CHAPTER 8Adult Intelligence Sketch of a Theory and Applications.docx

CHAPTER 8
Adult Intelligence: Sketch of a Theory and Applications to
Learning and Education
Phillip L. Ackerman
University of Minnesota
OVERVIEW
Intelligence theory and assessment methods have traditionally
been aimed at predicting academic success. As such, efforts
during the early part of this century first focused on predicting
the school success of children and young adolescents (for a
review, see Ackerman, 1996). Around World War I, intelligence
test content was extended upward—to allow for testing of young
and middle-aged adults. As the educational establishment
embraced intelligence testing, postsecondary institutions
increasingly relied on the use of tests for selection of college
and university applicants, starting in the 1920s. Today’s college
entrance tests, such as the Scholastic Assessment Tests (SAT)
and the American College Testing Program (ACT), show a
significant resemblance to the adult intelligence tests of the
1920s. Although these procedures may be useful predictors of
college success for young adults, they fail to take account of the
differences between child/adolescent intelligence and adult
intelligence. A perspective of intelligence that focuses
on knowledge as a key ingredient of adult intelligence is
presented in this chapter. By moving away from the traditional
process-oriented conceptualization of intelligence to a
knowledge-oriented conceptualization, many aspects of adult
intellectual development can be considered, especially in the
context of learning and education for adults. Such a shift in
emphasis provides a basis for considering other aspects of the
adult learner, such as personality, interests, and motivational
skills—and provides a framework for an integrated view of
adult development, learning, and education.
In this chapter, I first discuss the differences between child and

adult intelligence, as a contrast between process and knowledge
components of intellect. Next, a discussion is presented of
relations between intelligence and personality, interests, and
motivational skills. Putting all of these components together
provides for a perspective on adult development that stands in
contrast to the traditional view of intellectual decline with
increasing age. Finally, some implications of the knowledge-
based perspective for adult education and learning are
presented.
REVIEW OF DISTINCTION BETWEEN CHILD AND ADULT
INTELLIGENCE
Intelligence as Process?
When the first modern procedures were devised for assessing
intelligence, Binet and Simon (1905) distinguished between two
different approaches, which they called the psychological and
pedagogical methods. The psychological method, which they
adopted for assessment of children, was specifically oriented
toward aspects of intelligence that were believed to be less
influenced by cultural privilege—namely memory, reasoning,
following directions, and so on. Most of the measures that were
developed to assess intelligence were thus process measures.
Later developments in individual intelligence assessment have
generally adhered to the Binet-Simon formula. Such
assessments have been repeatedly shown to be effective
instruments for predicting elementary and secondary school
success, somewhat effective in predicting postsecondary
academic success, and somewhat less effective in predicting
occupational success (for reviews, see Ackerman & Humphreys,
1991; Anastasi, 1982).
In contrast, the pedagogical method described by Binet and
Simon (1905) attempts to assess intelligence by focusing on
what an individual knows—knowledge that is often specific to
individuals with differing educational or experiential
backgrounds. The pedagogical method essentially focuses on
the content of intelligence. A focus on process aspects of
intelligence seems logical for prediction of elementary and

early secondary academic achievement—when the curriculum is
relatively standardized. Ignoring knowledge differences
between individuals as they reach adulthood serves only to
ignore aspects of intelligence that are increasingly important
determinants of postsecondary academic performance,
occupational performance, and other attainments in intellectual
activities outside of the occupational domain.
In the 1940s, two investigators sought to partially remedy this
apparent oversight in mapping intelligence theory to adult
development. Hebb (1942) described two different types of
intelligence, which he called Intelligence A and Intelligence B.
Intelligence A was more of a process- or physiologically based
intelligence, and Intelligence B was more
cultural/educational/experiential. The preservation of
intellectual functioning in adults, even after instances of
significant neurological damage, was seen as an expression of
the distributed nature of Intelligence B. Similarly, Cattell
(1943) divided intelligence into two domains, which he called
fluid intelligence and crystallized intelligence. Fluid
intelligence (gf) was described as mainly innate and
physiological—important for early development and learning,
but decreasing as individuals reached adulthood. Fluid abilities
include memory and abstract reasoning. Crystallized
intelligence (gc), on the other hand, is strictly determined by
educational and experiential influences; gc forms out of gf, but
unlike gf, gc is maintained well into middle adult ages. The
kinds of abilities that are identified as prototypical for gc
include verbal comprehension, vocabulary, information, and so
on.
Several studies by Cattell and his colleagues (e.g., Cattell,
1963; Horn & Cattell, 1966) have supported the distinction
between gf and gc, though not without controversy over the
strength of the empirical data on which the demonstrations were
based (e.g., see Guilford, 1980; Humphreys, 1967). As far as
adult intellect is concerned, though, the main problem with
validation of this theory has been with the rather meager

sampling that has been done on the diverse domains of
knowledge that adults surely have. Cattell (1971/1987)
acknowledged this problem—noting that when one attempts to
assess adult gc, one either must develop as many tests as there
are specialized domains of knowledge, or else one must
concentrate on knowledge that has been obtained at
adolescence—again, when individuals are assumed to have
relatively common educational experiences. To date, researchers
have mainly relied on the later strategy—testing vocabulary,
reading comprehension, or information that examinees acquire
during their adolescent years. Such is the implicit rationale
behind the design of general entrance examinations for college
and university admissions, such as the SAT or the general
portions of the Graduate Records Examination (GRE).
Intelligence as Knowledge
From a pragmatic point of view, prototypical intelligence
assessments—that is, those that are predicated on a combination
of process measures and high school-type knowledge measures,
give adults literally little or no credit for knowledge and skills
that they have acquired through occupational or avocational
experience. Knowledge obtained through college study or
through work experience is simply ignored in such assessments.
With this basic feature of intelligence tests in mind, it should
provoke little wonder that, on average, adults tend to perform
more poorly than high school students on standard intelligence
tests. Such results are entirely predicted by the Cattell and Horn
theory of Gf and Gc—shown in Fig. 8.1. That is, even though gc
shows stable or slightly increasing levels through early and
middle adulthood, any gains in gc are more than offset by
declines in gf. If performance on intellectual tasks is
determined only by the kinds of intelligence that are measured
by such measures, we could expect that middle-aged and older
adults will substantially underperform their younger
counterparts.
However, recent research from the cognitive science and
artificial intelligence literature has demonstrated

that knowledge is an important determinant of both learning and
performance (e.g., Alexander, Kulikowich, & Schulze, 1994).
Indeed, the artificial intelligence field, which once supported
the notion of a process-oriented “general problem solver” that
could handle all kinds of intellectual demands—now has
discarded the concept in favor of “expert systems” that are
predominantly knowledge-driven (e.g., Schank & Birnbaum,
1994). That is, researchers in these fields have come to
understand that what that the individual knows is more likely to
determine success or failure in intellectual tasks than either the
number of novel facts that can be remembered in a single sitting
or the speed of abstract reasoning (although abstract reasoning
tests make up a substantial portion of traditional assessments of
intelligence—see Anastasi, 1982). Several empirical research
programs have provided substantiating evidence on the
importance of prior knowledge in content-specific problem-
solving situations (e.g., Chi & Ceci, 1987; Chi, Glaser, & Rees,
1982; Glaser, 1991; see Voss, Wiley, & Carretero, 1995, for a
recent review). Clearly then, there is a substantial empirical
basis for the idea that knowledge is a fundamental component of
intelligence (even if one is tempted to adopt a narrow view that
only embraces traditional gc abilities—see Cattell, 1971/1987;
Horn, 1989).
FIG. 8.1. Hypothetical growth/level of performance curves
across the adult life span for traditional measures of gf (fluid
intelligence) and gc (crystallized intelligence). Adapted from
Horn (1965).
When it comes to understanding the developmental nature of
knowledge acquisition and maintenance for adults, the field is
too new to provide much hard empirical evidence. However, the
few studies in the field (e.g., Holahan & Sears, in association
with Cronbach, 1995), tend to indicate that adults often
accumulate occupational knowledge well into their middle adult
years, and may continue to develop avocational knowledge
(such as knowledge about cultural interests and hobbies) well

into late adult years. From a theoretical perspective, we can
consider the likely developmental trajectories of occupational
and avocational knowledge, in comparison to traditional
assessments of gf and gc. As shown in Fig. 8.2, quite different
images of adult intellectual development will emerge,
depending on which aspects of intelligence one chooses to focus
on. A focus on knowledge only will likely yield a distinct
advantage to adults, whereas a focus on process only will yield
an advantage to adolescents and young adults. Any single
composite might yield an advantage to either group, depending
on the weighting scheme chosen by the investigator. Rather,
though, a more comprehensive description of an individual’s
intelligence may be derived by developing separate indices for
each of these four main components of intelligence.
Unfortunately, no measurement instruments have yet been
developed that can gauge where an individual stands on the
knowledge components of intelligence. There are many
assessment instruments that do assess specific knowledge—such
as Advanced Placement exams, College Level Examination
Program (CLEP), professional certification and proficiency
exams, and the GRE “Advanced” tests. Although such tests are
usually administered in isolation (i.e., an examinee typically
takes only one or two GRE Advanced tests), these tests are the
best current examples of assessment instruments that begin to
provide a measure of adult knowledge—at least in the academic
and professional domains. Some evidence exists showing that
these tests provide a more accurate prediction of postgraduate
academic success (Willingham, 1974) and occupational success
(Hunter, 1983) than do standard intelligence tests.
The problem for intelligence theorists and practitioners alike, as
identified by Cattell (1971/1987), is that to adequately assess
knowledge across individuals, one needs to develop many tests,
in order to cover the divergent areas of knowledge that adults
develop. Although we are still at the early stages of work, one
of the major goals of our current research program is to begin to
address this shortcoming of adult intellectual assessment. We

are starting with academic domains, as measured by the CLEP
tests, and supplementing these tests with our own measures of
art, music, electronics, technology, and so on. Our goal is not to
develop a test for every area of adult knowledge, but rather to
broadly sample what it is that people know, and use such
measures to study how knowledge develops across the life span.
Furthermore, our ultimate hope is to use a knowledge-based
approach to intelligence that can better predict intellectual
performance of adults than extant measures of intelligence that
are predominantly based on assessment of process and high
school knowledge.
FIG 8.2. Hypothetical growth/level of performance curves
across the adult life span, for intelligence-as-process,
traditional measures of gc (crystallized intelligence),
occupational knowledge, and avocational knowledge. From
Ackerman (1996). Copyright © by Ablex Publishing Corp.
Reprinted by permission.
LINKING INTELLIGENCE AND OTHER TRAITS
For children and adolescents, there is relatively little flexibility
in the educational curriculum, outside of a small number of
elective options (such as music, foreign language, shop, etc.).
When it comes to adult learning and education, there is much
greater flexibility in both curriculum and external demands on
the student’s time. Under these more fluid situations,
intelligence, as either process or knowledge, tells only part of
the story. The individual’s personality characteristics, interests,
and motivational skills also help determine the direction of the
individual’s effort, the individual’s likely persistence in a field
of study, and along with intelligence, jointly predict the
likelihood of success in acquiring new knowledge and skills.
Each of these domains is briefly discussed next.
Personality. In a recent review (Ackerman & Heggestad, 1997),
we found several personality domains that are related to
intelligence. Some personality characteristics appear to be
pervasively related to intelligence, both positively (Need for

Achievement, Extroversion) and negatively (Neuroticism).
However, two related personality traits appear to be especially
related to gc and knowledge/achievement, namely a trait called
Openness or Culture, and a trait we call Typical Intellectual
Engagement (TIE; Goff & Ackerman, 1992). Openness/Culture
refers to an individual’s orientation to novel experiences, such
as trying new restaurants, going to plays and concerts, and so
on. TIE is a term that describes an individual’s orientation
toward intellectual activities, such as reading, problem solving,
and acquiring knowledge. Neither of these personality traits is
much related to process-oriented intellectual abilities (Goff &
Ackerman, 1992), but they are positively associated with level
of verbal ability and with knowledge about diverse areas of the
humanities, arts, and social sciences (Rolfhus & Ackerman,
1996). Assessing Openness and TIE in particular may be
expected to help identify adults who are particularly oriented to
adult educational experiences.
Interests. In addition to personality traits, individuals differ in
their particular interests. Holland (1959, 1973) identified six
major areas of interests—Realistic, Investigative, Artistic,
Social, Enterprising, and Conventional. Even prior to Holland’s
work, interest assessment has been a staple component of
matching adults to educational and occupational activities (for a
review, see Campbell & Hansen, 1981). The premise of the
counseling orientation behind interest assessment is that
individuals with similar interests to job incumbents will most
likely fit into particular occupations, reflecting job satisfaction
(if not actual occupational success). However, a few
investigations have found overlap between interests and
personality traits (e.g., Lowman, Williams, & Leeman, 1985;
Randahl, 1991), and recently we have determined that it is
possible to align interests, personality, and abilities—to identify
four major “trait complexes,” shown in Fig. 8.3 (Ackerman &
Heggestad, 1997). The four trait complexes are Social,
Clerical/Conventional, Science/Math, and Intellectual/Cultural.
The existence of these trait complexes indicates that, for adults,

various intelligence, personality, and interest traits tend to
cluster together—in a way that suggests mutually supportive
roles among the different domains. Two of these trait
complexes (Social and Clerical/Conventional) represent
individuals whom we expect to be less likely to pursue adult
education and learning opportunities, whereas the other two
trait complexes (Science/Math and Intellectual/Cultural) appear
to be closely identified with an orientation toward education
and learning. However, the Science/Math trait complex is
mostly related to process aspects of intelligence—and thus the
most challenging for adult—learners, and the
Intellectual/Cultural trait complex most closely related
to knowledge aspects of intelligence—and thus is probably the
most closely identifiable with particularly fruitful domains of
adult education.
Motivational Skills. Recent research by Kanfer and her
colleagues (e.g., Kanfer & Ackerman, 1996) has identified two
major sources of motivational skills that appear to be essential
ingredients to learning and skill acquisition, namely Emotion
Control and Motivation Control (see also Kuhl, 1985). Emotion
Control refers to the skills that individuals use to maintain a
focus of attention on difficult and novel tasks, when failure is
frequent and frustrations common. A lack of Emotion Control
skills leads learners to divert their attention away from the
learning task toward emotions, such as worry and evaluation
apprehension. In learning situations where individuals have a
choice about continuing or abandoning the course of instruction,
it can be expected that learners with low Emotion Control skills
will be quite likely to express frustration and quit—even as they
make progress and improve performance. One of the most
common examples of such learning situations has to do with the
introduction of new technology—such as VCRs and computer
networks.
FIG 8.3. Trait complexes, including abilities, interests, and
personality traits showing positive commonalities. Shown are

(a) Social, (b) Clerical/Conventional, (c) Science/Math, and (d)
Intellectual/Cultural trait complexes. From Ackerman and
Heggestad (1997). Copyright © by APA (American
Psychological Association).
Although there are methods for ameliorating the demands for
Emotion Control (such as scaffolding of instruction and
providing a nonthreatening learning environment), probably the
biggest positive influences that serve to avoid Emotion Control
problems are to present new information in the context of
knowledge already available to the learner, or provide explicit
information about the nature of expected acquisition trajectories
to the learner. In the latter case, it is necessary to take into
account the difference between the processing abilities of adults
and adolescents, such that when an adult learner is presented
with normative information about the kind of progress that is
expected from other adults, the learner might obtain a
nonthreatening reference anchor (and therefore reduce
discrepancies between the expected and actual progress during
learning).
Reducing the demands on Emotion Control helps avoid the
problems associated with early stages of learning for adults.
This is probably the most salient issue in matching instruction
to the special capabilities and limitations of adult learners.
However, issues of Motivation Control are more subtle and
potentially more serious in the long run. Motivation Control
refers to an individual’s skills in persisting in a task until
mastery is attained—after a rough level of acceptable
performance is reached. That is, when learners reach an
acceptable threshold of learning or knowledge acquisition, some
individuals stop devoting attention to continued learning—
which indicates a lack of Motivation Control skills. Learners
with Motivation Control skills will maintain effort and attention
on a task, even though knowledge acquisition shows
diminishing returns over time and effort (Kanfer & Ackerman,
1996). That is, even though the gains in knowledge do not come
as quickly to a skilled learner as they do to a novice, the learner

with Motivation Control skills has a “mastery” orientation,
which will ensure both maintenance of current skills and
growth of knowledge over long periods of time. Such skills may
even be instrumental in determining which learners become
experts or world-class performers in a variety of different
intellectual domains (e.g., see Ericsson, Krampe, & Tesch-
Romer, 1993).
IMPLICATIONS FOR ADULT DEVELOPMENT
It is possible to provide an integrative perspective of
intelligence that takes account of the traditional process
components, but also a wider array of knowledge components,
along with personality and interest domains. Fig. 8.4 shows one
conceptualization, called PPIK—for intelligence-as-process,
personality, interest, and intelligence-as-knowledge. This
conceptualization combines these four sources of individual-
differences variance to yield individual differences in levels of
academic and occupational knowledge (Ackerman, 1996). This
perspective not only identifies a developmental progression
from process to knowledge, but also identifies potential cross-
influences between personality and interests and knowledge
acquisition. For adults, though, this perspective provides a
means for linking traditional measures of intelligence with
potential measures of intellectual knowledge and skills. That is,
although traditional intelligence measures may partly predict
adult knowledge, an adequate assessment of adult intellect
requires assessment of adult knowledge. Some areas of
knowledge can be adequately measured using existing scales of
college-level achievement and occupational proficiency, but
such scales only begin to identify adult intellect. Nonetheless,
by using a combined assessment strategy that takes account of
traditionally measured intelligence, personality, and interests, a
more comprehensive evaluation of adult intellect may be
possible. Moreover, one can also incorporate aspects of
motivational skills into the developmental model, inasmuch as
they influence the interface between interests and knowledge
acquisition (Kanfer & Ackerman, 1996). Of course, a full

understanding of adult development awaits longitudinal
evaluation of the changes in knowledge structures that occur
across the adult life span.
FIG 8.4. Illustration of the PPIK theory, outlining the
influences of intelligence-as-process, personality, interests, and
intelligence-as-knowledge during adult development, covering
academic and occupational knowledge. From Ackerman (1996).
Copyright © 1996 by Ablex Publishing Corp. Reprinted by
permission.
IMPLICATIONS FOR ADULT LEARNING AND EDUCATION
There are three specific applications of intellectual assessment
for adult educational purposes, namely, selection, classification,
and instruction. The PPIK approach adopted here suggests
several promising applications across these three fields of
application.
Selection. First of all, the PPIK approach to adult intellect
suggests that prediction of adult academic success will be
improved (i.e., higher validity) when assessments are made of
individual differences in relevant knowledge structures, rather
than the traditional college entrance examinations. Subject to
additional empirical validation (e.g., Willingham, 1974), it is
expected that tests of knowledge structures will show higher
validity for grades and degree progress, especially as students
proceed along the course of knowledge acquisition. Such a
result is entirely consistent with the repeated demonstrations
showing that, although traditional measures of intelligence well
predict first-semester college grades, the validity of these
indices declines as students progress through college
(Humphreys & Taber, 1973). In contrast, when course material
is novel for most students, knowledge tests may have low
validity—but as students progress, the knowledge tests are
expected to increase in validity. If the ultimate selection
criterion is degree completion, higher overall prediction validity
may be expected from knowledge tests. In addition, given the
developmental progression of knowledge acquisition, middle-

aged and older adults may be expected to perform better than
younger adults on the knowledge tests—a result that is
consistent with the fact that older adults tend to perform better
in postsecondary courses than younger adults with equivalent
scores on traditional college selection tests, such as the ACT
(see, e.g., Sawyer, 1986).
Classification. The task of finding an optimal field of study for
adults returning to school is currently more of an art than a
science. The counselor will try to integrate work experience
information with traditional ability and interest measures. The
PPIK approach provides a rationale for finding out, specifically,
what it is that the adult learner knows. A profile of knowledge
structures (along with an understanding of the knowledge
demands of various curricular choices) for prospective adult
learners could be used to choose a course of study that
optimally builds on the knowledge of each learner. Because
adults are likely to have lower levels of process-related
intelligence, an assessment along these lines could provide for a
scientifically determined “match” between field of study and
the learner’s strengths. Moreover, improving the match between
adult learners and field of study will help ameliorate problems
associated with Emotion Control, by placing the learner in
familiar fields of inquiry.
Instruction. Ideal instructional environments match the content
and difficulty of instruction to the knowledge and process
abilities of the individual learner. When it is impossible to
provide one-on-one instruction, it is often possible to provide at
least some tailoring of the educational experience to the
particular attributes of the learner (e.g., see Snow, 1989).
Numerous sources of interactions between individual
differences traits and optimal instructional methods have been
documented over the past 30 years (e.g., see Cronbach & Snow,
1977; Snow, Corno, & Jackson, 1996; Snow & Yalow, 1982).
For most learners, we can expect that taking account of trait
complexes (which include intelligence, personality, and
interests) can result in more effective instruction. In addition,

instructional systems need to take account of the learner’s
emotion control skills and motivation control skills, because
deficiencies in these skills may influence the likelihood that the
learner will persist in a course of study when confronted with
inevitable plateaus and failures that accompany any learning
situation. Thus, remediation of these motivational skills might
preclude actual substantive instruction. However, for adult
learners, it may be especially important to take account of age-
related decreases in process-related intelligence and increases in
knowledge-related intelligence. Particularly appropriate
instructional changes would attempt to minimize, for example,
rote learning of new facts (which requires process abilities), and
maximize the degree to which new material is built on
preexisting knowledge structures. Changes to instructional
methods might be as simple as increasing the use of analogy
examples in the classroom, to exploration of connections
between extant knowledge structures and new material.
Regardless, the main theme is that with the accompanying
changes to the structure of intelligence with adult development,
instruction must be adapted away from the current process-
based approach toward a knowledge-based approach.
ACKNOWLEDGMENTS
Research reported in this chapter was partially supported by
Grant F49620-93-1-0206 from the Air Force Office of Scientific
Research, Phillip L. Ackerman, principal investigator.
Correspondence concerning this chapter should be addressed to
Phillip L. Ackerman, Department of Psychology, University of
Minnesota, N218 Elliott Hall, 75 East River Road, Minneapolis,
Minnesota 55455 (e-mail: [email protected]).
CHAPTER 9

Mnemonic Strategies for Adult Learners
Russell N. Carney
Southwest Missouri State University
Joel R. Levin
University of Wisconsin
A good memory is a valuable commodity for adult learners,
bringing confidence to both social interactions and the
workplace. In contrast, difficulty in remembering leads to
hesitation and, perhaps, to second thoughts concerning one’s
mental state (especially for older adults). Such concerns may
even have led to the following exchange:
When the old Indians came in their file to speak to the
Governor, he would ask their names; then the governor would
ask Ben [Franklin], as he called him, what he must think of to
remember them by. He was always answered promptly. At last
one Indian came whose name was Tocarededhogan. Such a
name! How shall it be remembered? The answer was prompt:—
Think of a wheelbarrow—to carry a dead hog on. (Watson,
1830, cited in Pressley & McCormick, 1995, p. 301)
This interesting account illustrates how Franklin recoded the
unfamiliar name, Tocarededhogan, in order to make it more
concrete, meaningful, visualizable, and hence, more memorable.
Techniques such as Franklin’s, which represent
“systematic procedures for changing difficult to remember
material into more easily remembered material” (Pressley, J. R.
Levin, & Delaney, 1982), are referred to as mnemonic
strategies, and have been practiced since ancient times (Hrees,
1986; Yates, 1966). Such strategies often facilitate paired-
associate learning (e.g., associating names and faces) and serial
learning (learning a list of ordered items) in part by the use of
interactive imagery (Paivio, 1971). Over the years, a variety of
memory-improvement books have recommended mnemonic
techniques for learners of all ages (e.g., Bellezza, 1982;
Fenaigle, 1813; Furst, 1944; Higbee, 1993; Lorayne, 1990;
Lorayne & Lucas, 1974). Additionally, such strategies are
routinely presented in self-improvement courses (e.g.,“Dale

Carnegie Course in Effective Speaking and Human Relations,”
and “Where There’s A Will There’s An A”). Even the
ubiquitous Reader’s Digest has offered such strategies to its
readership from time to time (e.g., a condensed version of
Lorayne, 1985).
In this chapter, we begin by considering the memory concerns
of adults, especially the popular notions that (a) information-
processing abilities, including memory, decline substantially
with age, and (b) there is little that one can do to stop the
decline. We then summarize several mnemonic-strategy
applications that have, and may be adapted to have, utility for
dealing with the memory failures of such learners. We conclude
the chapter with some instructional implications stemming from
the mnemonic-strategies’ research literature.
MEMORY CONCERNS OF ADULT LEARNERS
As has been observed about the weather, everybody talks about
it but no one does anything about it. Likewise, many
individuals—particularly older adults—complain about their
memories, but do little in an effort to improve them. Such
everyday memory complaints are often heightened by the
perception that mental abilities decline with age. Indeed, on the
basis of an 8-year longitudinal study involving verbal learning,
Arenberg and Robertson-Tchabo (1977) concluded that there
was some decline after 60 years of age. Yet, Perlmutter and Hall
(1992) have observed that although, “… on average, aging is
accompanied by a decline in the ability to process information,”
this decline is “less severe, later in onset, and true for a smaller
proportion of the population than was once believed” (p. 213).
This is especially the case for those who continue to be
involved in intensive mental activity (Kausler, 1994). Notably,
in a large survey of adults over age 55, only 15% said they
often had trouble remembering in comparison to 25% who said
they never had memory problems (Cutler & Grams, 1988).
Likewise, on the basis of their research, Cerella, Rybash,
Hoyer, and Commons (1993) argued that aging is associated
with minimal decline in cognitive abilities, and that such

“counter” positive findings have been underreported.
By the year 2000, it is estimated that 12% of the population will
be 65 or older; by the year 2025, 17% (U.S. Senate, Special
Committee on Aging, 1987–1988). Despite the positive findings
cited earlier, the perception and worry remain among an
increasingly gray adult population that normal aging is
accompanied by a gradual decline in information-processing
abilities, including memory skills. Even worse, perhaps, is these
individuals’ perception of the immutability of the process—that
is, that there is little if anything that can be done to halt the
declining memory parade. True, age-related memory deficits
greater than 1 standard deviation below the mean on tests of
recent memory have been termed “age-associated memory
impairment” (Yesavage, 1990, p. 53), and true, many elderly
adults exhibit deficits of that magnitude. But also true, the
employment of mnemonic strategies, such as those described in
the next section, may enhance memory and, as a consequence,
allay the fears (both real and imagined) of adult and elderly
learners.
Acknowledging that there is some decline, Perlmutter and Hall
(1992) summarized a number of potential explanatory
hypotheses. Briefly, the speed hypothesis (Salthouse, 1989) and
the generalized slowing hypothesis (Cerella, 1990) both suggest
that aging slows down cognitive processing to some extent. A
direct consequence of this is that aging adult learners may
require more time to process and encode information.
The disuse hypothesis (Salthouse, 1989) suggests that as we
age, we are less often called upon to use the memory abilities
that are tested in the laboratory. Because adults can rely more
readily upon external memory aids (Intons-Peterson &
Newsome, 1992; Park, Smith, & Cavanaugh, 1990), such as
handwritten notes and reminders, they are less likely to make
use of associative memory techniques such as those described in
this chapter. Simply put, as we age, our memory skills may
become rusty through disuse—especially when tested in
artificial settings (laboratories) with artificial materials (e.g.,

lists of unrelated words). The resource reduction
hypothesis (Salthouse, 1988) attributes decline in cognitive
functioning to a reduction in cognitive resources. Aging may
lead to reductions in such resources as mental energy, speed of
processing, attentional capacity, and the capacity of
consciousness or working memory. As we see later, such
reductions, if real, would tend to make the use of mnemonic
strategies more difficult—especially if the strategies are
complex in nature. Relatedly, it is important to note that
although working memory may decrease in capacity, long-term
memory seems to remain intact (Poon, 1985). Hence, adults and
the elderly have what might be described as a “target-rich”
environment for associating new information with prior
knowledge. (The “down” side to this is that such a rich
environment may in turn contribute to interference during
retrieval.) Finally, the inefficient strategies
hypothesis (Salthouse, 1988) argues that older adults may tend
to select less effective strategies (e.g., rote repetition) for
processing information. This explanation is especially
appealing, in that memory decline might be offset by training or
instruction in more efficient memory strategies. It is to such
strategies that we now turn.
ENCODING (OR ASSOCIATIVE) MNEMONICS
A repeated theme in cognitive psychology is that “… learning
proceeds most efficiently when to-be-acquired information can
be meaningfully related to previously acquired information …”
(M. E. Levin & J. R. Levin, 1990, p. 316). In this regard, M. E.
Levin and J. R. Levin have proposed that different types of
material to be learned can be placed along a “relational
processing continuum.” Within this continuum, “efficient”
strategies are selected on the basis of the degree of
correspondence between new to-be-learned information and the
learner’s prior knowledge. In particular, semantic- and schema-
based strategies are well suited to the task when the
correspondence is high, whereas mnemonic strategies are most
beneficial when the correspondence is low. This chapter targets

the learning of the latter type of information—information that
is mnemonically ripe. J. R. Levin (1983) has proposed that there
are three common “R” components of associative mnemonic
techniques: recoding, relating, and retrieving. For example, take
the task of having to remember that George Washington Carver
devoted much of his time to researching the peanut. First, the
to-be-associated stimulus name is recoded into something more
concrete and familiar (e.g., the name Carver can be recoded as a
more concrete, familiar word, such as car). Second, the car and
peanuts are related by means of a meaningful, interactive
episode. Here, one might imagine a car driving over, and
crunching, a bag full of peanuts. Finally, retrieval is
accomplished by following the systematic retrieval path that has
been established: Carver → car → scene of a car driving over
crunching peanuts → peanuts. Levin has termed these steps the
“3 Rs” of associative mnemonic techniques. Our primary focus
in this chapter is on “encoding” mnemonics (Bellezza, 1981),
that is, on strategies that facilitate associative learning. Popular
encoding mnemonics (and mnemonic variations) include the
face-name mnemonic system, the keyword method, and the
phonetic mnemonic system.
The Face-Name Mnemonic Strategy. As was illustrated by the
Tocarededhogan example, an everyday task faced by adult and
elderly learners is that of remembering people’s names.
Although common, name recall represents a difficult task for
many individuals, and forgetting them is a frequent complaint
of the elderly (e.g., Cohen & Burke, 1993). For example, in a
survey of over 100 elderly individuals (Leirer, Morrow, Sheikh,
& Pariante, 1990), remembering people’s names was the number
one memory skill they wished to improve. In this regard, the
face-name mnemonic strategy has been routinely recommended
as a useful technique for facilitating memory for people’s names
(e.g., Higbee, 1993). The face-name mnemonic involves three
steps. Consider, for example, comedian and actor Jim Carrey.
The first step is to identify a prominent facial feature, such as
his huge grin. The next step is to recode his name, Carrey, into

an acoustically similar name clue, such as carry. Finally, an
interactive visual image is devised relating the name clue to the
prominent feature. For example, one might visualize a pet
detective carrying a Cheshire cat with a huge grin. Upon next
seeing Mr. Carrey, retrieval proceeds as follows: face → huge
grin → interactive image → carrying → Carrey.
The “representational” model of memory for proper names
argues that remembering names is difficult because they are
both arbitrary and meaningless (Cohen & Burke, 1993). The
face-name mnemonic strategy makes the name meaningful by
recoding it as a more concrete name clue, and then embedding
this clue in a meaningful, interactive image. The ability of
interactive visual imagery, in particular, to “glue” items
together has been well established, and is theoretically
supported by Paivio’s dual-coding hypothesis (e.g., Paivio,
1971). In the end, the procedure yields a systematic retrieval
path leading from a pictorial stimulus (the face) to a verbal
response (the person’s name).
With few exceptions (e.g., Lewinsohn, Danaher, & Kikel,
1977), research has supported use of the face-name mnemonic
technique with undergraduates and adults (e.g., Geiselman,
McCloskey, Mossler, & Zielan, 1984; L. D. Groninger, D. H.
Groninger, & Stiens, 1995; Hastings, 1982; McCarty, 1980;
Morris, Jones, & Hampson, 1978; Patton, 1994; Yesavage &
Rose, 1984a). In particular, McCarty’s analysis suggested that
all three components of the face-name approach (prominent
facial feature, name clue, and interactive image) were essential
for the device to be successful. (Note that the Tocarededhogan
anecdote does not describe a method of relating the
wheelbarrow [“to carry a dead hog on”] to a prominent feature
of the individual’s face. Hence, as best we can tell, Franklin
was not applying the face-name mnemonic strategy in toto.)
Especially relevant to this chapter, Yesavage and Rose
investigated the effects of the face-name mnemonic strategy
with young (21–38 years old), middle-aged (44–59 years old),
and elderly (60–70 years old) adults. They found that the

youngest participants remembered the most names, the middle
group was intermediate, and the oldest group recalled the fewest
names. Nevertheless, all three groups displayed gains in recall
after applying the mnemonic strategy. Gruneberg, Sykes, and
Hammond (1991), and Gruneberg, Sykes, and Gillett (1994)
have successfully used the technique with learning-disabled
adults.
Recently, Patton (1994) replicated the positive mnemonic
findings with undergraduates when the to-be-remembered
stimuli took the form of graduation photographs presented on
slides. However, when participants were required to engage in
conversation with actual individuals while learning their names,
no advantage was gained through use of the face-name
mnemonic strategy. Therefore, an important limitation may be
that the strategy seems to work only as long as one is able to
devote his or her full concentration to the task.1 This appears to
be a salient point in that so often our effort to learn names is
thwarted by the processing demands involved in greeting
someone and making conversation. Nevertheless, there are many
instances in which concentrated study is a possibility for adults.
For example, an elementary school principal can sit down with a
yearbook and study students’ names. Likewise, a minister can
peruse a photographic church directory and study parishioner
names.
As we have seen, elderly adults often have difficulty
remembering people’s names (Cohen & Burke, 1993; Cohen &
Faulkner, 1986), and a number of studies have examined the use
of the face-name mnemonic strategy in this regard. Jerome
Yesavage and his colleagues have been particularly active
researchers in this area (e.g., Brooks, Friedman, Gibson, &
Yesavage, 1993; Brooks, Friedman, & Yesavage, 1993;
Yesavage & Rose, 1984a, 1984b; Yesavage, Rose, & Bower,
1983; Yesavage, Sheikh, Friedman, & Tanke, 1990). This work
has generally validated the use of the technique with the
elderly, especially in conjunction with what they have termed
nonmnemonic pretraining. Such pretraining may focus on

relaxation, visual imagery, and semantic elaboration training
(Yesavage, 1990).
Although the research points to mnemonic benefits for face-
name learning, an adaptation of the strategy may be even more
effective when applied to other stimuli that are richer in
thematic content than, say, faces. For example, artwork often
contains features that can be incorporated into an image
involving a recoding of the artist’s name (Carney & J. R. Levin,
1991, 1994; Carney, J. R. Levin, & Morrison, 1988; Franke, J.
R. Levin, & Carney, 1991). More generally, it may be possible
first to identify an artist’s characteristic style or theme (e.g.,
Seurat’s pointillism), and then to construct an interactive scene
between that and a name clue (e.g., imagining that the painting
has been made by dropping tiny drops of syrup [Seurat] all over
the canvas). This mnemonic approach may facilitate transfer so
that the learner is subsequently able to identify new paintings of
similar style or theme (e.g., Seurat’s pointillism), and then to
construct an interactive scene between that and a name clue
(e.g., imagining that the painting has been made by dropping
tiny drops of syrup [Seurat] all over the canvas). This
mnemonic approach may facilitate transfer so that the learner is
subsequently able to identify new paintings of similar style or
theme by the same artist (Carney, J. R. Levin, & Hoyt, 1997).
Additional potential applications include labeling outlines of
countries in geography, naming parts of the body in anatomy,
mineral identification, and identifying unfamiliar animals at the
zoo or in the wild (Hoyt, Carney, & J. R. Levin, 1997).
The Keyword Method. The keyword method of vocabulary
acquisition (e.g, Atkinson, 1975; Raugh & Atkinson, 1975) is a
close cousin of the face-name mnemonic strategy, and is one of
the most frequently described mnemonic techniques in
educational psychology texts (e.g., Biehler & Snowman, 1997;
McCormick & Pressley, 1996; Woolfolk, 1995). Adults engaged
in second-language learning or in learning unfamiliar terms
related to a new job or interest (e.g., mountaineering) would do
well to scrutinize this technique. To illustrate the strategy,

consider the geological term bergschrund, which refers to the
crack (or crevasse) that forms where the head of a glacier pulls
away from the mountain. First, the vocabulary
word, bergschrund, can be recoded into a more
visualizable, acoustically similar keyword, such as burgers.
Next, the keyword and the definition are related by means of a
meaningful interactive scene (e.g., an avalanche
of hamburgers(bergschrund) tumbling down a mountain and
falling into the crack at the head of a glacier). Finally, encoded
in this manner, retrieval proceeds as follows: bergschrund →
burgers → tumbling hamburgers → crack at the glacier head.
More than 20 years of research has demonstrated the usefulness
of the keyword method for vocabulary acquisition and related
tasks—tasks in which an unfamiliar verbal stimulus prompts a
familiar verbal response (J. R. Levin, 1993). A versatile
technique, the keyword method has been adapted, extended, and
validated as a powerful memory strategy in a variety of
situations including: second language vocabulary learning
(Atkinson, 1975; Raugh & Atkinson, 1975), acquiring science
concepts (J. R. Levin, Morrison, McGivern, Mastropieri, &
Scruggs, 1986; M. E. Levin & J. R. Levin, 1990), associating
states and their capitals (e.g., J. R. Levin, Shriberg, Miller,
McCormick, & B. Levin, 1980), learning about “famous” people
(e.g., Shriberg, J. R. Levin, McCormick, & Pressley, 1982) and
remembering presidents of the United States (Dretzke & J. R.
Levin, 1996), and city attractions (J. R. Levin, Shriberg, &
Berry, 1983), to name but a few applications.
Recently, Gruneberg and Pascoe (1996) conducted an
experiment with the keyword method, involving a group of
healthy older adults whose mean age was about 70. Participants
studied 20 Spanish vocabulary words and their meanings. These
researchers concluded that “elderly individuals benefit from the
keyword method for both receptive and productive foreign
vocabulary learning” (p. 108) (although, in the latter instance,
the finding was only true given a liberal scoring criterion).
Regarding the relevance of the technique for adults, Gruneberg

and Pascoe pointed out that “[s]ome individuals may wish to
retire to countries where a foreign language is spoken, and these
individuals are likely to regard foreign vocabulary acquisition
as important” (p. 103).
Wang, Thomas, and their colleagues (Wang & Thomas, 1995;
Wang, Thomas, Inzana, & Primicerio, 1993; Wang, Thomas, &
Ouellette, 1992) have suggested that individuals using the
keyword method experience a faster rate of forgetting (over a
delay of several days) than do individuals using a repetition
strategy—especially in the absence of an immediate test.
However, using a comparable design, Carney, J. R. Levin,
Bingham, and Cook (1996) found delayed mnemonic recall
advantages after 2- and 5-day delays, even in the absence of an
immediate test on the items. At the same time, Carney noted a
slightly more rapid decline in the forgetting rate for
mnemonically instructed individuals after 5 days. As R. Krinsky
and S. G. Krinsky (1996) and others have suggested, the
overlearning of mnemonic associations, through additional
rehearsal, may be of critical importance in promoting robust
long-term mnemonic benefits. It would be interesting to
examine this issue with elderly participants.
The Phonetic Mnemonic System. Another common complaint of
adult and elderly learners is that it is difficult for them to
retrieve numerical information. In the survey mentioned earlier,
the second most common memory skill listed as needing
improvement by older adults was remembering dates (Leirer et
al., 1990). A mnemonic approach recommended by memory
improvement books for remembering numerical information
such as dates and telephone numbers is the phonetic (or digit
consonant) mnemonic system (e.g., Higbee, 1993; Lorayne,
1990; Lorayne & Lucas, 1974). The phonetic mnemonic system
is based on a phonetic code whereby numbers are recoded as
consonant sounds (e.g., 1 = t, 2 = n, 3 = m, …, 0 = s or z).
Because vowels are not used in the code, they may be inserted,
as needed, to form familiar words. Thus, the number 20 may be
recoded as n + s = nose, 32 as m + n = man, and so forth.

Ideally, these words are much more concrete and meaningful—
and hence more memorable—than the nominal abstract number
(in addition to the critical component of the words then being
able to be associated with other information through interactive
visual images).
Research evidence regarding the use of the phonetic mnemonic
system has been somewhat mixed. Slak (1970) formulated his
own memory code, practiced extensively, and then showed
improvement in memory span, serial learning, self-paced serial
learning, and recognition. Bruce and Clemmons (1982) used the
system in a rather complicated procedure for converting
between metric and standard measurement units but did not find
a mnemonic advantage. Morris and Greer (1984) found a
mnemonic advantage in serial recall of a list of two-digit
numbers. Patton (1986) found that use of the phonetic
mnemonic system actually impaired recall test performance
compared to performance by a control group. More recently,
Carney and J. R. Levin (1994) combined a simplified version of
the technique with the face-name mnemonic strategy to help
college students remember “who painted what when”—that is,
the dates of various artists’ paintings. They found that memory
for dates could be facilitated by recoding the last two digits (all
paintings included were from the 19th century) using the
phonetic mnemonic system (e.g., with 6 = soft g or j and 1 = t,
1861 could be recoded as jet). The jet, along with a name clue
for the artist’s name (e.g., messenger for Meissonier), could
then be made to interact with a prominent feature or theme of
the painting (e.g., a messenger walking down the steps from
his jet in the background). Carney and Levin found a mnemonic
advantage for this date-learning technique, in comparison to the
learning of students directed to use their own best method.
ORGANIZATIONAL MNEMONICS
Whereas the previous encoding mnemonics facilitate the
learning of associated pairs (or clusters) of
information, organizational mnemonics (Bellezza, 1981)
facilitate the acquisition of ordered information. In particular,

older adults are less likely to organize incoming information
automatically than are younger adults (Kausler, 1994).
Examples of organizational mnemonics include the link
mnemonic strategy, the method of loci, and the pegword
method. The link mnemonic (Higbee, 1993) is the simplest of
these mnemonic systems. Take, for example, the following list
of grocery items (found in this order in the store): bread, tuna,
milk, corn flakes, and dog food. This mnemonic strategy
involves forming sequential interactive images linking each
adjacent pair of items (e.g., two pieces of bread wrapped around
a tuna; a tuna swimming in a sea of milk; milk being poured
over cornflakes; and finally, cornflakes being mixed with dog
food. As Higbee pointed out, it is important to make a special
effort to remember the first item “starting point” on the list
(e.g., linking grocery store to bread in some way). Research has
generally supported use of the link mnemonic (e.g., Bugelski,
1977; McCormick & J. R. Levin, 1984; Roediger, 1980).
A second organizational mnemonic is the method of loci.
Perhaps the oldest mnemonic method, it dates back to ancient
Greece and was used as a memory aid in oratory (Yates, 1966).
To apply this strategy, one first selects a number of locations or
loci. For example, one might select specific locations
encountered sequentially on a walk around campus, such as a
specific bench, a statue, a rock wall, and so forth. Next, to-be-
remembered items are related to these locations by storing them
as interactive visual images. For example, with our ordered
grocery list just discussed, one might imagine
the bench upholstered with slices of bread, the statue holding
a tuna, the rock wall with milk cascading over it, and so on.
Finally, retrieval is accomplished by taking a mental walk
through these locations, which, in turn, cues the items stored
therein. Although the method of loci has been effectively used
by college students (e.g., Bower, 1970; Groninger, 1971; Krebs,
Snowman, & Smith, 1978), mnemonic benefits with the elderly
have been somewhat mixed. For example, Anschutz, Camp,
Markley, and Kramer (1985) successfully trained older adults to

use the method of loci to remember grocery items. However, a
3-year follow-up to this study found that even though the adults
remembered the strategy, they were no longer using it
(Anschutz, Camp, Markley, & Kramer, 1987; see Kausler, 1994,
for a recent review). Relatedly, a four-seasonal loci approach
has been adapted to facilitate students’ learning of the U.S.
presidents (J. R. Levin, McCormick, & Dretzke, 1981), and
Hwang et al. (1994) used 10 seasonal loci for learning the dates
of inventions and the atomic numbers.
A third serial, or ordered recall, mnemonic is the pegword
method (e.g, Higbee, 1993). To apply this technique, the learner
first memorizes a set of concrete rhyming pegwords, each
corresponding to a number from 1 to 10. Thus, 1 = bun, 2
= shoe, 3 = tree, 4 = door, 5 = hive, and so forth, up to 10
= hen. Next, given a list of to-be-remembered items, each of
these items is made to interact with the pegword in a
meaningful image. Again, let us consider our grocery list. First,
one could imagine a package of hamburger buns mashing a loaf
of bread in the same section. Next, imagine your shoe kicking a
can of tuna. Then, imagine a young tree planted in a
plastic milk carton, and so forth. Encoded in this manner, upon
entering the grocery store, one simply goes down the list of
numbers to cue the desired items. For example, one sounds like
bun, and bun brings back the image involving the package of
buns mashing the loaf of bread. College students seem to
benefit from the technique (e.g., Bugelski, Kidd, & Segmen,
1968), and modifications of the technique have been applied to
learning a variety of ordered information, such as presidents
(Dretzke & J. R. Levin, 1990), inventions (Hwang et al., 1994),
dinosaurs (Mastropieri, Scruggs, & J. R. Levin, 1987), and
mineral hardness levels (Scruggs, Mastropieri, J. R. Levin, &
Gaffney, 1985). Nevertheless, Kausler (1994), in his review of
the literature, described the effectiveness of the technique with
elderly learners as “questionable” (p. 106).
Recently, R. Krinsky and S. G. Krinsky (1994, 1996) published
studies in which fifth-graders in school settings applied the

pegword method to list learning. Their general findings were
that although the pegword method produced immediate
mnemonic recall advantages over control groups, the mnemonic
groups experienced a more rapid rate of forgetting. These
findings are in line with those of Wang, Thomas, and their
colleagues (Wang & Thomas, 1995; Wang et al., 1992, 1993),
which we cited earlier in our discussion of the keyword method.
Once again, perhaps overlearning mnemonically acquired
information through additional rehearsal is vital for mnemonic
benefits to be sustained over the long haul.
INSTRUCTIONAL IMPLICATIONS FOR ADULT LEARNERS
In describing efficient strategy use, Pressley, Borkowski, and
Johnson (1987) suggested that “a proficient strategy user knows
and can execute a variety of strategies that accomplish many
specific cognitive goals” (p. 274). Hence, it would seem to be a
simple matter to train adult learners directly to use the well-
established mnemonic techniques described in this chapter. One
could begin by providing a demonstration of a mnemonic
technique to illustrate its efficacy (such as remembering 10
ordered items using the pegword method). Next, various
mnemonic techniques could be applied to material pertinent to
adults to convince them of the techniques’ relevance (Carney, J.
R. Levin, & M. E. Levin, 1994). For example, adults could be
asked what is important for them to remember (e.g., medical
information), and then the strategies could be tailored to suit
their particular needs.
Nevertheless, research has suggested that adults “do not
generate mnemonic elaborations reliably in the absence of
instruction” (Pressley & McCormick, 1995, p. 301, citing
Beuhring & Kee, 1987). Indeed, Park et al. (1990) reported the
results of a survey of 69 memory researchers who were asked to
rate the frequency of their use of mnemonic techniques. Perhaps
surprisingly, the frequency of use was quite low. However, one
would suspect that active memory researchers in academe have
respectable memories to begin with—and are well stocked with
Post-it Notes adjacent to their computers! Additionally, such

individuals may be using certain techniques routinely, at some
level, without their necessarily being consciously aware of
those techniques (e.g., an alphabetic cuing scheme for retrieving
a name, leaving a concrete reminder in a strategic place,
constructing visual maps, etc.).
Earlier we described the findings of Wang, Thomas, and their
colleagues (Wang & Thomas, 1995; Wang et al., 1992, 1993),
who have suggested that mnemonically instructed individuals
display a faster forgetting rate over a span of several days.
Although our research has not supported such a dramatic
forgetting rate (e.g., Carney et al., 1996), we have nonetheless
noted a slightly faster rate of forgetting for mnemonically
instructed students. Again, as R. Krinsky and S. G. Krinsky
(1996) and others have speculated, the overlearning of
mnemonically acquired associations may be of critical
importance in promoting long-term retention. Thus, any training
program for adults should provide for additional rehearsal if
long-term benefits are to be anticipated. For example, a spaced
rehearsal approach (Bjork, 1988; Camp & McKitrick, 1991)
might be helpful. To emphasize the importance of this activity
in the effective use of mnemonic strategies, we might hereby
add a fourth “R” to Levin’s three: recoding, relating, retrieving
… and then, rehearsing!
INSTRUCTING OLDER ADULT LEARNERS IN MNEMONIC
STRATEGIES
At the beginning of this chapter we listed a number of
hypotheses that attempt to explain age-related declines in
information-processing abilities, such as memory. Through
“intensive and extensive” training in mnemonic techniques
(Kausler, 1994, p. 114), we would hope to overcome deficits
due to both the inefficient strategies and disuse hypotheses (see
also Roberts, 1983). Additionally, it is very important to keep
the speed and generalized slowing hypotheses in mind when
considering aged learners. Indeed, the “time needed by the
elderly to acquire and demonstrate proficiency with a mnemonic
technique may need to be extended, particularly if the technique

and stimuli are novel to the elderly learner” (Poon, Walsh-
Sweeney, & Fozard, 1980, p. 475, cited by Richardson, Cermak,
Blackford, & O’Connor, 1987). The slowing of information
processing, and the suggestion to provide more practice time is
echoed repeatedly throughout the literature (e.g., Finkel &
Yesavage, 1989, Pressley & J. R. Levin, 1977; Salthouse, 1985;
Treat & Reese, 1976; Yesavage, 1990)—especially regarding
more complex mnemonics, such as the method of loci
(Yesavage, 1990).
Likewise, the resource reduction hypothesis should be
considered. Among other things, the resource reduction
hypothesis involves reductions in the capacity of consciousness
or working memory. This is problematic, in that short-term
memory is “an important determinant of imagery strategy
execution” (Pressley et al., 1987, p. 280). As we mentioned
earlier, Yesavage (1990) and his colleagues have validated the
practice of “pretraining” in teaching the elderly to use
mnemonic techniques. Yesavage commented that the pretraining
interventions work because “they increase the efficiency of
processing” (p. 63), especially for more complex mnemonic
techniques. Pretraining consists of three parts: relaxation
training, training in visual imagery, and training in semantic
elaboration. Relaxation pretraining is analogous to the
techniques used in reducing test anxiety. Visual imagery
pretraining involves displaying slides, and then having
individuals practice visualizing what they have seen. Finally,
semantic elaboration pretraining involves asking older learners
to make verbal judgments related to their visual images
(Yesavage, 1990). These components are designed to offset the
finding that the elderly “… often have difficulty applying
complex mnemonic strategies because of performance anxiety,
difficulty in forming visual images used in associations, and
relatively superficial encoding of associated visual images”
(Finkel & Yesavage, 1989, p. 199).
Even when older adults are taught to use a strategy, they are
less likely to make use of such strategies spontaneously (Camp-

Cameron, Markley, & Kramer, 1983), and even when they have
discovered benefits in using them, they tend to prefer not to do
so (Brigham & Pressley, 1988). As Devolder and Pressley
(1992) have demonstrated, young adults are more likely to
attribute success to controllable factors, such as strategy use,
than are older adults. Additionally, personality traits such as
“openness to experience” (Costa & McCrae, 1988) may also
play a role in whether an elderly individual learns and
successfully uses a mnemonic technique (Gratzinger, Sheikh,
Friedman, & Yesavage, 1990). In Costa and McCrae’s model,
the open individual is more imaginative than down to earth,
prefers variety to routine, and tends to be independent as
opposed to conforming (Perlmutter & Hall, 1992).
As illustrated by the preceding points, getting older learners to
apply mnemonic strategies is difficult. Taking a pessimistic
view, Kausler (1994) suggested that such techniques are
“effortful to apply” and “require an imaginal ability that is
likely to be difficult for many elderly adults to apply” (p. 114).
However, it should be pointed out that the mnemonic benefits
we have described are not restricted to visual images per se. For
example, in many of our studies with college students, we have
routinely provided verbal descriptions of to-be-imagined
interactions (e.g., Carney et al., 1988), and have observed
comparable mnemonic benefits to those produced by learner-
generated images (see also Pressley et al., 1982). Thus, relating
to-be-learned items through verbal elaboration (i.e., meaningful
sentences tying the items together) might be helpful with
learners who seem to have difficulty using visual imagery.
One a more positive note, Kausler (1994) observed that “the
keyword method could serve as a means of enhancing the
acquisition of a limited foreign language vocabulary that elderly
adults could use in visiting a foreign country” (p. 114). More
generally, Perlmutter and Hall (1992) have suggested that
formal education is no longer reserved for young people. As the
proportion of older adults grows larger, and retirement comes
sooner, formal education appears to be “spreading across the

life span, with middle-aged and older adults enrolling in
traditional college programs, in special college programs
devised for ‘mature students,’ and in community adult education
courses” (p. 417). Our research with mnemonic strategies leads
us to be optimistic. We believe that, under the right
circumstances, mnemonic strategies can be useful memory
techniques for this growing body of graying learners.
CHAPTER 8
Phillip L. Ackerman
OVERVIEW
been aimed at predicting academic
success. As such, efforts during the early part of this century
first focused on predicting the
school success of children and young adolescents (for a review,
see Ackerman, 1996). Around

World War I, intelligence test content was extended upward
—
to allow for testing of young and
middle
-
aged adults. As the educational establishment embraced
intelligence testing,
postsecondary institutions increasingly relied on t
he use of tests for selection of college and
university applicants, starting in the 1920s. Today’s college
entrance tests, such as the Scholastic
Assessment Tests (SAT) and the American College Testing
Program (ACT), show a significant
resemblance to the a
dult intelligence tests of the 1920s. Although these procedures
may be useful
predictors of college success for young adults, they fail to take
account of the differences
between child/adolescent intelligence and adult intelligence. A
perspective
of intell
igence that
focuses on
knowledge
as a key ingredient of adult intelligence is presented in this
chapter. By
moving away from the traditional process
-
oriented conceptualization of intelligence to a
knowledge
-
oriented conceptualization, many aspects of adult

intellectual development can be
considered, especially in the context of learning and education
for adults. Such a shift in
adult learner, such as personality,
interests, and motivational skil
ls
—
and provides a framework for an integrated view of adult
development, learning, and education.
adult intelligence, as a contrast
between process and knowledge components of intellect. Ne
xt, a discussion is presented of
motivational skills. Putting all of
these components together provides for a perspective on adult
development that stands in
contrast to the traditional view of
intellectual decline with increasing age. Finally, some
implications of the knowledge
-
based perspective for adult education and learning are
presented.
INTELLIGENCE
When the first mode
rn procedures were devised for assessing intelligence, Binet and
Simon
(1905) distinguished between two different approaches, which

they called the psychological and
adopted for assessment of childr
en,
was specifically oriented toward aspects of intelligence that
were believed to be less influenced
CHAPTER 8
Phillip L. Ackerman
OVERVIEW
been aimed at predicting academic
success. As such, efforts during the early part of this century
first focused on predicting the
school success of children and young adolescents (for a review,
see Ackerman, 1996). Around
World War I, intelligence test content was extended upward—to
allow for testing of young and
middle-aged adults. As the educational establishment embraced
intelligence testing,
postsecondary institutions increasingly relied on the use of tests
for selection of college and
university applicants, starting in the 1920s. Today’s college
entrance tests, such as the Scholastic
Assessment Tests (SAT) and the American College Testing
Program (ACT), show a significant
resemblance to the adult intelligence tests of the 1920s.
Although these procedures may be useful
predictors of college success for young adults, they fail to take
account of the differences
between child/adolescent intelligence and adult intelligence. A
perspective of intelligence that
focuses on knowledge as a key ingredient of adult intelligence
is presented in this chapter. By

moving away from the traditional process-oriented
conceptualization of intelligence to a
knowledge-oriented conceptualization, many aspects of adult
intellectual development can be
considered, especially in the context of learning and education
for adults. Such a shift in
adult learner, such as personality,
interests, and motivational skills—and provides a framework for
an integrated view of adult
development, learning, and education.
adult intelligence, as a contrast
between process and knowledge components of intellect. Next, a
discussion is presented of
motivational skills. Putting all of
these components together provides for a perspective on adult
development that stands in
contrast to the traditional view of intellectual decline with
increasing age. Finally, some
implications of the knowledge-based perspective for adult
education and learning are presented.
INTELLIGENCE
When the first modern procedures were devised for assessing
intelligence, Binet and Simon
(1905) distinguished between two different approaches, which
they called the psychological and
adopted for assessment of children,
was specifically oriented toward aspects of intelligence that
were believed to be less influenced

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