Age Differences and Individual Differences in Cognitive Functions

Author(s):  
Klaus Oberauer
2019 ◽  
Vol 8 (1) ◽  
pp. 1 ◽  
Author(s):  
Anna-Lena Schubert ◽  
Dirk Hagemann ◽  
Christoph Löffler ◽  
Gidon T. Frischkorn

Several studies have demonstrated that individual differences in processing speed fully mediate the association between age and intelligence, whereas the association between processing speed and intelligence cannot be explained by age differences. Because measures of processing speed reflect a plethora of cognitive and motivational processes, it cannot be determined which specific processes give rise to this mediation effect. This makes it hard to decide whether these processes should be conceived of as a cause or an indicator of cognitive aging. In the present study, we addressed this question by using a neurocognitive psychometrics approach to decompose the association between age differences and fluid intelligence. Reanalyzing data from two previously published datasets containing 223 participants between 18 and 61 years, we investigated whether individual differences in diffusion model parameters and in ERP latencies associated with higher-order attentional processing explained the association between age differences and fluid intelligence. We demonstrate that individual differences in the speed of non-decisional processes such as encoding, response preparation, and response execution, and individual differences in latencies of ERP components associated with higher-order cognitive processes explained the negative association between age differences and fluid intelligence. Because both parameters jointly accounted for the association between age differences and fluid intelligence, age-related differences in both parameters may reflect age-related differences in anterior brain regions associated with response planning that are prone to be affected by age-related changes. Conversely, age differences did not account for the association between processing speed and fluid intelligence. Our results suggest that the relationship between age differences and fluid intelligence is multifactorially determined.


2017 ◽  
Vol 31 (4) ◽  
pp. 313-328 ◽  
Author(s):  
René Mõttus ◽  
Christopher J. Soto ◽  
Helena R. Slobodskaya ◽  
Mitja Back

Do individual differences in personality traits become more or less pronounced over childhood and adolescence? The present research examined age differences in the variance of a range of personality traits, using parent reports of two large samples of children from predominantly the USA and Russia, respectively. Results indicate (i) that individual differences in most traits tend to increase with age from early childhood into early adolescence and then plateau, (ii) that this general pattern of greater personality variance at older childhood age is consistent across the two countries, and (iii) that this pattern is not an artefact of age differences in means or floor/ceiling effects. These findings are consistent with several (noncontradictory) developmental mechanisms, including youths’ expanding behavioural capacities and person–environment transactions (corresponsive principle). However, these mechanisms may predominantly characterize periods before adolescence, or they may be offset by countervailing processes, such as socialization pressure towards a mature personality profile, in late adolescence and adulthood. Finally, the findings also suggest that interpreting age trajectories in mean trait scores as pertaining to age differences in a typical person may sometimes be misleading. Investigating variance should become an integral part of studying personality development. Copyright © 2017 European Association of Personality Psychology


2007 ◽  
Vol 30 (2) ◽  
pp. 161-162 ◽  
Author(s):  
Hamid Reza Naghavi ◽  
Lars Nyberg

AbstractA large body of evidence supports the idea that a common fronto-parietal network is activated across a range of diverse cognitive functions. Jung & Haier's (J&H's) review demonstrates a very similar pattern of activity, which correlates with individual differences in intelligence. We propose that these converging lines of evidence are best interpreted as a general role of the fronto-parietal network in integration and control.


2016 ◽  
Vol 30 (1) ◽  
pp. 4-11 ◽  
Author(s):  
René Mõttus ◽  
Jüri Allik ◽  
Martina Hřebíčková ◽  
Liisi Kööts–Ausmees ◽  
Anu Realo

In contrast to mean–level comparisons, age group differences in personality trait variance have received only passing research interest. This may seem surprising because individual differences in personality characteristics are exactly what most of personality psychology is about. Because different proposed mechanisms of personality development may entail either increases or decreases in variance over time, the current study is exploratory in nature. Age differences in variance were tested by comparing the standard deviations of the five–factor model domain and facet scales across two age groups (20 to 30 years old versus 50 to 60 years old). Samples from three cultures (Estonia, the Czech Republic and Russia) were employed, and two methods (self–reports and informant–reports) were used. The results showed modest convergence across samples and methods. Age group differences were significant for 11 of 150 facet–level comparisons but never consistently for the same facets. No significant age group differences were observed for the five–factor model domain variance. Therefore, there is little evidence for individual differences in personality characteristics being systematically smaller or larger in older as opposed to younger people. We discuss the implications of these findings for understanding personality development. Copyright © 2015 European Association of Personality Psychology


2019 ◽  
Author(s):  
Anna-Lena Schubert ◽  
Dirk Hagemann ◽  
Christoph Löffler ◽  
Gidon T. Frischkorn

Several studies have demonstrated that individual differences in processing speed fully mediate the association between age and intelligence, whereas the association between processing speed and intelligence cannot be explained by age differences. Because measures of processing speed reflect a plethora of cognitive and motivational processes, it cannot be determined which specific processes give rise to this mediation effect. This makes it hard to decide whether these processes should be conceived of as a cause or an indicator of cognitive aging. In the present study, we addressed this question by using a neurocognitive psychometrics approach to decompose the association between age differences and fluid intelligence. Reanalyzing data from two previously published datasets containing 223 participants between 18 and 61 years, we investigated whether individual differences in diffusion model parameters and in ERP latencies associated with higher-order attentional processing explained the association between age differences and fluid intelligence. We demonstrate that individual differences in the speed of non-decisional processes such as encoding, response preparation, and response execution, and individual differences in latencies of ERP components associated with higher-order cognitive processes explained the negative association between age differences and fluid intelligence. Because both parameters jointly accounted for the association between age differences and fluid intelligence, age-related differences in both parameters may reflect age-related differences in anterior brain regions associated with response planning that are prone to be affected by age-related changes. Conversely, age differences did not account for the association between processing speed and fluid intelligence. Our results suggest that the relationship between age differences and fluid intelligence is multifactorially determined. Data and analysis code are available at https://osf.io/hy5fw/.


2018 ◽  
Author(s):  
Chandra Sripada ◽  
Saige Rutherford ◽  
Mike Angstadt ◽  
Wesley K. Thompson ◽  
Monica Luciana ◽  
...  

AbstractDifficulties with higher-order cognitive functions in youth are a potentially important vulnerability factor for the emergence of problematic behaviors and a range of psychopathologies. This study examined 2,013 9-10 year olds in the first data release from the Adolescent Brain Cognitive Development 21-site consortium study in order to identify resting state functional connectivity patterns that predict individual-differences in three domains of higher-order cognitive functions: General Ability, Speed/Flexibility, and Learning/Memory. We found that connectivity patterns involving task control networks and default mode network were prominently implicated in predicting individual differences across participants across all three domains. In addition, for General Ability scores specifically, we observed consistent cross-site generalizability, with statistically significant predictions in 14 out of 15 held-out sites. These findings demonstrate that resting state connectivity can be leveraged to produce generalizable markers of neurocognitive functioning. Additionally, they highlight the importance of task control-default mode network inter-connections as a major locus of individual differences in cognitive functioning in early adolescence.


2021 ◽  
Author(s):  
Yuzhan Hang ◽  
Christopher J. Soto ◽  
Billy Lee ◽  
René Mõttus

Rationale: Personality traits change in both mean levels and variance from childhood through mid-adolescence, but the mechanisms underlying these developmental trends remain unknown. We tested the possible roles of social pressure and self-regulation. Methods: The Common-Language California Child Q-Set was used to measure youths’ mean-level personality, social expectations for youths’ behavior from multiple perspectives (parents, teachers, peers) and the self-regulatory requirements for achieving the desired trait levels. Results: There were consistent expectations for youths’ traits, regardless of who described the expectations or whether these pertained to children or adolescents. Mean trait levels were moderately commensurate with social expectations, but age differences in the means did not follow these expectations. Traits with strong expectations showed more pronounced individual differences and increased even more in variance with age. In contrast, traits’ self-regulation requirements did not predict their developmental trends. Implications: Strong social expectations may contribute to the development of individual differences.


Gerontology ◽  
2015 ◽  
Vol 62 (2) ◽  
pp. 238-246 ◽  
Author(s):  
Stefan T. Kamin ◽  
Frieder R. Lang

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