Cognitive Strategy Variations
During Aging
Current Directions in Psychological
Science
000(00) 1-7
ª The Author(s) 2010
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DOI: 10.1177/0963721410390354
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Patrick Lemaire
Université de Provence and Centre National de la Recherche Scientifique
Abstract
The last two decades of research in cognitive aging have seen a shift from simply describing age-related changes in cognitive
performance to determining the mechanisms underlying these changes. Recent findings on variations in the use of cognitive
strategies during aging further our understanding of how these changes in performance occur during adulthood. Data show
age-related differences in strategy repertoire, strategy distribution, strategy execution, and strategy selection. I illustrate these
findings in cognitive domains as varied as episodic memory, working memory, reasoning, decision making, problem solving, and
language. I discuss how strategic variations are best studied both conceptually and methodologically and how investigating
strategic variations helps us make significant progress in the study of cognitive aging. As I also show in this article, whichever the
cognitive domain being studied, there are no restrictions that would prevent us from adopting a strategy perspective.
Keywords
cognitive aging, strategies, problem solving
Research in cognitive aging has two fundamental goals. First,
researchers want to know how cognition changes with age.
Aging often has negative effects on cognitive performance;
sometimes has no effects; and, fairly rarely, has positive effects
(Craik & Salthouse, 2007). The second goal of research in
cognitive aging is to understand the mechanisms underlying
age-related changes and stability in cognitive performance.
To discover these mechanisms, researchers have tested two
types of factors: so-called quantitative and qualitative factors.
Quantitative factors include such parameters as processing
resources (e.g., processing speed, working-memory capacities).
Qualitative factors include factors like different cognitive
strategies used in problem solving.
A strategy perspective offers a great window to understanding cognitive aging and is not incompatible with quantitative
approaches. It helps to better describe and explain (in mechanistic terms) age-related differences and similarities and how
changes occur during adulthood. It enables us to understand
how people of different ages think and how (some) older adults
can compensate in order to moderate the effects of aging on
cognition. Recent data in a wide variety of cognitive domains
have shown individual variations in cognitive strategies during
aging. Age differences have been found in each strategy dimension involved in cognitive performance identified by Lemaire
and Siegler (1995). Young adults differ from older adults in
strategy repertoire (or which strategies they use to accomplish
a cognitive task), strategy distribution (or how often they use
each strategy), strategy execution (or how fast and accurate
they are with each strategy), and in strategy selection (or how
they choose among strategies on a given problem).
Strategic Variations During Aging
Age-related changes in strategy repertoire
Many theoretical and empirical works on aging assume that
young adults and older adults approach cognitive tasks the
same way. However, this is often not the case.
Strategy repertoires in different domains in which agerelated differences have been found are listed in Table 1. For
example, Dunlosky and Hertzog (2001) found that adults over
60 years old were less likely to spontaneously use effective
mediators (e.g., making a mental image or generating a sentence) for encoding paired associates than were adults in their
twenties. As another example, Lemaire and Arnaud (2008)
asked participants to solve two-digit addition problems and
assessed (via verbal protocols) the strategies participants used
Corresponding Author:
Patrick Lemaire, Université de Provence & CNRS, Case D, 3 Place Victor
Hugo, 13331 Marseille, France
E-mail: [email protected]
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Lemaire
Table 1. Strategies Used (or Not) by Young and Older People to (a) Solve Two-Digit Addition Problems (Lemaire & Arnaud, 2008), (b) Verify
Arithmetic Inequalities (Duverne & Lemaire, 2004), (c) Encode Pairs of Words in Episodic Memory (Dunlsoky & Hertzog, 2001), and (d) Search
Information in Decision-Making Tasks (Johnson, 1990).
Two-digit addition problem-solving
task (e.g., 12 þ 46)
Arithmetic problem
verification task
Rounding the first operand down
(10 þ 46) þ 2
Rounding the second operand down
(12 þ 40) þ 6
Interactive mental imagery strategy
Exhaustive-verification strategy
Encoding the problem, searching for Making a mental image that relates
the two words of each pair
correct solution in memory, com(e.g., for glass–water, imagining
paring this solution with the prosomeone drinking a glass of
posed solution, making a true/false
water)
decision, and responding
Sentence-generation strategy
(e.g., 8 5 < 41. True? False?)
Making a sentence with both
words of each pair (e.g., for
glass–water, giving the sentence
‘‘The boy drinks a glass of
water’’)
Rote repetition strategy
Approximate verification strategy
Mentally repeating both words of
Encoding the problem, judging the
each pair (e.g., ‘‘glass–water/
plausibility of the proposed answer,
glass–water/glass–water’’)
making a true/false decision, and
responding
Other or no strategy
(e.g., 8 5 < 47. True? False?)
Rounding both operands down
(10 þ 40) þ (2 þ 6)
Columnar retrieval
(2 þ 6) þ (10 þ 40)
Rounding the first operand up
(20 þ 46) – 8
Rounding the second operand up
(12 þ 50) – 4
Rounding both operands up
(20 þ 50) – 8 – 4
Borrowing units
18 þ 40
Retrieving
58
on each trial. It was found that young individuals used a larger
number of strategies than did older individuals. Similarly, participants’ performance in Duverne and Lemaire’s (2004) study
suggest that young adults used both the exhaustive and approximate verification strategies to verify arithmetic inequalities,
whereas older adults used only the exhaustive verification strategy (a result confirmed with event-related potentials, ERP, data
by El Yagoubi, Lemaire, & Besson, 2005). For example, to
determine whether 5 þ 7 < 13 is correct or incorrect, they used
an exhaustive verification strategy consisting in retrieving the
correct solution (12) in memory and comparing that to the proposed solution (13). To verify whether 5 þ 7 < 19, they do not
need to retrieve the correct answer in memory; they can quickly
reject the proposed answer, as it is too far from the correct
answer and is, thus, too implausible. When Johnson (1990)
asked participants to search information about different alternative cars (attributes such as purchase pricing or resale value)
in order to decide which car to buy, she found that young adults
used the so-called compensatory strategy (e.g., taking both into
account price and comfort) and older adults the noncompensatory strategy (e.g. focusing on one feature such as price). These
studies show that, whenever possible, assessing which strategy
is used on each item is the most appropriate way to determine
whether young and older individuals differ in how they accomplish a given task.
Age-related changes in strategy distributions
When young and older adults use the same strategies, they
may differ in how often they select each available strategy.
Episodic memory: Pairedassociate recall task
Information search
strategies in decision making
Compensatory attribute
comparison strategy
Comparing multiple
attributes for each alternative (e.g., price, comfort
for a Honda or a
Volkswagen)
Noncompensatory alternative
comparison strategy
Comparing multiple
alternatives for each
attribute (e.g., comparing
Honda and Volkswagen on
price only)
Age-related differences both in how many participants use each
available strategy and in how often each participant chooses
each strategy have been found.
Examples of domains in which age-related differences in
strategy distribution have been found are shown in Figure 1.
For example, Cohen and Faulkner (1983) found different numbers of participants using each available strategy in a rotatedfigure task. In this task, participants are presented with one
of eight versions of a human figure and are required to respond
left or right according to which hand of the figure is holding a
ball. Four strategies are available: rotation (inverted figures are
converted to an upright version by a mental rotation through a
180 in the two dimensional picture plane), flip (inverted
figures are mentally flipped through the third dimension around
the horizontal axis), body reference (people use their own body
as a reference and mentally align themselves to correspond
with the position of the figure), and the rule strategy (the individual formulates a rule such as ‘‘for inverted views, the left
hand is on the right’’). More young participants used the rule
strategy; more older adults used the rotation and bodyreference strategies, and only young adults used the flip strategy.
Hartley and Anderson (1983) reported similar age differences in
the number of participants using different inductive reasoning
strategies, with more young adults using the optimal strategy
and only older adults selecting the nonoptimal strategy.
Problem-by-problem assessments of strategies have also
revealed age-related differences in strategy distributions.
For example, in a study on numerosity estimation (i.e., participants are asked to estimate the number of dots briefly presented
on a computer screen), verbal protocols and eye-movement
a
3
Number of Participants
Strategic Variations During Aging
6
5
Young
Older
4
3
2
1
0
b
Number of Participants
Rule
Rotation
Percent Use
35
30
25
20
15
10
5
0
Flip
25
Young
Older
20
15
10
5
0
Optimal
c
Body Reference
Sub−Optimal
Non−Optimal
Young
Older
Anchoring
Estimation
Decomposition/
recomposition
Approximate counting
Exact counting
Fig. 1. Age-related differences in strategy distributions used by young and older participants. Panel a shows the number of participants using
rule, rotation, body reference, and flip strategies in a rotated-figure task (data from Cohen & Faulkner, 1983); panel b shows the mean number of
participants using optimal, suboptimal, and nonoptimal strategies in an inductive reasoning task (data from Hartley & Anderson, 1983); and panel
c shows participants’ mean percent use of five numerosity estimation strategies (anchoring, perceptual estimation, decomposition/recomposition, approximate counting, and exact counting; data from Gandini, Lemaire, & Dufau, 2008).
recordings on each trial revealed that participants used several
strategies (Gandini, Lemaire, & Dufau, 2008). Some strategies
(anchoring and perceptual estimation) were used equally often
by both age groups, whereas the groups differed in their use of
other strategies (decomposition/recomposition, approximate
counting, and exact counting).
Age-related changes in strategy execution
To assess age-related differences in strategy execution, Siegler
and Lemaire (1997) proposed using the choice/no-choice
method, in which participants in one condition are instructed
to use a given strategy on all items and in another condition
choose among available strategies. As all participants use all
available strategies on all items, strategy performance and age
differences in strategy performance are not contaminated by
strategy repertoire, distributions, and selection (i.e., a fast strategy could be used more often than a slow strategy by young
adults and equally often by older adults, thereby slowing down
older adults). Several studies have found true age differences
and similarities in strategy performance when other strategy
dimensions are controlled (Fig. 2).
For example, Mata, Schooler, and Rieskamp (2007) asked
participants to forage for fish in a virtual landscape and to
decide when to move between ponds so as to maximize the
number of fish caught. They found that older performed more
4
Lemaire
Young
Older
Short Travel
Time Environment
Accuracy Rates
Young
Older
70
65
60
55
Interactive Imagery
5000
Individual−Interactive
Imagery
Young
Older
4000
3000
2000
1000
0
Percptual Estimation
d
80
75
6000
Long Travel
Time Environment
Verification Times (in ms)
c
Solution Latencies (in ms)
b
100
90
80
70
60
50
40
30
20
10
0
Mean Number of Captures
a
Anchoring
2500
Young
Older
2000
1500
1000
500
0
Linguistic
Pictorial
Fig. 2. Age-related differences in strategy execution for young and older participants. Panel a shows mean number of fish caught in two environments in which participants were instructed to use one of two incremental strategies to accomplish a foraging task (data from Mata, Schooler,
& Rieskamp, 2009); panel b shows mean solution times to accomplish numerosity estimation tasks using perceptual estimation or anchoring
strategies (data from Gandini, Lemaire, Anton, & Nazarian, 2008); panel c shows mean number of correctly recognized pairs of words in an
associative recognition test, using interactive imagery versus individual þ interactive imagery (data from Patterson & Hertzog, 2010); panel d
shows mean verification times to determine whether sentences are consistent with patterns in a sentence verification task, using linguistic versus
pictorial strategies (data from Cohen & Faulkner, 1983).
poorly than young adults when executing the incremental strategy (i.e., deciding on an initial staying time at a pond and then
increasing it with each capture).
In some tasks, some strategies yield equal levels of performance in young and older adults, whereas other strategies yield
different levels of performance between age groups. For example, Gandini, Lemaire, Anton, & Nazarian (2008) asked participants to find the approximate number of dots in dot
collections briefly displayed on a computer screen. Older
adults were slower than young adults while executing the
anchoring strategy (i.e., counting groups of dots and adding the
number of groups before estimating the remaining dots), but
both age groups were equally fast when executing the perceptual strategy (i.e., scanning the whole pattern or a subset of
dots, searching a corresponding numerosity in memory, and
adding or subtracting small amounts to this retrieved numerosity before stating a response). Gandini, Lemaire, Anton, &
Nazarian (2008) have found different brain networks underlying execution of each strategy in young and older adults.
Similarly, Patterson, and Hertzog (2010) found that young
adults obtained better performance than older adults did in an
associative recognition test while using an interactive imagery
strategy (i.e., forming an interactive image for the two words in
each pair) but found no age differences when both groups
executed the individualþinteractive imagery strategy (i.e., first
forming an image of each word, then forming an interactive
image for the words in the pair). Cohen and Faulkner (1983)
found larger age-related differences in a sentence–picture verification task when participants executed a linguistic strategy (i.e.,
describing the picture with a sentence and comparing the two sentences) than when they executed a pictorial strategy. These data
illustrate a more general and interesting point: When we investigate cognitive aging with a strategy perspective, we can find true
age differences in participants’ performance but also true age
similarities even within the same domain or the same task.
Age-related differences in strategy selection
Even when young and older adults use the same strategies, use
available strategies equally often, and execute strategies
Strategic Variations During Aging
Young
1.6
Older
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Hard Items
c
Percent Use of the Best Strategy
Easy Items
b
Percent Use of the Best Strategy
1.8
Mean Number of Items Retrieved
a
5
80
Young
70
60
50
40
30
20
10
0
Small−Unit Problems
Young
70
Older
Large−Unit Problems
Older
60
50
40
30
20
10
0
No Emphasis
Emphasis
Fig. 3. Age-related differences in strategy selection. Panel a shows mean number of retrieves on easy and hard problems in a synthetic arithmetic problem-solving task (data from Onyper, Hoyer, & Cerella, 2008); panel b shows mean percent use of the best strategy on small-unit (e.g.,
31 82) and large-unit (e.g., 27 69) problems in a computational estimation task (data from Lemaire, Arnaud, & Lecacheur, 2004); panel c
shows mean percent use of the best strategy in a computational estimation task when participants were instructed to try to be most accurate
versus no accuracy emphasis (data from Lemaire et al., 2004)
equally efficiently, age-related differences in participants’
performance can be found. How participants choose among
strategies may vary with age. Previous research has found that
young and older adults differ in which strategies they select on
problems, in selecting the best strategy for a problem, or in
changing their strategies as a function of situation parameters
or relative strategy efficacy.
As an example, Lemaire, Arnaud, and Lecacheur (2004)
asked young and older adults to find approximate products for
arithmetic problems like 46 52. For each problem, participants were asked to choose the best among two strategies
(i.e., the strategy that yields the most accurate product): rounding up and rounding down. In the rounding-up strategy, participants rounded both operands up to the nearest 10 (e.g., 50 60). In the rounding-down strategy, participants rounded both
operands down (e.g., 40 50). One nice feature of this task
is that it is easy to know for each problem which is the best
strategy: The rounding-down strategy yields the best product
for problems like 51 62, whereas the rounding-up strategy
is best for problems like 57 69. Young adults selected the
best strategy on 65% of the problems, significantly more often
than did older adults (57%). Young adults were able to select
the best strategy on both small-unit problems (e.g., 41 74)
and large-unit problems (e.g., 57 38), whereas older adults
selected the best strategy only on small-unit problems. Moreover, young adults were more able than older adults to improve
their strategy selection under accuracy pressure (see Onyper,
Hoyer, & Cerella, 2008, for similar findings in skill
acquisition).
Mechanisms of Strategic Changes During
Aging
Why do young and older adults differ in strategy repertoire,
distribution, execution, and selection? Several recent studies
suggest that such age differences in strategies may result from
age-related decreases in processing resources. For example,
regarding episodic memory, Bouazzaoui et al. (2010) recently
found that 82% of age-related variance in mental memorystrategy use was accounted for by executive functions. Duverne
and Lemaire (2004) found that 70% of age-related variance in
strategy use in arithmetic problem-solving tasks was mediated
by processing speed. Whether decrease in all processing
resources (processing speed, working memory, inhibition,
6
executive control) or only in a subset is responsible for
age-related changes in strategies is unknown. Duverne and
Lemaire (2004) found that processing speed was the predictor
of age-related differences in arithmetic problem-solving strategies. Taconnat et al. (2009) found that executive functions were
the main mediator of age-related differences in memory strategies. Whether strategic changes with age are the result of
changes in all processing resources or just some (and in that
case, which ones) may depend on several parameters, such as
cognitive domain or how (and which) processing resources are
tested. Future research will determine this.
How can declines in processing resources with age lead to
strategic changes? Regarding changes in strategy repertoire,
there are two possibilities. First, it could be that older adults use
fewer strategies than young adults do because they know fewer
strategies and have never used strategies that young adults know.
Second, it could be that the same strategies are available in both
age groups but that older individuals restrict the set of strategies
they use in a given task. Lemaire and Arnaud’s (2008) data are
consistent with the second hypothesis. At the group level, the
same set of nine addition problem-solving strategies was found
in both older and younger adults. However, older individuals
used fewer strategies than young adults did. Decreased processing resources with age might lead older adults to restrict the set
of strategies. Indeed, fewer resources makes it harder to maintain
all strategies in working memory and harder to select a given
strategy among more (compared to fewer) strategies.
Regarding strategy selection, decreased processing
resources may lead older adults to select the best strategy on
each problem less often than do young adults. This could happen via different processes. Fewer resources makes it more difficult to encode problem features efficiently, to choose the most
appropriate strategy for each problem. Moreover, decreased
efficiency of executive-control processes reduces cognitive
flexibility for switching from one strategy to another on each
problem. Such decreased flexibility prevents older adults from
inhibiting a just-executed strategy on a given problem and to
activate the most appropriate strategy on the next problem.
Lemaire et al. (2004) found that older adults were as good as
young adults at selecting the best strategy on each item when
they were instructed to only select the best strategy for each
item and not to execute it. However, when asked to both select
and execute strategies on the same items in the same tasks,
older adults’ strategy choices were much poorer. Decreased
processing resources might lead to less-efficient strategy execution because executing strategies, especially those involving
more numerous and more complex processes, require cognitive
resources to be successfully executed. In all cognitive domains,
older adults obtained poorer performance than young adults
when they are asked to execute harder strategies. However,
if participants are asked to execute easier strategies, agerelated differences are much smaller and even sometimes nonexistent. Finally, changes in strategy distribution may also be
influenced by changes in cognitive resources with age. With
fewer resources, older adults tend to use the easiest strategies
more often, even if those strategies do not yield the best
Lemaire
performance. For example Bouazzaoui et al. (2010) and many
others have found that, in contrast to young adults, older adults use
less-consuming external memory strategies (e.g., making notes
on paper) more often than more-consuming internal (i.e., mental)
strategies.
Conclusions and Future Directions
Four main strategic changes occur during aging: decrease in the
size of strategy repertoire, increases and decreases in frequency
of use of strategies, less-efficient execution of strategies, and
poorer choices among strategies. Age differences in strategies
tend to be largest in most difficult tasks, domains, or conditions. Change mechanisms leading to these strategic changes
include, among others, reduced processing resources.
In addition to its usefulness for furthering our understanding
of cognitive aging, the strategy perspective helps us better
understand how older adults compensate for age-related
declines in human cognition. Neuroimaging studies have found
that comparable performance in young and older adults are
associated with neural compensations during aging, as older
adults often recruit brain regions not typically recruited by
younger adults (Cabeza et al., 2005). Behavioral studies point
to functional compensations (i.e., older adults select strategies
they can more easily and accurately execute) and suggest that
age-related differences in neural activations may result from
functional compensations (e.g., Gandini et al., 2008; Logan,
Sanders, Snyder, Morris, & Buckner, 2002), an issue that future
studies will more systematically address.
Strategic variations during aging have been more investigated
in some domains than in others. The strategy perspective has
mostly been adopted to investigate normal aging. Some recent
studies (e.g., Arnaud, Lemaire, Allen, & Michel, 2008) suggest
that it is a fruitful perspective to understand pathological aging,
as patients with Alzheimer’s disease undergo very important
strategic changes. Whichever the cognitive domain and whichever the cognitive status of the age group, I see no restrictions
that would prevent researchers from adopting a strategy perspective. The benefits of such a perspective for furthering our understanding of cognitive aging outweigh the costs (in relative time
and trouble) of assessing strategic variations.
Recommended Reading
Cabeza R., Nyberg L., & Park, D.C. (2005). (See References). An
overview of how changes in the brain produce changes in human
cognition.
Craik, F.I.M., & Salthouse, T.A. (2007). (See References). An up-todate, state-of-the art book on aging and cognition.
Lemaire, P. (2005). Strategic aspects of human cognition: Implications for understanding human reasoning. In M. Roberts (Ed.),
Cognitive strategies and human reasoning (pp. 11–29). London,
England: Psychology Press. A detailed discussion of strategic
aspects of human cognition.
Park D.C., & Schwarz N. (2000). Cognitive aging: A primer. Philadelphia, PA: Psychology Press. A comprehensive and accessible overview of cognitive aging.
Strategic Variations During Aging
Acknowledgments
I would like to thank two anonymous reviewers for their helpful
comments on a previous version of this manuscript.
Funding
Research reported in this article was supported by grants from Agence
Nationale de la Recherche (Grants #: ANR-06-BLAN-0241-01; ANRBLAN07-1_196867).
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