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hemisphere patients, however, acute subjects showed a far more substantial V > P discrepancy than chronic patients (14 versus 51 2 points). Both right-lesion groups showed the predicted pattern, but only the acute group showed a sizable discrepancy. Thus, the data in Table 8.8 suggest that left-lesion patients do not vary in their V P differences as a function of the recency of the lesion, but that acuteness chronicity is a key variable for patients with right-hemisphere damage. Generally, the most neurobehavioral recovery from traumatic brain injury is made in the first years after the injury. The most rapid recovery typically occurs within the first 3 6 months and then a plateau occurs around 2 years (Jones, 1992). Within these broad parameters, some abilities recover more quickly than others, and improvement from traumatic brain injury is a heterogeneous process. The relative improvement, over time, in rightbrain cognitive functioning by patients with damage to that side of the brain attests to the recuperative powers of brain-injured people. Lezak (1995) states: Cognitive functions, particularly those involving immediate memory, attention and concentration, and specific disabilities associated with the site of the lesion generally continue to improve markedly during the first six months or year, and improvement at a progressively slower rate may go on for a decade or more following a stroke or other single-event injury to the brain (p. 283). Nonetheless, it is still consistent with previous research and clinical experience to find a V > P discrepancy of 51 2 points in chronic rightlesion patients beause full recovery is rare, even for apparently slight injuries (Brodal, 1973; Schachter & Crovitz, 1977).

Bellevue investigations, showing once again that larger V P IQ discrepancies are more likely to emerge for patients who recently became brain injured than for those with long-standing injuries. Because Snow et al. (1986) grouped the leftlesion and right-lesion samples together for their correlational analysis, it was possible neither to deny nor confirm the hypothesis proposed here that acuteness versus chronicity is a more important variable for lesions to the right hemisphere. Paniak, Silver, Finlayson, and Tuff (1992) examined the time from injury to WAIS-R testing in a sample of head-injured patients and found that longer time from injury to testing was associated with higher Performance IQ (r = .29, p < .01). Like Snow et al., Paniak et al. did not provide data on the duration of injury across separate left- and right-hemisphere lesion patients.

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same, whether applied to tests for children, adolescents, or adults. Intelligent testing rests on five assumptions, discussed in the sections below: 1. IQ tasks measure what the individual has learned. 2. IQ tasks are samples of behavior and are not exhaustive. 3. IQ tests like the WAIS-III, KAIT, and WJ III assess mental functioning under fixed experimental conditions. 4. IQ tests are optimally useful when they are interpreted from an information-processing model. 5. Hypotheses generated from IQ test profiles should be supported with data from multiple sources.

This concept comes directly from Wesman s (1968) introduction of the intelligent testing approach. The content of all tasks, whether verbal or nonverbal, is learned within a culture. The learning may take place formally in the school, casually in the home, or incidentally through everyday life. As a measure of past learning, the IQ test is best thought of as a kind of achievement test, not as a simple measure of aptitude. Like the SAT, IQ tests assess developed abilities, broadly applicable intellectual skills and knowledge that develop slowly over time through the individual s experiences both in and out of school...[that are] not tied to the content of any specific course or field of study (Anastasi, 1988, p. 330). The interaction between learning potential and availability of learning experiences is too complex to ponder for any given person, making the whole genetics environment issue of theoretical value, but impractical and irrelevant for the interpretation of that person s test profile. Even the sophisticated scientific challenges to the IQ construct issued by Lezak (1988a) and Siegel (1999) or the emotional, less informed in-

It is almost certain you have used a wide variety of highlighter pens. These pens allow you to draw a see-through stripe (usually in yellow) on top of text in order to call it out as important. For instance, when reading a textbook, you may highlight certain portions so that when you reread it, you can focus on just the most important parts of the text. That is precisely the idea behind both highlighter tools in ProphetCharts. Each of them does the same thing: produces a see-through color in either an oval or rectangular shape (depending on the tool selected). You can control how transparent the color is as well as what color is used. You may be wondering what kinds of things you would want to highlight. The answer is: anything that merits attention. It could be a huge gap in a chart, a series of tops or bottoms, an area of consolidation, or an interesting price pattern. Just as with any of the other drawn objects, the principle is to embellish the chart in such a way that it is more meaningful to you both now and in the future. 257

[Schwartz et al 1990] DB Schwartz, VK Samalam, SA Solla, JS Denker, Exhaustive Learning, Neural Computation, Vol 2, 1990, pp 374 385 [Sejnowski 1977] T Sejnowski, Storing Covariance with Nonlinearly Interacting Neurons, Journal of Mathematical Biology, Vol 4, 1997, pp 303 321 [Seung et al 1992] HS Seung, M Opper, H Sompolinsky, Query by Committee, Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, 1992, pp 287 299 [Sevenster and Engelbrecht 1996] S Sevenster, AP Engelbrecht, GARTNet: A Genetic Algorithm for Routing in Telecommunications Networks, Proceedings of CESA96 IMACS Multiconference on Computational Engineering in Systems Applications, Symposium on Control, Optimization and Supervision, Lille, Prance, Vol 2, 1996, pp 1106 1111 [Shi and Eberhart 1999] Y Shi, RC Eberhart, Empirical Study of Particle Swarm Optimization, Proceedings of the IEEE Congress on Evolutionary Computation, Vol 3, 1999, pp 1945 1950.

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