Smarter every day: The deceleration of population ageing in terms of cognition
Introduction
Higher chronological age tends to be associated with lower cognitive functioning, which in turn correlates with a range of age-associated health risks such as hypertension and diabetes (McCrimmon et al., 2012, Slomski, 2014). Cognitive functioning is also a good predictor of future morbidity and mortality (Negash et al., 2011). Individuals with higher cognitive abilities tend to live longer and healthier lives (Batty et al., 2007, Der et al., 2009). Cognitive functioning can therefore be considered an important measure of differential ageing across cohorts and population groups.
Cognitive functioning is a characteristic of individuals that is associated with but not determined by chronological age. The maintenance of good cognitive functioning is thus one of the central components of successful ageing (Rowe & Kahn, 1987). Some parts of the population start ageing earlier than others, education being a central factor in this regard. Higher educated individuals tend to participate in more cognitively stimulating activities during their lifetime and therefore remain cognitively fit until a higher age (Wilson, Barnes, & Bennett, 2003). Recent evidence shows that the lower educated start ageing earlier than those with higher levels of education — in terms of physical health and fitness (Christensen et al., 2009, Mäki et al., 2013, Sanderson and Scherbov, 2014) as well as in terms of mental health and cognitive functioning (Lièvre, Alley, & Crimmins, 2008). The expansion of education has therefore been among the primary explanations for why steadily increasing average scores on common tests of cognitive functioning (including classic IQ tests) have been observed since the end of the 19th century — the so-called Flynn effect (Flynn, 1984, Flynn, 2000, Flynn, 2007, Hiscock, 2007).
The majority of studies on the Flynn effect have focused on children, adolescents, and prime-age adults. However, some recent studies have shown that the Flynn effect extends to older populations (Baxendale, 2010, Christensen et al., 2013, De Rotrou et al., 2013, Gerstorf et al., 2011, Skirbekk et al., 2013). In other words, older populations today have aged more successfully in terms of their cognitive functioning than did earlier generations. An important reason for such secular trends may lie in the expansion of education and thus the fact that more recent cohorts tend to be more highly educated (Baker et al., 2015, Ceci, 1991).
The factors responsible for the Flynn effect are commonly assumed to be environmental rather than genetic given the speed at which average cognitive abilities have been rising (Dickens and Flynn, 2001, Flynn, 2007, Pietschnig and Voracek, 2015). In addition to improved education (Baker et al., 2015, Teasdale and Owen, 2005), a range of environmental explanations for the Flynn effect have been proposed such as improvements in medical care and nutrition (Lynn, 2009), reduced family size (Sundet et al., 2008, Zajonc and Mullally, 1997), and reductions in pathogen stress (Eppig, Fincher, & Thornhill, 2010). These formerly independent theories for explaining the Flynn effect (i.e., better education, improved nutrition, reduced family size, and lower pathogen stress) have been integrated by Woodley in a model of life history speed (Woodley, 2012a) that points to the important insight that the explanations for the Flynn effect are likely to vary across space and time.
In the context of explanations based on changes in environmental conditions, Neisser (1997) put forward the thesis that the increasing exposure to visual and technical environments may be a central factor for explaining the Flynn effect. The increasing use of computers and gadgets with visual interfaces in everyday life may have contributed to a rising complexity of our cognitive environments and in turn higher levels of cognitive stimulation and performance (Charness et al., 2011, Dickens and Flynn, 2001, Neisser, 1997, Schooler, 1998). Especially the older parts of the population who used to withdraw from cognitively demanding tasks and environments at relatively young ages (i.e., mental retirement, cf. Rohwedder & Willis, 2010) are increasingly exposed to cognitive challenges in everyday life (Charness & Schaie, 2003). The thesis that the increasing use of modern technology in everyday life increases cognitive demands on the older population and in turn helps maintain cognitive capacities to higher ages is put to an empirical test in the present study.
This study aims to contribute to our understanding of why individuals at age 50 or above today perform better in tests of cognitive functioning than people of the same age did in the past. First, to ascertain the presence of a Flynn effect, we investigate change over time in the cognitive functioning of the population aged 50+ in England and Germany. We use a wide range of different cognitive tests that differ in their sensitiveness to the biological ageing processes of the brain (Hiscock, 2007), including tests of processing speed and accuracy, a test of verbal fluency, and two tests of verbal memory. Second, we test if education and the increasing use of modern technology help explain the Flynn effect. To date, evidence for the use of modern technology as a cause for the Flynn effect is still warranted (Pietschnig & Voracek, 2015).
Studies pointing to a sustained upward drift in mean cognitive abilities over time (i.e., the presence of a Flynn effect) have usually paid little attention to subgroup differences by age, sex, or level of education (Ang, Rodgers, & Wänström, 2010). Recent exceptions include the work by Pietschnig and Voracek (2015) who find significantly stronger Flynn effects for adults than for children and adolescents. Ang et al. (2010) find an accelerated Flynn effect for children of more educated mothers. No sex differences in the Flynn effect have been found (Pietschnig, Voracek, & Formann, 2011). The present study explores age and sex differences in the Flynn effect as well as differences by educational attainment.
Section snippets
Surveys and sampling
This study is based on data from two surveys, the German Socio-Economic Panel (SOEP) and the English Longitudinal Study of Ageing (ELSA). In order to avoid retest effects, we do not use the longitudinal samples of these surveys but restrict the samples of analysis to individuals taking part in the cognitive tests only once.
SOEP is a survey of private households that provides representative, longitudinal micro-data that have been collected on an annual basis since 1984. All samples are
Descriptive findings
The summary statistics shown in Table 1a, Table 1b suggest that average cognitive functioning is higher in the more recent survey wave as compared with the earlier one both in England and in Germany (i.e., confirming the existence of a Flynn effect). The effect sizes are similar across all measures with a Cohen's d (Cohen, 1988) of about 0.2 standard deviations (0.21 for SDT90 and DR, 0.20 for ANT, 0.17 for IR and LCT, 0.15 for SDT60), except for STD30 (0.09 SD). Based on an average IQ test
Discussion
This paper contributed to the discussion of the sustained upward drift in mean cognitive abilities over time, adding an analysis of the role played by the use of modern information and communication technology for maintaining cognitive functioning in the older population. We presented a simple procedure that uses measures of cognitive functioning as a characteristic-based age (Sanderson & Scherbov, 2013) and expresses the magnitude of the Flynn effect in terms of a secular ‘age-gain’ — a
Contributors
All authors contributed equally to the work. All three authors contributed to the overall study design. V.B. performed the analyses on ELSA data and N.S. the analyses on SOEP data. S.S. directed the study. N.S. and V.B. wrote the manuscript, which was edited by all the co-authors. All authors have approved the final article.
Acknowledgments
The authors acknowledge the funding from the European Research Council Grant no ERC2013-AdG 323947-Re-Ageing. The funder was not involved in the research.
The data on England were made available through the UK Data Archive. ELSA was developed by a team of researchers based at the University College of London, the National Centre for Social Research and the Institute for Fiscal Studies. The data were collected by the National Centre for Social Research. The developers and funders of ELSA and the
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