Psychology and Aging
2010, Vol. 25, No. 4, 767–778

© 2010 American Psychological Association
0882-7974/10/$12.00 DOI: 10.1037/a0020683

Working Memory Training in Older Adults:
Evidence of Transfer and Maintenance Effects
Erika Borella, Barbara Carretti, Francesco Riboldi, and Rossana De Beni
University of Padova
Few studies have examined working memory (WM) training-related gains and their transfer and
maintenance effects in older adults. This present research investigates the efficacy of a verbal WM
training program in adults aged 65–75 years, considering specific training gains on a verbal WM
(criterion) task as well as transfer effects on measures of visuospatial WM, short-term memory,
inhibition, processing speed, and fluid intelligence. Maintenance of training benefits was evaluated at
8-month follow-up. Trained older adults showed higher performance than did controls on the criterion
task and maintained this benefit after 8 months. Substantial general transfer effects were found for the
trained group, but not for the control one. Transfer maintenance gains were found at follow-up, but only
for fluid intelligence and processing speed tasks. The results are discussed in terms of cognitive plasticity
in older adults.
Keywords: working memory, cognitive training, transfer effects, older adults

To date, very few studies have examined the effects of WM
training in older adults along with its transfer and maintenance
gains. The training procedure is characterized by the presentation
of complex tasks for which participants either practice with the
task (see, Li et al., 2008) or else advance through the task according to their performance on previous set trials, that is, progress is
not automatic (adaptive procedure; see Buschkuehl et al., 2008).
The common objective of such training on WM is to seek to
improve the information processing system by limiting the development of task-specific strategies in order to favor transfer and
long-term effects and overcome the limitations of mnemonic training—which usually shows small or null transfer (Verhaeghen,
Marcoen, & Goosens, 1992; but see Carretti, Borella, & De Beni,
2007)—and maintenance effects (e.g., Rebok, Carlson, & Langbaum, 2007; Verhaeghen & Marcoen, 1996), usually attributed to
the strategy taught.
Buschkuehl et al. (2008) proposed an adaptive visual WM
training program to old-old adults: Their results showed substantial gains in the WM trained tasks. Short- and long-term transfer
effects were found only for tasks with the same stimuli content.
Similarly, Li et al. (2008) found in young and older adults
specific improvement in the task practiced—a spatial 2 n-back
WM task–that involved two conditions: one standard, one more
demanding. Transfer effects were found on a more demanding 3
n-back visual task as well as on numerical n-back tasks. Although
near transfer effects to the same (visual) and also different (numerical) modality were shown, no far transfer effects to more
complex WM tasks (operation and rotation span tests) were found.
With regard to maintenance effects, Buschkuehl et al. (2008)
failed to find any maintenance 1 year after completion of training,
in comparison with pretest. In contrast, Li et al. (2008) showed a
maintenance of practice gains and of near-transfer effects at
3-month follow-up; nonetheless, in contrast with young adults,
older participants showed a performance decrement from postpractice to follow-up.

Current training studies are seeking ways to enhance and maintain cognitive functioning of older adults through interventions
targeting specific processes (see Park, Gutchess, Meade, & StineMorrow, 2007) such as inhibition (Davidson, Zacks, & Williams,
2003), executive functions (i.e., updating: Dahlin, Nyberg, Bäckman,
& Stigsdotter Neely, 2008; and task switching: Karbach & Kray,
2009), and working memory (WM) (Buschkuehl et al., 2008; Li et
al., 2008). Developing such interventions involves determination
of the range of modifiability of the targeted functions, the extent to
which the trained ability transfers to other tasks, and therefore
establishment of whether the benefits are due to the “formation of
a new skill, . . . or whether cognitive mechanisms and capacities of
general applicability have been enhanced” (Li et al., 2008, pp.
731–732).
Of particular interest is the role of WM in view of its involvement in various different complex abilities such as reasoning
(Borella, Carretti, & Mammarella, 2006; de Ribaupierre & Lecerf,
2006; Nettelbeck & Burns, 2010) and reading comprehension (De
Beni, Borella, & Carretti, 2007; DeDe, Caplan, Kemtes, & Waters,
2004), as well as its linear decline with aging (Bopp & Verhaeghen, 2005; Borella, Carretti, & De Beni, 2008; Park & Payer,
2006). One of the challenges of aging research is therefore to
determine not only whether performance in such a basic cognitive
process can be improved, but also whether WM training produces
effects that are generalized to untrained tasks and maintained over
time.

This article was published Online First October 25, 2010.
Erika Borella, Barbara Carretti, Francesco Riboldi, and Rossana De
Beni, Department of General Psychology, University of Padova.
We wish to thank Sergio Morra for his helpful comments during the
preparation of this manuscript.
Correspondence concerning this article should be addressed to Erika Borella
or Barbara Carretti, Department of General Psychology, Via Venezia, 8, 35131
Padova, Italy. E-mail: erika.borella@unipd.it or barbara.carretti@unipd.it
767

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BORELLA, CARRETTI, RIBOLDI, AND DE BENI

Overall, these findings provide evidence for the modifiability of
WM performance and coordination processes in young-old and
old-old adults, and reveal a degree of cognitive plasticity in aging.
However, transfer and maintenance gains, when found, appear to
be limited and specific, being mainly related to tasks similar in
format to the trained task and specifically share processing requirements with the practice task.
The present study investigates the extent of transfer of WM
training in older adults. In previous WM training studies with older
adults, the training tasks were visual and the procedure was either
adaptive (Buschkuehl et al., 2008) or based on repetitive practice
with no adaptation to the participant performance outcome (Li et
al., 2008). Instead, in the current study the training stimuli were
verbal, and the intervention mixed a particular adaptive procedure
with variations in task requests. As with the adaptive procedure,
the task difficulty was increased if participants were successful at
a given level; however, in the case of failure the lowest level was
presented. In addition, in the present training, maintenance and
processing requests of the task were systematically varied. This
hybrid training procedure was thought to keep the task always
challenging and variable, aspects which should favor transfer
effects, as pointed out by the literature on training studies (e.g.,
Gardner, Strayer, Woltz, & Hill, 2000; Karbach & Kray, 2009).
We assessed whether WM training of this type produced: (i)
performance gains on the criterion task on which participants were
trained; (ii) short-term transfer effects (posttest), and (iii) longterm maintenance of gains—at 8 months (follow-up session)—
following completion of training.
Because one of the critical issues in training studies is the
definition of these effects, in particular the range of transfer
distance (e.g., Noack, Lövdén, Schmiedek, & Lindenberger,
2009), the effects were categorized along a conceptually based
continuum of nearest- to far-transfer tasks. Common measures
used in cognitive aging research, and theoretically related to WM,
were chosen: short-term memory, fluid intelligence, inhibition, and
processing speed (Craik & Salthouse, 2000; Verhaeghen, Steitz,
Sliwinski, & Cerella, 2003). For nearest-transfer effects, a visuospatial WM task (Dot Matrix task; adapted from Miyake, Friedman, Rettinger, Shah, & Hegarty, 2001) was included. This task
involves processes (elaboration and processing phase) similar to
the one practiced. However, the nature of the material and the
secondary requirement are different from those of the trained task.
The Forward and Backward Digit Span tests were used to assess
near-transfer effects because they are part of the general memory
factor, but the task requests were different from those of the WM
tasks (see Bopp & Verhaeghen, 2005). Because these tasks measure the same narrow or same broad ability, we expect transfer
effects onto them. To determine the presence of far transfer effects,
we chose classic tasks: the Cattell task to measure nonverbal
reasoning ability; the Stroop Color test to index inhibition-related
mechanisms; and the Pattern Comparison test to assess processing
speed. The transfer abilities were chosen with consideration of
their relationship to WM processes. Working memory impairment
in older adults is generally attributed to general mechanisms such
as inhibition and processing speed (Borella et al., 2008). Furthermore, WM is frequently advanced as one of the mechanisms that
also accounts for age-related differences in intelligence tasks (de
Ribaupierre & Lecerf, 2006; Rabbitt & Lowe, 2000; Schaie &
Hertzog, 1986), because WM and intelligence share a limited

processing capacity that has to keep a certain amount of information simultaneously active (Kyllonen & Christal, 1990) and, more
generally, because both require controlled and effortful processing
(Salthouse, Pink, & Tucker-Drob, 2008).
Given that these tasks tap mechanisms related to WM process
functioning in older adults, theory would lead us to expect fartransfer effects, despite the failure to find such effects in previous
studies (e.g., Buschkuehl et al., 2008; Li et al., 2008). In addition
the magnitude of modifiability of untrained tasks was evaluated,
given that the tasks selected vary in degree of processing overlap
with the practice task.

Method
Participants
Forty older adults, aged 65-75 years, participated in the study;
20 (13 women and 7 men) were randomly assigned to the trained
group; the remaining 20 (10 women and 10 men) were assigned to
the control group.
All participants were healthy, native Italian speakers and volunteered for the study; they were selected on the basis of a physical
and a health questionnaire. None fitted the “exclusion criteria”
proposed by Crook et al. (1986): that is, history of head trauma;
any neurological or psychiatric illness; history of brain fever;
dementia or any other state of consciousness alteration; use of
benzodiazepines in the previous 3 months; use of illicit drugs;
visual, auditory, or motor impairment; and any symptomatic cardiovascular condition, breathing problems, or pathologies causing
possible cognitive impairments. They were recruited from the
University of the Third Age or in social clubs in Mestre (Venice,
Italy); they were active in the cultural and social activities of their
neighborhoods.
Experimental and control group participants did not differ ( p ⬎
.05) in age, years of formal education, or Wechsler Adult Intelligence Scale—Revised (WAIS–R) vocabulary score (Wechsler,
1981). Demographics for each group are listed in Table 1.

Materials
Criterion task. The Categorization Working Memory Span
task (CWMS; Borella et al. 2008; De Beni, Borella, Carretti,
Marigo, & Nava, 2008) is similar to the classic WM tasks, such as
the Listening Span test (Borella et al., 2008), the only difference
being that it involves processing lists of words rather than sentences, limiting the role of semantic processing. The materials

Table 1
Demographic Characteristics of the Trained and
Control Groups
Trained group
(N ⫽ 20)

Control group
(N ⫽ 20)

Characteristic

M

SD

M

SD

Age
Education
Vocabulary

69.00
9.40
46.65

3.18
3.95
8.64

69.15
9.25
44.60

2.99
3.54
9.23

WORKING MEMORY TRAINING IN OLDER ADULTS

consisted of 10 sets of words, each set comprising 20 lists of
words, which were organized in series of word lists of different
lengths (from 2 to 6). Each list contained 5 words of high–medium
frequency. Furthermore, the lists contained zero, one, or two
animal nouns, present in any position, including last. An example
list is house, mother, dog, word, night. Of the total number of
words (200) in the task, 28% were animal words.
Participants listened to the lists of words audiorecorded presented at a rate of 1 s per word and had to tap their hand on the
table whenever they heard an animal noun (processing phase). The
interval between series of word lists was 2 s (the presentation was
thus paced by the experimenter). At the end of the series, participants recalled the last word of each string in serial order (maintenance phase). Two practice trials of 2-word length were given
before the experiment started.
Words recalled were written down by the experimenter on a
prepared form. The total number of correctly recalled words was
used as the measure of WM performance (maximum score 20).
This score has been demonstrated to show large correlations with
visuospatial (Jigsaw Puzzle test) and verbal (Listening Span test)
WM tasks (Borella et al., 2008), and measures of fluid intelligence
(Borella et al., 2006).
Half (five) of the sets were used as a pretest task, the other five
as posttest. The two sets were counterbalanced across testing
sessions.
Nearest-transfer effects: Visuospatial WM task. The Dot
Matrix task (adapted from Miyake et al., 2001) is a computerized
WM task that differs from the trained task in terms of: (i) the
nature of the materials (visuospatial), and (ii) the secondary requirement, that is to verify equations rather than to tap on hearing
an animal word.
Participants had to verify a simple visual matrix equation,
checking whether it was true or false while simultaneously remembering a dot location on a 5 ⫻5 grid. Each trial contained a set of
matrix equations for verification that consisted of either addition or
subtraction presented as lines drawn on a 3 ⫻3 matrix. Participants
were given a maximum of 4.5 s to verify each equation by saying
“True” or “False.” Immediately after each response, participants
were then given a 3-s presentation of a 5 ⫻ 5 grid containing a dot
in one of its squares. After a sequence of between two and six
equation-grid pairs, they had to indicate on a blank 5 ⫻ 5 grid the
sequence that had contained the dot. There was one practice trial
with two equations, each with one dot. The test was carried out at
five levels (Levels 2 to 6). A total of 28 equations and 28 matrices
were presented.
Positions recalled were written down by the experimenter on a
prepared form. The total number of dot positions correctly recalled
was considered as a dependent variable (maximum score of 14).
Half (14) of the sets were used at pretest, the other 14 at posttest
in a counterbalanced fashion across testing sessions.
Near-transfer effect: Short-term memory tasks.
In the
Forward Digit Span and Backward Digit Span tasks (De Beni et
al., 2008), series of digits were presented at a rate of 1 s per digit;
participants had to repeat the digits in the same (forward) or
reverse (backward) order. The series started from three digits and
rose to nine for the forward task, and went from two to eight for
the backward task. Each level contained two strings of digits. After
two consecutive recall errors, the task was discontinued. A practice

769

sequence of two digits was given for each task before the test
started. One point was awarded for each sequence correctly recalled.
The final score corresponded to the total number of correct trials
recalled (maximum score of 14 for both tasks).
For each task, two versions were created, exchanging the digit
strings within each level; one was administered at pretest, the other
at posttest in counterbalanced fashion across testing sessions.
Far-transfer effects: Fluid intelligence (Cattell test),
inhibition-related processes (Stroop Color task), and processing speed (Pattern Comparison test).
Culture Fair test, Scale 3 (Cattell & Cattell, 1963). Scale 3
of the Cattell test consists of two parallel forms (A and B), each
containing four subtests to be completed in 2.5 to 4 min, depending
on the subtest. In the first subtest, Series, participants saw an
incomplete series of abstract shapes and figures and had to choose
from six alternatives that best completed the series. In the second
subtest, Classifications, participants saw 14 problems comprising
abstract shapes and figures and had to choose which 2 of the 5
differed from the other 3. In the third subtest, Matrices, participants were presented with 13 incomplete matrices containing four
to nine boxes of abstract figures and shapes plus an empty box and
six choices: Their task was to select the answer that correctly
completed each matrix. In the final subtest, Conditions, participants were presented with 10 sets of abstract figures, lines, and a
single dot, along with five alternatives: Their task was to assess the
relationship among the dot, figures, and lines, then choose the
alternative in which a dot could be positioned in the same relationship.
The dependent variable was the number of correctly solved
items across the four subsets (maximum score of 50).
One of the two parallel forms (A or B) was administered at
pretest, the other at posttest in counterbalanced fashion across
testing sessions.
Stroop Color task (adapted from Trenerry, Crosson, De Boe, &
Lever, 1989). The task, administered in paper modality, consisted of six cards. The first two cards contained names of
colors printed in an incongruent ink color (Incongruent condition). The third and fourth cards contained names of colors
printed in a congruous ink color (Congruent condition). Finally,
two cards containing color patches were presented (Control
condition). Participants had to name the ink color of each
stimulus and were required to process stimuli as fast as possible
whilst being as accurate as possible. The experimenter recorded
response latencies for all conditions by using a stopwatch as
times between naming first and last stimuli, as is typical in
other studies using the paper version (e.g., Troyer, Leach, &
Strauss, 2006; Van der Elst, Van Boxtel, Van Breukelen, &
Jolles, 2006; West & Alain, 2000), and noted accuracy by hand
on a prepared form. An interference effect was calculated as the
relative difference, to control for individual difference in baseline (e.g., Borella, Delaloye, Lecerf, Renaud, & de Ribaupierre,
2009), between Incongruent and Control conditions calculated
for time and errors as follows: (incongruent condition⫺control
condition)/control condition. A higher score thus implies
greater difficulty in controlling the prepotent response in the
incongruent condition.

770

BORELLA, CARRETTI, RIBOLDI, AND DE BENI

Two versions of the task were created by exchanging card order
within each condition; one was administered at pretest, the other at
posttest in counterbalanced fashion across testing sessions.
Pattern Comparison task (adapted from Salthouse & Babcock,
1991). In this task, participants had to decide whether arrangements of line segments were identical or not. The items to be
compared were set out on two pages, each containing one column
of 30 items. Responses consisted of writing S (for Si, i.e., Yes, for
identical) or N (for No, for different) on the line between the two
members of each stimulus pair. The experimenter used a stopwatch
to record the time to complete each page. Three practice trials were
given before the experiment started. The dependent variable was
total time taken to complete responses for the two pages.
Two versions of the task were created by exchanging page
order; one was administered at pretest, the other at posttest in
counterbalanced fashion across testing sessions.

Procedure
Participants of the trained and control groups attended five
individual sessions: The first and last sessions were the pretest and
the posttest; in the other three, the control group carried out
alternative “activities,” as described below, while the trained group
attended the training itself. Each session lasted about 60 min; the
training was completed within a 2-week time frame, with a fixed
2-day break between training sessions. The schedule was identical
for the two groups, thus allowing amount of social interaction to be
matched.
The WM training consisted of three sessions (Sessions 2, 3, and
4) in which participants were trained on modified versions of the
CWMS task. The experimenter presented participants of the
trained group with lists of words, audiorecorded and organized as
for the CWMS task. Again, as for the CWMS task, the basic
instructions were to recall the target words (see below) and tap the
hand on the table whenever an animal noun arose. However, some
manipulations were made during the three sessions to favor generalized transfer and limit the development of task-specific strategies. The maintenance demand of the CWMS task was manipulated by increasing the number of words to be recalled in the case
of success, and presenting the lowest memory load in the case of
failure (Session 2). Moreover, the task requests were varied, requiring recall of: (i) the last or first word of each series (Sessions
2 and 4), and (ii) words that were followed by a beep sound
(Session 3). The processing request, tapping for occurrence of an
animal noun, was also manipulated by varying the frequency of
these animal words in the lists (Session 3).
There were no instructions to use specific strategies, and no
feedback was provided.
For the trained group the five sessions were organized as follows:
• In Session 1 (pretest session), participants were given the
following tasks (listed in order of presentation): Vocabulary, Forward Digit Span, Backward Digit Span, Pattern Comparison,
CWMS, Stroop Color, Dot Matrix, and Cattell. The order of
presentation was fixed for each participant. The CWMS was
presented in auditory modality (audiorecorded), the Dot Matrix
was computerized, the Stroop Color was in paper modality, and the
Vocabulary, Pattern Comparison, and Cattell tests were in paperand-pencil format.

Furthermore, before starting this and the training sessions, in
order to limit the influence of sensory variables (sight and hearing)
on the outcomes, the auditory presentation was adjusted to the
participant’s hearing level. Also, for the paper-and-pencil tasks, all
participants were asked whether they found it easy to read the
stimuli.
• In Session 2, series of word lists were grouped into sets of
different lengths (from 2 to 5), each with three series of word lists.
The task included three phases presented sequentially: In the first
phase, the experimenter instructed participants to recall the last
word of each series of words; in the second phase, the first word
of each series; and in the third phase, again the last word.
The session always began from the first phase (recall of the last
word of each series of words) and from the length 2 set. In the case
of success (correct recall of words for two of the three series), the
task was increased in difficulty up to length 5. Once the final level
was reached, phase 2 (requiring recall of the first word) was
presented, starting from the lowest level of memory load (i.e.,
length 2). In the case of success in all second-phase trials, the third
phase (recall of the last item) was administered, starting again from
the lowest level (2).
However, in the case of failure, the level of difficulty was not
increased, and the following phase was started. For example, if
participants failed at level 3 of the first phase, they started the
second phase from level 2; if they succeeded here, the level of
difficulty was increased, whereas for failure there was again a
change of phase (the third). The new phase always started from the
lowest level (2). The experimenter ended the task when, at phase
3, the participant failed to correctly recall the final words of two
out of the three series. Of the total number of words (185) per task
part, 20% were animal words.
• In Session 3, series of word lists audiorecorded were
grouped into sets of different lengths (from 2 to 5), each
consisting of four series of word lists. For each length, the first
two series contained fewer animal words than did the last two.
Specifically, word series of length 2 could contain from 2 to 8
animal nouns; length 3, from 4 to 9; length 4, from 6 to 11; and
length 5, from 8 to 17. For each series, participants were
instructed to remember each word that was followed by a beep,
in serial order. The task difficulty was not adjusted to participant performance. Of the total number of words (280) included
in the task, 55% were animal words.
• In Session 4, participants had to recall alternately words in
last, then first, position on the list. The task started with a set of
four series of two-word lists, the requirement for the first series
being to recall the last words of each list; for the second series, the
first words; for the third series, the last words; and for the fourth
series, the first words. The memory load increased progressively
from two to five, independently of participant performance (correct or incorrect recall). Of the total number of words (280)
included in the task, 20% were animal words.
• In Session 5 (posttest session), participants were administered
the same tasks (except for the Vocabulary test) as in Session 1,
with the same presentation order.
Control group participants met the experimenter and underwent
the same number of sessions as did the trained group for approximately the same amount of time. Sessions 1 and 5 were the same
as for the trained group. However, in Sessions 2, 3, and 4, participants were required to complete a questionnaire in which they had

WORKING MEMORY TRAINING IN OLDER ADULTS

771

For the Stroop Color task, in view of the very low/null number
of errors, analyses were run only on interference index for response times (RTs).

to try to remember common events related to their childhood,
adulthood, and recent events, and to rate their vividness (Autobiographic Memory Questionnaire, De Beni et al., 2008; Session 2);
rate the frequency of behavior dedicated to saving memories of life
events (Memory Sensitivity Questionnaire, De Beni et al., 2008;
Session 3); and rate personal satisfaction about life (past, present,
and future), emotional competencies (ability to understand one’s
own and other’s emotions), and coping strategies regarding everyday problems (Questionnaire of Psychological Well-Being, De
Beni et al., 2008; Session 4). Questionnaires were administered in
paper-and-pencil format.
For both trained and control groups, the sessions were guided by
the experimenter, who explained the activities of each session and
managed the presentation of materials (i.e., stopping the taperecorder to allow participants to recall the words). Sessions ended
with the experimenter asking participants their feelings about the
activities carried out and reminding them about the date of the
following meeting.
All participants took part in an 8-month follow-up maintenance
session, in which the tasks, in pretest versions, were administrated
in the same order.

Criterion Task: CWMS
Trained participants recalled more correct words than did controls (Mdiff ⫽ 4.23, p ⬍ .001). Performance consistently increased
from pretest to posttest and follow-up (Mdiff ⫽ ⫺2.65, p ⬍ .001,
and Mdiff ⫽ ⫺2.82, p ⬍ .001, respectively), the latter not differing
between each other. Post hoc comparisons revealed that trained
participants performed better at posttest ( p ⬍ .001) and follow-up
( p ⬍ .001) than at pretest. Furthermore, this group maintained
performance from posttest to follow-up. By contrast, no significant
difference was found for the control group. The trained group
outperformed the control group at both posttest and follow-up
( p ⬍ .001).
The trained group performed better at posttest than at pretest,
and the performance increased from pretest to 8-month follow-up.

Transfer Effect
Nearest-transfer effect. For the Dot Matrix task, all participants performed better at posttest and follow-up than at pretest
(Mdiff ⫽ 1.70, p ⬍ .001, and Mdiff ⫽ 0.67, p ⬍ .01, respectively);
however, from posttest to follow-up, performance fell (Mdiff ⫽
⫺1.02, p ⬍ .01).
For the trained group, post hoc comparisons indicated an improvement in performance between pretest and posttest ( p ⬍ .001)
and a decrease between posttest and follow-up ( p ⬍ .001). No
significant difference was found from pretest to follow-up for the
trained participants. For the control group, performance did not
differ between sessions. The two groups differed only in posttest
performance ( p ⬍ .001).
Near-transfer effect. In the Forward Digit Span test, the
trained group recalled significantly more correct digits than did the
control group (Mdiff ⫽ 1.18, p ⬍ .001). For the two groups,
performance was significantly higher at posttest than at pretest

Results
First, in order to interpret differences between the two groups on
the training procedure, separate analyses of variance (ANOVAs)
were carried out with group (trained and control) as the betweensubjects factor on the pretest performance on all tasks. Results
indicated that there were no baseline differences between the
trained and control groups (see Table 2).
To assess training effects, we analyzed the measures of interest
for each task using a 2 Group (trained and control) ⫻ 3 Session
(pretest, posttest, and follow-up) mixed-design ANOVA with
group as a between-subjects factor and session as repeated measures. Interactions were decomposed using post hoc pairwise comparisons with Bonferroni’s correction at p ⬍ .05, adjusted for
multiple comparisons. Descriptive statistics are given in Table 2,
and the ANOVA results are summarized in Table 3.

Table 2
Descriptive Data for Pretest, Posttest and Follow-Up Data by Group
Trained group
Pretest

Posttest

Control group
Follow-up

Pretest

Posttest

Follow-up

Measure

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

CWMS
Dot Matrix
Forward Span
Backward Span
Cattell
Stroop Color incongruent RTs
Stroop Color control II RTs
Stroop Color index on RTs
Stroop Color incongruent errors
Stroop Color control II errors
Pattern Comparison

9.60
5.70
6.65
5.30
16.00
31.58
15.35
1.10
1.10
0.00
174.60

3.25
2.13
0.99
0.98
4.79
5.88
3.44
0.39
1.62
0.00
40.35

15.45
8.90
8.45
7.60
22.05
22.88
13.05
0.78
0.15
0.00
135.55

1.54
1.52
0.51
0.94
3.59
3.51
1.81
0.35
0.37
0.00
36.75

14.75
6.5
6.35
5.54
20.30
24.37
12.32
1.01
0.00
0.00
146.85

1.41
2.04
0.49
0.51
2.92
3.24
2.10
0.35
0.00
0.00
33.70

9.05
6.15
6.40
5.55
15.70
33.50
15.95
1.15
1.30
0.00
188.01

4.22
2.25
1.47
1.54
4.16
8.28
4.67
0.37
1.52
0.00
30.34

8.50
6.35
5.65
5.10
16.54
32.25
15.66
1.09
0.85
0.00
186.50

3.57
2.11
1.14
1.07
5.29
7.57
4.32
0.34
0.81
0.00
28.99

9.55
6.70
5.85
5.40
16.55
31.47
15.77
1.03
1.15
0.00
182.43

3.96
1.63
1.18
0.94
4.36
8.34
4.46
0.33
1.27
0.00
30.01

Note. CWMS ⫽ Categorization Working Memory Span test; RTs ⫽ response times.

BORELLA, CARRETTI, RIBOLDI, AND DE BENI

772

Table 3
Mixed-Design 2 ⫻ 3 ANOVA Results for the Measures of
Interest, With Group (Trained, Control) as Between-Subjects
Factor and Session (Pretest, Posttest, Follow-Up) as
Repeated Measures
Measure

MSE

n2p

20.71ⴱⴱⴱ

8.65

0.35

42.77ⴱⴱⴱ
46.69ⴱⴱⴱ

2.34
2.34

0.53
0.55

2.69

0.03

2.34
2.34

0.36
0.35

0.72

0.34

0.49
0.49

0.32
0.51

0.69

0.18

.59
.59

0.34
0.50

13.65

0.16

6.69
6.69

0.33
0.22

0.22

0.05

0.80
0.80

0.10
0.08

855.51

0.25

411.00
411.00

0.23
0.19

F(1, 38)
Specific effect

CWMS
Between subjects
Group
Within subjects
Session
G⫻S

Transfer effects
Dot Matrix
Between subjects
Group
1.35
Within subjects
Session
21.70ⴱⴱⴱ
G⫻S
20.51ⴱⴱⴱ
Forward Digit Span
Between subjects
Group
19.48ⴱⴱⴱ
Within subjects
Session
18.26ⴱⴱⴱ
G⫻S
39.83ⴱⴱⴱ
Backward Digit Span
Between subjects
Group
8.53ⴱⴱⴱ
Within subjects
Session
19.27ⴱⴱⴱ
G⫻S
38.43ⴱⴱⴱ
Cattell test
Between subjects
Group
7.58ⴱⴱⴱ
Within subjects
Session
18.78ⴱⴱⴱ
G⫻S
10.80ⴱⴱⴱ
Stroop Color interference index
Between subjects
Group
2.07
Within subjects
Session
4.14ⴱ
G⫻S
3.34ⴱ
Processing speed
Between subjects
Group
12.97ⴱⴱⴱ
Within subjects
Session
11.38
G⫻S
8.67ⴱⴱⴱ

Note. ANOVA ⫽ analysis of variance; CWMS ⫽ Categorization Working Memory Span test; G ⫽ group; S ⫽ session.
ⴱ
p ⬍ .05. ⴱⴱⴱ p ⬍ .001.

(Mdiff ⫽ 0.52, p ⬍ .001). Nevertheless, a higher number of correct
digits at both pretest and posttest was recalled than at follow-up
(Mdiff ⫽ 0.42, p ⬍ .001, and Mdiff ⫽ 0.95, p ⬍ .001, respectively). Post hoc comparisons revealed that only the trained group
showed a consistent increase in performance between pretest and
posttest ( p ⬍ .001), whereas at follow-up, performance was not
different from that at pretest. A decrease in performance between
posttest and follow-up ( p ⬍ .001) was also found for the trained
participants. For the control group, performance did not differ

between sessions. The trained group performed better than did the
control group only at posttest ( p ⬍ .001).
In the Backward Digit Span test, the trained group performed
better than did the control group (Mdiff ⫽ 0.77, p ⬍ .001).
Participants performed significantly better at posttest than at either
pretest (Mdiff ⫽ 0.92, p ⬍ .001) or follow-up (Mdiff ⫽ 0.92, p ⬍
.001), but no significant difference was found from pretest to
follow-up. Although for the control group no significant differences were found across test occasions, the trained group showed
a significant increase in performance between pretest and posttest
( p ⬍ .001). Even so, for the trained participants a significant
decrease between posttest and follow-up ( p ⬍ .001) was found,
and the comparison between pretest and follow-up was not significant. The trained group performed better than did the control
group only at posttest ( p ⬍ .001).
For the visuospatial WM tasks and the two short-term memory
tasks, trained participants did not maintain immediate transfer over
time, performing at a level similar to that at pretest.
Far-transfer effect. For the Cattell test, results indicated that
trained participants performed significantly better than did controls
(Mdiff ⫽ 3.22, p ⬍ .001). Posttest and follow-up performances
were significantly better than on pretest (Mdiff ⫽ 3.40, p ⬍ .001,
and Mdiff ⫽ 2.75, p ⬍ .001, respectively). No significant difference was found between posttest and follow-up. Post hoc comparisons revealed that only the trained group showed significant
improvement in performance between pretest and both posttest
( p ⬍ .001) and follow-up ( p ⬍ .001), although posttest performance was not different from that of follow-up. By contrast, no
significant difference was found for the control group. The trained
group performed better at both posttest and follow-up than did the
control group ( p ⬍ .001).
With regard to the Stroop Color interference index, all participants showed stronger resistance to interference at posttest
than at pretest (Mdiff ⫽ 0.18, p ⬍ .01). Post hoc comparisons
indicated that trained participants, but not control ones showed
stronger resistance to interference at posttest than at pretest
( p ⬍ .01), whereas the trained group showed stronger resistance to interference than did the control group only at posttest
( p ⬍ .001). No other difference was significant for the two
groups.
Finally, for the Pattern Comparison test, the trained group
completed the task more quickly than did the control group
(Mdiff ⫽ 33.31, p ⬍ .001). For both groups, completion times were
shorter at both posttest and follow-up than at pretest (Mdiff ⫽
20.27, p ⬍ .001, and Mdiff ⫽ 16.65, p ⬍ .001, respectively); no
significant difference was found between posttest and follow-up.
Post hoc comparisons indicated that the trained group, but not the
control group, showed a significant decrease in time to complete
the task between pretest and posttest ( p ⬍ .001), and between
pretest ( p ⬍ .001) and follow-up, although posttest performance
was not different from that of follow-up. No significant difference
was found for the control group. The trained group performed
better at both posttest and follow-up than did the control group
( p ⬍ .001).
Trained older adults showed immediate transfer effect. After the
training, on the Cattell and Pattern Comparison tasks, they also
performed better than at pretest. However, the immediate transfer
effect for the Stroop Color interference index was not maintained
over time.

WORKING MEMORY TRAINING IN OLDER ADULTS

To obtain a closer understanding of the range of training
gains and transfer effects between pretest and posttest, Cohen’s
(1988) d — expressing the effect size of the comparisons—was
calculated. Values were corrected using the Hedges and Olkin
(1985) correction factor to avoid the small sample bias. Comparisons
of the gains from pretest to posttest within each group revealed a large
(above .80) effect size in the case of the trained group, and a small
effect size for the control group (see Figure 1).
For the pretest and follow-up, effect sizes were large for the
Criterion task (CWMS, d ⫽ 2.01) and Cattell test (d ⫽ 1.12), and
medium for the Pattern Comparison task (d ⫽ 0.73).

Discussion
Summary of Findings
The study investigated the transfer effects of a verbal WM
training program in older adults, along with the maintenance of
gains. It was found that the WM training benefits were indeed
effective, with only the trained group showing both specific gains
(on the Criterion task) and transfer gains. Evidence was found for
substantial and immediate posttraining effects of transfer not only
to similar abilities but also to abilities dissimilar but theoretically
related to WM. The magnitude of the effects was in fact large, as
was demonstrated by the values of the d index (Cohen, 1988).
Moreover, the training benefit gains are also supported by a
decrease of variability (standard deviation) for the trained group,
indicating an overall increase in test performance after the intervention. Transfer effects were found for tasks that represent a same
narrow ability (visuospatial WM task); different narrow but same
broad ability (short-term memory tasks); and different abilities
(fluid intelligence, inhibition, and processing speed tasks). In contrast, control group gains were very limited or null in comparison
with those of the trained group. This pattern of findings clearly
indicates that trained-group benefits cannot be interpreted as sim-

773

ple test–retest effects or as due to individual differences. Furthermore, because different task versions were used from pretest to
posttest, transfer effects are unlikely to be due to item-specific
practice effects.

Transfer Effects
Our results are somewhat surprising given that previous WM
training studies failed to find transfer effects, except for the practiced task or tasks closely sharing processes with it. One potential
source of these transfer effects may be the training procedure
adopted, which may have produced a change in the allocation of
attentional resources to enable efficient handling of the different
task demands of the transfer tasks (e.g., Jennings, Webster,
Kleykamp, & Dagenbach, 2005; Schmidt & Bjork, 1992). The
training program provided an adaptive and repetitive regimen for
participants, with repeated practice and gradual increase of difficulty: The WM training task difficulty was in fact manipulated by
changing the amount of information to be recalled (Session 2) or
by varying the processing (Sessions 3 and 4) and maintenance
requests (Phase 2 of Session 2, and Sessions 3 and 4), thus forcing
participants to quickly adapt to new task requests. Such a procedure ensures that the task is always challenging, cognitively demanding, and novel, thus probably requiring participants to adhere
to the task. Schmidt and Bjork (1992) have argued that challenging
training conditions often promote generalization. Furthermore, it is
clear that our practice task engages multiple processes including
encoding, maintenance of information, inhibition of no-longerrelevant information, simultaneous management of two tasks,
shifting attention, and ability to control attention. Thus, assuming
that all these processes were trained, it is logical that transfer
effects would be obtained. In the domain of executive functions, it
has been shown that if the training paradigm is flexible and
variable, transfer occurs (e.g., Kramer, Larish, & Strayer, 1995).
For instance, Kramer et al. (1995) found transfer effects when

Figure 1. Effect size d for pre- and posttest contrasts for transfer effects (nearest, near, and far) as a function
of transfer measure and group (trained and control). CWMS ⫽ Categorization Working Memory Span test.

774

BORELLA, CARRETTI, RIBOLDI, AND DE BENI

older participants were instructed to rapidly shift priority among
concurrent tasks (variable priority condition); however, effects
were not found when they were asked to treat the concurrent task
demand as equal in importance. Consistently, Karbach and Kray
(2009) found robust transfer effects on verbal and visuospatial
WM, fluid intelligence, and interference control in the Stroop
Color task in children, young adults, and older adults after taskswitching training. Basak, Boot, Voss, and Kramer (2008) found
transfer effects after videogame training for some executive control tasks such as task switching, focus switching in n-back, and
visual short-term memory. It is worth mentioning that in this study,
no transfer effects after a 23-hr training were found for a complex
span test (the operation span test) or for other executive control
tasks/measures (e.g., stop signal, accuracy on the n-back and task
switching, enumeration, attentional blink, and mental rotation latency).
The present findings, however, contrast with the outcome of the
WM training studies conducted with older adults (Buschkuehl et
al., 2008; Li et al., 2008), in which no transfer effects were found
either to other complex WM span tasks (Li et al., 2008) or to tasks
with different memory content (Buschkuehl et al., 2008). This
discrepancy cannot be due to the tasks selected to determine
transfer effects, because some were similar across our study and
previous investigations (short-term memory tasks, Buschkuehl et
al., 2008, and complex WM tasks, Li et al., 2008). However, some
sources of difference between our study and earlier investigations
can be identified.
First, the participants involved in our study were young-old
(mean age of 69 years), whereas in Buschkuehl et al.’s (2008)
study as well as that of Li et al. (2008), they were old-old adults
(mean age of 80.1 and 74.5 years, respectively). In the context of
episodic memory, the meta-analysis by Verhaeghen et al. (1992)
has pointed out that the benefit of interventions is negatively
related to participant age (see also Singer, Lindenberger, & Baltes,
2003). It has been shown that cognitive plasticity is reduced over
the adult life span (Jones et al., 2006), with young-old exhibiting
larger training-related gains than old-old (Singer et al., 2003). The
importance of participant age is evident from considering the
results of training focused on executive control tasks–for example,
task-switching (Buchler, Hoyer, & Cerella, 2008; Karbach &
Kray, 2009; Kramer, Hahn, & Gopher, 1999), dual tasks (Bherer et
al., 2005, 2008), or general executive functions (Basak et al.,
2008)—for which transfer effects emerged with a sample comprising young-old (age range between 60 and 75 years, mean age
between 65 and 71 years; Basak et al., 2008; Bherer et al., 2005,
2008; Karbach & Kray, 2009; Kramer et al., 1995). The question
of whether transfer effects of WM training can also be determined
by participant age range is of interest and should be addressed in
further research.
Second, as is mentioned at the beginning of this section, the task
and the procedure used to train participants can be considered an
important source of difference. For example, Buschkuehl et al.
(2008) reported that trained participants claimed to have generated
task-specific strategies in one of the variants of the WM task in
which they were trained, leading to greater training gains (62%)
with respect to the other two variants (44% and 15%, respectively).
The difficulty of transferring the gains obtained in a specific task
to other tasks suggests that the WM training by Buschkuehl et al.
did not foster an increase in flexibility, but simply the tendency to

find a strategy to recall as many items as possible but in the context
of each WM task. In the case of Li et al. (2008), the modest
transfer effects to the WM task can be explained by reflecting on
the nature of the trained task: n-back task, which involves the
manipulation and maintenance of information as well as updating
of temporal order and contextual information and binding processes between stimuli and certain representation (Oberauer,
2005). Although the n-back shares common processing mechanisms with complex span tasks, the underlying mechanisms of the
n-back are not completely understood (Schmiedek, Hildebrandt,
Lövdén, Wilhelm, & Lindenberger, 2009). Moreover, the few
studies that used it with other WM tasks— complex span tasks—
have shown variable correlations (from very low or null-Kane,
Conway, Miura, & Colflesh, 2007; Roberts & Gibson, 2002–to
large–Schmiedek et al., 2009; Shamosh et al., 2008).
Finally, our training consists of just three 1-hr sessions, in
comparison with larger numbers of training sessions used in the
other WM training studies: 24 sessions (2 per week, for 12 weeks)
for Buschkuehl et al. (2008), 45 (15 min per day) for Li et al.
(2008). Although obtaining transfer effects after such brief training
may appear counterintuitive, its intensive nature, concentrated in
terms of a timescale, may have facilitated the short-term transferability of WM training to other tasks, allowing participants to
maintain their interest toward the training activities. Indeed, other
successful training studies involving few training sessions can also
be found in the literature. For example, Karbach and Kray (2009)
found significant transfer effects in older adults with a training of
four sessions (each lasting 60 –70 min). Kramer et al. (1995) as
well as Bherer et al. (2005), in dual-task training, also proposed
limited numbers of training sessions (three and five, respectively),
showing transfer effects in different sets of stimuli and tasks.
Furthermore, in the domain of episodic memory training, intensive
(concentrated in time) interventions (e.g., Stigsdotter Neely &
Backman, 1995) have been shown to have slighter performance
advantages than do briefer interventions (e.g., Verhaeghen et al.,
1992). Future studies should investigate variability of dose, frequency, and intensity of training that produces benefits.
Another important variable might be the training setting, in
particular regarding the presence/absence of the experimenter during the sessions. In Buschkuehl et al. (2008), participants were
tested individually at pretest and posttest, while the training was
conducted in small groups (of 8 participants) under the experimenter’s supervision, with the training tasks carried out at a PC.
Interaction was thus limited to the initial and final parts of the
sessions. No explicit information is given in the study by Li et al.
In our training program, the experimenter guided the entire session
in a one-to-one interaction. The presence of human– human interaction, in contrast to machine– human interaction, might have led
to increased interest toward the activities proposed by the training.
In this sense, motivation and metacognitive aspects—promoting
self-awareness, monitoring progress in performance, reducing anxiety, and promoting higher confidence in participant cognitive
abilities (Dunlosky et al., 2007)—may have played a role in
explaining transfer effects immediately after the training (Carretti,
Borella, Zavagnin, & De Beni, 2010). Some training group participants reported that they were stimulated to “make more use of
their memory” in everyday life; this might have led to the development of new skills outside the training environment. Several
authors have suggested that metacognitive control and self-

WORKING MEMORY TRAINING IN OLDER ADULTS

efficacy might mediate the age effect on memory performance
(e.g., Valentijn et al., 2006), and that metacognition is related to
executive functions (Shimamura, 2000). A more active engagement in cognitive activities has been shown to be positively related
to mental functioning (e.g., Herzog, Hultsch, & Dixon, 1989;
Verghese et al., 2003). The opportunity for participants to experience success in recall during training sessions may have had
positive influence on their attitude, altering their beliefs about
efficacy and control over memory, making them much more flexible in their thinking and enabling them to improve control of
performance efficacy. By the same token, a decrease in motivation
and in self-initiated efforts to maintain such skills, leading to a
production deficit in use of cognitive operation to manage the
tasks, might have partly explained the lack of maintenance effects.
Interviews to ascertain how participants felt about their cognitive
functioning before, immediately after, and at follow-up could have
lent support to this interpretation; unfortunately, these were not
conducted in the present study, and should be included in future
investigations.

Maintenance Effects
To test maintenance of training gains, we presented the same
tasks used at pretest to the trained and control groups. We demonstrated an 8-month maintenance effect of WM performance on
the Criterion task. This result is in line with previous studies
demonstrating long-term maintenance of gains also in older adults
on the Criterion task in both WM training (Li et al., 2008) and in
dual-task training (e.g., Erickson et al., 2007; Kramer et al., 1995)
or updating training (Dahlin et al., 2008).
A novel finding is a selective maintenance that goes beyond that
found in the Criterion task: Performance on the Cattell and Pattern
Comparison tasks was better at follow-up than at pretest. It is
reasonable to suppose that these effects may somehow be related
to practice, or to the fact that participants are able to remember
items. However, the time lag between pretest and follow-up, and
the absence of such maintenance in the control group (which had
the same amount of practice and participated in the follow-up),
make this improbable. Moreover, no other maintenance effects
were found. Transfer effects were not maintained over time for
Stroop Color task, but the same was also true—surprisingly—for
visuospatial WM (nearest-transfer task) and short-term memory
tasks (near transfer). Nonetheless, for most of the tasks, training
effects dissipate in time, confirming findings suggesting that unless attempts are made to provide reinforcement to maintain the
benefit of an intervention, training gains are lost (see Ball et al.,
2002). Booster sessions, in this sense, seem to have positive effects
on the maintenance of training benefit (Brehmer, Li, Muller, van
Oertzen, & Lindenberger, 2007). Brehmer et al. (2007) showed
maintenance of the training benefit through re-presenting mnemonic instructions to reactivate the learned strategies, or more
generally the learned attitude. It is possible that repractice on the
WM task presented during training before follow-up could have
maintained immediate transfer effects.
The particular pattern of training maintenance suggests that the
training may have fostered self-initiated successful processing,
resulting in more efficient knowledge representation and greater
capacity and flexibility, which in turn led to specific maintenance
effect. Both processing speed and fluid intelligence are general and

775

basic abilities that determine capacity and are thus strictly interrelated (e.g., Bugg, Zook, DeLosh, Davalos, & Davis, 2006) and
also related to WM (e.g., de Ribaupierre & Lecerf, 2006; Salthouse, 1991). It can be postulated that because processing speed is
largely considered a basic aspect of cognitive processing that
explains the changes in WM performance with aging (Park et al.,
2002; Salthouse, 1996), improving WM performance increases the
efficiency of cognitive operations fostering ability to move among
the basic information processes. The same considerations apply to
fluid intelligence performance, because this is strictly related to
WM (Kane, Hambrick, Tuholski, Wilhelm, Payne, & Engle, 2004;
for a meta-analysis, see Ackerman, Boyle, & Beier, 2005). In
contrast, the other tasks may call upon more task-specific processes and abilities, leading to only transient (immediate) transfer
effects. The decrease in gains in the follow-up session for the
visuospatial WM task, though unexpected, could depend on the
differences in format of information to be processed (verbal vs.
numerical and spatial). In fact, although some models conceive
WM as a unitary system independently of the material to be
processed, some data suggest that the distinction based on format
is not irrelevant (see, e.g., Kane et al., 2004; Miyake & Shah, 1999;
Park et al., 2002). For tasks involving short-term memory, because
here capacity is determined by practiced skills and strategies, the
training may have only temporarily altered the domain-specific
abilities, after which participants re-engage in automatized skills.
For the finding related to inhibition, the lack of maintenance could
be ascribed to the choice of task. The Stroop Color task has been
shown to tap an inhibition-related function, prepotent response
inhibition, and requires the suppression of exogenous, external
irrelevant information (Borella et al., 2008; Friedman & Miyake,
2004). According to Friedman and Miyake (2004), this function is
indeed considered to be related to the control of information
coming from the environment rather than to information coming
from memory content; the Stroop Color task should therefore not
tax WM resources.

Conclusions
Overall, the findings do not actually disentangle whether the
WM training used has led to “formation of a new skill, . . . or
whether cognitive mechanisms and capacities of general applicability have been enhanced” (Li et al., 2008, pp. 731–732). Nonetheless, in general the patterns of results we have found inform
about the potential for short-term modifiability of WM-related
mechanisms and specific maintenance changes at the informationprocessing level, and certainly suggest some degree of plasticity in
older adults. The recent Scaffolding Theory of Aging (Park &
Reuter-Lorenz, 2009) proposes that the brain builds scaffolds in
response to age-related neural changes. This model suggests that
the brain adapts and reorganizes under conditions of new learning
and cognitive training, improving ability to scaffold and develop
new neural circuitry to compensate for age-related decline (Goh &
Park, 2009). It is possible that the training given in our study
allowed a general scaffold to be developed, but one that is too
fragile to remain erected over time, and with only some of its parts
resistant.
One limitation of the present study is the absence of a group of
young adults. In fact it was not possible to rule out the group
differences (trained vs. control) for transfer effects being due to

BORELLA, CARRETTI, RIBOLDI, AND DE BENI

776

overestimation, because the potential range of improvement in the
older trained group in relation to that in a younger group might
have been reduced. Typically, younger adults benefit more from
training than do older adults (Dahlin et al., 2008), a pattern that is
magnified on extended training (Kliegl, Smith, & Baltes, 1990).
Furthermore, plasticity decreases with age (Brehmer et al., 2007):
Young-old exhibit larger training-related gains than do old-old
(e.g., Verhaeghen, 2000). Our study involves a sample of youngold adults, and would benefit from replication with young adults
and old-old adults.
Our overall conclusion is that our results are consistent with a
general cognitive flexibility in older adults, as is shown by the
overall short-term general transfer effects. Cognitive plasticity can
be induced, but only for very general abilities. Determining the
nature of cognitive and neural changes of transfer gains would
allow for a better understanding of the importance of WM training
and also help clarify whether or not there are maintenance effects.
A further aspect to consider is the applicability of the intervention
gains to everyday life. An unsolved question is whether the benefits, both short-term and long-term, had any impact on the dayto-day lives of the older adults of our study. Despite the maintenance gains being limited to aspects related to the trained process,
our findings suggest the modifiability of different cognitive abilities and mechanisms in aging, supporting earlier studies showing
the possibility of enhancing WM performance in older adults.

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Received March 24, 2010
Revision received June 12, 2010
Accepted June 17, 2010 䡲

Correction to Hutchison, Balota, and Duchek (2010)
In the article “The Utility of Stroop Task Switching as a Marker for Early-Stage Alzheimer’s
Disease,” by Keith A. Hutchison, David A. Balota, and Janet M. Duchek (Psychology and Aging,
2010, Vol. 25, No. 3, pp. 545–549), author Janet M. Duchek’s name was misspelled as Janet M.
Ducheck. The online version has been corrected.
DOI: 10.1037/a0021733