pro-
cessing events. Contrary to the assumption that complex-span
performance relies more on long-term memory than standard
immediate serial recall (simple span), the Hebb effect was not
larger in complex-span than in simple-span performance. The
Hebb effect in complex span was also not modulated by the
opportunity for refreshing list items, questioning a role of
refreshing for the acquisition of the long-term memory repre-
sentations underlying the effect.
Keywords Immediate serial recall .Complexspantask .
Work i n g m emory .Long-term memory
Half a century ago, Donald Hebb (1961) asked participants in
an experiment to remember lists of random digits for imme-
diate recall in the order of presentation. Unbeknown to the
participants, Hebb presented the same list on every third trial,
interspersed with new random lists in the intervening two
trials. Across 24 trials, immediate serial recall improved for
the repeated but not the random lists. Although participants
were not asked to remember the lists beyond the time of im-
mediate test, more long-lasting memory traces accrued with
the repetitions, which gradually improved peoples ability to
remember lists matching those traces.
Tests of immediate serial recall are routinely used to inves-
tigate short-term or working memory, also known as primary
memory (from here on we will use the term working memo-
ry). Most tests of serial recall involve the simple-span proce-
dure, in which people must recall a list of items immediately
upon presentation in forward order. Because of its limited
capacity, working memory is commonly assumed to hold only
the current list, perhaps with a few traces of the immediately
preceding one, but it is not thought to be suited to acquire a
representation of the commonalities of lists spanning four tri-
als. Therefore, the pervasive Hebb effect documents the con-
tribution of some longer-lasting form of memory, referred to
as long-term memory or secondary memory, to tests of imme-
diate serial recall. The Hebb effect implies that lists main-
tained for immediate recall leave long-term memory traces,
and that these traces are used in immediate recall (Burgess &
Hitch, 2005,2006; Page & Norris, 2009).
Here we investigate whether the Hebb repetition effect is
also observed with two variants of the complex-span para-
digm. The typical complex-span task differs from the
simple-span task by the addition of a distractor task that is to
be carried out in between pairs of list items during encoding;
here we also investigate a less common variant in which
distractors are interspersed between items at retrieval. The
distractor task usually requires processing without any explicit
memory demand, for instance reading sentences (reading
span, Daneman & Carpenter, 1980), solving arithmetic
K. Oberauer (*)
Department of Psychology Cognitive Psychology, University of
Zurich, Binzmühlestrasse 14/22, 8050 Zürich, Switzerland
e-mail: k.oberauer@psychologie.uzh.ch
T. Jo nes :S. Lewandowsky
University of Bristol, Bristol, UK
S. Lewandowsky
University of Western Australia, Perth, WA, Australia
Mem Cogn
DOI 10.3758/s13421-015-0512-8
problems (operation span, Turner & Engle, 1989), or carrying
out a series of choice response tasks (Barrouillet, Bernardin,
Portrat, Vergauwe, & Camos, 2007). Complex-span tasks
have become popular in particular because their psychometric
properties render them suitable for measuring working-
memory capacity (Oberauer, Süß, Schulze, Wilhelm, &
Wittmann, 2000; Wilhelm, Hildebrandt, & Oberauer, 2013),
and they are good predictors of fluid intelligence (Conway
et al., 2005; Conway, Kane, & Engle, 2003; Engle, Tuholski,
Laughlin, & Conway, 1999). Although behavioral phenomena
from complex span tests bear many similarities with those
from simple-span tests, the two paradigms also differ in some
regards. For instance, whereas the majority of errors in simple-
span tests are order errors, item errors are more prevalent in
complex span tests (Oberauer, Lewandowsky, Farrell, Jarrold,
&Greaves,2012;Unsworth&Engle,2007b). In correlational
studies, when multiple simple span and complex-span tasks
are used, the two types of tasks load on separate factors
(Gathercole, Pickering, Ambridge, & Wearing, 2004;Kane
et al., 2004). Those observed differences between the two
types of span task render it plausible that simple-span and
complex-span performance may also differ with regard to
the Hebb effect.
As we discuss next, there are additional reasons to believe
that an examination of the Hebb effect in complex span should
be theoretically rewarding. Compared to the Hebb effect in
simple span, there are equally plausible theoretical reasons
to believe that the Hebb effect in complex-span should be
greater, or that it might not be present at all. The goal of the
present work is to investigate the empirical merits of these
competing theoretical expectations.
The Hebb effect could be diminished or even abolished in
complex span because the distractor task interrupts encoding
of the list, thereby disrupting the formation of an integrated list
representation. The Hebb effect in simple span appears to
depend on an integrated representation of the list, as demon-
stratedbyanexperimentbyCumming,Page,andNorris
(2003): After a learning phase with the standard repetition of
one list every third trial, they introduced transfer lists
matching the previously repeated list in every second list item,
whereas the intervening list positions were filled with new
items. There was no transfer from the learned list to recall of
the repeated items on these transfer lists. Hitch, Fastame, and
Flude (2005) investigated Hebb learning with training lists in
which only every second item was repeated, rather than the
complete list as in the standard Hebb paradigm. There
was no evidence of learning in this condition. On the
basis of their results, Cumming et al. (2003)aswellas
Hitch et al. (2005) argued that Hebb learning consists of
the formation of a unified (chunked) representation of
the list, or at least of segments of the list (for compu-
tational models implementing this idea see Burgess &
Hitch, 2006;Page&Norris,2009). The acquisition and
use of such chunks is disrupted if repetition is limited
to sub-components of learned chunks.
On this hypothesis, one might expect the Hebb effect to be
at least diminished if not absent altogetherin complex
span: It is known that people find it difficult to exclude repre-
sentations used for distractor-task processing from working
memory, and those attempts are often not entirely successful
(Oberauer, Farrell, Jarrold, Pasiecznik, & Greaves, 2012). If
distractor materials are selected at random, as in our experi-
ments reported below, then distractor representations arguably
play a similar role to the not-repeated items in the training lists
of Hitch et al. (2005), or in the transfer lists of Cumming et al.
(2003): When representations of not-repeated distractors are
interspersed with representations of repeated list items, forma-
tion or application of chunks could be disrupted, thereby
diminishing the Hebb effect or abolishing it altogether.
Moreover, Hebb learning is incidental after all, partici-
pants are not asked to remember a list any further after they
have finished recalling it immediately after presentation. It is
generally assumed that incidental learning does not depend on
the persons intention to learn but on the kind and degree of
processing of the material (Craik & Lockhart, 1972;Hyde&
Jenkins, 1969). Thus, any material that is attended to and
processed to some extent becomes a candidate for incidental
learning. It follows that incidental learning of events during a
complex-span trial is unlikely to be limited to list items at the
exclusion of distractors: People must process the distractors
just like list items, and indeed most complex-span experi-
ments enforce an accuracy criterion on the distractor task to
prevent people from focusing on the list alone (Conway et al.,
2005). One would therefore expect Hebb learning to apply
non-selectively to all events that a person attends to and pro-
cesses during a complex-span trial, in which case distractor-
task representations should be as much part of the long-term
memory trace as the memoranda. When the distractors have
no systematic relationship to the memoranda and differ across
repetition trials as well as was the case in all experiments
presented below such a composite trace would be largely
useless for improving recall of a repeated list in the current
Hebb paradigm. These reasons justify the prediction that the
Hebb effect should be diminished or even abolished in the
complex-span paradigm.
On the other side of the argument are considerations that
cite the presumed greater involvement of secondary or long-
term memory in complex span compared to simple span.
Unsworth and Engle (2006)havearguedthatinsimple-span
tests, up to four list items can be held in working memory,
whereas in complex span the distractor task pushes previous
list items out of working memory. In consequence, at the point
of recall after a complex-span list only the last one or two
items can be recalled from working memory, whereas the
remainder must be retrieved from long-term memory. If this
assumption is correct, the Hebb effect might be expected to be
Mem Cogn
larger in complex span than in simple span: The Hebb effect
reflects a gradually strengthened long-term memory represen-
tation of the repeated list, and the impact of that representation
on immediate-recall performance should be larger the more
that recall depends on long-term memory.
The assumption that long-term memory is more involved
in complex span than in simple span received support from an
observation first reported by McCabe (2008): When tested
with a final free recall test for the words on all memory lists
encountered in the experiment, participants were found to re-
call more words from complex-span than from simple-span
lists. This effect has been replicated several times (Loaiza &
McCabe, 2012; Loaiza, McCabe, Youngblood, Rose, &
Myerson, 2011). According to McCabe, the effect arises be-
cause in complex span people cannot hold the entire list in
working memory. They are thus forced to temporarily out-
source parts of the list to long-term memory, and to bring them
back into working memory through covert retrievalduring
the distractor-task phases. Because covert retrieval serves as
retrieval practice, stronger long-term memory traces are
established in complex-span than in simple-span, which does
not require covert retrieval during list presentation. If covert
retrieval during complex span contributes to the Hebb effect,
then the strength of the Hebb effect in complex span should
depend on the opportunity for covert retrieval. This opportu-
nity can be varied through the cognitive loadimposed by
the distractor task. Cognitive load, as defined by Barrouillet
et al. (2007), refers to the proportion of time available for the
distractor task during which attention is actually occupied by
the distractor task. When distractors are presented at a leisure-
ly pace (e.g., one arithmetic step every 2 s), then cognitive
load is said to be low because processing only takes up a
fraction of the available time. When distractors are presented
at a fast pace (e.g., one arithmetic step every 500 ms), cogni-
tive load is high because the entire available time is required
for processing. According to Barrouillet and colleagues, any
remaining time in between distractors that is not taken up by
processing can be used to attend to the representations of the
memoranda, thereby refreshing them. The concept of refresh-
ing as used by Barrouillet and colleagues (c.f. Raye, Johnson,
Mitchell, Greene, & Johnson, 2007) is very similar to the
concept of covert retrieval, as McCabe (2008) recognized.
The two processes might not be the same, but for both it is
assumed that they can be carried out only when attention is not
occupied by a distractor task. It follows that varying cognitive
load arguably also varies the opportunity for covert retrieval
during a complex-span task. If covert retrieval plays a role in
building the long-term memory representations underlying the
Hebb effect, then the Hebb effect in complex span should be
larger at low than at high cognitive load.
To summarize, theoretical considerations and existing evi-
dence provide equally strong reasons for predicting that the
Hebb effect in complex span should be larger than in simple
span, or that it should be smaller or even non-existent.
Through the following experiments we tested these contrast-
ing predictions. Experiments 1and 2established that there is a
Hebb effect with complex span, suggesting that distractors did
not disrupt the formation of integrated list representations.
Experiment 3generalizes this finding to a version of complex
span in which distractor processing is interspersed between
recall rather than encoding of memoranda, thereby showing
that the effect is resilient to disruptions at test. Finally,
Experiment 4directly compared the Hebb effect for simple
and complex span. In addition, Experiment 4varied the op-
portunity for covert retrieval (or refreshing) in a complex span
paradigm, thereby testing the assumption of McCabe (2008)
about the role of covert retrieval in that paradigm. We found
that the size of the Hebb effect was unaffected by cognitive
load. We conclude that the Hebb effect is a highly robust
attribute of list learning that is unaffected by disruptive
distractors at encoding or test.
Experiments 1 and 2
Participants performed a complex-span task, which required
them to remember lists of consonants and to make size judg-
ments on words displayed after each consonant. The same list
was used on every third trial; we refer to those trials as the
repetition trials. Each set of three consecutive trials, including
one repetition trial and two non-repetition trials, will be called
a cycle. We expected a Hebb repetition effect, that is, better
immediate recall for repetition trials than new trials, especially
at later cycles. Experiments 1and 2differed in only two
regards: Experiment 1involved memory lists of seven conso-
nants; for Experiment 2we increased list length to eight to
create more room for improvement through learning. To com-
pensate for the longer duration of trials, in Experiment 2we
reduced the number of trials from 27 to 24.
Method
Participants Participants were 32 (Experiment 1)and27
(Experiment 2) members of the University of Western Austra-
lia community. They took part in a single 1-hour session in
exchange for AUD$10 or course credit.
Materials For each new trial a memory list was constructed
by sampling the required number of consonants without re-
placement from the set of all consonants except Q and Y. The
list for the first repeated trial was constructed in the same way
and then held constant for all repetitions. The repeated list was
used in every third trial, beginning with trial three.
Materials for the distractor task consisted of 264 English
nouns referring to concrete objects. They were selected from a
larger set of nouns referring to objects varying across a broad
Mem Cogn
range of size, from ladybirdto sun.The participantstask
was to judge for each word whether the object was smaller or
larger than a soccer ball. To make the task unambiguous, we
selected only the words referring to the 25 % largest and the
25 % smallest objects in the original set. Each word from the
experimental set was used three times throughout the experi-
ment. Words for each size judgment were drawn at random on
every trial, including for the repetition trials. Thus, repetition
trials had a constant memory list but variable distractor-task
stimuli. The random selection of distractors maximizes the
chance of distractor representations disrupting the formation
of an integrated list representation, thereby creating a condi-
tion for which there are good theoretical reasons to expect the
Hebb effect to disappear.
Procedure Each trial started with a fixation cross, followed
after 3 s by the first letter displayed centrally in red for 1.5 s.
The letter was immediately replaced by the first distractor
word, displayed centrally in black. Participants judged wheth-
er the word referred to an object larger or smaller than a soccer
ball by pressing the /(slash) key or the Z key, respectively,
on the computer keyboard. Once a response was made, or after
the maximum time of 2 s elapsed, the distractor disappeared
and was replaced by the next word. Each letter was followed
by four size judgments. The fourth size judgment was imme-
diately followed by the next to-be-remembered letter, and so
on until presentation of the list was completed. The very last
size judgment was followed by a red question mark,
prompting participants to commence recall by entering the
first letter on the keyboard. The entered letter was displayed
for 0.3 s, and was then replaced by the question mark again to
prompt recall of the second letter, and so on until participants
had given as many responses as there were letters in the list.
Omissions were not allowed. The next trial commenced 2.5 s
after the last recall response.
Results
We first report memory accuracy to test for the classic Hebb
repetition effect. Next we ask whether repetition of the mem-
ory list had an impact on speed and accuracy of the distractor
task. We analyzed all data with a Bayesian linear regression
model, using the BayesFactor package (Morey & Rouder,
2012; Rouder, Morey, Speckman, & Province, 2012)forR
(R Development Core Team, 2012). The lmBF function in
the BayesFactor package estimates linear models and returns
the Bayes factor (BF) of the model relative to a null model that
predicts the data by the intercept alone. Two alternative
models M
1
and M
2
can be compared to each other by dividing
their BFs (relative to the null model). The ratio of the BFs of
M
1
vs. null and M
2
vs. null is the BF of M
1
vs. M
2
.
For each analysis we investigated two predictors, cycle and
repetition. Cycle refers to the ordinal number of the eight sets
of three consecutive trials, each including one repeated and
two non-repeated lists. Cycle was entered as a continuous
variable, centered on zero. For each analysis we estimated four
models: M
c
, with only a main effect of cycle; M
r
,withonlya
main effect of repetition versus new trials; M
add
,withadditive
effects of cycle and repetition; and M
full
, with both additive
effects and their interaction. Each of these models included
subjects as a random effect, and therefore we also estimated
M
b
as a baseline model with only the intercept and the random
effect of subjects. We assessed the strength of evidence for the
main effect of cycle by BF(M
c
)/BF(M
b
), and the main effect of
repetition by BF(M
r
)/BF(M
b
). Evidence for the interaction
was assessed by BF(M
full
)/BF(M
add
). BFs larger than 1 reflect
evidence in favor of the model in the numerator; Bayes factors
smaller than 1 reflect evidence in favor of the model in the
denominator. The strength of evidence for the model in the
denominator can be gauged by the reciprocal of the BF. For
instance, if BF(M
full
)/BF(M
add
) = 0.5, then the BF in favor of
the additive model is 2. BFs <3 are usually regarded as evi-
dence barely worth mentioning;BF between 3 and 10 as
substantial evidence,BF between 10 and 100 as strong
evidence,and BF >100 as decisive(Kass & Raftery, 1995).
Memory accuracy Memory performance was scored as the
proportion of letters reported in their correct list positions.
Figure 1shows proportion correct by cycle and repetition
(new vs. repeated). Table 1summarizes the BFs reflecting
the strength of evidence for the main effects and the interac-
tion. The evidence for a main effect of cycle was substantial in
Experiment 1but weak in Experiment 2.Therewascompel-
ling evidence for the main effect of repetition in both experi-
ments. The interaction was supported only weakly in both
cases.
The Hebb effect was primarily reflected in the main effect
of repetition. Its size can be estimated by sampling from the
posterior distribution, using the posterior function in the
BayesFactor package (Morey & Rouder, 2012). The sample
provides information about the mean and the 95 % credible
interval of the effect, which are given in Table 2. The 95 %
credible interval is the range in which the true effect size lies
with a posterior probability of .95. Based on the findings from
the first two experiments, we can say that the Hebb effect
increases memory performance in complex span by 616 per-
centage points over 89 list repetitions.
Size-judgment performance Failures to respond to a size-
judgment trial were scored as errors. Response times (RTs)
of correct trials only were analyzed. We estimated Bayesian
linear models with the same predictors as for memory accura-
cy. The resulting BFs are reported in Table 1; the data are
plotted in Fig. 2. In both experiments accuracy improved
and RTs declined over cycles. The BFs for the main effects
of repetition show that list repetition had a beneficial effect on
Mem Cogn
RTs in Experiment 1, and on both accuracies and RTs in
Experiment 2. Evidence for the interaction was non-existent
in Experiment 1and modest at best in Experiment 2.
Discussion
Experiments 1and 2established that there is a Hebb effect
with complex span. Memory was better for repeated than for
new lists. The beneficial effect of repetition emerged fairly
rapidly by the third cycle it was already strong and this
explains why there was only weak evidence for the interaction
of repetition and the linear effect of cycle that would be ex-
pected from more gradual learning.
The repetition benefit extended to the distractor task: Size
judgments were made faster, and in Experiment 2also more
accurately, in the context of repeated lists. This is a novel
finding that we did not predict. Several post-hoc explanations
could be offered. From the perspective of a resource theory, it
could be argued that encoding and maintaining repeated lists
consumes a smaller share of a limited resource, leaving more
of that resource for concurrent processing. Other explanations
could start from the assumption that people notice the list
repetition and find the repeated lists easier to encode and
maintain. In previous experiments with the Hebb paradigm,
the majority of participants became aware of the list repeti-
tions at some point during the experiment (McKelvie, 1987;
Sechler & Watkins, 1991). McCabe (2010) has shown that
merely anticipating an easier memory task leads people to
respond faster to a concurrent processing task in a complex-
span paradigm. When people perceive the memory task to be
harder, they apparently devote more of the time in between
items to further processing of the memoranda this could
involve consolidation, refreshing, covert retrieval, or elabora-
tion and therefore delay responding to the distractors.
Experiment 3
Before moving on to a direct comparison of the Hebb effect in
simple and complex span we need to examine one possible
explanation for the results of the first two experiments. It has
been claimed that the Hebb effect arises primarily from learn-
ing of the output sequence, as opposed to the presented list
(Cunningham, Healy, & Williams, 1984). Cunningham et al.
(1984) found a Hebb effect only for lists that were initially
recalled, not for lists repeatedly encoded but not recalled, sug-
gesting that Hebb learning occurs only during recall. A later
study with better control of learning times observed a robust
Hebb effect also for not-recalled lists, but the effect was slight-
ly larger for recalled lists (Oberauer & Meyer, 2009), implying
that learning occurred both during encoding and recall. To the
extent that the Hebb effect arises from learning during recall,
our finding of a Hebb effect in Experiments 1and 2would be
unsurprising, because in the standard complex-span paradigm
that we used in those experiments, the output sequence
consisted of uninterrupted recall of all list items, just like in
simple span.
To test the possibility that the Hebb effect in
Experiments 1and 2relied on learning during uninter-
rupted list recall, in Experiment 3we used a variant of
complex span in which the distractor episodes interrupt
the output sequence instead (Lewandowsky, Duncan, &
Brown, 2004): Recall of each item was preceded by a
brief series of distractor operations. If Hebb learning
occurred primarily during output, and if distractors dis-
rupt the formation of associations between list items
that support the Hebb effect, then we might expect the
Hebb effect to disappear in Experiment 3.
Fig. 1 Memory accuracy in Experiment 1(top) and Experiment 2
(bottom). Error bars are 95 % confidence intervals (CIs) for within-
subject comparisons (Bakeman & McArthur, 1996). The CIs can be
interpreted in terms of classical null-hypothesis tests for pair-wise
comparisons between data points: Two means differ significantly (p <
.05) when their CIs overlap by less than 50 % of the interval between
each mean and the corresponding CI boundary (G. Cumming & Finch,
2005). The straight lines are regression lines from the mean posterior
parameters of linear models with cycle as predictor, applied separately
to each repetition condition
Mem Cogn
Method
Participants Twenty-three members of the University of
Western Australia campus community took part in a single
1-hour session in exchange for AUD$10 or course credit.
Materials and procedure The experiment was identical to
Experiment 2with one exception: The distractor episodes
were moved from the encoding to the recall phase. Specifical-
ly, recall of each letter prompted by a red question mark
was preceded by four size judgments on words.
Results
We analyzed the data in the same way as for the first two
experiments.
Memory accuracy The proportion of letters recalled in correct
order is plotted in Fig. 3as a function of cycle and repetition
(new vs. repeated). The comparison of Bayesian regression
models returned strong evidence for the main effects of both
cycle and repetition (repeated vs. new), as well as their inter-
actions. The BFs are given in Table 1. The size of the Hebb
effect, reflected in the posterior density of the main effect of
repetition, was comparable to the effect sizes of the first two
experiments (see Table 2)numerically it was somewhat
larger than in the preceding two experiments, perhaps because
there was more room for improvement, given that accuracy
started at a lower level in the first cycle. That said, the credible
intervals of the posterior distributions of the effect sizes over-
lap considerably, so that this numerical difference is unlikely
to be systematic. The conservative conclusion is that the Hebb
effect in Experiment 3was at least as large as in Experiments 1
and 2. Clearly, interrupting recall by a distractor task does not
reduce or abolish the Hebb effect.
Size-judgment performance Proportion correct and mean RTs
of the size judgments are presented in Fig. 4. The BFs for the
linear models on these data are included in Table 1. There was
no evidence for any main effect or their interaction on the
judgment accuracies. Participants responded increasingly
faster over the course of the experiment, reflecting practice
with the size judgments. The main effect of repetition shows
shorter RTs for distractors in trials with repeated lists. This
result replicates the Hebb effect on distractor RTs already ob-
served in the first two experiments.
Discussion
Experiment 3demonstrates the Hebb effect for a variant of
complex span in which distractor processing is interleaved
with recall rather than study. To the extent that the Hebb effect
relies on learning during retrieval, interruption of the recalled
list sequence does not disrupt learning the list, and does not
disrupt application of what has been learned.
Experiments 13provide an existence proof for the Hebb
effect in complex span. This is a novel result that rules out the
possibility that distractors whether at encoding or at test
Tabl e 2 Means and 95 % credible intervals for the Hebb Effect
Experiment Mean 95 % credible interval
1 0.13 [0.10, 0.16]
2 0.10 [0.06, 0.14]
3 0.15 [0.11, 0.19]
4: Simple span 0.12 [0.09, 0.15]
4: Complex span, low CL 0.08 [0.05, 0.12]
4: Complex span, high CL 0.08 [0.04, 0.11]
CL = cognitive-load
Tabl e 1 Bayes Factors for the linear models for Experiments 13, and the three span conditions of Experiment 4
Effect E1 E2 E3 E4 Simple E4 Complex low CL E4 Complex high CL
Memory accuracy
Cycle 11.7 1.7 4.3 × 10
7
1.5 × 10
5
1.1 49.2
Repetition 1.0 × 10
12
4446.7 6.6 × 10
7
1.3 × 10
17
1723.2 863.7
Cycle × Repetition 4.6 2.1 47.8 2.4 0.2 3.2
Size-judgment accuracy
Cycle 6922.2 38.3 0.2 25.4 34.9
Repetition 0.16 121.3 0.3 2.6 × 10
5
0.3
Cycle × Repetition 1.6 0.15 0.2 0.4 0.1
Size-judgment response time
Cycle 4 × 10
26
5×10
17
3.0 × 10
17
6×10
5
2×10
32
Repetition 243.3 139.9 112.5 0.7 0.2
Cycle × Repetition 2.8 5.3 0.11 0.2 0.9
Note.E1E4 denote Experiments 14; CL = cognitive-load condition
Mem Cogn
disrupt the formation of integrated list representations that are
thought to support Hebb learning. The next experiment exam-
ined whether the Hebb effect might benefit from covert re-
trieval practiceduring encoding, by manipulating the oppor-
tunity for such covert retrieval in a conventional complex-
span paradigm.
Experiment 4
In Experiment 4we compared the Hebb effect in
simple- and complex-span tasks, using the standard
complex-span paradigm with distractors during
encoding. In addition, we varied the cognitive load in
the complex-span task to manipulate the opportunity for
covert retrieval during a complex-span task. If covert
retrieval causes long-term memory traces to be laid
down and strengthened, and if these memory traces un-
derlie the Hebb effect, then the Hebb effect should be
reduced when we increase cognitive load.
Method
Participants Thirty students from the University of Western
Australia took part in three 1-hour sessions for financial reim-
bursement (at the rate of AUD$10/hr) or course credit. Two
participants took part in only one session, and therefore we
excluded their data from analysis.
Fig. 2 Performance in the size-judgment task in Experiment 1(left)and
Experiment 2(right). Error bars are 95 % confidence intervals for within-
subject comparisons. The straight lines are regression lines from the mean
posterior parameters of linear models with cycle as predictor, applied
separately to each repetition condition
Mem Cogn
Materials and procedure The materials and procedure were
as in Experiment 2, with the following modifications: In one
session, participants were tested with the complex-span task
exactly as in Experiment 1; here we will refer to this as the low
cognitive-load condition. In a second session participants
were tested on the complex-span task with high cognitive
load. We increased cognitive load by shortening the time win-
dow for each size-judgment trial from 2 s to 1.2 s. Thus, the
next stimulus already appeared 1.2 s after each word, irrespec-
tive of whether people had made a response during that time.
In the third session, participants were tested on a simple-span
task, created by cutting out the distractor-task periods. That is,
presentation of each to-be-remembered letter was immediately
followed by presentation of the next letter. Because simple-
span trials took less time than complex-span trials, we ran two
blocks of 24 trials of simple span; each block used a different
repeated list. The order of sessions was counterbalanced
across participants.
Results
We ran two Bayesian linear-model comparisons for each de-
pendent variable. One included span type (simple vs. com-
plex) as a third predictor (in addition to cycle and repetition),
Fig. 4 Performance in the size-judgment task in Experiment 3. Error bars are 95 % confidence intervals for within-subject comparisons. The straight
lines are regression lines from the mean posterior parameters of linear models with cycle as predictor, applied separately to each repetition condition
Fig. 3 Memory accuracy in Experiment 3. Error bars are 95 % confidence intervals for within-subject comparisons. The straight lines are regression
lines from the mean posterior parameters of linear models with cycle as predictor, applied separately to each repetition condition
Mem Cogn
collapsing over cognitive load. The other focused on the
complex-span conditions, contrasting high and low cognitive
load, omitting the data from the simple-span session. Both
analyses now involved three predictors, so that a more com-
plex set of model comparisons was needed to assess all inter-
actions. We estimated the full model, including all main ef-
fects and all interactions, and compared that against a progres-
sion of reduced models created by eliminating first the three-
way interaction, then additionally each of the two-way inter-
actions. We determined the BF for each interaction by divid-
ing the BF of the model including that interaction by the BF
for the model that eliminated that interaction but was other-
wise identical. For instance, evidence for the interaction of
repetition and cognitive load was assessed by the ratio of the
BF for a model including all three two-way interactions (but
not the three-way interaction) to the BF for a model including
only the other two two-way interactions (cycle × repe-
tition, and cycle × cognitive load), but eliminating the
repetition × cognitive-load interaction. Table 3summa-
rizes the resulting BFs for the main effects and interac-
tions in both analyses.
In addition, we ran the regression analysis with cycle and
repetition as predictors within each of the three span-type
conditions separately. The resulting BFs are added to Table 1
for comparison with the preceding experiments.
Memory performance Memory accuracy (plotted in Fig. 5)
increased over cycles, and was better for repeated than for
new lists; the BFs for those two main effects imply decisive
evidence. The interaction of cycle with repetition was moder-
ately supported (BF >5), implying continued Hebb learning of
the repetition list over cycles. Unsurprisingly, participants did
better in simple- than in complex-span tasks.
None of the interactions involving span type was
supported. In fact, the BFs provided some evidence
against these interactions, and in favor of an additive
model excluding the interactions. The evidence in favor
of the model excluding an interaction compared to the
same model including it can be gauged by the recipro-
cal of the BF for that interaction in Table 3.TheBFin
favor of omitting the interaction of repetition with span
type was 1/0.56 = 1.7. Thus, the data speak somewhat
more in favor of an additive model of repetition with
span type than against it, although the evidence is weak.
The analyses of each span-type condition separately
yielded strong evidence for the main effect of repetition
in each condition, demonstrating that the repeated lists
benefited from learning regardless of span type.
Tab le 2shows that the posterior mean of the Hebb
effect was slightly smaller for the complex span (for
both levels of cognitive load) than for the simple span.
We can assess the strength of evidence against each
directed hypothesis separately by computing one-sided
BFs (Wagenmakers & Morey, 2013). First, we calculat-
ed the one-sided BF for the hypothesis that the Hebb
effect for complex span is smaller than that for simple
span, compared to the alternative that it is equal to or
larger than that for simple span. This BF was 1.09,
implying ambiguous evidence. Second, we calculated
the one-sided BF for the hypothesis that the Hebb ef-
fect for complex span is larger than that for simple
span, compared to the alternative that it is equal to or
smaller than that for the simple span. This BF was 0.03, im-
plying strong evidence against the directed hypothesis (BF =
1/0.03 = 33.1). Thus, the present data provide evidence that is
equally compatible with the hypotheses that the Hebb effect is
equal for both span types or that it is smaller for complex than
for simple span; at the same time, the data provide strong
evidence against the assumption that the Hebb effect is larger
for complex span.
The analysis focusing only on complex span confirmed the
main effects of cycles and of repetition, with scant evidence
for their interaction. As expected, memory was better with
lower cognitive load. There was no evidence for any interac-
tion involving cognitive load. Rather, the results provided
fairly substantial evidence against the possibility that the Hebb
effect was reduced with higher cognitive load: The BF
against the interaction of repetition with cognitive load
was 1/0.11 = 9.1.
Tabl e 3 Bayes Factors for linear models, Experiment 4
Memory
accuracy
Size-judgment
accuracy
Size-judgment
response time
Simple span vs. complex span
Cycles 76,353.0
Repetition 1.8 × 10
14
Simple vs.
complex
(SC)
9.3 × 10
6
Cycles ×
Repetition
5.6
Cycles × SC 0.18
Repetition ×
SC
0.56
Three-way 0.14
Complex span: low vs. high CL
Cycles 57.0 101.8 1.5 × 10
6
Repetition 470,926.2 21.1 0.17
CL 1.4 × 10
6
9.6 × 10
57
6.6 × 10
147
Cycles ×
Repetition
1.5 0.20 0.39
Cycles × CL 0.20 0.16 0.32
Repetition ×
CL
0.11 0.56 0.17
Three-way 0.26 0.21 0.13
CL = cognitive-load
Mem Cogn
Size-judgment performance Accuracy and mean RTs of
size judgments in the complex-span conditions are plot-
tedinFig.6. The BFs for the linear models on these
data (see Table 3) reflect a simple pattern: Performance
improved over cycles. With higher cognitive load, re-
sponses were faster but less accurate, reflecting the in-
creased time pressure. On repetition trials accuracy was
slightly improved but RT was unaffected by list repeti-
tion. None of the interactions was supported by the
data. In particular, there was no evidence that cognitive
load modulated the repetition effect, and for RTs there
was even modest evidence against that proposition, BF
= 1/0.17 = 5.9.
Discussion
Experiment 4replicated the Hebb effect in complex span. The
effect was observed for memory performance, and for the
distractor task it was reflected in accuracy but not RT. We
found no evidence that the Hebb effect differed in magnitude
between complex span and simple span. The effect was nu-
merically smaller in complex span, but this trend must be seen
in light of the fact that the effect of list repetition in complex
span was smaller in Experiment 4than in Experiments 1and
2, which used the same complex-span task (see Table 2). The
true effect is therefore probably somewhat larger than estimat-
ed in Experiment 4, and hence even closer to the effect
Fig. 5 Memory accuracy in Experiment 4. Error bars are 95 % confidence intervals for within-subject comparisons. The straight lines are regression
lines from the mean posterior parameters of linear models with cycle as predictor, applied separately to each repetition condition
Mem Cogn
estimated for simple span. In addition, we obtained substantial
evidence against the hypothesis that the Hebb effect is modu-
lated by cognitive load.
When focusing on the low cognitive-load condition in iso-
lation, the absence of a Cycle × Repetition interaction (BF =
0.2) might lead some readers to conclude that in this condition
there was no Hebb effect. This conclusion would be premature
for two reasons. First, this interaction was statistically support-
ed in the joint analysis of all three conditions (BF = 5.6), and
the evidenceagainst the three-way interaction (BFs = 0.14and
0.26, see Table 3) speaks against the possibility that the Cycle
× Repetition interaction differed between conditions. Note
that unlike conventional frequentist statistics, which can at
best fail to find evidence for a three-way interaction, our
Bayesian analysis provided evidence against its existence.
Second, throughout all experiments and conditions of this
article, the Hebb effect manifested itself most clearly through
the main effect of repetition, whereas the evidence for the
Cycle × Repetition interaction was much weaker, as reflected
in the substantially smaller BFs. One reason for the relative
weakness of the interaction, supported by the learning curves,
is that the growth of accuracy over cycles in the repeated
condition is not linear but rather decelerating, so that the Hebb
effect is not well captured by the interaction of repetition with
a linear trend over cycles. We argue that the main effect of
repetition is sufficient evidence for the existence of a Hebb
effect, because Hebb learning is the only conceivable expla-
nation for why serial recall is better for repeated than for non-
repeated lists: The repeated lists were chosen at random for
each participant, so the only systematic difference between
Fig. 6 Performance in the size-judgment task in Experiment 4. Error bars are 95 % confidence intervals for within-subject comparisons. The straight
lines are regression lines from the mean posterior parameters of linear models with cycle as predictor, applied separately to each repetition condition
Mem Cogn
repeated and non-repeated lists was the fact that the former
occurred at trial numbers divisible by three, and that they were
repeated.
One potential concern with the present comparison of the
Hebb effect between simple and complex spans, and between
two levels of cognitive load in complex span, could be that the
three span types differed in trial duration. By implication, the
time between successive repetitions of the repeated list was
shortest in simple span, and longest in the low cognitive-load
condition of complex span. These time differences were prob-
lematic if we had reasons to suspect that the memory traces
that gradually build up for the repeated list are sensitive to the
passage of time. This is not the case: When interference across
lists is minimized, the Hebb effect is not affected by the num-
ber of non-repeated lists between two successive repetitions
(for at least up to 12 intervening trials), and by implication, by
the time between repetitions (Page, Cumming, Norris,
McNeil, & Hitch, 2013).
General discussion
The present experiments provide an existence proof for the
Hebb repetition effect in complex-span tasks. The effect is of
approximately the same size as for a comparable simple-span
task, and is not modulated by cognitive load. These findings
weaken both the theoretical arguments for expecting a reduced
or abolished Hebb effect in complex span, and the arguments
forpredictinganevenlargereffectthaninsimplespan.
Theinterruptionoflistencoding(Experiments1,2,and4)
or of list recall (Experiment 3) by the distractor task did not
hinder the formation of a long-term representation of the
memory list. Despite being incidental, long-term learning of
repeated lists appears to largely exclude the distractor-task
materials. If the long-term representation of lists included rep-
resentations of the distractor task, transfer across repeated lists
would have been impaired because the distractors were unre-
lated across list repetitions. One possible explanation for the
selective long-term learning of lists is that long-term learning
applies only to information held in working memory for some
time, not to material that is just briefly processed. Against that
proposition stands the well-established finding that mere pro-
cessing without intention to learn, especially semantic pro-
cessing such as the size judgments used in our experiments,
generates long-term memory traces (Craik & Lockhart, 1972).
Moreover, even the content of working memory does not con-
sist exclusively of memory items distractors are involuntari-
ly encoded into working memory (Oberauer, Farrell, et al.,
2012), and removing them by unbinding their representa-
tions from their context takes time, so that working-memory
representations are contaminated with distractor information,
particularly at high cognitive load, when there is little oppor-
tunity for removing distractor representations (Oberauer,
Lewandowsky, et al., 2012). Therefore, it is unlikely that
long-term learning during complex-span tasks entirely ex-
cludes distractors.
We conclude that the Hebb effect in complex span relies on
a memory trace that, despite its contamination with elements
of the processing task, is strong enough to support the gradual
improvement of list recall. Perhaps the memory traces of
words from the size-judgment task were sufficiently dis-
tinct from the letter lists to keep interference at a min-
imum. Future studies could investigate the contribution
of the processing task to the long-term memory trace by
manipulating the similarity between memory and
processing-task materials (e.g., using word lists com-
bined with size-judgment tasks), and manipulating
whether in the repeated condition the distractor-task ma-
terials also repeat across trials.
Our findings provide no support for the proposition that
long-term memory contributes more strongly to complex span
than simple span performance (Unsworth & Engle, 2007a).
Whereas better long-term retention for complex span than
simple span materials was observed in final free recall tests
(McCabe, 2008), there was no indication in the present exper-
iments of better long-term memory for repeated lists in com-
plex than in simple-span tasks. A possible resolution of this
discrepancy is that stronger long-term memory traces are cre-
ated during complex span trials, but that these traces are not
used in subsequent complex span trials using the same list.
One conceivable reason why that might be the case is that the
McCabe effect arises from elaborative encoding and rehearsal,
which is known to improve delayed recall (Craik & Tulving,
1975). Due to their longer duration, complex-span trials pro-
vide more opportunity for elaboration than simple-span trials,
and therefore could result in better memory for list words in
final free recall, whereas immediate recall might not benefit
much from elaboration (Rose & Craik, 2012). In contrast, the
Hebb effect is unlikely to rely on elaboration because it has
been observed with lists of digits and, in the present case,
letters, which are less amenable to elaboration than words,
the material used to demonstrate the McCabe effect.
Finally, we observed no modulation of the Hebb effect in
complex span by the opportunity for rehearsing or refreshing
the memory items, as afforded by cognitive load. With low
cognitive load, there is more opportunity for refreshing than
with high cognitive load, and yet the magnitude of the Hebb
effect was indistinguishable for the two loads in Experiment 4.
This finding strengthens our conclusion that the Hebb effect
does not arise from rehearsal (elaborative or otherwise), and
that it does not arise from refreshing or covert retrieval. It is
possible and we think plausible that the McCabe effect
arises from one of these processes. If this is the case, it implies
that there are two forms of long-term learning during
complex-span tasks, one driven by elaborative rehearsal or
covert retrieval and underlying the McCabe effect, and the
Mem Cogn
other not relying on any of those processes, underlying the
Hebb effect.
We propose that the McCabe effect reflects episodic long-
term memory for items, whereas the Hebb effect reflects the
acquisition of semantic long-term memory for sequences.
More specifically, the Hebb effect reflects a form of long-
term learning that results in unified representations of lists,
or sub-sequences in lists, akin to the learning of words by
forming unified representations of sequences of phonemes
or letters (Page & Norris, 2009). Converging evidence sup-
ports the close link between the Hebb effect and word learn-
ing: The size of the Hebb effect correlates with individual
differences in the ability to learn new word forms
(Mosse & Jarrold, 2008; Szmalec, Loncke, Page, &
Duyck, 2011). Transfer experiments showed that sylla-
ble sequences learned in a Hebb repetition procedure
interfered with access to similar words in a lexical-
decision task, showing that the sequences acquired dur-
ingHebblearningwereintegratedintothementallexi-
con (Szmalec, Duyck, Vandierendonck, Mata, & Page,
2009; Szmalec, Page, & Duyck, 2012).
To conclude, we demonstrated that the Hebb effect is ob-
served with equal strength in complex- and simple-span tasks,
regardless of cognitive load. This is remarkable because there
are good reasons to expect that interleaving list items with
distractor episodes disrupts the formation of an integrated
chunk of the list in long-term memory. The Hebb effect re-
ported here is the first direct evidence that long-term learning
of memory lists improves immediate recall in a complex-span
test, and as such opens a window into investigating the role of
long-term memory in tests of working memory.
Acknowledgments The research reported in this article was supported
by a grant from the URPP Dynamics of Healthy Agingat the Univer-
sity of Zurich to the first author, by a World University Network grant to
the first and the second authors, and a Discovery Grant and a Discovery
Outstanding Researcher Award from the Australian Research Council to
the third author.
References
Bakeman, R., & McArthur, D. (1996). Picturing repeated measures:
Comments on Loftus, Morrison, and others. Behavior Research
Methods, Instruments, & Computers, 28, 584589.
Barrouillet, P., Bernardin, S., Portrat, S., Vergauwe, E., & Camos, V.
(2007). Time and cognitive load in working memory. Journal of
Experimental Psychology: Learning, Memory, and Cognition, 33,
570585.
Burgess, N., & Hitch, G. J. (2005). Computational models of working
memory: Putting long-term memory into context. Tre nd s in
Cognitive Sciences, 9, 535541.
Burgess, N., & Hitch, G. J. (2006). A revised model of short-term mem-
ory and long-term learning of verbal sequences. Journal of Memory
and Language, 55, 627652.
Conway,A.R.A.,Kane,M.J.,Bunting,M.F.,Hambrick,D.Z.,
Wilhelm, O., & Engle, R. W. (2005). Working memory span tasks:
A methodological review and user s guide. Psychonomic Bulletin &
Review, 12, 769786.
Conway, A. R. A., Kane, M. J., & Engle, R. W. (2003). Working memory
capacity and its relation to general intelligence. Trends in Cognitive
Sciences, 7, 547552.
Craik, F. I. M., & Lockhart, R. S. (1972). Levels of processing: A frame-
work for memory research. Journal of Verbal Learning and Verbal
Behavior, 11, 671684.
Craik, F. I. M., & Tulving, E. (1975). Depth of processing and the reten-
tion of words in episodic memory. JournalofExperimental
Psychology: General, 104, 268294.
Cumming, G., & Finch, S.(2005). Inference by eye: Confidence intervals
and how to read pictures of data. American Psychologist, 60,
170180.
Cumming, N., Page, M., & Norris, D. (2003). Testing a positional model
of the Hebb effect. Memory, 11, 4363.
Cunningham, T. F., Healy, A. F., & Williams, D. M. (1984). Effects of
repetition on short-term retention of order information. Journal of
Experimental Psychology: Learning, Memory, and Cognition, 10,
575597.
Daneman, M., & Carpenter, P. A. (1980). Individual differences in work-
ing memory and reading. Journal of Verbal Learning and Verbal
Behavior, 19, 450466.
Engle, R. W., Tuholski, S. W., Laughlin, J. E., & Conway, A. R. A.
(1999). Working memory, short term memory and general fluid
intelligence: A latent variable approach. Journal of Experimental
Psychology: General, 128, 309331.
Gathercole, S. E., Pickering, S. J., Ambridge, B., & Wearing, H. (2004).
The structure of working memory from 4 to 15 years of age.
Developmental Psychology, 40, 177190.
Hebb, D. O. (1961). Distinctive features of learning in the higher animal.
In J. F. Delafresnaye (Ed.), Brain mechanisms and learning (pp. 37
46). Oxford: Blackwell.
Hitch, G. J., Fastame, M. C., & Flude, B. (2005). How is the serial order
of a verbal sequence coded? Some comparisons between models.
Memory, 13, 247258.
Hyde, T. S., & Jenkins, J. J. (1969). Differential effects of incidental tasks
on the organization of recall of a list of highly associated words.
Journal of Experimental Psychology, 82, 472481.
Kane, M. J., Hambrick, D.Z., Tuholski, S. W., Wilhelm, O., Payne, T. W.,
& Engle, R. W. (2004). The generality of working-memory capac-
ity: A latent-variable approach to verbal and visuo-spatial memory
span and reasoning. Journal of Experimental Psychology: General,
133, 189217.
Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the
American Statistical Association, 90, 773795.
Lewandowsky, S., Duncan, M., & Brown, G. D. A. (2004). Time does not
cause forgetting in short-term serial recall. Psychonomic Bulletin &
Review, 11, 771790.
Loaiza, V. M., & McCabe, D. P. (2012). Temporal-contextual processing
in working memory: Evidence from delayed cued recall and delayed
free recall tests. Memory & Cognition, 40, 191203.
Loaiza, V. M., McCabe, D. P., Youngblood, J. L., Rose, N. S., &
Myerson, J. (2011). The influence of levels of processing on recall
from working memory and delayed recall tasks. Journal of
Experimental Psychology: Learning, Memory, and Cognition, 37,
12581263.
McCabe, D. P. (2008). The role of covert retrieval in working memory
span tasks: Evidence from delayed recall tests. Journal of Memory
and Language, 58, 480494.
McCabe, D. P. (2010). The influence of complex working memory span
task administration methods on prediction of higher level cognition
and metacognitive control of response times. Memory & Cognition,
38, 868882.
Mem Cogn
McKelvie, S. J. (1987). Learning and awareness in the Hebb digits task.
JournalofGeneralPsychology,114,7588.
Morey, R. D., & Rouder, J. N. (2012). BayesFactor (Version 0.9.0).
Retrieved from http://cran.at.r-project.org/web/packages/
BayesFactor/index.html
Mosse, E. K., & Jarrold, C. (2008). Hebb learning, verbal short-term
memory, and the acquisition of phonological forms in children.
Quarterly Journal of Experimental Psychology, 61, 505514.
Oberauer, K., Farrell, S., Jarrold, C., Pasiecznik, K., & Greaves, M.
(2012a). Interference between maintenance and processing in work-
ing memory: The effect of item-distractor similarity in complex
span. Journal of Experimental Psychology: Learning, Memory,
and Cognition, 38, 665685. doi:10.1037/a0026337
Oberauer, K., Lewandowsky, S., Farrell, S., Jarrold, C., & Greaves, M.
(2012b). Modeling working memory: An interference model of
complex span. Psychonomic Bulletin & Review, 19, 779819.
Oberauer, K., & Meyer, N. (2009). The contributions of encoding, reten-
tion, and recall to the Hebb effect. Memory, 17,774781.
Oberauer, K., Süß, H.-M., Schulze, R., Wilhelm, O., & Wittmann, W. W.
(2000). Working memory capacity - facets of a cognitive ability
construct. Personality and Individual Differences, 29, 10171045.
Page, M. P. A., Cumming, N., Norris, D., McNeil, A. M., & Hitch, G. J.
(2013). Repetition-spacing and item-overlap effects in the Hebb
repetition task. Journal of Memory and Language, 69, 506526.
doi:10.1016/j.jml.2013.07.001
Page, M. P. A., & Norris, D. (2009). A model linking immediate serial
recall, the Hebb repetition effect and the learning of phonological
word forms. Philosophical Transactions of the Royal Society of
London, 364, 37373753.
R Development Core Team (2012). R: A language and environment for
statistical computing (Version 2.15.2). Vienna, Austria: R
Foundation for Statistical Computing. Retrieved from URL: http://
www.R-project.org
Raye, C. L., Johnson, M. K., Mitchell, K. J., Greene, E. J., & Johnson, M. R.
(2007). Refreshing: A minimal executive function. Cortex, 43, 135145.
Rose, N. S., & Craik, F. I. M. (2012). A processing approach to the
working memory/long-term memory distinction: Evidence from
the levels-of-processing span task. Journal of Experimental
Psychology: Learning, Memory, and Cognition, 38, 10191029.
Rouder, J. N., Morey, R. D., Speckman, P. L., & Province, J. M. (2012).
Default Bayes factors for ANOVA designs. Journal of
Mathematical Psychology, 56, 356374.
Sechler, E. S., & Watkins, M. J. (1991). Learning to reproduce a list and
memory for the learned. American Journal of Psychology, 104,
357394.
Szmalec, A., Duyck, W., Vandierendonck, A., Mata, A. B., & Page, M. P.
A. (2009). The Hebb repetition effect as a laboratory analogue of
novel word learning. Quarterly Journal of Experimental
Psychology, 62, 435443.
Szmalec, A., Loncke, A., Page, M. P. A., & Duyck, W. (2011). Order or
disorder? Impaired Hebb learning in dyslexia. Journal of
Experimental Psychology: Learning, Memory, and Cognition, 37,
12701279. doi:10.1037/a0023820
Szmalec, A., Page, M. P. A., & Duyck, W. (2012). The development of
long-term lexical representations through Hebb repetition learning.
Journal of Memory and Language, 67, 342354. doi:10.1016/j.jml.
2012.07.001
Turner, M. L., & Engle, R. W. (1989). Is working memory capacity task
dependent? Journal of Memory and Language, 28, 127154.
Unsworth, N., & Engle, R. E.(2006). Simple and complex memory spans
and their relation to fluid abilities: Evidence from list-length effects.
Journal of Memory and Language, 54, 6880. doi:10.1016/j.jml.
2005.06.003
Unsworth, N., & Engle, R. E. (2007a). The nature of individual differ-
ences in working memory capacity: Active maintenance in primary
memory and controlled search from secondary memory.
Psychological Review, 114, 104132.
Unsworth, N., & Engle, R. E. (2007b). On the division of short-term and
working memory: An examination of simple and complex span and
their relation to higher order abilities. Psychological Bulletin, 133,
10381066.
Wagenmakers, E.-J., & Morey, R. D. (2013). Simple relation between
one-sided and two-sided Bayesian point-null hypothesis tests.
Unpublished Manuscript, University of Amsterdam.
Wilhelm, O., Hildebrandt, A., & Oberauer, K. (2013). What is working
memory capacity, and how can we measure it? Frontiers in
Psychology, 4. Retrieved from doi:10.3389/fpsyg.2013.00433
Mem Cogn
... It is well-known that repeated learning and testing can significantly improve recall performance [10][11][12][13][14][15] . For example, a study showed that, compared to one presentation, five-fold repetition of word pairs increased both the familiarity of every individual word in the pair and the associative strength of the word pairs, leading to higher memory accuracy 13 . ...
... Thus, we predicted lower memory accuracy for gesture-letter pairs as the memory load became higher. It is well-known that repeated learning and testing can significantly improve recall performance [10][11][12][13][14][15] . For example, a study showed that, compared to one presentation, five-fold repetition of word pairs increased both the familiarity of every individual word in the pair and the associative strength of the word pairs, leading to higher memory accuracy 13 . ...
... In line with our prediction, we also showed that memory performance of gesture-letter pairs can be significantly improved by five successive memorizing sessions, the so-called "learning effect" of repeated learning and testing 10 . The learning effect was identified in previous studies on free recall [11][12][13] and paired-associates recognition 14,15 . ...
Article
Full-text available
In this study, we employed a recall test to investigate how memory load affects the learning curve of gesture-letter pairs for younger and older users. The gesture-letter pairs were carefully designed to mimic real-world gesture-function/command associations on a touchscreen mobile phone. Both younger and older user groups showed lower recall accuracy as the memory load of gesture-letter pairs increased, and recall performance improved with repeated memory training. More specifically, younger users improved rapidly over repeated memory sessions under all memory loads, whereas older users benefited much less from repeated memory sessions except the lowest memory load of 6 gesture-letter pairs. These results reveal that the memory load differentially modulated younger and older users’ learning curves of gesture-letter pairs. Thus, our work suggests an upper limit when adding new gesture-function associations on mobile phones and special attention should be devoted to old users.
... Complex span tasks refer to a variant of serial recall in which, unlike simple span tasks, stimuli that do not need to be remembered (i.e., distractors) are interspersed between each memory item (Daneman & Carpenter, 1980). Given the previous findings, in complex span tasks one can expect the absence of the Hebb repetition effect (Oberauer et al., 2015) for the following reason: Distractors, like memory items, are encoded into working memory (Oberauer et al., 2012a, b;Oberauer & Lewandowsky, 2016). Therefore, when Hebb lists are repeated in a complex span paradigm, while the distractors in between items are always novel, the relation between neighboring list items are interrupted by the intervening distractors, so it is difficult to learn associations between neighboring items. ...
... However, Oberauer et al. (2015) showed evidence of the Hebb repetition effect in a complex span task. They initially suggested that one possibility was that the participants can learn the sequence during the recall phase. ...
... Therefore, either at encoding or at recall, both relations deemed necessary for Hebb repetition learning (i.e., relations of items to positions, and relations between neighboring items) remain constant, giving participants a chance to create integrated representations, perhaps contributing to the Hebb repetition effect. To conclude, the experiments of Oberauer et al. (2015) gave the first insights on the topic, but these were not enough to confirm that the Hebb repetition effect can occur when the items are never presented and recalled in immediate succession. ...
Article
Full-text available
The Hebb repetition effect on serial-recall task refers to the improvement in the accuracy of recall of a repeated list (e.g., repeated in every 3 trials) over random non-repeated lists. Previous research has shown that both temporal position and neighboring items need to be the same on each repetition list for the Hebb repetition effect to occur, suggesting chunking as one of its underlying mechanisms. Accordingly, one can expect absence of the Hebb repetition effect in a complex span task, given that the sequence is interrupted by distractors. Nevertheless, one study by Oberauer, Jones, and Lewandowsky (2015, Memory & Cognition , 43 [6], 852–865) showed evidence of the Hebb repetition effect in a complex span task. Throughout four experiments, we confirmed the Hebb repetition effect in complex span tasks, even when we included distractors in both encoding and recall phases to avoid any resemblance to a simple span task and minimized the possibility of chunking. Results showed that the Hebb repetition effect was not affected by the distractors during encoding and recall. A transfer cycle analysis showed that the long-term knowledge acquired in the complex span task can be transferred to a simple span task. These findings provide the first insights on the mechanism behind the Hebb repetition effect in complex span tasks; it is at least partially based on the same mechanism that improves recall performance by repetition in simple span tasks.
... Some evidence suggests HRL is resilient to distraction at both encoding and retrieval. Oberauer et al. (2015) required both processing and storage within Hebb tasks, effectively making the immediate serial recall task a 'complex' memory span task. Participants were presented with Hebb and filler sequences in the usual way, but had to make judgements (about the sizes of pictured objects) after the presentation of each item in the sequence (or make these judgements at recall -interspersed between the recall of each to-be-remembered item in the sequence). ...
... Participants were presented with Hebb and filler sequences in the usual way, but had to make judgements (about the sizes of pictured objects) after the presentation of each item in the sequence (or make these judgements at recall -interspersed between the recall of each to-be-remembered item in the sequence). Surprisingly, this degree of distraction did not minimise HRL effects in adults (Oberauer et al., 2015). Such findings suggest that the HRL effect could still promote long-term memory mechanisms despite interruptions, and is robust to immediate distraction. ...
Article
Full-text available
Background Hebb repetition learning is a form of long-term serial order learning that can occur when sequences of items in an immediate serial recall task are repeated. Repetition improves performance because of the gradual integration of serial order information from short-term memory into a more stable long-term memory trace. Aims The current study assessed whether adolescents with non-specific intellectual disabilities showed Hebb repetition effects, and if their magnitude was equivalent to those of children with typical development, matched for mental age. Methods Two immediate serial recall Hebb repetition learning tasks using verbal and visuospatial materials were presented to 47 adolescents with intellectual disabilities (11–15 years) and 47 individually mental age-matched children with typical development (4–10 years). Results Both groups showed Hebb repetition learning effects of similar magnitude, albeit with some reservations. Evidence for Hebb repetition learning was found for both verbal and visuospatial materials; for our measure of Hebb learning the effects were larger for verbal than visuospatial materials. Conclusions The findings suggested that adolescents with intellectual disabilities may show implicit long-term serial-order learning broadly commensurate with mental age level. The benefits of using repetition in educational contexts for adolescents with intellectual disabilities are considered.
... Each condition consisted of 20 (Experiments 1 and 5) or 24 (Experiments 2, 3, and 4) mini-blocks of four trials: The first three trials presented unique arrays, and the fourth trial, the repeated array. This repetition schedule (i.e., presentation of the repeated array after a constant number of unique arrays) is frequently used in Hebb studies (Couture & Tremblay, 2006;Cumming et al., 2003;Gagnon et al., 2005;Oberauer et al., 2015;Oberauer & Meyer, 2009). ...
... One way to disrupt refreshing is by imposing an attentionally-demanding distractor task in between encoding of the memoranda, as commonly done in complex span tasks. Oberauer et al. (2015) compared learning of Hebb lists in simple span (aka the traditional Hebb task) and complex span trials. ...
Preprint
Full-text available
Repeated exposure is assumed to promote long-term learning. This is demonstrated by the so-called “Hebb-effect”: when short lists of verbal or spatial materials are presented sequentially for an immediate serial recall test, recall improves with list repetition. This repetition benefit, however, is not ubiquitous. Previous studies found little or no performance improvement for repetitions of visuospatial arrays (e.g., arrays of colored squares). Across eight experiments with college students and Prolific samples, we investigated which factors promote visuospatial learning by testing all combinations of variables distinguishing between visual-array tasks (brief + simultaneous presentation + a single recognition test) and tasks showing the Hebb effect (slow + sequential presentation + recall test probing all items). Participants profited from repetitions when all items were tested with a recall procedure, but not if the test consisted of recognition. Hence, the key to promote long-term learning is to recall all of the memorized information over the short-term.
... Serial recall performance gradually improves for the repeated lists (Hebb lists) compared with the randomized lists (i.e., Filler lists). This effect was replicated in several studies that used this paradigm (Cumming et al., 2003;Cunningham et al., 1984;Fendrich et al., 1991;Oberauer et al., 2015;Oberauer & Meyer, 2009;Page & Norris, 2009). Researchers hypothesized that over the course of repetitions, the memory for the sequence of items in the repeated lists becomes more and more robustly established in LTM. ...
Article
When encoding task-relevant information in working memory (WM), we can use prior knowledge to facilitate task performance. For instance, when memorizing a phone number, we can benefit from recognizing some parts as known chunks (e.g., 911) and focus on memorizing the novel parts. Prior knowledge from long-term memory (LTM), however, can also proactively interfere with WM contents. Here, we show that WM selectively recruits information from LTM only when it is helpful, not when it would interfere. We used variants of the Hebb paradigm in which WM is tested through immediate serial recall of lists. Some lists were repeated frequently across trials, so they were acquired in LTM, as reflected in increasing serial-recall performance across repetitions. We compared interference conditions in which that LTM knowledge could interfere with holding another list in WM to a neutral condition in which that knowledge could be neither beneficial nor harmful. In Experiments 1-3, lists in the interference conditions shared their items with the learned lists but not their order. We observed no proactive interference. In Experiments 4 and 5, the interference lists' first three items overlapped exactly with the learned lists, and only the remaining items had a new order. This made LTM knowledge partially beneficial and partially harmful. Participants could use LTM flexibly to improve performance for the first part of the list without experiencing interference on the second half. LTM-mediated learning of the first part even boosted memory for the unknown second part. We conclude that there is a flexible gate controlling the flow of information from LTM and WM so that LTM knowledge is recruited only when helpful. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
... There are also many approaches to studying incidental learning, many of which involve relatively complex regularities, such as the learning of predictable sequences of trials (Nissen & Bullemer, 1987;Turk-Browne, Jungé, & Scholl, 2005), of artificial grammars (Reber, 1967; for a review, see Pothos, 2007), or lists of repeated digits (Oberauer, Jones, & Lewandowsky, 2015;Mckelvie, 1987;Vachon, Marois, Lévesque-Dion, Legendre, & Saint-Aubin, 2018). A particularly interesting and simple incidental learning procedure that will be at the center of this review is the colour-word contingency learning task (Schmidt, Crump, Cheesman, & Besner, 2007). ...
Article
Full-text available
In this article, I review research on incidental learning of simple stimulus-response regularities. The article summarizes work with the colour-word contingency learning paradigm and related simple learning procedures. In the colour-word contingency learning paradigm participants are presented with a coloured neutral word on each trial and are asked to ignore the word and respond to the print colour (e.g., similar to a Stroop procedure). Distracting words are typically colour-unrelated neutral stimuli. However, each distracting word is presented most often in one target colour (e.g., "move" most often in blue, "sent" most often in green, etc.). Learning of these contingencies is indicated by faster and more accurate responses to high contingency trials (in which the word is presented with its frequent colour) relative to low contingency trials. This procedure has proven useful for investigations in incidental learning. The present manuscript summarizes the existing work with this (and related) learning procedures and highlights emerging directions.
... Serial-recall performance gradually improves for the repeated lists (Hebb lists) compared to the new lists (Filler lists). This effect was replicated in several studies that used this paradigm (Cumming et al., 2003;Cunningham et al., 1984;Fendrich et al., 1991;Oberauer et al., 2015;Oberauer & Meyer, 2009;Page & Norris, 2009). Researchers hypothesized that over the course of repetitions, the memory for the sequence of items in the repeated lists becomes more and more robustly established in LTM. ...
Preprint
Full-text available
Increasing time to process information in working memory (WM) improves performance. Free time given after an item is often assumed to enable maintenance processes to counteract forgetting of this item, suggesting that time has a retroactive benefit. Two other hypotheses – short-term consolidation, and temporal distinctiveness – entail a local effect of time on immediately preceding and following items. Here, we show instead a novel, global and proactive, benefit of time in WM. In three serial-recall experiments (21,25,26 young-adults), we varied the position and duration of the free time within a seven-item list of consonants. Experiment 1 showed that the effect is global and not local. Experiments 2a and 2b showed increased inter-item time only benefited the subsequent items, implying a proactive benefit. This finding rules out maintenance processes, short-term consolidation, and temporal distinctiveness as explanations of the free-time benefit but is consistent with the proposal of a gradually recovering encoding resource.
... It may even have a temporal advantage, because it does not require the prior experiential knowledge built up over a long time-span (Dobbins et al., 2004). In this context, educational research refers to the so-called Hebb-Effect which states that the more immediate and frequent the repetition, the greater the long-term effect (Oberauer et al., 2015). In this process, content is gradually transferred from short-term to long-term memory (Szmalec et al., 2012). ...
Article
In times of pandemic‐related university shutdowns and a shift of teaching to homeschooling, alternative educational methods are more in demand than ever. The class peer‐review (CPR) method offers the opportunity for students to evaluate each other and share knowledge during their private learning time. This study reports on a CPR which was conducted out‐of‐class with 39 students in Business Management. Participants were asked to write an essay about a case on Marketing and then conduct two reviews. Subsequently, the difference between reviews in the same topic of the own manuscript or in two different exam‐relevant subjects was investigated and the effect on exam performance and participants’ attitude was measured. The results showed that the final grades after CPR with thematically similar reviews were on average better than those of the comparison group. This was due to the fact that the reviews were more critical and technically more profound and generated a greater amount of knowledge among the authors. Carrying out several reviews in other subject areas led to a higher self‐assessment of knowledge uptake, but was too superficial for the exam. If the learning objective is more of a narrowly defined and in‐depth topic area, a CPR out‐of‐class with two to three reviews in the manuscript's own topic is recommended, in order to generate the highest possible level of knowledge for others as well as for oneself.
Article
Full-text available
Recent research indicates that visual long-term memory (vLTM) representations directly interface with perception and guide attention. This may be accomplished through a state known as activated LTM (though not in all cases, see: Plater et al., 2020), however, little is known about the nature of activated LTM. Is it possible to enhance the attentional effects of these activated representations? And furthermore, is activated LTM discrete (i.e., a representation is either active or not active, but only active representations interact with perception) or continuous (i.e., there are different levels within the active state that all interact with perception)? To answer these questions, in the present study we measured intrusion effects during a modified Sternberg task. Participants saw two lists of three complex visual objects, were cued that only one list was relevant for the current trial (the other list was, thus, irrelevant), and then their memory for the cued list was probed. Critically, half of the trials contained repeat objects (shown 10 times each), and half of the trials contained non-repeat objects (shown only once each). Results indicated that repetition enhanced activated LTM, as the intrusion effect (i.e., longer reaction times to irrelevant list objects than novel objects) was larger for repeat trials compared to non-repeat trials. These initial findings provide preliminary support that LTM activation is continuous, as the intrusion effect was not the same size for repeat and non-repeat trials. We conclude that researchers should repeat stimuli to increase the size of their effects and enhance how LTM representations interact with perception.
Article
Full-text available
The present report investigated whether nonmusicians can incidentally learn musical skills needed for sight-reading. On each trial, participants identified a note name written inside of a note on the musical staff. In Experiment 1, each note was presented frequently with the congruent note name (e.g., "do" with the note for "do") and rarely with the incongruent names (e.g., "do" with the note for "fa"). With or without deliberate learning instructions, a robust contingency learning effect was observed: faster responses for congruent trials compared to incongruent trials. Participants also explicitly identified the meaning of the note positions more accurately than chance. Experiment 2 ruled out the potential influence of preexisting knowledge on the contingency learning effect by presenting notes most often with an incongruent note name. Robust learning was again observed, suggesting that participants acquired sufficient knowledge of musical notation to produce automatic influences on behavior (e.g., akin to the interference effect previously found in skilled musicians). A congruency effect was additionally observed in Experiment 2, however. Experiment 3 further explored to what extent this congruency effect might be due to prior music knowledge and/or spatial stimulus-response compatibility between note and response locations (analogous to the SMARC effect). Overall, our results open up new avenues for investigating the incidental learning of complex material, musical or otherwise, and for reinforcing learning even further.
Article
Full-text available
A latent variable study examined whether different classes of working-memory tasks measure the same general construct of working-memory capacity (WMC). Data from 270 subjects were used to examine the relationship between Binding, Updating, Recall-N-back, and Complex Span tasks, and the relations of WMC with secondary memory measures, indicators of cognitive control from two response-conflict paradigms (Simon task and Eriksen flanker task), and fluid intelligence. Confirmatory factor analyses support the concept of a general WMC factor. Results from structural-equation modeling show negligible relations of WMC with response-conflict resolution, and very strong relations of WMC with secondary memory and fluid intelligence. The findings support the hypothesis that individual differences in WMC reflect the ability to build, maintain and update arbitrary bindings.
Article
A study was conducted in which 133 participants performed 11 memory tasks (some thought to reflect working memory and some thought to reflect short-term memory), 2 tests of general fluid intelligence, and the Verbal and Quantitative Scholastic Aptitude Tests. Structural equation modeling suggested that short-term and working memories reflect separate but highly related constructs and that many of the tasks used in the literature as working memory tasks reflect a common construct. Working memory shows a strong connection to fluid intelligence, but short-term memory does not. A theory of working memory capacity and general fluid intelligence is proposed: The authors argue that working memory capacity and fluid intelligence reflect the ability to keep a representation active, particularly in the face of interference and distraction. The authors also discuss the relationship of this capability to controlled attention, and the functions of the prefrontal cortex.
Recent researchers have attempted to correlate measures of working memory (WM) with measures of higher level cognitive skills and abilities focusing on the functions of this limited capacity system, i.e., processing and storage. Relationships between three span measures of the functional model of WM capacity and two measures of reading comprehension were investigated. The magnitude of the correlations found between reading comprehension and the two spans embedded in reading processing tasks was similar to that of the correlation found between a third span measure embedded in a quantitative task with reading comprehension. These results indicated that these span measures of WM capacity were independent of the nature of the concurrent processing task.
Article
In a 1935 paper and in his book Theory of Probability, Jeffreys developed a methodology for quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now called the Bayes factor, which is the posterior odds of the null hypothesis when the prior probability on the null is one-half. Although there has been much discussion of Bayesian hypothesis testing in the context of criticism of P-values, less attention has been given to the Bayes factor as a practical tool of applied statistics. In this article we review and discuss the uses of Bayes factors in the context of five scientific applications in genetics, sports, ecology, sociology, and psychology. We emphasize the following points:
Article
One of the main challenges facing potential users of Bayes factors as an inferential technique is the difficulty of computing them. We highlight a useful relationship that allows certain order-restricted and sign-restricted Bayes factors, such as one-sided Bayes factor tests, to be computed with ease.
Article
The complex span measure of working memory is a word/digit span measured while performing a secondary task. Two experiments investigated whether correlations between the complex span and reading comprehension depend on the nature of the secondary task and individual skill in that task. The secondary task did not have to be reading related for the span to predict reading comprehension. An arithmetic-related secondary task led to correlations with reading comprehension similar to those found when the secondary task was reading. The relationship remained significant when quantitative skills were factored out of the complex span/comprehension correlations. Simple digit and word spans (measured without a background task) did not correlate with reading comprehension and SAT scores. The second experiment showed that the complex span/comprehension correlations were a function of the difficulty of the background task. When the difficulty level of the reading-related or arithmetic-related background tasks was moderate, the span/comprehension correlations were higher in magnitude than when the background tasks were very simple, or, were very difficult.
Article
In four experiments using a variation of the Hebb repetition task, we investigated the effects on learning rate, of repetition spacing and of the overlap in experimental items between repeating and nonrepeating lists. In the first two experiments it was shown that when repeating and nonrepeating lists were all permutations of the same items, learning was slower than when they shared no items. Under no-item-overlap conditions in a third experiment, the learning rate for a repeating sequence was shown to be substantial and essentially equivalent for repetitions spaced at every 6th, 9th and 12th trial. Concurrent learning of several different sequences was also demonstrated. When participants were retested after several months on lists that they had previously learned, there was evidence that the learned representations were long-term and order-specific. The results are discussed in relation to two recent models of the Hebb effect.
Article
This study clarifies the involvement of short- and long-term memory in novel word-form learning, using the Hebb repetition paradigm. In Experiment 1, participants recalled sequences of visually presented syllables (e.g., la-va-bu-sa-fa-ra-re-si-di), with one particular (Hebb) sequence repeated on every third trial. Crucially, these Hebb sequences contained three orthographic nonword neighbors of existing Dutch base-words (e.g., lavabu – lavabo [kitchen sink]). Twenty-four hours later, the same participants performed two auditory lexicalization tests involving the actual Dutch base-words (e.g., lavabo, safari, residu). Both tests yielded slower reaction times for these Dutch base-words compared with matched control words, which reflects lexical competition between the base-words and the Hebb sequences, therefore demonstrating lexical engagement of the Hebb sequences. In Experiment 2, we subsequently used the Hebb paradigm as an analogue of word-form learning, in order to investigate whether the creation of novel lexical memories requires sleep. Whereas earlier findings indicate that overnight sleep plays a crucial role in lexical consolidation, the current results show that Hebb learning of phonological sequences creates novel word-forms representations in the mental lexicon by the mere passage of time, with sleep playing no necessary role.
Article
Bayes factors have been advocated as superior to pp-values for assessing statistical evidence in data. Despite the advantages of Bayes factors and the drawbacks of pp-values, inference by pp-values is still nearly ubiquitous. One impediment to the adoption of Bayes factors is a lack of practical development, particularly a lack of ready-to-use formulas and algorithms. In this paper, we discuss and expand a set of default Bayes factor tests for ANOVA designs. These tests are based on multivariate generalizations of Cauchy priors on standardized effects, and have the desirable properties of being invariant with respect to linear transformations of measurement units. Moreover, these Bayes factors are computationally convenient, and straightforward sampling algorithms are provided. We cover models with fixed, random, and mixed effects, including random interactions, and do so for within-subject, between-subject, and mixed designs. We extend the discussion to regression models with continuous covariates. We also discuss how these Bayes factors may be applied in nonlinear settings, and show how they are useful in differentiating between the power law and the exponential law of skill acquisition. In sum, the current development makes the computation of Bayes factors straightforward for the vast majority of designs in experimental psychology.