Cognitive Plasticity and Training across the Lifespan
Cognitive Plasticity and Training across the Lifespan
Yee-Lee Shing, Yvonne Brehmer, and Shu-Chen Li1
Lifespan psychology posits that throughout the entire lifespan, individuals adapt to the opportunities and constraints embedded within the developmental contexts. These adaptive processes entail the essential notion of developmental plasticity, which can be broadly defined as the modifiability of the individual's possible ranges of performance and function. This chapter presents a selective review of evidence on lifespan differences in cognitive plasticity. Special attention is given to cognitive training studies in the domains of fluid intelligence and episodic memory, focusing on the magnitude, scope, and maintenance of training gains. Results from these studies suggest three key features. First, plasticity remains present throughout the lifespan, albeit decline in the extent of plasticity occurs in old age. Second, within the cognitive domain, training benefits tend to be narrow, with little cross-ability transfer. Third, training gain can be maintained or reactivated over time. Using results from a preliminary empirical study, the chapter also illustrates how a lifespan age comparative design can be utilized to directly compare the relative extent of plasticity across different life periods.
COGNITIVE PLASTICITY AND TRAINING ACROSS THE LIFESPAN
At the phenomenological level, there exists a seeming symmetry in the human lifespan: we grow during youth and decline during old age. Such pervasive inverted U-shaped function characterizes various aspects of cognitive functions and intellectual abilities (Dempster, 1992; Kail & Salthouse, 1994; Li et al., 2004). Surface performance similarities observed at the two ends of the lifespan notwithstanding, it is important to underscore that old age should not be considered merely as the reversal of child development, because mechanisms underlying cognitive changes may differ at these two life periods (Baltes, Lindenberger, & Staudinger, 2006a; Bialystok & Craik, 2006; Li et al., 2004). In accord with the general theme of this volume, the current chapter deals with the modifiability of cognitive functioning, and focuses on research stemming from lifespan psychology (Baltes et al., 2006a).
One of the core tenets of the lifespan approach is that the interplay of maturational, senescence, and learning mechanisms extends across the entire course of human life. In contrast to a deterministic, “grow-oriented” model of development (Segalowitz & Rosekrasnor, 1992), the lifespan approach holds that development is multidimensional and multidirectional as it constitutes multiple pathways, of which only some are expressed due to the interaction of contextual and individual selection factors (Baltes, 1987; Chapman, 1988). Most recently, in line with the earlier notion of probabilistic epigenesis (e.g., Gottlieb, 1998), meta-the-oretical conceptions of biocultural coconstructivism were proposed (Baltes, Reuter-Lorenz, & Roesler, 2006b; Li, 2003) to highlight the importance of research integration among dynamic processes and mechanisms encompassing neurobiological, cognitive/behavioral and sociocultural levels in order to better understand lifespan development.
Co-constructive processes necessarily imply plasticity (i.e., modifiability of the individuals) at multiple levels. Plasticity is conceptualized as the driving force for human adaptation to environmental and experiential factors (e.g., Amedi, Merabet, Bermpohl, & Pascual-Leone, 2005; Baltes, 1987; Lindenberger & von Oertzen, 2006). As the lifespan approach suggests, plasticity is not a privilege only for early periods in life but it remains throughout the lifespan, although to varying extents (for reviews of plasticity across different levels and life periods, see Hensch, 2004; Li, 2003). In order to investigate plasticity, intervention and training studies have been pursued over the past few decades to explore the range and modifiability of cognitive functioning across different age periods. Due to space limitations, the review only focuses selectively on representative empirical studies addressing plasticity in fluid intelligence and episodic memory, as empirical findings concerning these two domains have been more consolidated.
Magnitude, Scope, and Maintenance of Cognitive Intervention Effects
Since the early 1970s, adult developmental studies on plasticity have been undertaken to examine the extent to which the observed age-related decline in fluid intelligence can be reversed through cognitive interventions. Subsequently the research focus was also extended to the inquiry of limiting constraints on plasticity, especially within the domain of episodic memory (Baltes & Lindenberger, 1988). These lines of research have been guided by a few key considerations. First, is improvement in performance possible for both young and old adults? If so, where does the upper limit of plasticity lie? Second, are the improvements, if and when they occur, related to a change in general skills or are they mostly only specific to the mechanisms involved in the tasks that have been trained? Third, can these changes be maintained over time or are they mainly transient short-term boosts in performance?
Unlike adult developmental studies, cognitive developmental research has adopted a different focus. Tracking back to Piaget's theory, there is the distinction between “development” as an “active” construction of knowledge and “learning” as a “passive” formation of associations. This distinction has led to a focus on questions regarding the development of knowledge representation and metacognition in natural context, rather than on basic learning mechanisms (Brown, 1982; Siegler, 2000). Thus, the review below will mainly be drawn from the field of cognitive aging, with insertions of studies from child development when the evidence is available and relevant. The organization of the review follows the three key research questions, namely the magnitude, scope, and maintenance of training gain.
Training and Plasticity of Fluid Intelligence
Given conspicuous improvement and decline of cognitive functions observed in childhood and in old age, respectively, investigations of the plasticity of fluid intelligence in these age periods are noteworthy. In particular, until the emergence of training research (Schaie, 1983;Willis, Blieszner, & Baltes, 1981), earlier models of adult intelligence focused on the normative or average pattern of intellectual aging and did not address the potential for modifiability in intellectual functioning in later adulthood.
Magnitude of training gain . One of the earliest empirical studies on training in the domain of fluid intelligence was pursued within the context of the Seattle Longitudinal Study (SLS) since the late 1960s (Schaie, 1983). In the SLS, participants were classified on whether they had shown reliable decline in inductive reasoning (e.g., letter, number, and word series tasks) over a 14-year interval. Individuals who showed a decline were then assigned to a training program with a pretest-posttest design. The study's results revealed that 60% of the individuals showed significant performance gains after the training. Within these individuals, about half of them increased their performance to the level of their baseline performance 14 years prior to the training program.
Another study conducted by Baltes, Dittmann-Kohli, and Kliegl (1986) demonstrated that elderly participants who attended a ten-session fluid intelligence training program evinced substantial increases in levels of performance both in tasks of figural relations and inductive reasoning. The benefits accrued from training mainly stemmed from more accurate problem solving, especially for items with high difficulty levels. A recent large scale study (ACTIVE: Advanced Cognitive Training for Independent and Vital Elderly) carried out by Ball et al. (2002) utilized a similar multiple-session training paradigm on inductive reasoning for participants between 65 and 94 years of age. This study also revealed impressive amounts of immediate training gain as an outcome of reasoning training (see Figure 4.1 top panel for change between baseline and posttest); the amount of immediate gain was comparable to the amount of decline, reported in the literature, that occurs to elderly persons without dementia over a 14-year interval. Taken together, these studies have reliably shown that healthy older adults still possess sizeable plasticity to benefit from intervention programs that train fluid intelligence.
As for evidence from the field of child cognitive development, a recent study conducted by Siegler and Svetina (2002) utilized a micro-genetic method to compare short-term and long-term changes in children's performance on matrix completion (a logical reasoning task). The microgenetic method resembles the training paradigm from intervention research with dense sampling of performance and an emphasis on intraindividual variability (e.g., Siegler, 1996). However, the main focus of the microgenetic paradigm is typically to examine the natural unfolding of behavior in response to dense exposure to the experimental task. In Siegler and Svetina (2002), 6-year-olds attended five testing sessions in which they solved matrix completion problems, were asked to explain their solutions for each trial, and subsequently received feedback on their performance. Their pre- and post-microgenetic assessments of performance were found to be comparable to cross-sectional age difference between the age of six and seven. This finding showed that the amount of change that emerged from a microgenetic paradigm may parallel long-term age-related performance increase, demonstrating the importance of the microgenetic method as a tool for investigation of developmental mechanism within a condensed time frame.
Transfer of training gain. The issue of whether fluid intelligence training generalizes to other dimensions of intellectual performance is another important aspect for evaluating the effects of intervention, as questions of transfer/generalization pertain to the scope of processes that can be impacted by a specific training program. In a pioneering study by Willis, Blieszner, and Baltes (1981), elderly participants were enrolled in training sessions aimed to provide them experience in problem-solving skills requisite for good performance on figural relation task. Assessment of training effectiveness and transfer of training were examined by using several tests that indexed ability factors beyond figural relation, ranging from induction, speed, and vocabulary (ordered from nearest to furthest in similarity). A pattern of differential transfer was found with greater training effects to near fluid transfer measure, and less training effects to far transfer measure (see similar results in Blieszner, Willis, and Baltes, 1981 with training on induction). These findings indicate that the spectrum of transfer to tasks not explicitly trained is limited. Such a limited “within-ability” transfer indicates that adult psychometric intelligence and the associated systems of skills involve a heterogeneous set of processes.
Among the related findings from the developmental literature, a no-table study conducted by Chen and Klahr (1999) involving the training of a scientific reasoning strategy (Control of Variables Strategy or CVS) is of particular relevance here. The CVS improves the ability to make appropriate inferences from the outcomes of unconfounded experiments as well as an understanding of the inherent indeterminacy of confounded experiment. In this study, 7- to 10-year-old children were trained on the CVS and a pre- and posttest design was utilized to assess the children's abilities to design and evaluate experiments, and make inferences from the experimental outcomes. The results showed that when provided with both explicit training and probe questions, children were able to learn and transfer the basic strategy for designing unconfounded experiments (see bottom panel of Figure 4.1). This pattern was also correlated with age, with older children showing higher ability in transferring learned strategies to remote situations. Two main conclusions can be made from this study: (1) explicit training and support through probe questions can benefit even early elementary school children in learning to use the CVS; and (2) in comparison to older children, younger children may face developmental constraints in their ability to transfer the use of strategy to new contexts. The combination of training and age-comparative design in this study demonstrated its value for examining the interplay of experiential and maturational factors.
Maintenance of training gain. A third feature important for evaluating the effects of cognitive intervention besides the magnitude and scope of training gain is the maintenance of learned skills. Willis and Nesselroade (1990) set out to examine the long-term impact of a three-phase cognitive intervention on figural relations for participants as they advanced from being younger old (first phase: mean age of 69; second phase: mean age of 71) to older old adults (third phase: mean age of 77). It was found that significant training effects occurred at all phases, with the largest gain occurring at the first phase of training. More importantly, long-term accumulative effects of training were demonstrated in the finding that at the third phase assessment, participants performed on average 5 T-score points above their baseline performance assessed at the first phase. Similar maintenance pattern has also been found in the more recent ACTIVE project (Ball et al., 2002). As can be seen in the top panel of Figure 4.1, participants of the experimental group that received training on reasoning outperformed their counterparts in the control group both in the first and second annual reassessments.
On the other hand, developmental study that examines long-term maintenance effect of training in fluid intelligence in childhood is comparatively lacking. The few studies that examined maintenance effect (e.g., maintenance of problem-solving strategies; Ferretti & Butterfield, 1992) typically involved very short time frames (e.g., two weeks). Further investigation is necessary to provide more information regarding the extent of long-term maintenance of training in children.
Taken together, the findings reviewed above leads to the conclusion that plasticity of fluid intelligence is present from childhood to old age. Microgenetic and training interventions can speed up developmental progress in childhood (Siegler & Svetina, 2002), and also slow down or, to some extent, reverse aging-related reduction in the functioning of fluid intelligence (e.g., Baltes & Lindenberger, 1988). Transfer of training gains is mostly limited to near-transfer measures, but in limited cases, also to far-transfer measures (specifically in older children). Long-term maintenance of training gains has also been demonstrated, showing that the elderly participants were able to perform above their own baseline before receiving the training at least up to a few years. However, due to a lack of study that directly compares children, younger adults, and older adults, extant findings do not provide conclusive information on the relative extent of plasticity across different life periods. For future research, it would be a fruitful endeavor to utilize lifespan age-comparative longitudinal designs in order to examine the extent, scope and maintenance of fluid intelligence training across different age groups.
Training and Plasticity of Episodic Memory
We now turn to the domain of episodic memory. A specific paradigm that deserves mentioning is the testing-the-limits procedure because it has been applied widely in memory training studies in aging (Linden-berger & Baltes, 1995). This method bears similarity to the microgenetic approach reviewed above, which focuses on detailed analysis of time-compressed developmental-change function. The aim for using this paradigm is to search for limits of performance plasticity (i.e., identifying what is possible and impossible) by providing instruction and extensive practice combined with systematic variations in task difficulty.
Magnitude of training gain. A robust finding from the adult memory training literature is that memory plasticity remains in cognitively healthy older adults. Instruction and extensive practice in a mnemonic memory technique can lead to considerable performance improvements in healthy older adults. For example, Kliegl, Smith, and Baltes (1989; 1990) showed that after multiple sessions of training and practice in using the Method of Loci (MoL) mnemonics, both younger and older adults greatly improved their memory performance. This finding converged with findings of many other studies (Derwinger, Neely, Persson, Hill, & Bäckman, 2003; Verhaeghen & Marcoen, 1996), indicating continued existence of memory plasticity in old age. Ball et al. (2002) also found in their large-scale intervention study that the participants who received memory strategy training showed an amount of immediate training gain comparable to the amount of expected decline reported in the literature over a 7-year interval in elderly persons without dementia.
Despite an overall positive picture of resilient plasticity in old age, Baltes and Kliegl (1992), however, found that extensive practice and training in MoL resulted in a close-to-perfect separation of the young and old age groups: at the end of the training all younger adults consistently outperformed older adults. This finding demonstrated the existence of age-related differences in upper limits of performance (see top panel of Figure 4.2). A follow up study demonstrated even further reduction of upper limits of performance in very old age, i.e., ranged from 75 to 101 years (Singer, Lindenberger, & Baltes, 2003), as most of the older adults in this study did not improve any further after four sessions of practice on MoL. These findings support the amplification/magnification model of plasticity (Verhaeghen & Marcoen, 1996), demonstrating the latent influence of initial level of performance on subsequent training gain, and clearly suggest negative adult age differences in the extent of memory plasticity.
A further question can then be raised concerning memory plasticity encompassing wider age ranges. Given the assumption regarding lifespan changes in the adaptive capacity of fluid intelligence, one may hypothesize that peak performance levels can be identified even at younger ages given an appropriate amount of training. A recent study conducted by Brehmer, Li, Müller, von Oertzen, and Lindenberger (2007) was designed to explore this possibility. In this study, 108 participants aged 9–10, 11–12, 20–25, and 65–78 years learned and practiced on an imagery-based mnemonic strategy related to the MoL. The findings showed that both older adults and children demonstrated comparable baseline performance and improvement through instruction. However, children profited much more from further practice of the strategy and subsequently reached higher levels of final performance than older adults. This finding demonstrates a greater amount of plasticity in child development in comparison to old age in the domain of episodic memory (see bottom panel of Figure 4.2).
Transfer of training gain. A robust finding in the literature of memory skill transfer is that the effects of memory strategy, similar to transfer effects found with respect to inductive reasoning, are quite specific in their applicability. Several studies have found that transfer effects of a learned strategy to tasks not specifically trained are typically small or non-existent (e.g., Ball et al., 2002; Derwinger et al., 2003; Stigsdotter Neely & Bäckman, 1993).
On the other hand, examination of transfer effect of memory training in the developmental literature typically involves strategies that organize (or categorize) the to-be-remembered materials (Bjorklund & Buchanan, 1989). The transfer task used in child developmental studies is typically very similar to the original task, in which children are tested whether they can simply generalize the use of the categorization strategy to a new list that contains new categories. Limited extant evidence suggests that the pattern of generalization is related to typicality of item in the list and age of the participant (Bjorklund & Buchanan, 1989).
Taken together, to better understand the nature of transfer in memory training, future investigations both with respect to child development and aging would benefit from more systematic variation of tasks that draw on abilities ranging from near to far transfer (as in the studies of inductive reasoning).
Maintenance of training gain. Studies investigating memory skill maintenance in adulthood have provided a mixed picture. Many studies found long-term maintenance of memory training benefits over time periods up to three years (e.g., Derwinger et al., 2003; Neely & Bäckman, 1993; Stigsdotter & Bäckman, 1989), while others did not (Anschutz, Camp, Markley, & Kramer, 1987; Sheikh, Hill, & Yesavage, 1986). The inconsistency of the findings may have stemmed from differences in study-specific parameters, including the amount of training and practice (e.g., two sessions in Anschutz et al., 1987 versus eight sessions in Neely and Bäckman, 1993).
A follow-up study of Brehmer and associates examined, in a lifespan sample, the long-term maintenance of the use of an imagery-based mnemonic strategy 11 months after training (Brehmer, Stoll, Straube, von Oertzen, Li, & Lindenberger, 2007). Maintenance performance was tested in two sessions, the first without and the second with mnemonic re-instruction (i.e., spontaneous versus reactive maintenance of skill, respectively). Children of both age groups (aged 10–11 and 12–13) spontaneously showed performance improvement beyond the level attained 11 months earlier and did not gain any further from renewed instruction in the second follow-up session. Older adults, however, showed a slight trend of decrease in performance after 11 months, but improved reliably from the first to the second retest session. Taken together, this study demonstrated that plasticity in middle childhood reflected a powerful alliance between learning and maturation that permitted enhancement of skilled episodic memory performance without the need of re-instruction. On the other hand, with the support of re-instruction, older adults were able to reactivate the skill that they gained 11 months before.
Preliminary Findings from a Lifespan Study Comparing Two Aspects of Memory Plasticity in Childhood and Old Age
Lifespan research provides a unique contribution to the study of plasticity as they reveal age-associated differences in the possible range and constraint of cognitive and memory plasticity. A further step can be taken to examine the explanatory mechanisms and factors underlying these age differences. As an illustration here, we report preliminary findings from a recent study that undertook a process-oriented approach to explore two aspects of age-related differences in episodic memory that requires binding (Zimmer, Mecklinger, & Lindenberger, 2006). Specifically, this study investigates the strategic and associative components of episodic memory and age-related changes in their interactions across the lifespan. The strategic component refers to the organization and elaboration of episodic features of memory items during memory encoding and retrieval, whereas the associative component refers to the basic mechanisms that bind together the memory items. These two components are assumed to be functionally related but show differential developmental trajectories.
In the development literature, the ability to integrate memory features together seems to be relatively matured by early childhood (Sluzenski, Newcombe, & Kovacs, 2006), whereas the ability to utilize memory strategy is not fully functional until early adulthood (Schneider & Pressley, 1997). In the aging literature, accumulating evidence shows that older adults have difficulty both in remembering relational information (e.g., Mitchell, Johnson, Raye, Mather, & D'Esposito, 2000; Naveh-Benjamin, 2000) and in initiating effective strategy use (e.g., Dunlosky, Hertzog, & Powell-Moman, 2005). At the neural level, children's late emergence of the strategic component in development parallels the late maturation of prefrontal cortex in similar ways as older adults' disproportionate deficits in the strategic component parallel the relatively early deterioration of certain areas of prefrontal cortex during adulthood. The associative component of episodic memory is mainly supported by medio-temporal brain circuitry. Functions of the medio-temporal lobes are already well developed in middle childhood but impaired in older adults (Menon, Boyett-Anderson, & Reiss, 2005; Raz et al., 2005; Sowell et al., 2003).
Utilizing the evidence of differential lifespan age gradients of the two functional brain circuitries, we investigated strategic and associative components of episodic memory and their interactions in a lifespan sample of Germans. Extending Naveh-Benjamin's paradigm (2000), we examined age differences in associative recognition memory that varied in associative demand (German—German versus German—Malay) under instructions that emphasized item, pair, or elaborative strategy encoding (a fully crossed design). The sample included four age groups: children (10–11 years), teenager (13–14 years), younger adults (20–23 years), and older adults (70–74 years).
Preliminary results based on the thus far acquired subsample (with 8 individuals per age group) are shown in Figure 4.3. The proportion of hits minus false alarms was computed for each participant and for every recognition test.2 Due to the preliminary nature of the data, the results presented here are mainly descriptive in trends. In the German—German condition (see left panel in Figure 4.3), children seemed to initially show lower performance than the teenagers and older adults (who showed similar performance in the item and pair instruction conditions). Interesting patterns emerged after the imagery strategy was taught to the participants (post-strategy 1 and 2). Children's performance showed the trend of reaching the level of performance of the older adults, whereas the teenagers clearly outperformed older adults after strategy instruction. Taken together, both children and teenagers showed more gain from the strategy instruction than the older adults did. Similar age patterns specifically between teenagers and older adults were found in the German—Malay condition: the teenagers showed more robust gain from the instruction of strategy compared to older adults. Younger children, on the other hand, did not show significant improvement across the sessions. This might have been due to the high difficulty of the German—Malay condition.
Taken together, in terms of baseline performance, younger children in this study performed at the lowest level, followed by teenagers and older adults, while younger adults performed at the highest level. However, when provided with an optimal strategy of encoding, children and teenagers improved more than older adults in forming exact associations between memory items, demonstrating their latent potential in the associative component. Older adults, on the other hand, owing to their decreased associative component functioning, tended not to gain as much as from the strategy instruction as the young participants.
Younger adults, despite being given a shorter presentation rate, exhibited high levels of performance both before and after strategy instruction, demonstrating optimal functioning of both associative and strategic component. If replicated in a larger sample, these preliminary findings of divergence between lifespan age gradients of the strategic and associative components can be expected to further our understanding of their unique and interactive contributions to episodic memory plasticity.
Outlook: Neural Correlates of Plasticity
Age-related differences and changes in cognitive functioning and plasticity have their implementations and correlates at the neural level. Although plasticity is not a novel concept to the field of neuroscience, recent advances in brain imaging technologies have gathered new evidence concerning the neural correlates of plasticity, allowing cross-level mappings between behavioral and neuronal functioning (Lindenberger, Li, & Bäckman, 2006). In this section, we review some stimulating recent findings from this line of research.
There are multiple aspects of neural plasticity, ranging from the molecular and synaptic levels to the levels of cortical maps and large-scale neural networks (Buenomano & Merzenich, 1998). During development, the cortex alters its functional and structural organization in response to experiences (see Elbert, Heim, & Rockstroh, 2001 for an overview). In particular, neural plasticity entails the production (i.e., synaptogenesis) and the subsequent experience-dependent elimination of neuronal connections (i.e., synaptic pruning). According to Huttenlocher and Dabholkar (1997), the rate of synaptic pruning differs across regions. For example, the synapses in the visual and auditory cortices reach adult level in early childhood (two to six years), whereas in the frontal gyrus adult level is not reached until adolescence. This pattern of rise and fall in synaptic density has been shown to be mediated by experience-dependent activity (e.g., Greenough, Black, & Wallace, 1987). As pointed out by Nelson (1999), neural plasticity can be conceptualized as the subtle but orchestrated relationship that occurs between the brain and the environment. It represents the ability of the brain to be shaped by experience, and in turn, for this newly remolded brain to accommodate and embrace new experiences (see Nelson, 1999 also for maladaptive forms of adaptation).
With respect to cognitive and brain aging, cognitive decline has been related to declines at neuroanatomical, neurochemical, and neurofunctional levels that are indicated by brain atrophy (Raz, 2000), increase in neural noise that may be attributable to the deficiency of dopaminergic modulation (Li, Lindenberger, & Sikström, 2001), decline in binding operations (Mitchell et al., 2000), and reorganization/dedifferentiation of brain activation (Park et al., 2004). However, recent findings from laboratory training studies also demonstrate that neural plasticity is an emergent and continuous state of all age periods (Draganski et al., 2004; Olesen, Westerberg, & Klingberg, 2003), albeit there is a possible greater limit on plasticity in old age (Nyberg et al., 2003). For example, there exists a growing body of research indicating that physical fitness training may have beneficial effects on cognitive and brain aging. Several rigorous micro-longitudinal studies have demonstrated that improvements in cardiovascular fitness impart positive effects on human cognitive abilities, with the largest benefits occurring for executive-control processes (see review by Colcombe & Kramer, 2003). Taken together, these findings provide evidence for neural plasticity in the aging human brain through physical training that enhance cardiovascular fitness, which in turn positively affects cognitive domains that show great extents of aging related decline.
Another line of research that has recently attracted much attention is the observation of reorganized functional brain activations in the aging brain compared to those observed in the young adult brain. The patterns of aging-related reorganization take the forms of contralateral recruitment at the opposite hemisphere, unique recruitment of additional brain regions, or substitution with different brain structures activated (for a comprehensive review, see Park, Polk, Mikels, Taylor, & Marshuetz, 2001). The functional relevance of these observations is currently still being explored and has spurred ongoing debates of whether such reorganized activation reflects neuropathological aging processes (e.g., the dedifferentiation hypothesis, Logan, Sanders, Snyder, Morris, & Buckner, 2002; Park et al., 2004), or compensatory processes (e.g., the CRUNCH hypothesis by Reuter-Lorenz & Mikels, 2006). Notwithstanding the controversies of different interpretations, the fact that the human brain reorganizes itself in response to both biological and experiential factors reflects plasticity of the aging brain. For example, a recent training study by Erickson et al. (2007) showed training-induced increased asymmetry in ventral prefrontal cortex and reduction in age differences in activation in both dorsal and ventral prefrontal cortex. Despite the prefrontal regions being commonly associated with the largest age-related atrophy, findings from Erickson et al. (2007) suggest that age-related functional decline in these regions is not an inevitable process of aging, but can be altered and reversed with training. Furthermore, the mappings between brain and behavior are not static but changing throughout ontogeny. Some of these mappings may be relatively universal and age-normative, reflecting biological maturational and senescence processes (e.g., progression of neural plasticity in childhood, brain atrophy in old age), others may be relatively idiosyncratic, reflecting individuals' genetic profiles and/or histories in developmental pathways (Lindenberger et al., 2006). Therefore, the distinctions among maturational, senescence, and learning mechanisms on plasticity are necessary in the investigations of brain-behavior mappings, because they offer guidance for interpretation of the regularities, diversity, and malleability of ontogeny processes.
In this chapter, we have drawn evidence from several lines of research as an attempt to summarize basic principles and key findings in relation to cognitive and neural plasticity of different age periods. An important concept of lifespan psychology is that developmental processes are pluralistic and dynamic, as development itself constitutes lifelong selective adaptation on the individuals. Lifespan research, in combination with experimental methods such as training and microgenetic paradigms, provides unique contributions to the study of plasticity as they reveal age-associated differences in possible range and constraint of functioning, and provide explanatory accounts for the observations.
Extant evidence suggests that plasticity most likely is at the peak level throughout childhood (see also Knudsen, Heckman, Cameron, & Shonkoff, 2006). However, there is also clear empirical support for the availability of plasticity in aging at the behavioral and neurobiological levels. Lifespan age-comparative design affording direct comparisons of development plasticity in different life periods, however, are notably lacking in the literature and should be utilized further in future studies.
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1 We thank Professor Ulman Lindenberger for his helpful discussions and comments on an earlier version of the chapter.
2 To avoid the ceiling effect, younger adults were given a presentation rate of three seconds instead of six seconds (as in the case of other age groups). Therefore, a direct interpretation of mean performance comparison between younger adults and other age groups should be cautioned. We focused on the comparison of the other three age groups for this current chapter.
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