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Abstract (English):
Hunting down the source: How amnesic
patients avoid fluency-based memory errors.
Geurten, Marie. Cyclotron Research Center, University of
Liege, Liege, Belgium,
[email protected]
Bastin, Christine. Cyclotron Research Center, Psychology and
Neuroscience of
Cognition Unit, University of Liege, Liege, Belgium
Salmon, Eric. Cyclotron Research Center, University of Liege,
Liege, Belgium

Willems, Sylvie. Psychology and Neuroscience of Cognition
Unit, University of
Liege, Liege, Belgium
Geurten, Marie, Cyclotron Research Center, University of
Liege, B33 Trifacultaire—
Quartier Agora, Place des Orateurs 1, 4000, Liege, Belgium,
[email protected]
Neuropsychology, Jun 20, 2019.
Neuropsychology
US : American Psychological Association
US : Educational Publishing Foundation
US : Philadelphia Clinical Neuropsychology Group
United Kingdom : Taylor & Francis
0894-4105 (Print)
1931-1559 (Electronic)
English
amnesia, fluency, metacognition, recognition memory
Objective: The primary aim of this study was to test whether
differences in the
ability of amnesic and healthy participants to detect alternative
sources of fluency
can account for differences observed in the use of fluency as a
cue for memory.
Method: Patients with severe memory deficits and matched
controls were
presented with 3 forced-choice recognition tests. In each test, an
external source of
fluency was provided by manipulating the perceptual quality of

the studied items
during the test phase. The detectability of the perceptual
manipulation varied in
each test (i.e., a 10%, 20%, or 30% contrast reductions were
given). Results: The
results indicated that all participants were able to rely on
fluency when making
recognition decisions as long as the perceptual manipulation
remained unnoticed.
It is interesting that our data also revealed that the level of
contrast reduction at
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which the alternative source is detected differs between healthy
controls and
amnesic patients. Specifically, patients with amnesia appeared
to disqualify fluency
as a cue for memory even when the contrast reduction was
moderate, whereas
healthy participants disqualified fluency only when the contrast
reduction was
clearly visible. Conclusion: Overall, our results seem to suggest
that the ability to
use fluency is probably not impaired in amnesia but undergoes
metacognitive
changes resulting in the implementation of explicit or implicit
strategies aiming at
tracking alternative sources in order to reduce memory errors.
(PsycINFO
Database Record (c) 2019 APA, all rights reserved)
General Scientific Summary: Despite amnesic patients’ severe
deficits, some of
their memory processes are preserved. Unfortunately, they do
not appear to take
full advantage of these spared memory abilities. This study is an
attempt to

determine whether adaptive metacognitive changes could
account for the apparent
inability of amnesic patients to rely on their preserved memory
skills. (PsycINFO
Database Record (c) 2019 APA, all rights reserved)
Journal Article
*Amnesia; *False Memory; *Memory; *Metacognition; *Verbal
Fluency; Errors; Implicit Memory; Memory Disorders; Patients;
Test Items
Neurological Disorders & Brain Damage (3297)
Human
Male
Female
Belgium
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Contrast Reduction Test
Wechsler Abbreviated Scale of Intelligence--Second Edition
DOI: 10.1037/t15171-
000
Wechsler Memory Scale III
Sponsor: Fund Maria-Elisa and Guillaume de Beys (FRB)
Recipients: No recipient indicated
Sponsor: National Fund for Scientific Research
Recipients: No recipient indicated

Empirical Study; Quantitative Study
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Journal; Peer Reviewed Journal
Online First Posting
Accepted: Apr 20, 2019; Revised: Apr 2, 2019; First Submitted:
Jan 7, 2019
20190620
American Psychological Association. 2019
http://0-dx.doi.org.wizard.umd.umich.edu/10.1037/neu0000566
neu-neu0000566
2019-33413-001
Hunting Down the Source: How Amnesic
Patients Avoid Fluency-Based Memory Errors

By: Marie Geurten
Cyclotron Research Center and Psychology and Neuroscience of
Cognition Unit, University of Liège;
Christine Bastin
Cyclotron Research Center, Psychology and Neuroscience of
Cognition Unit, and National Fund for
Scientific Research, University of Liège
Eric Salmon
Cyclotron Research Center and Psychology and Neuroscience of
Cognition Unit, University of Liège
Sylvie Willems
Psychology and Neuroscience of Cognition Unit, University of
Liège
Acknowledgement: This research was supported by a grant from
the Fund Maria-Elisa and
Guillaume de Beys (FRB) and by the National Fund for
Scientific Research. The authors have no
conflict of interest to declare.
Over the past 50 years, research focusing on memory
impairments associated with amnesia has
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generated a large array of findings, which in turn has led to
many theoretical advances.
Specifically, much attention has been directed toward the
identification of increasingly refined and
sophisticated dissociations. Thereby, researchers have learned
that amnesic patients usually show
spared short-term memory versus impaired long-term memory
(Baddeley & Warrington, 1970)
and demonstrate intact nonconscious long-term memory versus
altered conscious long-term
memory (Squire & Zola, 1996). Within the conscious long-term
memory deficits, patients with
amnesia have also been found to exhibit pronounced deficits in
recollection, defined as the ability
to mentally relive past events in vivid details, while showing no
or less impairment in familiarity,
defined as a vague feeling of “oldness” associated with past
experiences (e.g., Addante,
Ranganath, Olichney, & Yonelinas, 2012; Bastin et al., 2004;
Yonelinas, Kroll, Dobbins, Lazzara, &
Knight, 1998). Indeed, in a comprehensive review of studies
focusing on recollection and familiarity
in amnesia, Yonelinas et al. (1998) concluded that both
processes are compromised in amnesic
patients but that the impairment in familiarity is typically less
severe than that in recollection. Since
then, the question of whether and when familiarity is impaired
in amnesia has been hotly debated
(e.g., Keane, Orlando, & Verfaellie, 2006; Ozubko & Yonelinas,

2014; Squire, 2004).
According to many authors, processing fluency, defined as the
speed and ease with which a
stimulus is processed, is a key factor to understand familiarity-
based memory decisions (e.g.,
Jacoby & Dallas, 1981; Whittlesea, 1993; Willems, Germain,
Salmon, & Van der Linden, 2009).
Specifically, because people intuitively know that an earlier
encounter with a stimulus generally
enhances processing fluency, it is usually assumed that a feeling
a familiarity can result from
attributional processes whereby people ascribe fluency to the
past. This view, however, has been
challenged by studies showing that some patients with amnesia
are not able—or at least less able
—to use fluency as a cue for recognition memory, despite
successfully completing a priming task
conducted on the same set of stimuli (e.g., Levy, Stark, &
Squire, 2004). These data suggest that
processing fluency can occur without giving rise to better
explicit memory judgments, leading
several authors to conclude that fluency has no or only small
influences on amnesic patients’
memory decisions (Conroy, Hopkins, & Squire, 2005; Squire &
Dede, 2015).
In contrast with this radical vision, research documenting
metacognition as a key factor to better
understand the circumstances under which processing fluency
can generate a subjective
experience of familiarity has revealed that several steps have to
be completed for people to make
familiarity-based memory decisions on the basis of fluency: (a)
Participants have to understand at
some general level that fluency is a cue that can be used to

inform memory judgments, (b) they
have to experience a feeling of fluency when processing a
stimulus, and (c) they have to attribute
this feeling of fluency to their memory (Jacoby, Kelley, &
Dywan, 1989). In other words, fluency
experiencers have to rely on metacognitive skills to decide
whether fluency can be used as a
source of evidence when making a memory decision (Whittlesea
& Williams, 1998). This
inferential process may not necessarily come in the form of a
conscious strategy. Rather, people
may simply subconsciously note the occurrence of a feeling of
fluency and with modest amounts of
cognitive effort decide whether it is relevant to use this feeling
to inform memory judgments. As
evidence for this heuristic processing, studies have revealed that
although people can sometimes
verbalize that fluency is a cue to memory (Schwarz, 1998),
these verbal reports did not appear to
be related with the actual use of the fluency rule when making a
decision (Geurten, Willems, &
Meulemans, 2015). At any rate, according to this theory,
familiarity results from the interaction
between metacognition and fluency experiences that both have
to be preserved for familiarity-
based decisions to occur.
By manipulating fluency at the time of test through masked
visual priming, numerous studies have
shown that the ability to experience fluency is spared in
amnesia (Conroy et al., 2005; Squire,
2004; Verfaellie & Keane, 2002). What remains a subject of
debate is the extent to which

attributional (metacognitive) processes are also preserved in
amnesic patients. Indeed, although it
is generally assumed that a decrease in the ability to engage in
attributional processes accounts
for the impairment in familiarity observed in amnesia (Keane et
al., 2006; Verfaellie, Giovanello, &
Keane, 2001), two recent studies have shown that it is possibly
not so much an impairment than a
change in these processes that explains amnesic patients’
pattern of results in fluency-driven
recognition tests (Geurten & Willems, 2017; Ozubko &
Yonelinas, 2014).
More specifically, Geurten and Willems (2017; Experiment 1)
examined the influence of the
introduction of an alternative source of fluency on patients’
recognition decisions by manipulating
the perceptual quality of stimuli during a forced-choice
recognition test. Their results revealed that
healthy participants relied on the absolute level of fluency when
making recognition decisions,
whereas amnesic patients appeared to disqualify fluency as a
cue to memory when an external
source of fluency was detected. The authors suggested that
patients’ underuse of fluency could
result from a learned reinterpretation of fluency as a poor cue
for memory rather than from a real
inability to rely on it. Because of the high number of situations
where fluency leads to memory
errors in patients’ daily lives, the ecological validity of the
correlation between fluency and past
occurrence gradually decrease. Consequently, to reduce fluency-
based memory errors,
participants progressively learn to implement—possibly
unconscious—strategies to track biasing
fluency sources. Behaviorally, this leads them to rely on fluency

only when they can attribute it to
preexposure with a high level of confidence.
In another experiment, Geurten and Willems (2017; Experiment
2) tested the first part of their
hypothesis, showing that healthy participants repeatedly
exposed to evidence that perceptual-
quality-driven fluency led to memory errors started to
disqualify fluency as a cue for memory,
mimicking the pattern of responses demonstrated by amnesic
patients. To date, however, the
second part of their hypothesis—according to which patients
with amnesia should be able to track
alternative sources of fluency more effectively than do healthy
participants—has still to be
investigated.
In this context, the primary aim of the present study was to test
whether differences in the ability of
amnesic and healthy participants to detect alternative sources of
fluency can account for
differences observed in the use of fluency. To this end, patients
with severe memory deficits and
matched controls were recruited. The same procedure as the one
used in the study by Geurten
and Willems (2017; Experiment 1) was employed except that
participants were presented with
three forced-choice recognition tests instead of one. In each
test, in addition to exposure-related
fluency, an external source of fluency was provided by
manipulating the perceptual quality of either
the studied or the unstudied items during the test phase. To do
so, we prepared three types of

target−distractor pairs by combining stimuli with high and low
visual quality. It has been shown that
pictures with a high figure-ground contrast are perceived as
clearer and easier to process than are
low-contrast ones (Checkosky & Whitlock, 1973; Whittlesea,
Jacoby, & Girard, 1990). Critically
here, the detectability of the contrast reduction varied in each of
the three recognition tests (i.e.,
the pictures included in the three tests were, respectively, given
a 10%, 20%, or 30% contrast
reduction). It is important to note that the representation of the
stimuli was not manipulated in our
study. Indeed, the representation of each item—created during
the encoding phase—was the
same in our three experimental conditions. However, we did
manipulate factors that should
influence the results of the attributional processes (for a recent
integrative memory model
presenting the distinctions and the interactions between the
level of representation and the level of
attribution in memory, see Bastin et al., 2019).
In a similar experiment conducted with three different samples
of healthy participants, Willems and
Van der Linden (2006; Experiments 1–3) found that fluency due
to preexposure influenced
recognition responses less when the perceptual manipulation
associated with the target was
obvious compared to when it was only detectable or barely
noticeable. In this context, as in the
studies by Geurten and Willems (2017) and Willems and Van
der Linden (2006), we expected
participants to produce a greater correct recognition rate for
targets with higher picture quality
when the picture quality manipulation remained undetected
(Jacoby & Whitehouse, 1989).

However, when the perceptual manipulation was detected and
judged to be the principal source of
the feeling of fluency, we expected participants to attribute
fluency to this external source
(Whittlesea & Williams, 2000). In the latter case, fluency was
not expected to be used as a guide
for recognition decisions. In addition, if amnestic patients truly
implemented strategies to more
effectively detect alternative sources of fluency, we
hypothesized that they would demonstrate
reluctance to use fluency at a low level of contrast reduction
(i.e., when the external source is
relatively difficult to detect; i.e., 20% contrast reduction),
whereas healthy patients would disqualify
fluency only at a high level of contrast reduction (i.e., when the
external source is easily
detectable; i.e., 30% contrast reduction). Finally, if attributional
processes were truly preserved in
amnesia, all participants should be able to rely on fluency at a
very low level of contrast reduction
(i.e., when the alternative source is barely noticeable; i.e., 10%
contrast reduction).
Method
Participants
Eight French-speaking patients (three female) with amnesia
participated in this study. They were
recruited from various neuropsychological rehabilitation units
in Belgium. Major attentional and
executive function deficits constituted an exclusion criterion.
The time since diagnosis ranged from
1 to 11 years (M = 3.88, SD = 3.48). The mean age was 37.4

(SD = 12.09) years, and the mean
education level was 13.4 (SD = 2.4) years. General intellectual
efficiency was estimated using the
Wechsler Abbreviated Scale of Intelligence (2nd ed.; WASI-II;
Wechsler & Hsiao-pin, 2011). The
Wechsler Memory Scale (3rd ed.; WMS-III; Wechsler, 1997)
was used to appraise patients’
working memory and episodic memory abilities. All patients
showed normal intellectual
functioning (IQ = 98.4, SD = 7.5) and working memory
performance (working memory index =
93.25, SD = 8.5). However, they had severe episodic memory
deficits (general memory index =
58, SD = 5.8; visual delay index = 58.9, SD = 7.6; and auditory
delay index = 64, SD = 7.1).
Patients’ characteristics are presented in Table 1.
Amnesic Patients’ Demographic and Neuropsychological
Characteristics
Moreover, two healthy participants who had no history of
psychiatric or neurological illness were
matched with each amnesic patient for age, gender (n = 16; six
female), and education level. Their
ages ranged from 21 to 55 years (M = 43.2 years, SD = 12.6);
they had a mean IQ of 96 (SD =
10.15) and a mean education level of 13.7 (SD = 23.6) years.
The control and amnesic groups did
not differ significantly in age, education, or IQ (all ps > .50).
Required sample size was determined a priori on the basis of the
medium to large effects that
were observed in similar studies focusing on fluency use in
amnesia (e.g., Geurten & Willems,

2017). Specifically, sample size was thus set to reach a
predicted power of .80 for a within–
between interaction (medium effect size).
Materials
As in the study by Geurten and Willems (2017), unfamiliar
drawings created from abstract
paintings were used as stimuli in order to limit preexperimental
familiarity. Specifically, three series
of 60 drawings were created and randomly assigned to one
recognition test. Each of the 60 figures
of the three tests was randomly assigned to Sets A and B. Half
of the participants were presented
with Set A as targets and Set B as distractors; the other half of
the participants were presented
with the reverse design.
A high-fluency and low-fluency version of each drawing was
created by manipulating the figure-
ground contrast quality of the figures. To do so, we used the
same method as the one employed by
Reber, Winkielman, and Schwarz (1998), who degraded both the
picture foreground and the
picture background. This manipulation has repeatedly been
shown to influence processing fluency
through its impact on various types of judgments inside and
outside the memory domain (e.g.,
Reber, Schwarz, & Winkielman, 2004; Willems & Van der
Linden, 2006). Specifically here, in each
of the three recognition tests, the high-contrast version of the
figures was always the same (i.e.,
white on black). However, the quality of the low-contrast
version of each abstract picture varied as
a function of the test. In the first test, figures were given a 10%
contrast reduction so the external
source of fluency was barely noticeable. In the second test,

figures were given a 20% contrast-
reduction so the fluency manipulation was detectable but
without attracting participants’ attention
(Willems & Van der Linden, 2006). In the third test, figures
were given a 30% contrast reduction so
the external source of fluency was clearly visible. The level of
contrast manipulation used in the
second test was the same as the one used by Geurten and
Willems (2017).
For each of the three test phases, 30 pairs of target−distractor
figures were prepared based on the
60 figures: 10 Target+/Distractor− (i.e., targets had high
alternative fluency), 10 Target=/Distractor=
(i.e., no alternative fluency), and 10 Target−/Distractor+ (i.e.,
distractors had high alternative
fluency) pairs. The “+” symbol indicates that the stimulus had a
high contrast (i.e., high perceptual
fluency), whereas the “−” indicates that the stimulus had a low
contrast (i.e., low perceptual
fluency). Stimuli that were assigned to these three contrast
conditions were randomly
counterbalanced between subjects. Figure 1 displays some
examples of stimuli used in each
contrast-reduction test.
Figure 1. Examples of pairs of abstract pictures used in each
contrast-reduction test (10%, 20%,
and 30% contrast-reduction). The items with the reduced
contrast are on the left.
Procedure
The study was conducted in accordance with the ethics

committee of the participating institutions.
Written consent was obtained before the study began.
Participants were tested individually in a
quiet room. They underwent an approximatively 60-min session
during which they completed three
forced-choice recognition tests. These three tasks were
conducted in the following order: (a) the
test in which the contrast manipulation was barely noticeable
(contrast reduction of 10%), (b) the
test in which the contrast manipulation was detectable (contrast
reduction of 20%), and (c) the test
in which the contrast manipulation was visible (contrast
reduction of 30%). These three tasks were
completed in that specific order so that the inevitable detection
of the contrast manipulation in the
30% contrast reduction test would not induce participants to
look for contrast differences in the
other tests. The three recognition tests were composed of two
experimental phases (i.e., a study
phase and a test phase) and separated by approximatively 10-
min delays filled with cognitive tasks
(i.e., the subtests of the WASI-II).
Study phase
As in the study of Geurten and Willems (2017), participants
were shown and told to study 30 white-
on-black figures, four times each, in random order. Each study
stimulus was presented in the
center of the screen for 50 ms, followed by a 17-ms interval. A
rapid serial visual presentation
(Potter & Levy, 1969) was used to promote fluency-based
recognition and eliminate the influence

of declarative memory (Whittlesea, Masson, & Hughes, 2005).
Test phase
A forced-choice recognition test immediately followed the study
phase. Participants were randomly
presented with the 30 target−distractor pairs (10
Target+/Distractor−, 10 Target−/Distractor+, and
10 Target=/Distractor=). Both figures of each pair were
presented simultaneously to each
participant for 2,000 ms followed by a self-spaced interstimulus
interval. The side of the screen in
which the target stimulus was displayed was randomized over
the trials. Participants were asked to
point to the drawing they had previously seen.
Contrast detection
At the end of the experiment, participants were randomly
presented with 45 target−distractor pairs
of abstract pictures (i.e., 15 pairs retrieved from each
recognition test) and were asked to judge
which of the two pictures was of better perceptual quality. This
procedure was used to examine
whether patients with amnesia and healthy participants truly
differed in their ability to detect
alternative sources of fluency when their attention is clearly
focused on the picture’s perceptual
quality.
Manipulation Check
To ensure that the levels of detection of the three contrast
manipulations (10%, 20%, or 30%) truly
differed from one another but were still sufficient for
participants to develop fluency expectations,
we carried out a pretest. A group of 12 participants (between 21

and 55 years of age) was
randomly presented with the 90 pairs of pictures
(Target+/Distractor–, Target=/Distractor=, and
Target–/Distractor+) and asked to judge which of the two
pictures of the pairs (if any) was of better
perceptual quality. Statistical analyses revealed that high-
contrast stimuli were selected in a
proportion greater than chance when targets were given a 10%
contrast reduction (M = .57), t(29)
= 2.8, p = .015, d = 1.03; a 20% contrast reduction (M = .70),
t(29) = 3.2, p < .001, d = 2.09; and a
30% contrast reduction (M = .95), t(29) = 11.28, p < .001, d =
4.70. They also revealed that the
level of detection was significantly lower with a 10% contrast
reduction than with a 20% contrast
reduction (p = .004), which was significantly lower than with a
30% contrast reduction (p < .001).
These results indicated that, when the participants’ attention
was focused on the detection of
perceptual differences, the level of detection of the contrast
manipulation differed across the three
conditions while remaining noticeable in each of them.
Results
Contrast Detection Rate
A 2 (group: control or amnesic) × 3 (contrast reduction: 10%,
20%, 30%) mixed-variables analysis
of variance (ANOVA) was carried out to determine whether the
ability of participants to detect the
perceptual manipulation differed across groups. The results
revealed that the effect of contrast
reduction was significant, F(2, 34) = 184.27, p < .001, ηp =

.92. Specifically, the high-contrast
stimuli were selected more often after a 30% contrast reduction
(M = .98) than after a 20% contrast
reduction (M = .71) and after a 10% contrast reduction (M =
.61). No other result reached
significance (Fs < 1.01).
Recognition Rate
A 2 (group: control or amnesic) × 3 (contrast reduction: 10%,
20%, 30%) × 3 (target fluency:
Target+/Distractor–, Target=/Distractor=, Target–/Distractor+)
mixed-variables ANOVA was carried
out to examine the influence of the perceptual fluency
manipulation on participants’ correct
recognition decisions. The group was the only between-subjects
variable. The results revealed a
Contrast Reduction × Target Fluency interaction, F(4, 88) =
6.74, p < .001, ηp = .23, and a Group
× Contrast Reduction × Target Fluency triple interaction, F(4,
88) = 4.17, p = .004, ηp = .16. The
triple interaction resulted from the fact that, in the 10% contrast
reduction test (i.e., barely
noticeable manipulation), both healthy participants (M = .57 vs.
.43), F(1, 22) = 5.21, p = .03, ηp =
.30, and patients with amnesia (M = .63 vs. .41), F(1, 22) =
6.83, p = .016, ηp = .42, produced
more correct old responses when the visual manipulation
induced a strong feeling of fluency
(Target+/Distractor–) compared to when it induced a weak
feeling of fluency (Target–/Distractor+).
Conversely, in the 30% contrast reduction test (i.e., obvious
manipulation), both groups gave fewer
correct old responses when the competing source induced a
strong feeling of fluency
(Target+/Distractor–) than when it induced a weak feeling of
fluency (Target–/Distractor+), M = .46

versus .65, F(1, 22) = 3.96, p = .05, ηp = .17, and M = .38
versus .65, F(1, 22) = 4.25, p = .04, ηp
= .63, for controls and amnesic patients, respectively. Finally,
an opposite profile was observed
between our two groups after a 20% contrast reduction (i.e.,
detectable manipulation). Indeed, our
data showed that the controls produced more correct old
responses when the visual manipulation
induced a strong feeling of fluency than when it induced a weak
feeling of fluency (M = .60 vs. .41),
2
2
2
2
2
2 2
2
F(1, 22) = 3.79, p = .05, ηp = .19, whereas patients with
amnesia seemed to give fewer correct
old responses when the competing source induced a strong
feeling of fluency than when it induced
a weak feeling of fluency (M = .31 vs. .65), F(1, 22) = 7.04, p =
.015, ηp = .63. No other result
reached significance (F < 2; see Figure 2).
Figure 2. Mean proportion of old responses for targets in the

three contrast reduction tests (10%,
20%, and 30%) and the quality of the three pictures for each
group (control vs. amnesic
participants). Error bars display the standard deviations. T+D–
= Target+/Distractor– (high-contrast
target, low-contrast distractor); T=D= = Target=/Distractor=
(high-contrast target, high-contrast
distractor); T–D+ = Target–/Distractor+ (low-contrast target,
high-contrast distractor).
Finally, to further ensure that the contrast reduction
manipulation was truly successful to enhance
participants feeling of fluency, we compared whether
participants truly showed a higher rate of
correct recognitions for the pairs where the fluency of the target
was high (Target+/Distractor–)
than for the pairs where fluency was not manipulated
(Target=/Distractor=), at least when the level
2
2
of contrast reduction was discreet enough not to induce a
disqualification of the fluency cue. In
control participants, results revealed a trend toward a higher hit
rate for pairs with a high-fluency
target than for pairs where the fluency was not manipulated in
the 10% contrast reduction test (M =
.57 vs. .52), F(1, 22) = 2.96, p = .08, ηp = .15. A higher hit rate
was also found for pairs with a
high-fluency target than for pairs where fluency was not

manipulated in the 20% contrast reduction
(M = .60 vs .50), F(1, 22) = 4.16, p = .04, ηp = .18. Similarly,
in the 10% contrast reduction,
amnesic patients gave more correct responses when the fluency
of the target was high than when
the perceptual fluency of the pairs were not manipulated (M =
.63 vs. .49), F(1, 22) = 6.18, p = .02,
ηp = .52. Overall, these findings confirm the validity of the
fluency manipulation.
Discussion
The main goal of this experiment was to determine whether
differences in how patients with
amnesia and healthy controls rely on fluency can be explained
by the fact that amnesic patients
detect alternative sources of fluency more effectively than do
healthy participants, leading them to
more often disqualify fluency as a cue for memory. Our findings
seem to confirm this hypothesis.
Indeed, our results indicate that all participants relied on the
absolute level of fluency when making
recognition decisions (i.e., the higher the fluency, the higher
their correct recognition rate) as long
as the perceptual manipulation (i.e., contrast reduction) that
served as an alternative source of
fluency remained unnoticed. The main finding of the present
study is that the level of contrast
reduction at which the alternative source was detected differed
between our groups.
Specifically, in the 10% contrast reduction test, our results
revealed that both healthy participants
and amnesic patients gave more correct responses on pairs
where recognition of the target was
facilitated by a high-contrast picture than on pairs where the

processing of the distractor was
facilitated. This pattern suggests that when the perceptual
manipulation is sufficient to induce a
feeling of fluency but inconspicuous enough not to be explicitly
detected, patients with amnesia are
able to rely on fluency to guide their memory decisions in the
same way as do healthy
participants. Many studies in which participants remain
unconscious of the artificial manipulation of
their processing experience have demonstrated that type of
pattern in healthy participants (e.g.,
Jacoby & Whitehouse, 1989; Willems & Van der Linden, 2006).
In the 30% contrast reduction test, our data showed that both
healthy and amnesic participants
performed better on pairs where the distractor was made easier
to process than on pairs where
the target was made easier to process. This pattern indicates that
all participants disqualified
fluency as a relevant cue for memory when an external source
was clearly visible. Consistent with
this view, our analyses revealed that when participants were
explicitly asked to compare the
2
2
2
perceptual quality of these pairs, their detection rate was nearly
perfect (M = .97), suggesting that
the experimental manipulation is easily detectable. It is
interesting that these results can be

interpreted within the discrepancy-attribution framework
(Whittlesea & Williams, 2000, 2001a,
2001b; Willems & Van der Linden, 2006). According to this
model, high processing fluency is
interpreted as a sign of memory when the degree of fluency that
is experienced is surprisingly
greater than expected given the context. However, if an external
source is detected that produces
more fluency expectations than in past experience, even healthy
participants are likely to attribute
their feeling of fluency to this source rather than to the past. In
recognition tests, this usually leads
them to give more “yes” responses to items with a lower level
of fluency.
Taken together, the results obtained in the 10% and 30%
contrast reduction tests are interesting
because, to our knowledge, it is the first time that, in the same
experiment, a sample of patients
with amnesia showed either a strong reliance or a
disqualification of fluency depending on the
characteristic of the test items. These findings are crucial
because they could help to explain why,
in previous studies, the influence of processing fluency on
patients’ recognition decisions varied
from large (Keane et al., 2006) to small (Verfaellie & Cermak,
1999) or even inconsistent (Levy et
al., 2004) as a function of the experimental manipulation. For
instance, using a subtle manipulation
of fluency including one condition in which the constituent
letters for studied and unstudied words
were distinct (nonoverlap) and another condition in which the
constituent letters for studied and
unstudied words were the same (overlap), Keane et al. (2006)
found a large influence of fluency
on patients’ recognition judgments. Conversely, using a

procedure manipulating fluency through
(probably) detectable perceptual priming (83 ms), Verfaellie
and Cermak (1999) found only a small
effect of their manipulation on patients’ memory performance.
Finally, the results observed in the 20% contrast reduction test
are particularly important because
they replicated those of Geurten and Willems (2017) by
showing different patterns of responses
between healthy controls and patients with amnesia.
Specifically, control participants performed
better on pairs where the processing fluency of the target was
high than on pairs where the
processing fluency of the distractor was high. Conversely, in
amnesic patients, poorer recognition
performance was observed for pairs where the processing of the
target was facilitated by higher
picture quality, whereas better recognition performance was
observed for pairs where the
processing of the distractor was facilitated by higher picture
quality. According to the discrepancy-
attribution hypothesis, these findings suggest that patients with
amnesia, but not controls, have
detected the perceptual manipulation and judged it as the source
of their feeling of fluency, leading
them to disqualify fluency as a relevant memory cue. All this
occurred although our analyses
revealed that both patients and controls showed similar
detection rates when they were explicitly
asked to focus on the differences in perceptual quality between
stimuli (Ms = .69 and .74 for
control and amnesic participants, respectively). These findings
indicate that differences observed

in the correct recognition rate between our two groups are not
due to a better ability of the patients
to detect the contrast manipulation per se. Indeed, all our
participants were shown to be able to
detect the manipulation when their attention was focused on the
pictures’ perceptual quality. In this
context, we hypothesize that differences in fluency use between
our groups resulted from the fact
that patients with amnesia could allocate resources to the
detection of perceptual differences
during the recognition test, leading them to more readily detect
the alternative source, which
remained unnoticed by control participants.
Overall, the findings of the present study seem to confirm the
hypothesis of Geurten and Willems
(2017) according to which patients with amnesia progressively
start to track alternative sources of
fluency to reduce the frequency of their fluency-based memory
illusions. Specifically, given that
recollection control processes are disturbed in amnesia (Bastin
et al., 2004; Yonelinas et al., 1998),
it is possible that amnesic patients frequently experience
situations where fluency leads to
memory errors in their daily life, creating the need to implement
strategies to help them to decide
with a high level of certainty whether their feeling of fluency
results from prior exposure or from
another source. This could explain why patients appear to use
fluency only in a context where the
external manipulation is hardly noticeable. On the other hand,
healthy participants have no reason
to closely track alternative sources of fluency in an attempt to
compensate for impaired recollection
control processes. Consequently, as in the study of Willems and
Van der Linden (2006), the

manipulation of the perceptual quality of the picture has to be
glaringly obvious for them to
disqualify fluency as a cue to memory.
Despite the relative clarity of these results, the question of
whether the monitoring processes
involved in the tracking of external sources of fluency are
explicit−effortful or implicit−automatic still
has to be investigated. Indeed, according to the cue-utilization
approach of memory (Koriat, 1997,
2007), monitoring processes can sometimes occur without
explicit goals and even without
consciousness. To test this hypothesis, future experiments in
which patients with amnesia would
have to verbally report the strategies they used while
completing some recognition tasks should be
conducted. Another option could be to put patients in a divided-
attention situation while performing
our three recognition tests in order to determine whether a
disqualification of fluency is still
observed in the 20% contrast reduction.
Moreover, it is important to note that other metacognitive
mechanisms may be suggested to
account for the findings reported in the present study. Indeed,
we postulate that patients with
amnesia implement (implicitly or explicitly) strategies to track
alternative sources of fluency to
avoid memory errors. However, a pattern of responses similar to
the one obtained in the present
experiment would have been observed if patients had simply set
a more conservative response
threshold on their global feeling of familiarity to effectively

discount fluency as a diagnostic cue of
information. Indeed, as the perceptual manipulation has
presumably produced more fluency in the
20% contrast reduction test than in the 10% contrast reduction
test, if patients changed their
response criterion, the experienced fluency would logically be
more likely to be disqualified in the
former than in the latter test. Because patients with amnesia do
not expect their impaired memory
to produce a strong memory feeling, they would be more likely
to reject strong as compared to
weak feelings of familiarity. Within this framework, patients
are not supposed to allocate more
resources than do healthy participants to the tracking of
alternative fluency sources but are
assumed to react differently to the absolute level of fluency that
is experienced. This could explain
why in one study, Ozubko and Yonelinas (2014) found that
amnesic patients’ recognition decisions
were driven by fluency for new, but not old, items. However,
because in their experiment the prime
used to enhanced fluency was detectable, the hypothesis that
patients had tracked the alternative
source of fluency is still plausible. To truly disentangle these
two hypotheses, an experimental
manipulation designed to induce a strong feeling of familiarity
while the external source of fluency
remains undetectable should be carried out. If the changing
criterion hypothesis is correct, such a
manipulation would give rise to a disqualification of fluency in
amnesic patients. On the reverse, if
the tracking hypothesis is correct, patients with amnesia should
rely on fluency to inform their
recognition decisions in such a design.
There are several limitations in this study. First, the small

number of patients with amnesia means
that the results of our statistical analyses must be interpreted
with caution. Nevertheless, the fact
that, in the 20% contrast reduction test, we replicated the results
of Geurten and Willems (2017)
seems to speak in favor of the robustness and validity of our
findings. Moreover, to determine
whether our results could be generalized, it would be interesting
to replicate these results in other
clinical populations where severe memory problems are
widespread and where, as in amnesia,
fluency-based memory decisions are not shown to translate into
better recognition performance
(e.g., Simon, Bastin, Salmon, & Willems, 2018). In the same
vein, the impact of the etiology of the
amnesia could also be investigated. In this study, the
recognition performance of all our patients
was quite homogeneous. Of note, patients were selected to
present only memory deficits.
However, it could be interesting to explore whether all types of
amnesic patients in more
heterogeneous samples would have the same profile of results
on our tests. Given the potential
involvement of frontal lobes in attributional processes, it is
possible that amnesic patients with
head trauma or Korsakoff syndrome (i.e., who frequently show
frontal damage) demonstrate more
deficits in attributional processes than, for example, patients
with anoxia.
A second limitation of this study is that the three recognitions
tests (10%, 20%, and 30% contrast
reduction) were always presented in the same order. Although

this specific procedure was
selected because we wanted the fluency manipulation to remain
undetected as long as possible,
this confounding of test order may have influenced our results
through, for example, an increase of
proactive interference for the last tests. Even though the global
performance of our participants
was shown to remain stable across tests, which seems to rule
out the possibility of an interference
effect, our results should nevertheless be replicated using other
types of designs. One possibility to
overcome this problem could be, for example, to replace the
block design used in this experiment
with a between-subjects design where three groups of patients
see pairs of stimuli with either a
10%, a 20%, or a 30% contrast reduction at test.
Another concern is the fact that, in the present study,
participants performed mostly at chance in
the control condition (Target=/Distractor=). This poses the
question of whether the current results
could generalize to tests in which the recognition performance
is above chance. Although future
experiments should be conducted to formally test this issue,
some responses are already available
in the literature. For instance, in studies where a counterfeit
encoding was used (i.e., a procedure
where participants are told that stimuli are presented in a
subliminal manner at study when, in fact,
there are not), participants’ performance was usually at chance
on subsequent tests. Despite this,
however, data have revealed similar variations in fluency
effects after a counterfeit encoding than
after a classic encoding condition that leads to above-chance
recognition performance (e.g., Lloyd,
Westerman, & Miller, 2003; Westerman, Miller, & Lloyd,

2003).
Finally, one last point to discuss is the low detectability of the
contrast manipulation in the 10%
contrast reduction test. This condition allowed us to confirm
that, in some circumstances, patients
with amnesia are able to rely on fluency to guide their memory
decisions to an extent that was
similar to that for healthy participants. However, because the
contrast reduction of most pairs
included in this condition was not detectable (i.e., correct
detection rate of 57% in the pilot data),
we could not determine whether patients relied on fluency in
this condition because they failed to
find an alternative source of fluency or because their
experienced level of fluency was not high
enough to prompt them to search for an alternative source.
Despite these limitations, our results could already have major
implications. From a theoretical
perspective, our findings could help to resolve the conceptual
debate on the question of whether
and when familiarity is impaired in amnesia. Specifically, our
study adds to the small amount of
literature showing that attributional processes—which have long
been assumed to account for the
emergence of familiarity (Jacoby & Dallas, 1981)—are probably
not impaired in amnesia but
undergo some metacognitive changes that are the product of
both a decrease in the ecological
validity of the fluency−memory correlations in daily life and the
implementation of a more
conservative response criterion or of strategies aiming at

tracking alternative sources to reduce
memory errors (Geurten & Willems, 2017; Ozubko &
Yonelinas, 2014). More generally, our
findings emphasize the importance of looking beyond the mere
behavioral pattern that is observed
following a memory task in amnesia. Indeed, what could, at first
sight, appear to be an impaired or
abnormal test performance may actually result from subtle
metacognitive changes that are very
adaptive for patients’ day-to-day functioning.
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Submitted: January 7, 2019 Revised: April 2, 2019 Accepted:
April 20, 2019
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Abstract (English):
Impact Statement:
Individual differences in long-term memory.
Unsworth, Nash. University of Oregon, Eugene, OR, US,
[email protected]
Unsworth, Nash, Department of Psychology, University of
Oregon, Eugene, OR,
US, 97403, [email protected]

Psychological Bulletin, Vol 145(1), Jan, 2019. pp. 79-139.
Psychol Bull
US : American Psychological Association
US : Psychological Review Company
US : The Macmillan Company
US : The Review Publishing Company
0033-2909 (Print)
1939-1455 (Electronic)
English
Individual differences, long-term memory, working memory
The literature on individual differences in long-term memory
(LTM) is organized
and reviewed. This includes an extensive review of the factor
structure of LTM
abilities as well as specific individual differences in criterial
tasks such as free
recall, paired associates recall, and recognition. It is
demonstrated that individual
differences in LTM abilities are represented by various lower
order factors based
on criterial tasks as well as by a more general higher-order LTM
factor. These
individual differences are linked with multiple different
constructs including working
memory, intelligence, and attention control. Individual
differences in forgetting,
interference control, false memory, testing effects, general
retrieval abilities, and
the influence of strategies are also examined. Overall, it is clear

that there are
substantial and robust individual differences in LTM abilities
and that these abilities
demonstrate important relations with other cognitive abilities.
Future directions and
an integration of individual differences in a general framework
of memory are
discussed, and it is suggested that combined experimental and
correlational
approaches are needed to better understand individual
differences in LTM and that
individual differences in LTM should be used to better test and
revise theories of
LTM processes. (PsycINFO Database Record (c) 2018 APA, all
rights reserved)
Public Significance Statement—This systematic review
indicates that there are
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large and important individual differences in long-term memory.
These individual
differences are related to other important abilities including
working memory,
intelligence, and attention control. (PsycINFO Database Record
(c) 2018 APA, all
rights reserved)
Journal Article
*Cognitive Ability; *Individual Differences; *Long Term

Memory; *Short Term
Memory; Attention; False Memory; Forgetting; Free Recall;
Intelligence; Cognitive
Control
Learning & Memory (2343)
Human
Literature Review; Systematic Review
Tables and Figures Internet
Text Internet
Electronic
Journal; Peer Reviewed Journal
Accepted: Sep 26, 2018; Revised: Sep 20, 2018; First
Submitted: Jan 29, 2018
20181231
American Psychological Association. 2019
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Individual Differences in Long-Term Memory

By: Nash Unsworth
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Department of Psychology, University of Oregon;
Acknowledgement: Thanks to Gene Brewer, Ashley Miller, Matt
Robison, and Colin MacLeod.
Our ability to encode, store, and retrieve vast amounts of
information in our memory system is one
of the most important functions of our cognitive system. This
memory system allows us to perform
a number of important and routine tasks daily. Although our
memory system is typically very
efficient, sometimes failures occur that have minor or major
consequences. Furthermore, the
efficiency of the memory system differs across individuals.
Even within the normal range of
abilities there are large and important individual differences in
memory abilities. Some of us find it
difficult to remember names, dates, and other events from our
lives, whereas others can seemingly
remember the most mundane of past activities. These individual
differences in memory abilities
can result not only in fairly commonplace differences (such as
differences in the ability to
remember your e-mail password), but they can also give rise to
differences related to more
important real-world outcomes. For example, students with poor
memory abilities will likely have
difficulties learning and retrieving information in educational
contexts leading to poor exam scores.

Understanding the nature of this variation in memory abilities is
critical not only for providing a
better understanding of our memory system more broadly, but it
is also important for potentially
reducing memory problems for the less able.
Researchers have long been interested in the scientific study of
memory processes (Ebbinghaus,
1885/1964) as well as individual differences in memory abilities
(e.g., Jacobs, 1887; see also
Blankenship, 1938). Indeed, in discussing memory abilities,
Ebbinghaus (1885/1964) noted “how
differently do different individuals behave in this respect! One
retains and reproduces well; another,
poorly” (p. 3). Although these two research areas have
flourished over the past 100 years, there
have been few attempts to integrate experimental and
differential approaches despite this having
been advocated by several researchers in both fields (Cohen,
1994; Cronbach, 1957; Kosslyn et
al., 2002; Underwood, 1975). For example, at the conclusion of
a conference on Learning and
Individual differences in 1967, Arthur Melton noted:
[T]he sooner our experiments and our theory on human memory
and human learning consider the
differences between individuals in our experimental analyses of
component processes in memory and
learning, the sooner we will have theories and experiments that
have some substantial probability of
reflecting the fundamental characteristics of those processes.
(Melton, 1967, pp. 249–250)
To better understand individual differences in memory, it is
critical that experimental and
differential methods be combined. In the present review, both of

these methodologies will be
considered to examine individual differences in memory
abilities, how these abilities relate to other
cognitive abilities, how these abilities are related to particular
components of cognitive tasks, and
how these abilities interact with various experimental
manipulations (see the Appendix for an index
of the organizational structure of the review).
Background
Individual differences in memory abilities have long interested
psychologists and have played an
integral role in psychometric batteries of intelligence (e.g.,
Binet & Simon, 1905; Terman, 1916).
When examining correlations among various ability measures
including various memory
measures, a number of memory factors tend to be present and
strongly correlate with other ability
factors (Carroll, 1993). Furthermore, there is a long and rich
history of examining individual
differences in learning (see Ackerman, Kyllonen, & Roberts,
1999; Gagne, 1967; Kanfer,
Ackerman, & Cudeck, 1989 for reviews) as well as examining
individual differences in cognition
based on more cognitive oriented frameworks (Hunt, Frost, &
Lunneborg, 1973; Hunt, Lunneborg,
& Lewis, 1975). Thus, the notion that there are important
individual differences in memory abilities
has been researched for a long time (see Bors & MacLeod,
1996; Kane & Miyake, 2008; MacLeod,
1979; MacLeod, Jonker, & James, 2014 for reviews). For
example, Cohen (1994) suggested a

zeroth law of memory such that “individuals differ reliably in
their memory capacities” (p. 270).
More recently, in discussing various principles of memory,
Surprenant and Neath (2008) also
suggested that individual differences in memory were a
fundamental property. Yet, contemporary
research on memory abilities still remains relatively scarce.
That is, despite many calls in the
literature for the need to examine individual differences in
memory abilities more thoroughly, this
remains a neglected area of research. Indeed, Carroll (1993)
noted that “the available literature on
individual differences in learning and memory abilities leaves
much to be desired” (p. 302).
Jenkins’ Tetrahedral Model of Memory Experiments
Jenkins (1979) presented a tetrahedral model of memory
experiments that suggested that the
outcomes of experiments on memory are due to four interacting
factors (see Figure 1; see
Roediger, 2008, for an updated view). These factors include
encoding conditions, to-be-
remembered materials, retrieval conditions, and subject factors.
The encoding factor refers to the
fact that various aspects of encoding will undoubtedly influence
performance. These include
instructions to the participants (intentional versus incidental
learning), various strategies that might
be used (rehearsal, imagery, grouping, etc.), the setting the
study is conducted in, and different
activities participants might engage during encoding (judgments
on the items, performing a dual-
task during encoding). The materials factor refers to the
different to-be-remembered items or
events that are presented to the participant. These include
variations in sensory modality (items

seen versus heard), words, letters, numbers, sentences, pictures,
or even answers to general
knowledge questions. The retrieval factor refers to the type of
task used to measure performance
and retention. Jenkins referred to these as the criterial tasks.
These include tasks like serial recall,
free recall, cued recall, item recognition, source recognition,
and various other judgments (e.g.,
judgments of frequency and recency). Finally, Jenkins
suggested that subject factors will also
influence performance. These subject factors include innate
abilities, interest (interest in the
materials, interest in the experiment), knowledge (prior
knowledge with the materials, prior
knowledge with the type of experiment being conducted or
criterial task), motivation (motivation to
do well on the current experiment), personality traits, as well as
age. Similarly, Kelley (1964) noted
“that an individual’s performance on a task or ‘test’ is
determined in part by the abilities that are
called for by the test and in part by the degree to which the
individual himself possesses these
abilities” (p. 1). Thus, Jenkins, a prominent researcher of
learning, memory, and individual
differences suggested that it was critical that experiments of
memory take into consideration basic
variation in subjects reflecting differences in abilities and other
differential variables.
Figure 1. Jenkins’ tetrahedral model of memory experiments,
suggesting that performance is
determined by a combination of encoding, materials, retrieval,
and subject factors. Adapted from

“Four points to remember: A tetrahedral model of memory
experiments,” by J. J. Jenkins, 1979,
Hillsdale, NJ: Erlbaum. Copyright 1979 by Erlbaum; and From
“Relativity of Remembering: Why
the Laws of Memory Vanished,” by H. L., III, Roediger, 2008,
Annual Review of Psychology, 59,
pp. 225–254. Copyright 2009 by Annual Reviews, Inc. Adapted
with permission.
Jenkins further noted that these different “variables interact
vigorously with one another” (p. 431).
That is, performance will depend on the particular combination
of these four factors being
manipulated and controlled. Thus, encoding and retrieval
factors will interact and will tend to result
in the best performance when there is a match between the two
(Fisher & Craik, 1977; Morris,
Bransford, & Franks, 1977; Tulving & Thomson, 1973).
Importantly, subject factors will also likely
interact in important ways with the other factors. For example,
differences in memory abilities will
interact with encoding factors to the extent that individuals can
understand and adhere to the
instructions. Likewise, memory abilities will interact with
different types of retrieval tasks. Tasks
that require more effort, attention, strategic control, and self-
initiated processing may result in
larger individual differences than tasks where more automatic
processing can be used (Craik,
1983, 1986; Salthouse, 2001; Unsworth, 2009a). Furthermore,
individual differences in motivation
will likely be important in terms of how much effort and

attention is allocated during encoding and
retrieval resulting in differential performance (e.g., Kanfer &
Ackerman, 1989). Thus, while
examining individual differences in memory abilities it is
critical that interactions with other
variables are examined and considered to obtain a fuller account
of variability between individuals.
In the current review, some of these interactions will be
examined in more detail, but much remains
to be done.
Dual-Store Models of Memory
To frame our understanding of individual differences in memory
abilities, we will need to consider
not only how subject factors interact with other factors in
memory experiments, but also how these
differences fit in the context of memory theories. Perhaps the
most prominent notion in memory
theory is that there are two main memory states: working
memory and long-term memory
(Atkinson & Shiffrin, 1968; James, 1890; see Norris, 2017, for
a recent review). The notion that
there are separate memory systems for information over the
short-term and the long-term is an
old and enduring one (James, 1890). Many contemporary
theories of memory suggest that a
small subset of information can be actively maintained over the
short-term via a working memory
system, whereas the vast amount of information a person has at
their disposal is usually stored in
a long-term system (e.g., Healy & McNamara, 1996;
Raaijmakers, 1993). Early theories of working
memory (WM) and long-term memory (LTM) suggested that
these two constructs represented
qualitatively distinct and independent memory systems (e.g.,
Baddeley, 2007; Healy & McNamara,

1996; Jonides et al., 2008). In these theories, the WM system is
responsible for maintaining and
manipulating a small amount of information over a relatively
short interval whereas the LTM system
is responsible for maintaining all of the memories a person has
acquired over the lifespan. The
WM system also utilizes various control processes that are
needed to maintain information in WM
and to build strong and durable memories in LTM. For example,
as suggested by Atkinson and
Shiffrin (1968), these control processes include setting up a
retrieval plan, selecting and utilizing
appropriate encoding strategies, selecting and generating
appropriate cues to search memory, as
well as various monitoring strategies and decisions to continue
searching or not. Thus, it was
postulated that these two systems represented functionally
different aspects of memory and had
different properties and limits in terms of capacity and duration.
To differentiate these two constructs, there must be reliable and
valid measures of both WM and
LTM. Traditionally, two task characteristics have differentiated
WM and LTM: number of to-be-
remembered (TBR) items and retention interval (Cowan, 2008).
Specifically, WM tasks usually
consist of a set of TBR items that are within theoretical capacity
limits (i.e., 4 ± 1, Cowan, 2001; 7 ±
2, Miller, 1956), whereas LTM tasks usually consist of a set of
TBR items that exceed these
capacity limits. Additionally, WM tasks are usually associated
either with no retention interval (i.e.,
immediate recall) or with a very brief retention interval of only

a few seconds (e.g., Cowan, 2008;
Jonides et al., 2008; Ranganath, Johnson, & D’Esposito, 2003),
whereas in LTM tasks the
retention interval is usually much longer. Based on this
distinction, research has found that there
are large and important differences in WM and these differences
are important predictors of
performance on a wide array of laboratory and more real-world
measures (Ackerman, Beier, &
Boyle, 2002; Conway, Cowan, Bunting, Therriault, & Minkoff,
2002; Cowan et al., 2005; Daneman
& Carpenter, 1980; Engle & Kane, 2004; Engle, Tuholski,
Laughlin, & Conway, 1999; Kane et al.,
2004; Kyllonen & Christal, 1990; Süß, Oberauer, Wittmann,
Wilhelm, & Schulze, 2002; Unsworth,
2016a; Unsworth & Engle, 2007; Unsworth, Fukuda, Awh, &
Vogel, 2014).
Although there has been extensive research examining
individual differences in WM, there is
decidedly less research examining individual differences in
LTM. The current review will primarily
focus on natural variation in LTM abilities, rather than variation
attributable to age or
neuropsychological conditions. Much of the research that has
been done examining LTM has
focused on various list-learning tasks thought to tap episodic
memory. In these tasks, participants
are presented with lists of items at encoding which they are
asked to remember for later. Following
a delay period participants are given one out of several different
types of memory tests. The tests
include various recall tasks like free recall, serial recall, and
cued recall in which participants are
presented with a set of TBR items and after a brief delay are
required to recall the TBR items. LTM

may also be tested via various judgment tasks including item
recognition, associative recognition,
source recognition, judgments of frequency, and judgments of
recency, to name a few. Unlike
recall tests where items must be generated from memory, in
different judgment tasks participants
are presented with the items and must make different judgments
about the items. These two types
of tasks have a long history in memory research and have been
used to elucidate the nature of
different memory processes. As will be seen below, these
different types of tasks have been used
to examine individual differences in LTM abilities and their
relation with WM and other cognitive
abilities.
Methods and Approaches for Studying Individual Differences
To study individual differences in LTM abilities, one must rely
on various different methods and
approaches that will best address the specific question being
asked (see Wingert & Brewer, 2018,
for a recent review). Within the domain of individual
differences there are two general types of
studies: Cognitive correlates and cognitive components
(Pellegrino & Glaser, 1979). First, the
cognitive correlates approach seeks to specify correlations
among various cognitive abilities. For
example, to what extent are WM and LTM related to one
another and to intelligence? In this
approach measures of each putative construct are obtained and
correlated to determine potential
relations. This approach is also useful for examining potential
unique sources of variance in a

construct. For example, if WM and LTM are both related to
intelligence is this because WM and
LTM share considerable variance or are the relations
independent with WM and LTM each
contributing uniquely to the intelligence? This approach is also
useful for examining possible
mediation. For example, is the relation between LTM and
intelligence attributable to WM? Second,
the cognitive components approach investigates a particular
cognitive task attempting to identify
the various mechanisms that give rise to performance and
examine whether there are individual
differences in those components. For example, is variability in
performance on free-recall tasks
due in part to individual differences in encoding strategies?
Both approaches are important and
necessary for examining individual differences in LTM abilities
because they provide a means of
examining both construct representation (i.e., theoretical
mechanisms that underlie performance)
and nomothetic span (network of relations of task performance
with other variables; Embretson,
1983).
In both approaches a number of different methods can be used
to examine individual differences.
Perhaps the simplest approach is to have participants perform
tasks thought to tap the construct of
interest (WM and LTM) and then simply examine whether
performance on the two tests are
correlated. This univariate method provides a simple way of
assessing whether two theoretical
constructs are related. However, because no task is a process-
pure measure of the construct of
interest and because single measures can be associated with
poor psychometric properties (like

poor reliability), a multivariate method can be beneficial. In
this method multiple measures of each
construct can be obtained and factor analysis can be used to
examine relations among various
tasks to determine whether there is sufficient common variance
to form latent factors. For
example, do WM measures load onto one factor and LTM
measures onto a separate factor? Early
research primarily relied on exploratory factor analysis which is
a data-driven approach. More
recent research relies on confirmatory factor analysis where
relations among tasks and among
factors are specified beforehand based on theory. Both methods
allow for an examination of
correlations at the latent factor level where measurement error
has been reduced. Although
knowing that two tasks or two factors correlate is important, we
also want to know whether these
relations are due to unique variance or due to shared variance
with other constructs. To examine
these types of issues regression techniques at the zero-order or
latent level (e.g., structural
equation modeling) are useful. With such techniques one can
move beyond simply stating that
there is a relation among constructs of interest, to specifying
structural relations based on prior
theory. All of these methods provide an assessment of the
degree and magnitude of relations
among various constructs of interest in line with cognitive
correlates approach.
Another important method for examining individual differences
in cognitive abilities is to combine

correlational and experimental methods to assess various
Aptitude × Treatment interactions.
Cronbach and Snow (1977) and others (see Snow, 1991 for a
review) argued for the importance of
examining Aptitude × Treatment interactions where aptitude
refers to characteristics of the
individual and treatment refers to manipulated variables. In
these types of designs a traditional
experiment is conducted where generally a single dependent
variable is examined for different
experimental conditions and interactions with different person
characteristics can be examined. For
example, one may consider whether individual differences in
LTM are greater under intentional
learning conditions compared with incidental learning
conditions. These types of studies seek to
not only examine whether a relation exists between the
individual differences variable and
performance (a main effect), but to also examine how this
relation changes as a function of various
experimental manipulations. As Engle and Kane (2004) noted
“the presumption is that if we can
make the correlation appear and disappear with a given
manipulation, some aspect of the
manipulation controls the correlation” (p. 156). There are
various methods for examining Aptitude ×
Treatment interactions including analysis of covariance, linear
mixed models, multiple regression,
and latent change and latent growth curve modeling. As
reviewed throughout, both the cognitive
correlates and cognitive components approaches and various
different methodologies have been
used to examine individual differences in LTM abilities.
Caveats to the Present Review

The present review will examine individual differences in LTM
abilities by primarily examining
normal variation in this cognitive ability. It is beyond the scope
of the current review to examine
variation attributable to age, personality, gender, or
psychopathologies. Although each of these are
likely important sources of variance in LTM abilities, the
current focus is on normal cognitive
abilities within a particular age range (young adults). Some
studies will be examined that include a
wide range of ages (19–90 e.g.), but the main focus will be on
relations seen regardless of age.
Furthermore, the current review will primarily focus on episodic
LTM abilities given that much of the
literature is concerned with list-learning tasks. Where
appropriate other types of LTM will be
examined, but there is a clear need for research examining
individual differences in other types of
LTM such as semantic memory, prospective memory,
autobiographical memory, procedural
memory, and implicit memory to name a few. See for example
research by Ball et al. (2018),
Brewer, Knight, Marsh, and Unsworth (2010), and Unsworth,
Brewer, and Spillers (2012)
examining individual differences prospective memory and
research by LePort, Stark, McGaugh,
and Stark (2017) on individuals with highly superior
autobiographical memories. Furthermore, it is
beyond the scope of the current review to review the long and
important history of work done on
learning and individual differences (see Ackerman et al., 1999;
Gagne, 1967; Kanfer et al., 1989
for reviews). This work mainly examined changes in

performance as a function of learning,
whereas the current review is primarily focused on list-learning
tasks where multiple learning
episodes of the same information does not generally occur.
Finally, throughout the paper I report
reanalyzes of data sets from several published papers. Many of
these reanalyses include data
from my own laboratory and data from other studies that were
accessible. This is a clear limitation
of these analyses, and future research is needed to ensure their
replicability and generalizability.
Factor Structure of LTM Abilities
One of the first and most heavily studied aspects of individual
differences in LTM abilities is the
factor structure of LTM. In these studies participants perform a
large sample of different LTM tasks
and factor analysis (primarily exploratory factor analysis for
early studies) was used to examine the
overall factor structure. Early work by Carothers (1921), Kelley
(1928), Anastasi (1932), Carlson
(1937), Garrett (1938), and Brener (1940) suggested the
presence of one or more memory factors
based on a number of different memory tests. In Thurstone’s
(1938) primary mental abilities one
factor was specifically devoted to memory and consisted
primarily of paired-associates test.
Thurstone (1938) also included a word fluency factor relating to
how quickly words could be
retrieved from LTM. By 1940, Wolfe in his review of factor
analysis up to that point suggested that
a memory factor was the fourth most identified factor (Wolfe,
1940). In his review of the field in
1951, French suggested that there were four memory factors
(Associated or Rote Memory,

Musical Memory, Span Memory, and Visual Memory). Thus,
when different memory tasks are
utilized, scores on these tasks tend to correlate and form one or
more factors potentially delineated
by type of task and content of the materials.
Following French’s (1951) review a number of additional factor
analytic studies were done to better
examine the overall factor structure. For example, Ingham
(1952) had 80 participants perform eight
different paired associates tasks and several intelligence
measures. Factor analysis suggested the
presence of a specific memory factor in addition to an overall g
factor. In subsequent research
Christal (1959) carried out a large-scale factor analytic study of
visual memory (see Beier &
Ackerman, 2004, for a reanalysis). In this Study 718 Air Force
personnel completed 17 memory
tests and 14 reference tests of ability (including tests of verbal
abilities, mechanical knowledge,
mathematic abilities, etc.). Factor analysis suggested the
presence of four memory factors
identified as Memory for Position in Space, Memory for Color,
Memory for Position in Temporal
Sequence, and Paired Associates Memory along with four
additional ability factors (Mechanical
Experience, Numerical Facility, Verbal Comprehension, and
Perceptual Speed). Games (1962)
had 100 university students perform 17 memory tests (primarily
memory span or paired
associates). A subsequent factor analysis suggested the presence
of five factors including
Memory Span and Rote Memory (which were correlated at r =

.32). Building on the work of
Christal (1959) and others, this work suggested the presence of
separate memory factors.
In one of the largest studies of individual differences in
memory, Kelley (1964) had 442 Air Force
Cadets perform 27 different memory tests along with 13
reference tests of ability (see Beier &
Ackerman, 2004, for a reanalysis). The memory tests consisted
of recognition tests, paired
associates tests, different tests of meaningful memory (e.g.,
remembering sentences,
remembering stories, remembering limericks, etc.), memory
span tests, and different visual
memory tests (e.g., reproducing a geometrical design from
memory, remembering map
locations). Based on a factor analysis, Kelley identified 11
different factors. Of these, three were
consistent memory factors of Rote Memory (paired associates),
Memory Span, and Meaningful
Memory. A fourth memory factor was identified as consisting of
only paired associates of
nonsense syllables. Finally, there was some indication of a fifth
memory factor, but it was not
clearly identified. Examining correlations among the memory
factors suggested that Rote Memory
and Meaningful memory factors were correlated (r = .28), but
neither were related to the Memory
Span factor (rs of −.04 and .06, respectively). Furthermore, the
paired associates factor for
nonsense syllables correlated with the Meaningful Memory
factor (r = .25), but not with the Rote
Memory factor (r = .03). Kelley suggested that these factors
were somewhat general in that both
visual and auditory presentations of the material were used and
both recognition and recall (paired

associates recall) were used. As such the results of this study
provide some of the best evidence
for different memory factors initially suggested by French
(1951) and others.
Brown, Guilford, and Hoepfner (1968) tested aspects of
Guilford’s (1967) structure of intellect
model in which it was hypothesized that there are 24 distinct
memory abilities. Brown et al. had
175 eleventh graders perform 50 different ability tests. Brown
et al. found six different memory
factors, identified as Memory for Isolated Items, Memory for
Class ideas, Memory for Meaningful
Connections, Memory for Order, Memory for Transformations,
and Memory for Arbitrary
Connections. Hakstian and Cattell (1974) examined the
existence of different primary abilities by
administering 57 ability tests to 343 participants. Of these tests
nine were fairly standard memory
tests with six being paired associates and three being memory
span tasks. The factor analysis
suggested the presence of 19 factors of which three were
memory factors. These were identified
as Associative Memory (paired associates for simple stimuli
like number-word pairs), Memory
Span, and Meaningful Memory (paired associates for
meaningful stimuli such as object-attribute
pairs). Furthermore, they found that all three factors were
correlated with one another (Associative
Memory to Memory Span r = .28; Associative Memory to
Meaningful Memory r = .58; Memory
Span to Meaningful Memory r = .20). Thus, similar to prior
research three distinct, yet correlated

memory factors arose. Following up on this research Hakstian
and Cattell (1978) administered 20
primary ability tests thought to tap each primary ability factor
to 280 participants. Three of these
tests represented the factors of Associative Memory, Memory
Span, and Meaningful Memory.
Hakstian and Cattell found that Associative Memory and
Memory Span were correlated (r = .23),
Associative Memory and Meaningful Memory were correlated (r
= .36), and Memory Span and
Meaningful Memory were correlated (r = .14). Importantly, they
found evidence for a higher-order
memory factor that they called General Memory Capacity. The
highest loadings on this factor
were Associative Memory (.66) and Meaningful Memory (.38).
Interestingly, Memory Span loaded
weakly on this factor (.11) and had its highest loading on the
Perceptual Speed factor (.31).
Hakstian and Cattell also found evidence for a higher-order
factor that they called General
Retrieval Capacity whose highest loadings were from an
ideational fluency task (.78). This factor is
similar to Thurstone’s (1938) fluency factor. Hakstian and
Cattell suggested that whereas the
General Memory Capacity factor represented the ability to
commit items to memory, the General
Retrieval Capacity factor represented the ability to rapidly
retrieve items from LTM that had already
been committed to memory. Importantly these two higher-order
factors were correlated (r = .22),
suggesting some shared abilities. This study is important for not
only examining different memory
factors, but for also providing some of the first evidence for a
more general higher-order memory
factor.

In 1978 Underwood, Boruch, and Malmi conducted what is
perhaps still the largest individual
differences study of episodic memory. In this study 200
participants completed (over the course of
10 sessions) 28 different episodic memory tasks along with
measures of vocabulary, spelling, and
SAT scores. The episodic memory tests consisted of free recall,
paired associates, recognition
memory, serial learning, discrimination (list-discrimination,
verbal discrimination; frequency
discrimination), an interference susceptibility measure, and
memory span tasks. Underwood et al.
found evidence for five separate episodic memory factors. The
first factor was identified as a
paired associates factor given that all of the paired associates
tasks loaded on it. Interestingly, the
serial learning tasks also tended to load on this factor. The
second factor was identified as a free
recall factor with all of the free-recall tasks loading on it. This
factor also had loadings from the
serial learning tasks and from the list-discrimination task. The
third factor was identified as a
memory span factor. The fourth factor was identified as a
recognition/frequency factor. Finally, the
fifth factor was identified as a discrimination factor with the
verbal discrimination tasks and list
discrimination task loading on it. This study provides important
evidence for distinct memory
factors based on differences in the criterial tasks used (see also
Malmi, Underwood, & Carroll,
1979). Whereas prior research primarily relied on different
psychometric memory tests that had
been used many times previously in factor analytic work,
Underwood et al.’s study stands out for
using more standard experimental tests of episodic memory. As
such this study provides

important evidence for the notion that the factor structure of
LTM abilities is driven by abilities
needed on different LTM tasks.
In his comprehensive review of factor analytic studies, Carroll
(1993) summarized the prior
research examining the factor structure of LTM (including the
studies summarized here) and
determined that a number of distinct factors were evident.
Specifically, examining data from 117
different samples in memory abilities Carroll identified five
first-order memory factors. These were
Memory Span (identified in 70 data sets), representing the
ability to recall items in their correct
order. Associative Memory (identified in 51 data sets),
representing the ability to form arbitrary
associations. Free Recall (identified in 12 data sets),
representing the ability to recall arbitrary
information that exceeds the capacity of WM. Meaningful
Memory (identified in 17 data sets),
representing the ability to recall or recognize meaningful
material. Visual Memory (identified in five
data sets), representing the ability to remember visual
information that is not easily transformed
into a verbal code. Given the scare evidence for this factor, in
later work Carroll (1994) did not
include it as one of the primary first-order factors.
In reanalyzing the data, Carroll found that although there was
evidence for five distinct memory
factors, these factors tended to all correlate with one another,
suggesting the presence of a
common higher-order factor. Similar to prior work by Thurstone

(1938) and Hakstian and Cattell
(1978), Carroll (1993, 1994) also suggested a second-order
general retrieval capacity indexing the
ability to rapidly retrieve information from LTM. Collectively,
this work suggests that not only are
there distinct abilities that are required in different memory
tests, but also that there are common
abilities that are needed across a wide array of different
memory tests and those individuals who
score high on one test of memory tend to score high on other
tests of memory.
More recent conceptualizations of human cognitive abilities
also suggest the presence of both
lower-order and higher-order memory factors. For example, the
Cattell-Horn-Carroll theory is an
integration of the Horn-Cattell fluid and crystallized
intelligence theory with Carroll’s (1993) three-
stratum theory (McGrew, 2009; Schneider & McGrew, 2012). In
this conceptualization, WM
(labeled as short-term memory [STM]) and LTM (labeled as
long-term storage and retrieval) are
distinct higher-order factors. The general WM (or Gsm) factor
represents the ability to apprehend
and maintain in awareness a small number of items for
immediate report. This factor is composed
of simple and complex memory span tasks. The general LTM (or
Glr) factor represents the ability
to encode and store new information in LTM and to later
fluently retrieve information from LTM.
This general factor can be further broken down into Learning
Efficiency and Retrieval Fluency
factors. The learning efficiency factor is composed of tasks
measuring Associative Memory, Free
Recall, and Meaningful Memory, whereas the retrieval fluency
factor is composed of various

fluency tasks. Thus, whereas prior research combined WM and
LTM into a more general memory
factor, more recent conceptualizations suggest that these are
separate and distinct higher-order
factors and each of these higher-order factors can be further
subdivided.
Following Carroll’s (1993) review there has been a relative lull
in examining the factor structure of
LTM abilities. Despite this lull, a number of advances have
been made. One important advance
has been the reliance on confirmatory factor analysis rather than
exploratory factor analysis. Much
of the prior research relied on exploratory factor analysis which
is primarily a data-driven process
in which the factor structure is not specified a priori based on
theory. In confirmatory factor
analysis, however, the overall measurement model (loadings of
measures onto factors and
relations among factors) is specified based on prior theory. By
testing various models one can
better examine the theoretical structure of the data with
confirmatory factor analysis. For example,
Nyberg (1994) examined whether declarative memory could be
broken down into episodic and
semantic memory factors (see also Cohen, 1984; Mitchell,
1989). Nyberg (1994) had 300
participants perform multiple measures of free recall, cued
recall, recognition, and various word
fluency tasks. Nyberg found that a two-factor model
differentiating episodic memory (free recall,
cued recall, and recognition) from semantic memory (word
fluency) fit the data better than a single

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