General Slowing Hypothesis

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Age-related slowing of responses to cognitive tasks is a ubiquitous phenomenon in research on cognitive aging. The general slowing hypothesis is built upon broad observations in many studies of age-related slowing and suggests that age-related general slowing is the primary contributor to the declines in cognitive functioning associated with aging. This entry provides a brief overview of the general slowing hypothesis and its research background, the evolution of models and debates, and cognitive training research based on the hypothesis.

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... Alternatively, these age differences could reflect true age effects. The general slowing hypothesis (Choi and Feng 2015) attributes age-related declines in literacy during adulthood to a general loss in mental capacities that is due to biological ageing processes. In line with this idea, recent longitudinal findings suggested that age-related declines in reasoning (fluid intelligence) and perceptual speed are responsible for literacy losses among older adults ). ...
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Background: In Germany, three large-scale surveys – the Level One Study (LEO), the Programme for the International Assessment of Adult Competencies (PIAAC), and the National Educational Panel Study (NEPS) – provide complementary data on adults’ literacy skills that can be harnessed to study adults with low literacy. To ensure that research on low-literate adults using these surveys arrives at valid and robust conclusions, it is imperative to ascertain the comparability of the three surveys’ low-literacy samples. Towards that end, in the present study, we comprehensively assess the comparability of adults with low literacy across these surveys with regard to their sociodemographic and socioeconomic characteristics. Methods: We used data from LEO, PIAAC, and NEPS. We identified features of the sample representation and measurement of (low) literacy as potential causes for variations in the low-literacy samples across the surveys. We then compared the low-literacy samples with regard to their sociodemographic and socioeconomic characteristics and performed logistic regressions to compare the relative importance of these characteristics as correlates of low literacy. Results: The key insight our study provides is that – despite different sample representations and measurement approaches – the low-literacy samples in the three surveys are largely comparable in terms of their socioeconomic and sociodemographic characteristics. Although there were small differences between the surveys with regard to the distribution of gender, educational attainment, and the proportion of non-native speakers within the group of low-literate adults, results revealed that both the prevalence of low literacy and its correlates were largely robust across LEO, PIAAC, and NEPS. Across all three surveys, lower educational attainment emerged as the most significant correlate of low literacy, followed by a non-German language background, unemployment and low occupational status. Conclusions: Our study provides evidence that all three surveys can be used for investigating adults with low literacy. The small differences between the low-literacy samples across the three surveys appear to be associated with sample representation and certain assessment features that should be kept in mind when using the surveys for research and policy purposes. Nevertheless, our study showed that we do not compare apples with oranges when dealing with low-literate adults across different large-scale surveys.
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Research concerned with relations between adult age and cognitive functioning is briefly reviewed. The coverage is necessarily selective, and is organized in terms of five major questions. These are what abilities are related to age, how many distinct influences are contributing to the relations between age and cognitive functioning, do the differences between people increase with advancing age, what is responsible for the discrepancies between cross-sectional and longitudinal age comparisons of cognitive functioning, and what methods can be used to identify causes of age-related influences on cognition. Although definitive answers are not yet possible, quite a bit of information relevant to the questions is now available. Moreover, the existing information has implications for the design, analysis, and interpretation of cognitive and neuropsychological research concerned with aging.
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We combined data from six studies, all using the same speed of processing training program, to examine the mechanisms of training gain and the impact of training on cognitive and everyday abilities of older adults. Results indicated that training produces immediate improvements across all subtests of the Useful Field of View test, particularly for older adults with initial speed of processing deficits. Age and education had little to no impact on training gain. Participants maintained benefits of training for at least 2 years, which translated to improvements in everyday abilities, including efficient performance of instrumental activities of daily living and safer driving performance.
IntroductionAge-Related Cognitive Slowing as a PhenomenonAge-Related Cognitive Slowing as an Explanatory ConstructIntraindividual VariabilityThe Diffusion ModelExplanatory Models for Cognitive SlowingPlasticity of Processing SpeedConcluding RemarksReferences
Studies of cognitive aging typically contrast the performance Y of a group of elderly subjects with the performance X of a group of college-aged subjects on some set of information-processing tasks. A scatter plot of old and young latencies was assembled from 189 tasks extracted from 35 published studies. Two multiplicative functions Y = mX described these data well, accounting for 96% of the variance in the old latencies. Fitting linear functions to each of the 35 studies separately resulted in a family of regression lines with coupled slopes and intercepts that could be derived from the preceding coefficients. The coefficients further predicted old latencies in 69 newly published information-processing tasks. Thus, a simple "slowing" model seems to describe the principal effects of age on group mean reaction times. The two coefficients suggest that the slowing of sensory-motor processes is less severe than the slowing of higher order processes.
We evaluated the debates concerning brinley plots and the associated theories of age-related slowing. We concluded that an explicit debate regarding a single-factor, general slowing model was no longer a debate as most, if not all, agree to the disconfirmation of that model. We address sources of confusion in the debates that have muddled the areas of genuine disagreement. When the confusion is lifted, the remaining debate centers, rightly, on evaluation of theories of aging. We show that Brinley plot analyses can lead to both falsely accepting and falsely rejecting theories of agerelated slowing. Although plotting data most certainly can assist with the evaluation of cognitive theory, we argue that models of performance and learning must play a more central role in advancing theories of cognitive aging
Research into the effects of aging on response time has focused on Brinley plots. Brinley plots are constructed by plotting mean response times for older subjects against those for young subjects for a set of experimental conditions. The typical result is a straight line with a slope greater than 1 and a negative intercept. This linear function has been interpreted as showing that aging leads to a general slowing of cognitive processes. In this article, we show that the slope of the Brinley plot is actually a measure of the relative standard deviations of older versus young subjects' response times; it is not a measure of general slowing. We examine current models of the effects of aging on mean response time and show how they might be reinterpreted. We also show how a more comprehensive model, Ratcliff's diffusion model (1978), can account for Brinley plot regularities and, at the same time, provide an account of accuracy rates, the shapes of response time distributions, and the relative speeds of error and correct response times, aspects of the data about which models designed to account for Brinley plots are mute. We conclude by endorsing a research approach that applies explicit models to response time data in aging in order to use the parameters of the model to interpret the effects of aging.
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