I - Theory-Driven Guidelines for Cognitive Rehabilitation Strategies in Older Adults

Editors: Backman, Lars; Hill, Robert D.; Neely, Anna Stigsdotter

Title: Cognitive Rehabilitation in Old Age, 1st Edition

Copyright 2000 Oxford University Press

> Table of Contents > Part I - Theory-Driven Guidelines for Cognitive Rehabilitation Strategies in Older Adults > 2 - Theoretical and Methodological Issues in Memory Training

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Theoretical and Methodological Issues in Memory Training

Robert Hill

Lars B ckman

A difficult issue that adults typically face as they as they grow older, and especially as they move into very late life, is declining memory function. However, the magnitude of age-related memory deficits varies across different forms of memory. Specifically, whereas performance in tasks assessing semantic memory, primary memory, procedural memory, and various forms of priming tends to be relatively little affected by the normal aging process, age-related impairments are legion in tasks assessing episodic memory (see Backman, Small, & Larsson, in press; Craik & Jennings, 1992, for reviews). Specific examples of this kind of memory loss include forgetting names and faces, misplacing important objects (e.g., house keys), forgetting telephone numbers, missing appointments, and even losing the train of a conversation in a social setting (e.g., Bolla, Lindgren, Bonaccorsy, & Bleecker, 1991).

Episodic memory deals with conscious retrieval of information that is encoded in a particular place at a particular time (Tulving, 1983). This form of memory is typically assessed by having subjects recall or recognize information acquired in the laboratory (e.g., a list of words or a series of faces). To the extent that age-related deficits are particularly likely to be observed in episodic memory, it is not surprising to find that attempts to enhance memory in old age have focused primarily on this form of memory. An example of this kind of memory deficit is age-related cognitive decline (see Larabee, 1996; Rediess & Caine, 1996), which has been labeled in the fourth edition of the Diagnostic and Statistical Manual for Mental Disorders as a psychological factor affecting a medical condition (American Psychiatric Association, 1996, p. 684).

This chapter will focus primarily on issues related to the efficacy of memory training as a psychological intervention for remediating memory loss in old age. Research has documented that modest gains in verbal and name-face recall are achievable in

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community-dwelling, high-functioning older adults through memory-training interventions (for an overview, see the chapter by Paul Verhaegen, in this volume). Focused short-term interventions in which older adults are taught specific mnemonic strategies (e.g., the method of loci) to facilitate encoding of to-be-remembered items (e.g., high-imagery nouns) represent the major training paradigm.

A central goal of this chapter is to examine the memory-training literature to ascertain whether such an approach to cognitive rehabilitation can be extended to a wider range of older adults, as well as to a broader spectrum of cognitive problems in everyday living. In this regard, we will explore several methodological and theoretical issues underlying memory training: (a) the unique strengths of a mnemonic device for addressing the kinds of deficits that are inherent in cognitive changes associated with old age, (b) the role that individual characteristics play in mediating training outcomes, (c) The extent to which mnemonic techniques can be effectively applied to everyday problems, and (d) The degree to which training can be extended beyond traditional contexts (e.g., computer-based instruction).

Mnemonic Training for the Acquisition and Retention of Information

It is our belief that to take full advantage of training in utilizing a mnemonic technique, its nature and purpose should be clearly delineated. In this section, we explore how mnemonic techniques are used to improve acquisition of information and to facilitate retention (or minimize forgetting). Acquisition refers to the initial encoding, of information at a specific point in time. This might consist of an item (e.g., a name) that is to be remembered when one is confronted with a stimulus cue (e.g., a face). Retention, on the other hand, refers to the ability to encode information in such a way that it can be recalled at a later point in time (e.g., remembering a newly learned phone number several weeks after it was initially learned). The extent to which a mnemonic device is designed for these purposes and its influence on acquisition and retention in an older adult population are described below.

Issues in the Acquisition of Information

One of the more highly publicized features of a mnemonic device is that it represents a kind of encoding that facilitates retrieval of large amounts of information. Highly practiced users of mnemonics (called mnemonists) have demonstrated that it is possible to encode large amounts of information (e.g., a complete list of phone numbers in a local phone book, line and verse from the Bible, and very long strings of random numbers) using mnemonic procedures (Cermak, 1975; Higbee, 1988). It is also noteworthy that such feats of memory power can be achieved by persons of average intellectual ability who learn to use a mnemonic proficiently. For example, Belleza, Six, and Phillips (1992) trained two individuals to produce nearly perfect recall of 80-digit number strings after only a few minutes of study.

In a meta-analysis of research studies with older adults where mnemonic strategies were employed to enhance acquisition, Verhaeghen, Marcoen, and Goossens (1992) concluded that, on average, active memory training produces a very modest gain of

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10% over placebo training. This magnitude of gain was associated with brief interventions that varied in terms of the number of training sessions and the amount of material covered in each session. Training typically involved limited exposure and practice in using a specific technique (e.g., the method of loci), as well as additional skills to facilitate its use (e.g., imagery pretraining).

As a way to enhance acquisition, more intensive interventions employing the same subject selection procedures, but increasing the number of training sessions, have also been tried (Stigsdotter & B ckman, 1989; Stigsdotter Neely & B ckman 1993a, 1995), and reviews of this research appear in several chapters of this volume (see chapter 4 by Anna Stigsdotter Neely). Interestingly, these studies have reported only slight performance advantages over briefer interventions, suggesting that training intensity itself may not be the most important component for achieving skill competency, but characteristics of the mnemonic strategy, as well as the subjects, should also be considered. As a demonstration of this point, Kliegl, Smith, and Baltes (1989, 1990) utilized an intensive training approach to determine the limits of age differences in cognitive reserve capacity for learning verbal material via the method of loci. In these studies, not only were training procedures intensive, but participants were also specifically selected who were intellectually high-functioning, motivated, and in optimal physical health. In addition, training was designed to ensure that participants achieved competence in utilizing the mnemonic procedure; that is, they could not progress through the training until they had achieved predetermined levels of performance proficiency. These studies demonstrated that with properly designed training, high-functioning older adults can substantially alter their baseline memory performance by applying mnemonic procedures to the material to be remembered. In fact, some of their subjects showed more than 10-fold increases in acquisition. From this research, it appears that simply providing additional training is not, in and of itself, sufficient to appreciably change an individual's ability to acquire information. What needs additional consideration is more systematic examination of how the specific characteristics of those being trained can influence the degree to which training leads to the actual mastery of the mnemonic itself, and the extent to which the mnemonic can be applied to acquiring material in question.

Issues in Retention and Forgetting

Perhaps the most powerful feature of a mnemonic device is its ability to facilitate the retention of information even after a long time. For example, Bellezza (1981) argued that one of the most useful characteristics of a mnemonic device is that it produces a kind of encoding that prohibits forgetting of information even without continual rehearsal. Specifically, a mnemonic produces a more distinctive encoding (Craik & Jennings, 1992; Craik & Simon, 1980; Hill, Storandt, & Simeone, 1989) by connecting to-be-remembered information with information stored in permanent memory. The stronger this connection, the greater the likelihood that acquired information will be retained for longer periods.

From a practical standpoint, a mnemonic device should help the individual retain important information (e.g., names and faces, phone numbers) over longer intervals even though the material may not be frequently used (Higbee, 1988). Thus, what is important in this regard is not the sheer quantity of information that can be retrieved, but the relative permanence (or durability) of a specific memory trace.

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Although little information is available that has investigated the role of mnemonics in facilitating retention of information in older adults, there is a large body of research on younger-aged groups that has examined the nature of the forgetting process (see Bogartz, 1990; Loftus, 1985; Slameka, 1985; Slameka & McElree, 1983; Wixted & Ebbesen, 1991) and the role that mnemonics play in minimizing forgetting (Belleza, 1981; Higbee, 1988). In this regard, the operational definition of forgetting is the performance difference between two or more retention intervals, retention being a memory measure that is obtained at a single point in time. Analysis of the forgetting rate, then, has involved examining the slope of the line that connects these different points of measurement.

In some samples, data suggest that older individuals may forget information at a faster rate than young adults (Brainerd, Reyna, Howe, & Kingma, 1990). Giambra and Arenberg (1993) also reported that in their sample of adults over 60 years there may be small and, depending on statistical power, reliable age-related differences in forgetting rate for longer retention intervals. Some research in both old and young adults indicates that both the quantity of learned material and the rate of forgetting, particularly when the retention interval is long, may be influenced by mnemonic skill (Boltwood & Blick, 1970; Bower & Clark, 1969).

In a study examining the impact of naturally occurring memory strategies on the rate of forgetting in older adults, Hill, Allen, and Gregory (1990) documented that those who spontaneously employed a mnemonic to encode a list of high-imagery nouns experienced less forgetting than did those who reported using no memory strategy (or only rote rehearsal). This study suggests that some older adults can spontaneously generate and utilize mnemonic strategies to facilitate retention. For example, nearly half the subjects in this study reported spontaneously using first-letter pegword techniques, categorizing the words, or creating stories that incorporated the words, and it is noteworthy that these strategy users outperformed those who reported using no strategy at each follow-up. Note also that this was a highly educated and relatively young group of older adults. Thus, the extent to which mnemonics are effectively employed in old age for the purpose of retention of information may depend, in part, on individual characteristics such as education and previous exposure to mnemonic strategies.

Most of the research investigating the retention effects of memory training in older adults has examined only retention of the mnemonic strategy itself (see Anschutz, Camp, Markley, & Kramer, 1985; Scogin & Bienias, 1988; Stigsdotter & Backman, 1989; Stigsdotter Neely & B ckman, 1993a, 1993b, 1995) rather than maintenance of the materials encoded at study via the mnemonic. Thus, the forgetting rate of learned material has not been systematically explored in this literature. From a cognitive rehabilitation standpoint this is a serious omission, because what matters most in many everyday situations is remembering encoded information (e.g., a name or a telephone number) over an extended time rather than retrieving only information about the mnemonic procedure itself (e.g., the steps necessary to encode a telephone number).

Individual Differences as a Mediating Factor in Memory Training

This section examines the extent to which individual difference variables mediate response to memory training. As alluded to previously, individual differences with

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regard to motivation and intellectual abilities can be an important factor in influencing gains associated with more intensive training (see Kliegl et al., 1989, 1990). Thus, by way of background, we will first highlight research that has documented the relationship between individual difference variables and episodic memory performance. We will then examine the role that individual differences play in mediating the utilization of cognitive support (e.g., the provision of retrieval cues; see Backman & Larsson, 1992) in experimental memory tasks. Finally, we will highlight those studies that have investigated individual difference variables in memory-training interventions with adults.

Individual Differences in Episodic Memory

A well-known phenomenon regarding aging and episodic memory is the influence of subject-related characteristics on performance. Studies have consistently found that subject-related variables within demographic, cognitive, lifestyle-related, and health-related domains can mediate episodic memory performance in old age (see Backman, Mantyla, & Herlitz, 1990; Hultsch & Dixon, 1990; Salthouse, 1991b, for reviews). Exploring how specific characteristics of the individual covary with memory performance may yield important theoretical information regarding the optimization and modifiability of memory in late life (e.g., Hill, Wahlin, Winblad, & Backman, 1995; Zelinski, Gilewski, & Schaie, 1993). Although there are a wide range of potential individual-difference variables, those that most frequently appear in the published literature can be roughly classified within four general content domains: (a) demographics, (b) cognitive factors, (c) lifestyle, and (d) disease.

Demographics

In this section, age and education are highlighted as subject demographics; however, it should be noted that other demographics have been found to mediate episodic memory performance, including gender (Herlitz, Nilsson, & B ckman, 1997). Only age and education are highlighted in this section because they are the two most commonly cited demographic variables predicting episodic memory performance.

It is well known that there are reliable declines in episodic memory performance from early to late adulthood (see Kausler, 1991; Salthouse, 1991b, for reviews). In addition, comparisons of older adults of different ages suggest that age-related deterioration of episodic memory continues into very late life (e.g., B ckman, 1991; Backman & Larsson, 1992; Wahlin et al., 1993; West, Crook, & Barron, 1992), with marked declines after 70 years of age (Christensen, Mackinnon, Jorm, Henderson, & Korten, 1994; Colsher & Wallace, 1991; Giambra, Arenberg, Zonderman, Kawas, & Costa, 1995; Johansson, Zarit, & Berg, 1993). However, it has been suggested that chronological age per se may be only a marker of other, more critical variables related to memory performance (e.g., education, intellectual ability, health). Some studies have found that age may play a relatively minor role in change in episodic memory when factors such as health and intellectual ability are controlled (see Hill et al., 1995). Thus, disentangling the true effects of age on episodic memory has been a difficult research issue, particularly in advanced age.

The relationship between education and episodic memory has also been extensively

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evaluated (Kausler, 1991; Perlmutter, 1978; Salthouse, 1991b). Education has generally been defined as the number of years of formal schooling, and in this context, it has been a potent ordinal predictor of memory performance (Hultsch & Dixon, 1990; Salthouse, 1993; West et al., 1992; Zelinski et al., 1993). Likewise, studies that have grouped individuals according to educational attainment have reported education-related advantages across various memory tasks (Craik, Byrd, & Swanson, 1987; Inouye, Albert, Mohs, Sun, & Berkman, 1993; Wiederholt et al., 1993). Educational status may also be linked to a variety of factors, including genetic selection, neuronal growth during critical periods in life, cognitive stimulation during active work periods, physical health, and use of memory strategies (Hill et al., 1995; Mortimer & Graves, 1993).

Cognitive Factors

This section highlights three cognitive variables and their relationship to episodic memory: verbal ability, working memory, and processing speed, all of which have been found to influence memory performance. Verbal ability has received attention in this context as a component of crystallized intellectual function that is positively associated with episodic memory. Zelinski and Gilewski (1988) found that across 3 years, older adults with high vocabulary scores declined less on a measure of episodic memory than those with lower scores. West et al. (1992) reported that vocabulary mediated the degree to which age influenced performance across all of their measures of episodic memory. Similarly, Craik et al. (1987) found that in a sample of older adults who were trichotomized according to vocabulary scores, those in the highest vocabulary grouping outperformed the two lower groupings.

Both working memory (Foos, 1989; Hartley, 1986; Morris, Gick, & Craik, 1988) and speed of processing (Salthouse, 1985a, 1985b, 1991a, 1992, 1993) also exert a strong influence on episodic memory performance. With regard to working memory, distinctions between capacity and efficiency have been made with the notion that working memory capacity may show the greatest age-related declines, and that working memory efficiency may be less influenced by age and more likely to be amenable to modification (see Foos, 1989). Processing speed, as described by Salthouse (1996), has received considerable attention as a theoretical construct that involves the speed with which an individual can perform the requisite operations to negotiate a given cognitive task. Since episodic memory is defined as a higher order cognitive process, processing speed has been examined as an elementary factor in memory performance (Salthouse, 1985a, 1991b; Salthouse & Coon, 1993; Salthouse & Kersten, 1993) and in gauging the benefits of memory training (Kliegl et al., 1990).

Lifestyle

Lifestyle variables examined in this section include behavioral indices, such as level of physical and social activity and use of substances such as alcohol and tobacco. It is well documented that these variables are highly interrelated; however, they have also been evaluated individually for their relative contribution to the prediction of memory performance

Research indicates that level of social participation is positively associated with

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memory performance (Cockburn & Smith, 1991; Craik et al., 1987). Furthermore, self-reported physical exertion has been found to predict performance on memory tasks, although its predictive value may be restricted to relatively strategy-free tasks (Christensen & Mackinnon, 1993; Stones & Kozma, 1989). Although the use of substances such as tobacco and alcohol have not shown a consistent relationship to episodic memory, self-report measures of these indices may not be adequate to detect subtle differences in memory performance (Hill, 1989; Hill, Storandt, & Malley, 1993). What seems to be the case, however, is that those older adults who are actively engaged in demanding, cognitively stimulating pursuits and who avoid lifelong patterns of substance abuse tend to be advantaged with respect to episodic memory (Hultsch, Hammer, & Small, 1993). It should be noted, however, that the causal direction of the relationship between activity and cognitive performance is unclear. As much as social activities may be beneficial to intellectual functioning, it may also be that persons who are cognitively adept tend to engage more in social activities than those who have lower cognitive functioning.

Disease

Although the individual difference variables discussed hitherto influence episodic memory in old age, it is clear that the most powerful characteristic is the presence or absence of disease. Dementia is the most prominent in this regard, and studies document that in dementia, episodic memory performance deteriorates markedly across the disease process (e.g., Almkvist & B ckman, 1993; Herlitz, Adolfsson, B ckman, & Nilsson, 1991; Herlitz & Viitanen, 1991; Welsh, Butters, Hughes, Mohs, & Heyman, 1992). M. M. Baltes, Kuhl, Gutzmann, and Sowarka (1995) have proposed that changes in cognitive processes due to Alzheimer's disease (AD) may most prominently display themselves in terms of a reduced cognitive reserve capacity. More specifically, older adults who are at high risk for developing AD may show deficiencies in developmental reserve capacity, or in the ability take advantage of opportunities for optimizing episodic memory function in supportive task conditions.

Research has also identified clinical depression as a strong predictor of memory performance in elderly adults (Hart, Kwentus, Hamer, & Taylor, 1987; La Rue, 1989; Lichtenberg, Ross, Millis, & Manning, 1995), especially in task situations requiring expenditure of cognitive effort and self-initiated operations at encoding and retrieval (B ckman & Forsell, 1994; Weingartner, Cohen, Murphy, Martello, & Gerdt, 1981). These findings have been extended to mood and motivational characteristics of aspects of depression in nondepressed older adults, particularly with regard to motivational factors (Backman, Hill, & Forsell, 1996).

Individual Differences in Utilization of Cognitive Support

In this section, a theoretical framework involving the influence of the provision of cognitive support on cognitive reserve capacity (see Baltes, 1987; Baltes, Dittmann-Kohli, & Kliegl, 1986) is examined. Special attention is given to examining the impact of the previously highlighted individual difference variables on the size of gains following the provision of cognitive support.

Although the aging process exerts a negative influence on episodic memory functioning,

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most older adults also possess substantial cognitive reserve capacity a potential for memory improvement. Specifically, when different forms of cognitive support are provided (e.g., directed encoding instructions, item organizability, motor activity at learning, a rich material, retrieval cues), older adults typically exhibit sizable performance gains relative to control conditions in which these forms of support are lacking (for reviews, see B ckman et al., 1990; Craik & Jennings, 1992).

An interesting question concerns whether there are individual differences among older adults with regard to the ability to benefit from cognitive support in episodic memory tasks. Most research addressing this issue has compared groups of elderly individuals who suffer from dementia or depression with normal elderly adults in tasks varying in the level of cognitive support provided. Results indicate pronounced dementia-related deficits in the ability to utilize cognitive support for remembering (see B ckman & Herlitz 1996; Herlitz, Lipinska, & B ckman, 1992, & B ckman 1995, for reviews); demented patients require a substantial amount of cognitive support in both encoding and retrieval in order to demonstrate memory facilitation. In clinical depression, the empirical picture is less clear (see the chapter by Pachana, Marcopulos, and Takagi in this book), although there is research indicating that depressed older adults may show deficits in utilizing item organizability (B ckman & Forsell, 1994; Watts, Dagleish, Bourke, & Healy, 1990), visual imagery (Hart et al., 1987), and an increased presentation rate (B ckman & Forsell, 1994) to improve memory performance.

Other research has examined the influence of demographic and lifestyle variables on the ability to utilize cognitive support within samples of healthy older adults. There is evidence to suggest that the level of support required to demonstrate memory improvement may increase from early to later portions of the late adult life span (B ckman, 1991; B ckman & Larsson, 1992; Hill et al., 1995). In addition, older individuals who are highly educated and socially active have been found to benefit more from cognitive support than less educated and less active individuals (Craik et al., 1987; Hill et al., 1995). In contrast, self-reported exercise habits, as well as gender, have not been found to be related to the size of performance gains from the provision of support in episodic memory tasks (Hill et al., 1995). Thus, it appears that individual difference variables may affect not only general level of episodic memory functioning in old age, but also the degree to which memory performance can be modified by means of supportive task conditions. The relationship between overall memory function and potential for memory improvement is further discussed in the next section, which reviews current approaches to memory-training research with older adults that have specifically examined the effects of various individual difference variables on the size of training-related gains.

Mnemonic Training for Modifying Episodic Memory Performance

Investigations have examined the extent to which subject characteristics are predictive of performance gains from memory training. In brief, these have included age (Rose & Yesavage, 1983), verbal ability (Yesavage, Sheikh, Decker-Tanke, & Hill, 1988), mental status (as measured by the MMSE; Hill, Yesavage, Sheikh, & Friedman, 1989; Yesavage, Sheikh, Friedman, & Tanke, 1990), cognitive factors such as processing

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speed (Kliegl et al., 1990), and personality characteristics (Gratzinger, Sheikh, Friedman, & Yesavage, 1990). These studies have suffered from various problems, including limited range of variation in the predictors, inadequate sample size, and a lack of theoretical rationale for guiding variable selection. In spite of these deficiencies, what follows is a review of how these studies have examined individual characteristics in memory-training interventions.

Age has received the most attention as an individual characteristic that could influence memory-training outcomes. As noted previously (e.g., B ckman, 1991; B ckman & Larsson, 1992; Craik et al., 1987; Hill et al., 1995), age has been found to be a critical characteristic determining the utilization of cognitive support to improve episodic memory. With regard to memory-training research, age has been specifically manipulated by contrasting old and young adults (Kliegl et al., 1989; Rose & Yesavage, 1983; Yesavage & Rose, 1984). Results indicate that young adults may gain more than older adults from mnemonic training even when both groups achieve a similar level of skill competency (Kliegl et al., 1989). Thus, this research supports the contention that cognitive reserve capacity may decrease with advancing age.

Level of education, primarily defined as years of formal schooling, has also been found to predict utilization of cognitive support to improve memory even after controlling for the effects of age (Hill et al., 1995). Interestingly, education as a control variable has been included in virtually every memory-training study to equate treatment and control groups. Although it has never been formally evaluated as a mediator of memory-training gains, most researchers believe that it could exert sufficient impact on training gains to be experimentally manipulated as a between-groups factor.

Verbal ability, as measured primarily by vocabulary proficiency, has been found to be a strong predictor of memory performance. In one study, Yesavage et al. (1988) found that verbal ability predicted response to memory training inasmuch as those who were high in verbal ability benefited more from training in the method of loci than low-verbal-ability subjects.

Individual characteristics that may have the most promise for influencing memory-training outcomes are processing speed and working memory. Some theorists have postulated that processing speed may supersede other subject characteristics, including age, education, and verbal ability, in accounting for age-related performance deficits in episodic memory (Salthouse, 1993, 1996). Processing speed may also be a critical component for accessing a mnemonic strategy, as well as decoding stored information in order to retrieve stimulus material, particularly under paced performance conditions. In one study, processing speed, as measured by the digit symbol substitution test, was found to predict response to memory training (Kliegl et al., 1990), although the percentage of the variance explained by this variable was minimal. The same may be true of working memory, which may play a critical role in mnemonic training, since it can be anticipated that learning and accessing a mnemonic require the individual to mentally transfer and recode information (e.g., change numbers to letters), as well as to refer back to transformed information as a way to decode the stimulus material (Baddeley, 1981). To date, working memory has not been used to predict response to memory training.

As described earlier, clinical depression has been found to negatively influence episodic memory performance, as well as the ability to benefit from cognitive support in old age (e.g., Backman & Forsell, 1994; Hart et al., 1987; La Rue, 1989). Like

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education, depression nas not been specifically examined as a predictor or response to memory training; however, it is clear that clinical depression in late life affects motivational processes. Therefore, it is conceivable that older individuals with depressive symptoms may also be deficient in accessing sufficient processing resources to utilize a mnemonic (as a form of cognitive support) even when they have learned and practiced the procedure via a specific intervention. B ckman et al. (1996) observed that nondepressed individuals who experience depressive symptoms showed deficits in the ability to utilize cognitive support (e.g., more study time) to improve memory. In summary, subject-related characteristics range along a continuum where the anchor points are represented by young versus old, highly versus poorly educated, high versus low verbal ability, active versus inactive, fast versus slow processing speed, efficient versus inefficient working-memory capability, and positive versus negative mood states. Intermediate positions on this continuum would be occupied by individuals who vary on these and other characteristics. As one progresses in the negative direction along this continuum, there is a gradual decrease in episodic memory functioning, a decrease in the ability to benefit from memory-training interventions for improving memory, and a concomitant increase in the degree of support required to show memory improvement. Thus, the observed relationship between memory proficiency and memory plasticity points to specific individual difference variables that may exert a direct influence on memory-training outcomes.

Expanded Application of Mnemonic Techniques

Mnemonics as a Number Memory Aid

As described earlier, the vast majority of memory-training interventions have focused on remediating two kinds of memory loss: verbal material (e.g., word lists composed of high-imagery nouns) and name-face associations (e.g., recalling the names of faces). One area that has received relatively little attention in older adult samples is the potential benefit of mnemonic training for numeric material. In their meta-analytic review of memory interventions, Verhaegen et al. (1992) highlighted over 30 published research papers. Of these, 20 were exclusively designed to facilitate recall of word lists, and 9 targeted name-face recall; the remaining 2 involved both word and name-face mnemonic training.

From a practical standpoint, it is evident that in contemporary society, gaining access to essential goods and services requires remembering personalized number sequences (e.g., four-digit personal identification numbers [PINs], lock combinations, telephone access codes, door codes to senior housing complexes, home-based security systems). As an example, the pervasiveness of automated banking services is dramatic. There are over 105,000 automated teller machines (ATMs) in the United States, and 480,000 worldwide, and it has been estimated that a typical homeowner will hold PINs for three different credit and/or debit cards (Harrow, 1995). While it is possible to preselect these security numbers, most are randomly generated in order to prevent the possibility of theft. With the proliferation of ATMs (Engley, 1995), it is becoming increasingly difficult to access banking services without ready access to a PIN, and the degree to which older adults use such services may depend, in part, on their ability

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to retrieve specific number sequences from memory at the point of service (e.g., standing in front of a bank teller machine; Bulkeley, 1995).

Although studies exist examining the effectiveness of mnemonic procedures for nonverbal material including random number strings, addresses, and phone numbers (Bellezza et. al., 1992; Higbee, & Kunihira, 1985; Kilpatrick, 1985; Kliegl, Smith, Heckhausen, & Baltes, 1987; Slak, 1970, 1971), such research has been confined to younger groups. For example, Kliegl et al. (1987) trained two young adults in an intensive procedure that involved number and letter transformation, as well as a method of linking numbers to familiar dates. They found large increases in number recall in these two individuals. As has been reported in related research with verbal material, special selection of subjects, a high frequency of sessions, and assurance that participants would achieve skill competency may have been critical factors in producing these large performance gains. Thus, in addition to providing novel stimuli to test theoretical assumptions about the role of individual differences in memory training, improving recall of numbers may be useful in enhancing day-to-day functioning in older adults.

Several recent studies have examined the efficacy of specialized mnemonic techniques as a way to improve both the acquisition and the retention of numerical material in older adults. In one study, Hill, Campbell, Foxley, and Lindsay (1997) utilized a visual imagery mnemonic that involved the linking of numbers to letters in the alphabet and the forming of meaningful words that could be decoded to the numbers. Participants were randomized into a placebo and a training group. Participants in both groups committed four six-digit number strings to memory at posttesting. Those in the training group were instructed to use the mnemonic procedure. There were no differences in recall between the groups at the immediate posttest; however, differences appeared at a 3-day follow-up favoring the mnemonic group and were highly significant at 7 days. This study provides preliminary evidence not only that older adults can benefit from number mnemonic training, but such training is effective in facilitating retention of numerical material. It is important to note that subjects trained in this study were high-functioning older adults, and although individual difference variables were not assessed for predicting training gains, such characteristics may be very important in determining whether an older adult can learn such a strategy and utilize it in novel contexts.

Mastering Components of the Mnemonic

A general problem in the published memory-training literature is the lack of research examining the extent to which individuals can learn the mnemonic procedure itself, and as alluded to earlier, learning a mnemonic procedure has often been confounded with proficient use of the mnemonic once it is learned. As the focus of many memory-training studies has been to compare memory interventions with placebo training, the degree to which specific components of the mnemonic are mastered has been of secondary importance.

In this regard, consider the well-known name-face mnemonic described by McCarty (1980) and later by Yesavage and Rose (1983; see also Yesavage, Lapp, & Sheikh, 1989), and described in detail in the the chapter by Stigsdotter in this book, in reference to older adults. To utilize this mnemonic the learner must successfully

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accomplish several steps: The first step requires identifying a prominent facial feature (e.g., a large nose). Although this may seem simple at the outset, such identification involves a number of abilities, including visual acuity and good visual search capability. The second step involves the mental transformation of this facial characteristic into a familiar image ( His nose reminds me of a Hill ). It is important to note that this step not only requires abstract reasoning and feature integration but must be performed in a time-limited context (e.g., meeting other people at a party). In the third step, this new image must be linked to the person's last name. Here, the older adult must have sufficient verbal knowledge to form such a link. Finally, the person must decode the integrated image in reverse order to retrieve the name. McCarty (1980) provided the following example that describes how this mnemonic should work:

To pair a face with the name Conrad, a person might choose the nose as the prominent facial feature, select the words con rat as the name transformation, and create a visual image in which a man wearing a prisoner's uniform (a con) rides on a rat that slides down the nose. (p. 145)

What is clear from this brief description is that failure to execute any one of these component steps will result in a breakdown of the total mnemonic procedure itself. Interestingly, most mnemonic procedures involve multiple concrete steps that may require very different kinds of cognitive skills. In fact, training curricula for mnemonic procedures are often organized around such steps. For example, Hill et al. (1997) used a multistep training procedure for utilizing the number-consonant mnemonic to encode number sequences.

It may be critical in future research to assess the degree to which the older learner is able to master each step, and the training that is needed to ensure competency in performing each step quickly as well as in the correct sequence. It is this kind of research that may ultimately determine whether a given mnemonic is useful as a practical cognitive rehabilitation tool. Only a few studies have examined the level of training necessary to master specific steps of the mnemonic and the extent to which individual differences can predict overall performance competency within and across steps. Not surprisingly, findings from these few studies suggest that those who are more proficient in learning component skills underlying the mnemonic gain more from training (Hill, Sheikh, & Yesavage, 1989; Kliegl et al., 1990).

Novel Training Methods and Individual Differences

This final section describes the potential benefits of incorporating advanced technology to enhance memory-training interventions and issues that may be associated with the application of such technology to older adults. We noted earlier in this chapter that tailored instruction that ensures skill mastery yields the greatest benefits from training. We also noted that the effectiveness of mnemonic training depends, in part, on the characteristics of those being trained and on the match of those characteristics with the training format. The question arises, then: What additional benefit might be accrued from an instructional format that can capitalize on the unique strengths of the individual learner?

Since the late 1980s, there has been an explosion in computer-based systems designed

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to facilitate individualized training objectives. Finkel and Yesavage (1990) were, perhaps, the first to test the idea that computer-based programming could be applied to memory training in older adults as well. Specifically, they evaluated the effectiveness of a computerized tutorial for training older adults in the method of loci. Their results provide evidence that computer-based training was at least as effective as in vivo training and had the added benefit that a computer tutorial was more efficient in presenting the material, could be modified to fit the individual's learning pace, and required less face-to-face instruction than in vivo training. More recent research has demonstrated that it is possible to extend this method of training by using CD-ROM technology in contexts that may be even more familiar to the older adult than the computer terminal (e.g., a television set; see Baldi, Plude, & Schwartz, 1996).

There are multiple advantages to incorporating computer-based technology into memory-training programs for older adults: First, the interactive and user-paced nature of contemporary computer programs allows the older adult to tailor the training experience to his or her specific learning needs (e.g., speed of presentation, level of complexity). Such flexibility has been shown to maximize concept learning and mastery (Baldi et al., 1996). Second, it is possible, using programming features such as graphics animation and interactive presentation mediums, to augment the learning environment with more supportive conditions, such as extra practice opportunities and varied support in the form of additional retrieval cues. Further, practice can be modified to conform more closely to real-world contexts. For example, a name could be presented on-screen by an actor who states, Hi, my name is Robert McDougal. I am from Salt Lake City, Utah. What is your name?

From the point of view of the experimenter, computerized testing also has several inherent advantages. For example, multiple learning parameters (e.g., the number of items correct, the amount of time required to complete a task) can be measured simultaneously. Further, the learner can provide continuous input as to what does and does not work in training (computer training has the distinct advantage of preventing the learner from progressing through steps in the mnemonic before previous steps are mastered). This input is very important, given that research has shown that skill mastery is a critical aspect of effective training (see Kliegl et al., 1990).

As such technology becomes more common in memory-training research, a number of additional issues will arise. Prominent in this regard are the added individual difference variables associated with computer familiarity. For example, a 60-year-old adult who was born in the 1930s will not have had the benefit of exposure to the computer at an early age. Thus, initial exposure to computers will vary widely across older adults, with some having virtually no experience in using computers. Finally, attitudes (and/or fears) associated with computer usage may be influential in predicting both comfort in using and willingness to experiment with computer-aided technology. Thus, although computer-aided instruction represents a new and promising horizon for memory-training interventions for older adults, the ability to utilize such technology to improve memory may vary greatly across individuals. As in our previously articulated conceptual scheme, those who possess greater facility and confidence in computer use are likely to gain the most from memory training that incorporates this kind of technology as part of the training format. To date, training studies employing computer-based formats as the instructional medium (see Baldi et al., 1996; Finkel & Yesavage, 1990)

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have not examined these individual difference variables; therefore, these variables represent a new avenue for testing the role of individual differences in memory-training efficacy.

Summary and Implications

This chapter highlighted a number of theoretical and methodological issues to consider when developing memory-training interventions for older adults. Foremost among these concerns is the kinds of outcomes from training that are important to measure, that is, the acquisition as well as the retention of information. Most of the research to date has focused on issues of acquisition, leaving retention (or forgetting) of learned material relatively unexplored. We also reviewed the role that individual characteristics play in influencing training gains, including demographic, lifestyle, cognitive competencies, and disease-related variables. In particular, these characteristics were examined for their ability to influence gains from training interventions. Finally, we highlighted several future directions for memory intervention research, including the application of techniques to a broader range of stimuli (e.g., numbers) and the incorporation of advanced technology in training regimens (e.g., computer-assisted training tutorials). The future of memory training is likely to involve extending what is learned theoretically and methodologically in the controlled classroom environment to issues and problems facing older adults in the everyday world.

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Cognitive Rehabilitation in Old Age
Cognitive Rehabilitation in Old Age
ISBN: 0195119851
EAN: 2147483647
Year: 2000
Pages: 18

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