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As indicated, Todd et al. (1977) identified a V > P profile on the WAIS for patients having lesions in both cerebral hemispheres. Smith (1966a) also found V > P patterns on the Wechsler-Bellevue for 55 patients with bilateral damage; only 27.3% had P > V patterns. In fact, the nine studies cited by Matarazzo and Herman (1985, Table 2) that included patients with diffuse brain damage spanning both cerebral hemispheres produced a WAIS V > P profile by an average of 6.2 points (range = 1.8 to 13.4). Munder (1976) found WAIS V > P profiles of similar magnitude for both of her samples with diffuse brain damage,

Auditory Delay Rebus Delay 15-16 17-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 Age Group

Mean standard scores on two measures of Glr (KAIT Rebus Delayed Recall and KAIT Auditory Delayed Recall), based on norms for ages 15 94 and adjusted for education, for 14 groups of adults between the ages of 15 and 94 years.

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Artificial neural networks have been developed to model the pattern association ability of the human brain. These networks are referred to as associative memory NNs. Associative memory NNs are usually two-layer NNs, where the objective is to adjust the weights such that the network can store a set of pattern associations - without any external help from a teacher. The development of these associative memory NNs is mainly inspired from studies of the visual and auditory cortex of mammalian organisms, such as the bat. These artificial NNs are based on the fact that parts of the brain are organized such that different sensory inputs are represented by topologically ordered computational maps. The networks form a topographic map of the input patterns, where the coordinates of the neurons correspond to intrinsic features of the input patterns. An additional feature modeled with associative memory NNs is to preserve old information as new information becomes available. In contrast, supervised learning NNs have to retrain on all the information when new data becomes available; if not, supervised networks tend to focus on the new information, forgetting what the network has learned already. Unsupervised learning NNs are functions which map an input pattern to an associated target pattern, i.e. FNN : M7 -> R* (4.1) as illustrated in Figure 4.1. The single weight matrix determines the mapping from the input vector z to the output vector o.

broadcast in the Crystallized Auditory Comprehension subtest; the Delayed Rebus task assesses how well individuals remember the words that are paired with unfamiliar symbols (rebuses) in the Fluid learning task known as Rebus Learning. Even though the initial subtests are administered a few minutes apart, as are the two delayed-recall tasks, how much a person remembers from each initial subtest is quite dependent on his or her age. As Figure 5.15 indicates, adolescents ages 15 16 earned mean scores that were about 9 points higher on Rebus Delayed Recall than on Auditory Delayed Recall; in contrast, elderly adults (ages 65 and above) scored about 3 points lower on the Rebus subtest than on the Auditory subtest. Horn (personal communication, September 1994) con-

sidered the different patterns for the two Glr tasks suggestive of intriguing hypotheses about the storage, consolidation, and retrieval of information. In the Kaufman, Kaufman, Chen et al. (1996) study, the aging pattern for long-term retrieval tasks depended on the material to be recalled: When the stimuli were learned during a Gc task, the amount of retention displayed a Gc-like maintained pattern; when the stimuli were learned during a Gf task, the pattern of retention was vulnerable to the effects of aging. Data for the WJ III Glr scale, which measures long-term retrieval with tasks that resemble KAIT s Fluid subtest (Rebus Learning), are consistent with Kaufman et al. s finding: This WJ III scale demonstrates a vulnerable pattern (see 14), although the data are

tentative because education was not controlled. The results with the KAIT and WJ III require cross-validation, especially with longer delays between initial learning and later recall. Though the KAIT interval of 30 to 45 minutes clearly qualifies as measuring Glr and not Gsm from a Horn standpoint, it is nonetheless true that Horn and colleagues (e.g., Horn & Hofer, 1992; Horn & Risberg, 1989) have been primarily interested in storage for hours, weeks, or years when investigating and theorizing about Glr. Furthermore, the use of Horn s terminology to define memory tasks is one of many theoretical approaches that can be taken. For example, in the next section, the aging patterns observed for WMS-III scales are interpreted within an episodic versus semantic memory context (Tulving, 1983), and there are numerous other ways of categorizing distinctions among memory tasks, and of interpreting changes due to

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