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Increasing Variance as a Function of Aging

Increasing Variance as a Function of Aging

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Increasing Variance as a Function of Aging

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    1. Increasing Variance as a Function of Aging Harvey Babkoff Elisheva Ben-Artzi Leah Fostick Miriam Geal-Dor

    2. Changes in Variance Due to Aging Changes in Mean and in Variance due to Aging Inter-Individual Variance and Aging Intra-Individual Variance and Aging

    3. Inter-Individual Variance and Aging

    4. Lovden et al. (2004) and Schie (2000) Differences in physiological, socio-demographic and educational factors become greater as individuals age and therefore begin to have greater weighting than the genetic factors in determining cognitive performance.

    5. Quantitative Genetic Analysis of Latent Growth Curve Models of Cognitive Abilities in Adulthood Chandra A. Reynolds University of California, Riverside (2005) Increased variability in cognitive performance with age has been reported primarily in, but has not been limited to, cross-sectional analyses (e.g., Christensen, Mackinnon, Korten, Jorm, Henderson, & Jacomb, 1999; Christensen, Mackinnon, Korten, Jorm, Henderson, Jacomb, & Rodgers, 1999; Morse, 1993). In the present case, such a pattern was seen with respect to systematic variances, that is, those explained by the latent growth model, for most measures. Variance increases have been ascribed to non-shared environmental or non-genetic stochastic processes (e.g., Finch & Kirkwood, 2000). This interpretation was supported by our findings. The increasing environmental variation seen for nearly all cognitive traits in the present study could reflect stochastic processes that may have their seeds in early development and that are magnified in late life (Finch & Kirkwood, 2000).

    6. Quantitative Genetic Analysis of Latent Growth Curve Models of Cognitive Abilities in Adulthood Chandra A. Reynolds University of California, Riverside (2005) Though many cognitive abilities exhibit marked decline over the adult years, individual differences in rates of change have been observed. In the current study, biometrical latent growth models were used to examine sources of variability for ability level (intercept) and change (linear and quadratic effects) for verbal, fluid, memory, and perceptual speed abilities in the Swedish Adoption/Twin Study of Aging. Genetic influences were more important for ability level at age 65 and quadratic change than for linear slope at age 65. Expected variance components indicated decreasing genetic and increasing non-shared environmental variation over age. Exceptions included one verbal and two memory measures that showed increasing genetic and non-shared environmental variance. The present findings provide support for theories of the increasing influence of the environment with age on cognitive abilities.

    7. Examples of psychophysical data showing no changes in either mean performance or variance as individuals age

    8. An example of stable performance across age in accuracy of discrimination of auditory target (tone) from among non-target tones. There is no significant change in performance either in the mean or in the variance of the distributions as a function of aging

    9. An example of stable performance across age in RT to auditory target (tone) from among non-target tones. There is no significant change in performance either in the mean or in the variance of the distributions as a function of aging

    10. Another example of stable performance across age in RT to auditory target (phonological stimulus) from among non-target stimuli. There is no significant change in performance either in the mean or in the variance of the distributions as a function of aging

    12. The following data are an example of increased inter-individual variance in the elderly together with an increased mean but with a scaling problem and change in distribution

    13. Note what appears to be an increase in variance from the young to the elderly subjects. This seems, however, to reflect the significant decrease in mean percent correct for the group of elderly subjects relative to the younger subjects and a resultant change in the distribution from non-Gaussian to Gaussian.

    14. Note the changes in the accuracy distributions of the young versus the elderly subjects. While the distribution of the young is not Gaussian, that of the elderly appears to approach a Gaussian distribution.

    15. An example of change in performance as subjects age in the accuracy of discrimination of a semantic target (word) from among non-target words. There is a significant change in mean performance but not in the variance of the distributions as a function of aging.

    16. There was a significant increase in the variance of the RT distributions to the semantic targets from the young to the elderly (F= 2.8278; p<.016). Both distributions are Gaussian.

    20. Some cross-sectional studies have reported results that were interpreted to mean that inter-individual variability among the elderly is related to variability in hearing loss (Helfer and Wilber, 1990; Humes et al., 1994). However, the results of other studies have found inter-individual variability to be relatively large even among elderly subjects with normal audiograms for their age (Brasz et al., 2002; Scneider & Pichora-Fuller, 2001; Versfeld and Dreschler, 2002).

    24. Both the distributions of the hemispheric index for the young and for the elderly are Gaussian. There is a tendency for the variance of the distribution of the hemispheric index of the elderly to be larger than for the younger subjects (F = 1.985); p<.07

    28. Intra-Individual Variance and Aging Speech Discrimination in Speech Noise

    29. INTRA-INDIVIDUAL VARIANCE and AGING In recent years there has been growing interest in within-person variability as a potentially informative individual difference parameter. At least five factors may contribute to this interest. (Correlates of within-person (across occasion) variability in RT, Salthouse and Berish,Neuropsychology, 2005) First, high levels of within-person variability may signify health-related problems. Second, unusual levels of within-person variability might function as an early indicator of impending cognitive change. That is, large fluctuations in ones momentary level of cognitive performance may be a precursor to certain types of cognitive pathology, and information about variability might provide a more useful baseline against which to evaluate the severity of extreme behaviors. Third, large within-person variability (i.e., low across-occasion consistency) could distort the evaluation of an individuals level of cognitive functioning. Fourth, within-person variability could contribute to inconsistency in research results across studies because in a single-occasion study, it is impossible to distinguish relatively stable trait variance from fluctuating state variance. For example, if there are age differences in within-person variability, it could lead to spurious conclusions of age-related increases in between-persons variability (Nesselroade, 2001). The possibility of large within-person variability in the performance of neuropsychological tests would also raise questions about the basis of the correspondence typically assumed between test performance and brain function. Fifth, identification of correlates of within-person variability may be informative about possible causes of individual differences, and particularly age-related individual differences, in cognitive functioning.

    31. (Correlates of within-person (across occasion) variability in RT. Salthouse and Berish,Neuropsychology, 2005) There are two major results of these studies. The first is the demonstration of very large within-person variability in measures of RT. In two studies involving a total of 420 individuals, the median RT from one occasion to the next was found to vary as much as the mean (across occasion) RT varied among people who ranged from 18 to 91 years of age. Because the reliability estimates indicate that for most participants the within-occasion RTs were more similar to one another than were the between-occasions RTs, this across-occasion variability cannot merely be attributed to random fluctuation. The second major result of the present studies is the consistent finding that measures of within-person variability appear to be secondary to measures of central tendency with respect to relations with age and with a variety of cognitive variables. For many variables, the mean and the SD are highly related because the variability around the mean is frequently greater when the mean is larger, but all of the analyses indicated that statistical control of the mean has a greater attenuating effect on correlations involving the SD than vice versa.