Plasticity In Human Behavior Research Paper

Neuronal plasticity research, for example, addresses the biochemical and neuronal processes underlying the development and the variability of behavior over the lifespan; that is, it refers to the mechanisms of brain development that involve changes in structure and function (brain plasticity). A key concept in this field was the ‘critical period’ (Wiesel and Hubel 1963) which refers to a phase in the lifespan when neuronal structures are highly malleable based on the level and type of activity exposed to. For organisms at increasing maturity levels it was expected that neurostructural plasticity decreases with age and is absent in the mature cortex. However, subsequent research revealed substantial plasticity in the mature adult cortex, for example after lesions, strokes, and nerve damage or—even more important—after learning occurs (e.g., Buonomano and Merzenich 1998, Recanzone 2000). Studies showing activity-dependent plasticity on brain structures and transmitter systems in the aging brain provided convincing evidence that brain plasticity is not restricted to juvenile stages of development, but at least in the human species covers the entire lifespan (e.g., Cotman and Neeper 1996).

In contrast to neuronal plasticity research, the focus in lifespan psychological plasticity research lies on identifying the range and the conditions of behavioral modifiability as well as their age-related constraints across the entire life course (behavioral plasticity). In that context, the concept of plasticity generally refers to the ability of human beings to display rather different patterns of behavior as well as different life trajectories (Baltes and Willis 1982, Lerner 1984). So, plasticity can refer to variability of life course patterns (ontogenetic plasticity) or to the variability of a performance in a specific moment in time (concurrent plasticity). Moreover, the study of intraindividual behavioral variability is not restricted to cognitive functioning but extends to noncognitive domains such as motivation and personality. To understand changes in behavioral and ontogenetic plasticity across the lifespan, broader theoretical frameworks of human functioning have to be considered that take the dynamic and complex interplay between evolutionary/biology-based and culture-based influences on the nature of lifespan development into account (Baltes et al. 1998, Finch 1996).

1. Human Behavioral Plasticity As A Product Of Evolution And The Foundation Of Culture

Much of the behavioral and ontogenetic plasticity is based on the phylogeny of mammals, a biological strata characterized by open behavioral programs (Mayr 1974). With few exceptions, human behavior is not regulated by preprogrammed stimulus–response patterns, but entails the potential to adapt to a variety of ecologies and stimulus constellations. This plasticity opens behavioral patterns both in terms of concurrently available behavioral options and—even more important for ontogenetic change—in terms of acquiring new behavioral patterns. This holds too and is even accentuated when one views the evolution of the human mind from a perspective of evolved domain-specific behavioral and cognitive modules (Cosmides and Tooby 1994, Fodor 1983), which are open to ecological and cultural variations. In the course of human phylogeny such evolved mental modules have escaped domain-specific encapsulation (see Fodor’s original conception of encapsulated modules, Fodor 1983) and thus provided the building blocks of human intellectual capacities and behavioral adaptiveness. This variability in behavior and development is as much a product of evolution as the more narrowly constrained behavioral systems of other species (e.g., Wilson 1980).

Behavioral plasticity opens the potential for systematic influences of experiences, most notably influences of socialization and instruction. Both on the part of the socializing agent and recipient, flexible regulation of behavior in the interaction allows the partners to move beyond preformatted parenting and attachment programs of behavior, and reach out for the social—and ultimately cultural—transmission of experience between generations, and more generally between experts and novices. The ability to instruct and to profit from instruction probably is the critical difference in behavioral regulation which sets homo sapiens apart from other primates (Tomasello et al. 1993). Only humans seem to intentionally instruct their children (or nonexpert adults) to modify behavior in accordance with a standard or goal. They correct, model subcomponents, and thus selectively modify error-prone behavior of a partner to fit a certain standard. This specific instructional potential of human interactions is the vehicle of fidelity in information transmission (Tomasello et al. 1993), the prerequisite of cultural heritage. In modern societies this instructional potential has led to the institutionalization of instruction in educational institutions, such as schools and universities. Instructional interactions between humans and the training in educational systems have in turn greatly amplified the range of plasticity in human behavior. The combination of evolutionary potential and cultural transmission can thus generate an immense, albeit not infinite, universe of behavioral plasticity in many potential developmental pathways. Indeed, this great potential for variability, plasticity, and adaptivity in human ontogeny is a cornerstone of lifespan developmental theories (Baltes 1997, Lerner 1984).

2. Plasticity Of Performance Across The Lifespan

The range of plasticity in performance in diverse domains of functioning (e.g., athletic, intellectual, creative) is determined by the joint influences of either the genotype-related component of performance, that is, genetically-based interindividual differences in performance, or the phenotype-related component of performance, that is, intraindividual differences based on current performance factors (e.g., motivation, alertness), accumulated investment, and instruction. While genotype-related influences delimit the range of plasticity for any given individual, the phenotype-based influences determine whether this potential of plasticity is actually realized (Kliegl and Baltes 1987). Specifically, these four influences are the following. First, there are age-graded differences in the baseline opportunities for maximum performance. It is well known and empirically established that across the human lifespan, performance in various domains of functioning undergoes radical changes, typically reflecting an inverted U-shaped trajectory, with a maximum performance potential at a given age (Lehman 1953, Simonton 1988). Age of maximum performance is relatively early for most athletic disciplines, particularly the track-and-fields sports (Schulz and Curnow 1988), and moves further into adulthood for areas of peak performance which require extensive expertise, such as soloist musical performance (Ericsson et al. 1993), science, and poetry (Simonton 1988). Second, a great many contextual factors can influence an individual’s baseline reserve capacity (Kliegl and Baltes 1987). Thus, the component of reserve capacity which influences current performance can be optimized by improving motivation, alertness, clarity of task instruction, and other contextual factors. Albeit opening up many avenues for intervention to promote improved performance, these performance factors are problematic from the perspective of basic research (Salthouse 1985). The performance factors can vary with age and thus confound age differences, so that accurate assessments of latent performance potential at different ages is compromised. Baltes, Kliegl and their colleagues have proposed the methodology of Testing-the-Limits as a research strategy to assess cognitive reserve capacity in terms of latent performance potentials. This research paradigm optimizes performance factors for all individuals and assesses their ability to profit from a training designed to construct a specific cognitive skill, for which the different age groups do not hold differential experiences (Kliegl and Baltes 1987). This latter component of instruction-related plasticity of performance is the third influencing factor, addressed next. Third, performance can be optimized by providing appropriate instruction. The influence of instruction on performance is a longstanding research topic in diverse fields of study and approaches to psychological functioning. Vygotsky conceptualized the zone of proximate development as the range of potential performance an individual can attain with the help and instruction of an expert interactive partner (Vygotsky 1978). This conception about developmental reserve capacity in socially constructive interactions has sprawned much research on social facilitation in parent–child dyads (scaffolding, Wood et al. 1976; apprenticeship, Heckhausen 1987, Kaye 1982, Rogoff and Wertsch 1984), child–child interactions (Rogoff 1990), cognitive aging (development-enhancing interventions, Kliegl and Baltes 1987), and educational psychology (see zone of potential development, Brown and French 1979). Finally fourth, plasticity in performance is a function of the individual’s long-term investment in a given area of skill or expertise. Research on life-course trajectories of peak performance across a wide variety of domains has demonstrated repeatedly that world-class levels of performance require extensive and high-quality expert-guided training and practice over long periods of the lifespan, particularly in the early phases of acquisition (Ericsson and Charness 1994, Simonton 1988). This powerful effect of focuses and prolonged practice and training is itself the best witness of the impressive plasticity of human performance across the lifespan.

3. Cognitive Functioning In Old Age: A Case In Point

An extensively studied area of human behavioral plasticity is cognitive functioning in old age. Numerous studies have demonstrated declining performance in fluid intelligence performance with increasing age among older adults (Salthouse 1985). This pattern of age related decline has been challenged to be reflective of true latent performance potential, since many factors such as motivation or lack of recent practice could produce performance differences such as those found between older and younger adults in intellectual functioning. This critique prompted researchers to optimize performance conditions for older adults for instance by using ecologically valid tasks. Moreover, specifically designed training programs were administered to promote improved performance in older adults (Verhaeghen et al. 1992, Willis 1989). Examples of early training studies are among others the Penn State Adult Development and Enrichment Project (ADEPT) and the Berlin PROALT project (for review see, Baltes and Lindenberger 1988). These studies demonstrated substantial benefits of older adults’ fluid intelligence performance from cognitive training. For tasks that were similar to those addressed by the cognitive training, older adults improved their performance. However, the training effect showed no transfer to other fluid intelligence tasks. Additional studies comparing the benefit of guided instruction with self-regulated practice showed that self-instruction was as effective in improving cognitive performance in older adults as was guided instruction. This suggests that a large share of the old age plasticity in cognitive functioning is due to the activation of already available cognitive skills, which have been fallen into disuse over the adult lifespan.

The findings of preserved substantial learning potentials up to old age and the possibly arising optimistic view of unchanged cognitive plasticity over the entire lifespan had been put in perspective by Testing-the-Limits studies aiming to delimit maximum performance potentials (Kliegl and Baltes 1987). In this research, cognitive skills are taught to subjects using specific training programs to construct an expertise in a certain cognitive domain, for example in the domain of memory for nouns. In this way, an expertise is cognitively engineered, so that the researchers know the component abilities, and can thus specifically compare subjects at different ages in terms of specific subskills. Subjects are trained extensively until they have reached asymptotic performance levels. Once maximum performance levels are achieved, time constraints are introduced. The findings indicate that although older adults can reach high levels of performance (e.g., in recalling nouns), their maximum (asymptotic) performance level after extensive training is much lower compared to younger adults. Moreover, their functioning breaks down when they encounter severe time constraints. Thus, at limits of functioning, age differences are magnified and reveal the potentially underlying neurobiological constraints associated with the biology of the aging brain (Baltes and Kliegl 1992). Finally, Testing-the-Limits methodologies have been successfully employed to identify elderly at risk of dementia (Baltes et al 1992) and distinguish pathological from normal cognitive aging (Reischies and Lindenberger 1996).

4. Plasticity In Personality Across The Lifespan

Processes of self-regulation, personality, and subjective well-being show much less systematic age variation than cognitive functioning. Indeed, the stability of life satisfaction, self-esteem, perceived control, and overall happiness in itself is proof of the adaptive plasticity of these processes. The age-normative shift from a predominance of perceived gains to ever increasing risks for decline (Heckhausen et al. 1989) would lead one to expect concomitant declines in indicators and components of subjective well-being. Instead, emotional balance remains resilient across the life span (Staudinger et al. 1993).

This resilience of the emotional balance to lifespan changes is one of the key achievements of the human motivational system, which has evolved to manage the challenges associated with the relative dearth of biologically-based predetermination of behavior. The relatively weak biological regulation of behavior furnishes a regulatory need to achieve and maintain selectivity in behavioral investment, and to compensate for the ever-present failure and loss of a behavioral system that is underdetermined and thus highly error prone. Both selectivity and loss compensation, key concepts in lifespan developmental research (Baltes et al. 1998, Schulz and Heckhausen 1999, Carstensen et al. 1999), require adaptive motivational processes, which focus volitional investment into a chosen goal and buffer the potentially negative effects of agingrelated losses. Typical examples of such metamotivational processes (i.e., secondary control, Heckhausen and Schulz 1995) which facilitate goal striving, are upward social comparisons with superior or advanced others, who can serve as models for self-improvement (Heckhausen and Krueger 1993), and enhanced valuation of the chosen compared to competing goals. Examples of metamotivational processes, which compensate for the negative effects of losses or failures, are self-enhancing comparisons with inferior others, selfprotective attributions which take credit for gains but refuse to take blame for losses, and the adjustment of goals to fit one’s real potential (for the latter example see also Brandtstadter and Renner’s conception of accommodation, Brandtstadter and Renner 1992). In general, the age-graded structure of opportunities and constraints sets the stage for the individual to use the age window of maximal opportunities to strive for a given goal. The regulation of motivational investment requires substantial plasticity in that the individual needs to enter cycles of engagement and disengagement with various developmental goals (Heckhausen 1999). Motivational engagement and disengagement with specific life goals should be contingent and flexibly adapted to changes in opportunities and constraints across the lifespan.

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