From the age of about 20 onwards the brain decreases in weight, and by the age of 90 the normal aged individual will have lost between 5 and 10 percent of their brain weight. Total brain mass also shrinks, especially after the age of 50, by approximately 15 to 20 percent. The majority of the tissue loss is in the cerebral cortex but depletion is most marked in the temporal and frontal cortex (Rabbitt, 1991). As the individual ages there are also structural changes to the brain most generally reflected by the loss of neurons, or nerve cells, which receive and transmit information, and, alterations to the architecture of the remaining cells. According to Petit (1982) the marked atrophying of dendritic process is one of the most
For a full discussion of the physiology of aging see Ivy, Petit & Marfcus (1992). _ _
Striking effects of the aging brain. Dendrites are branch like connections which receive and integrate information from other neurons; with age the dendrites decrease in both length and number and eventually disappear altogether, resulting in the loss of receptive ability. Axons - which are responsible for relaying signals to other neurons - also atrophy. Additionally, neurons undergo alteration of their internal structure by developing protein filaments which are helically wound and are known as neurofibrillary tangles. The development of these inside the cell causes harm by forcing the nucleus to one side and interfering with normal cell functioning (Adams, 1980; Bondareff^ 1980).
Neuronal functioning is also adversely affected by the accumulation of lipofuscin - a yellowish pigment found to a degree in all normal cells but which accumulates in excessive amounts in the £ ^ g brain. It has been suggested that an imbalance of lipofuscin in neuronal cytoplasm may cause a fundamental breakdown in cellular homeostasis, thus interfering with neuronal functioning and ultimately leading to the death of the neurone - although there is some debate as to the exact relationship between excessive lipofuscin and neuronal damage (Ivy, Macleod, Petit & Markus, 1992). Senile plaques (abnormally hard clusters of damaged or decaying neurons) are also likely to form, which again affect the natural workings of the brain. According to Ivy et al. (1992), although senile plaques are a feature of normal aging it is generally recognized that the greater the concentration of senile plaques than the greater the degree of dementia.
Although these changes are relatively heterogeneous, as has already been stated, certain subsets of cells and areas of the brain are more prone to age-related damage than others. Much of the limbic system (which is strongly linked to intellectual functions such as learning and memory) appears to undergo variable amounts of cell death with about five-percent of the neurons in the hippocampus decaying with each decade in the second half of life, the total loss approximating 20 percent during that period (Adams, 1980; Kermis, 1983, 1986; Selkoe, 1992). A number of researchers have suggested (e.g.. Ivy, Macleod, Petit & Markus, 1992) that neuronal loss is a concomitant of reduced cerebral blood flow. Aging blood vessels are no longer able to supply oxygen and other important nutrients to the brain so efficiently, which results in the death of neurons and surrounding tissues. However, there is some debate as to whether nerve cells die as a result of lack of oxygen or whether the decrease in neuronal activity means that a less fulsome cerebral blood supply is needed (Ivy et al., 1992; Stuart-Hamilton, 1991). Even if the change in cerebral blood flow does not affect the number of neurons it may have an adverse effect on the efficiency and speed with which the CNS can operate. A fijrther serious consequence of reduced blood flow is that the blood-brain barrier which filters out toxins may be affected resulting in the brain being exposed to toxic products or infectious substances (Ivy et al., 1992).
3.2.1: Degenerative Changes to the CNS and the Consequence For Adult Intelligence
Horn (1982) suggests that the difficulties involved in separating neurological functioning fi'om individual differences in human abilities are such that one should expect to uncover no more than simple correlates between the two. However, it is reasonable to presume that
fluid intelligence, as a biological function, would be sensitive to changes in the central nervous system. A concomitant of aging is an inevitable decrease in neurophysiological functioning, therefore, fluid intelligence might also be expected to decline with age. Conversely, although crystallized intelligence is, to an extent, a function of neurological
factors, analysis of performance on crystallized intelligence type tasks indicate few concrete links to neurological functioning (Horn, 1982).
Kausler (1991) concurs that the capacity of fluid intelligence is likely to decrease with increasing age, but that the capacity of crystallized intelligence would be expected to increase throughout the life-span. A proviso to this however, as far as crystallized abilities are concerned, is that a reduction in the eflSciency of fluid abilities means that less crystallized knowledge can be assimilated, and hence the increments in crystallized intelligence must implicitly become progressively smaller. A considerable amount of research (see Section 3 .3 below) has shown that when tests are used which tap accumulated knowledge, older subjects may be equivalent, or superior, to younger ones, but they are likely to show deficits in tests which are heavily dependent on fluid intelligence, i.e., those tests whidi reflect abilities not dependent upon previous learning but which are more directly reliant upon the neural and other physiological processes which support intellectual functioning and which may be held to deteriorate with age (e.g., Horn and Cattell, 1967).
Salthouse (1991), in an extensive review of the literature concerning diflferential aging effects, presents a list of cognitive characteristics found by researchers to be commonly associated with the eflfects of aging. The attributes that represent both the smallest and the
greatest aging eflfects, show support for the position that age brings about an increase in knowledge but at the expense of taking longer to think things through. For example, tasks showing the greatest age differences include: perceptual-motor and speed functions (Botwinick, 1977); the capacity to acquire new concepts or to apply existing concepts quickly and accurately to complex situations (Bromley, 1974); non-verbal information, speed of response, and novel situations (Burger, Botwinick & Storandt, 1987); immediate problem solving ability (Fitzhugh, Fhzhugh, & Reitan, 1967); current processing eflSciency (Salthouse, 1988c); and, abstract reasoning (Willis, 1987). All the above can clearly be placed under the heading of abilities which utilize fluid intelligence. However older adults show much smaller age deficits on tasks which draw on the so called crystallized abilities, for example, verbal abilities and stored information (Botwinick, 1967); intellectual attainments (Bromley, 1974); verbal abilities (HaJpem, 1984); use of knowledge to deal with problems and form new knowledge (Horn, 1982); and leniently timed ‘knowledge inventories’ (Spieth, 1965). These findings lend support to the notion that age differences can be measured in terms of the two distinct abilities delineated by Cattel (1963).
Bromley (1988) asserts that if one defines intelligence as the ability to formulate abstract and novel ideas in relatively straightforward test situations, then intellectual creativity will show considerable depletion with age, and this should be particularly the case when the individual is expected to respond within the constraints of certain time limits. That this depletion begins at a relatively early age, and is especially noticeable in tasks demanding
speeded responses, was demonstrated in a study carried out by Rabbitt, Baneiji, &
Szemanski (1989) who showed that there is impairment in the learning of complex, fast interactive video-games for individuals between the ages of 18 and 36 years. These findings are characteristic of circumstances which require the individual to process novel information as rapidly as possible.
In this respect, and because psychometric measures of intellectual creativity have been found to correlate significantly with measures of fluid intelligence (Bromley, 1988), a legitimate parallel could be drawn between the concepts of fluid and crystallized intelligence and the intellectual capacities of creativity and wisdom; creativity in the sense of the abstract capacity for problem solving, and wisdom as the acquisition of practical experience and expertise (Simonton, 1990).
The nature of the difference between the two modes of intelligence was highlighted in a comprehensive study of life-span achievements carried out by Lehman (1957, 1966). His examination of biographical data suggested that, regardless of profession, there is a relatively early peak in intellectual abilities which is followed by a long slow decline. These peaks occur later and the declines are less protracted in areas such as history and literature which require extensive application of acquired knowledge, as opposed to those areas such as mathematics which focus rather on the ability to identify and solve new problems. Although Lehman's conclusions have been criticized and qualified (e.g., Dennis, 1966) there is much support for the concept of a lifetime pattern of cognitive achievement being represented as a progressive, homeostatic, process involving a shifting balance between the
loss o f raw* information-processing capacity and problem-solving ability and the continuing enhancement of information and usefiil learned skills (Rabbitt, 1991).
While there appears to be little disagreement amongst researchers that fluid intelligence declines in old age while crystallized intelligence remains largely unaffected, the question of how best to map such patterns of adult intellectual change remains a matter of some debate and controversy.