Variation In Cognitive Ability Research Articles

Task-specific topology of brain networks supporting working memory and inhibition.

Network neuroscience explores the brain's connectome, demonstrating that dynamic neural networks support cognitive functions. This study investigates how distinct cognitive abilities-working memory and cognitive inhibitory control-are supported by unique brain network configurations constructed by estimating whole-brain networks using mutual information. The study involved 195 participants who completed the Sternberg Item Recognition task and Flanker tasks while undergoing electroencephalography recording. A mixed-effects linear model analyzed the influence of network metrics on cognitive performance, considering individual differences and task-specific dynamics. The findings indicate that working memory and cognitive inhibitory control are associated with different network attributes, with working memory relying on distributed networks and cognitive inhibitory control on more segregated ones. Our analysis suggests that both strong and weak connections contribute to cognitive processes, with weak connections potentially leading to a more stable and support networks of memory and cognitive inhibitory control. The findings indirectly support the network neuroscience theory of intelligence, suggesting different functional topology of networks inherent to various cognitive functions. Nevertheless, we propose that understanding individual variations in cognitive abilities requires recognizing both shared and unique processes within the brain's network dynamics.

Physical function predicts declarative memory

AbstractBackgroundNo cure currently exists for Alzheimer’s disease; however, early detection may help create therapies and treatments to slow cognitive decline. The concept of early detection is simple, but is complicated by the inability of primary care physicians to recognize symptoms until late disease manifestation and failure to provide appropriate screening tools for disease detection. Thus, the purpose of the present investigation was to determine predictors of cognitive performance among community‐dwelling older adults.Method63 community‐dwelling older adults (80.0 ± 5.5 years) participated in the present investigation. Declarative memory was used as the measure of cognition. Physical function was measured using the Short Physical Performance Battery (SPPB), dual‐task fast walking time, and sit‐to‐stand velocity. Linear regression analysis was used to predict cognition from physical function. Odds ratios were used to determine the magnitude of influence of physical function on cognitive function. ANOVA was used to determine differences in individual physical function variable between low and high cognitive groups.ResultsThe linear regression model included dual‐task fast 10‐m walking time, sex, education, and SPPB score and accounted for 39% of the variance in cognitive ability (p<.001). The physical function variables accounted for 8.8% and 8.5% of unique variance in cognition, respectively. For participants performing below the 50th percentile for dual‐task fast walking time, there was a 3.6‐fold increase in declarative memory scores (CI: 1.44‐9.35). Similarly, SPPB values indicative of mobility dysfunction was associated with a 3.2‐times increase in cognitive impairment (CI: 1.31‐7.87). Results of the one‐way ANOVA revealed significant differences in SPPB (p = .002), peak sit‐to‐stand velocity (p = .007), and dual‐task fast walking time (p = .001). The high cognitive group had significantly better physical function compared to the lower cognitive group.ConclusionResults from the present investigation support the use of physical function in predicting current declarative memory among a sample of community‐dwelling adults with no diagnosed cognitive impairment. Future research is needed to determine the ability to predict changes in cognition longitudinally.

Cognitive ability impact on life insurance lapsation


 
 
 Life insurance is an important household risk management and financial tool. Policy lapsation has economic effects on life insurance companies, policyholders, and beneficiaries that may be detrimental when these lapses are unexpected. Prior literature examined several hypotheses of life insurance lapse focusing mainly on macroeconomic factors using aggregate data and household microeconomic factors using household-level data. We introduce and test individual cognitive ability variables in a model of the life insurance voluntary lapse decision by individual policyholders using household-level data from the Health and Retirement Study. We find that one measure of cognitive ability, in particular, numer- acy, is related to the voluntary lapse decision. While controlling for numeracy, we find evidence that those individuals with higher levels of net worth are less likely to voluntarily lapse a policy which is consistent with the emergency fund hypothesis. We introduce a new measure of liquidity shock, kids moving home, into the model and find it has a strong positive relationship with the decision to voluntar- ily lapse a policy. Consistent with life insurance demand theory, we find that those who have recently entered retirement are more likely to lapse their policy.
 
 

The impact of early adversity and education on genetic and brain morphological predictors of cognitive ability.

Cognitive ability is a strong predictor of occupational achievement, quality of life and physical health. While variation in cognition is strongly heritable and has been robustly associated with early environment and brain morphology, little is known about how these factors combine and interact to explain this variation in cognition. To address this, we modelled the relationship between common genetic variation, grey matter volume, early life adversity and education and cognitive ability in a UK Biobank sample of N = 5237 individuals using structural equation modelling. We tested the hypotheses that total grey matter volume would mediate the association between genetic variation and cognitive ability, and that early life adversity and educational attainment would moderate this relationship. Common genetic variation, grey matter volume and early life adversity were each significant predictors in the model, explaining ~15% of variation in cognitive ability. Contrary to our hypothesis, grey matter volume did not mediate the relation between genetic variation and cognition performance. Neither did early life adversity or educational attainment moderate this relation, although educational attainment was observed to moderate the relationship between grey matter volume and cognitive performance. We interpret these findings in terms of the modest explanatory value of currently estimated polygenic scores accounting for variation in cognitive performance (~5%), making potential mediating and moderating variables difficult to confirm.

Few temporally distributed brain connectivity states predict human cognitive abilities

Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10 min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual's network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.

A massive dataset of the NeuroCognitive Performance Test, a web-based cognitive assessment.

We present a dataset of approximately 5.5 million subtest scores from over 750,000 adults who completed the NeuroCognitive Performance Test (NCPT; Lumos Labs, Inc.), a validated, self-administered cognitive test accessed via web browser. The dataset includes assessment scores from eight test batteries consisting of 5-11 subtests that collectively span several cognitive domains including working memory, visual attention, and abstract reasoning. In addition to the raw scores and normative data from each subtest, the dataset includes basic demographic information from each participant (age, gender, and educational background). The scale and diversity of the dataset provides an unprecedented opportunity for researchers to investigate population-level variability in cognitive abilities and their relation to demographic factors. To facilitate reuse of this dataset by other researchers, we provide a Python module that supports several common preprocessing steps.

A Machine-Learning Based Approach for Predicting Older Adults’ Adherence to Technology-Based Cognitive Training

Adequate adherence is a necessary condition for success with any intervention, including for computerized cognitive training designed to mitigate age-related cognitive decline. Tailored prompting systems offer promise for promoting adherence and facilitating intervention success. However, developing adherence support systems capable of just-in-time adaptive reminders requires understanding the factors that predict adherence, particularly an imminent adherence lapse. In this study we built machine learning models to predict participants’ adherence at different levels (overall and weekly) using data collected from a previous cognitive training intervention. We then built machine learning models to predict adherence using a variety of baseline measures (demographic, attitudinal, and cognitive ability variables), as well as deep learning models to predict the next week's adherence using variables derived from training interactions in the previous week. Logistic regression models with selected baseline variables were able to predict overall adherence with moderate accuracy (AUROC: 0.71), while some recurrent neural network models were able to predict weekly adherence with high accuracy (AUROC: 0.84-0.86) based on daily interactions. Analysis of the post hoc explanation of machine learning models revealed that general self-efficacy, objective memory measures, and technology self-efficacy were most predictive of participants’ overall adherence, while time of training, sessions played, and game outcomes were predictive of the next week's adherence. Machine-learning based approaches revealed that both individual difference characteristics and previous intervention interactions provide useful information for predicting adherence, and these insights can provide initial clues as to who to target with adherence support strategies and when to provide support. This information will inform the development of a technology-based, just-in-time adherence support systems.

Links between personality traits and problem-solving performance in zebra finches (Taeniopygia guttata).

Consistent individual differences in behaviour across time or contexts (i.e. personality types) have been found in many species and have implications for fitness. Likewise, individual variation in cognitive abilities has been shown to impact fitness. Cognition and personality are complex, multidimensional traits. However, previous work has generally examined the connection between a single personality trait and a single cognitive ability, yielding equivocal results. Links between personality and cognitive ability suggest that behavioural traits coevolved and highlight their nuanced connections. Here we examined individuals' performance on multiple personality tests and repeated problem-solving tests (each measuring innovative performance). We assessed behavioural traits (dominance, boldness, activity, risk-taking, aggressiveness and obstinacy) in 41 captive zebra finches. Birds' scores for boldness and obstinacy were consistent over two years. We also examined whether personality correlated with problem-solving performance on repeated tests. Our results indicate that neophobia, dominance and obstinacy were related to successful solving, and less dominant, more obstinate birds solved the tasks quicker on average. Our results indicate the importance of examining multiple measures over a long period. Future work that identifies links between personality and innovation in non-model organisms may elucidate the coevolution of these two forms of individual differences.

Individual differences in spatial learning are correlated across tasks but not with stress response behaviour in guppies

Cognition is vital for carrying out behaviours required for survival and reproduction. Cognitive performance varies between species, but also between individuals within populations. While variation is a prerequisite for natural selection, selection does not act on traits in isolation. The extent to which cognitive traits covary with other aspects of phenotype (e.g. personality traits) may be important in shaping evolutionary dynamics. Here we adopted a multivariate approach to test spatial learning in male Poecilia reticulata and asked whether differences in cognitive performance are associated with (repeatable) differences in stress response behaviour. Functional links between cognitive traits and ‘stress coping style’ have been hypothesized. Furthermore, individual level studies of cognitive performance typically rely on multiple testing paradigms that may themselves be a stressor. There is a risk that variation in stress responsiveness is itself a cause of apparent, but artefactual variance in cognitive ability. Fish repeatedly experienced two spatial learning tasks (maze layouts) and an acute stress response test (open field trial). We found repeatable differences between individuals in performance within and across maze layouts. On average, performance improved with experience in the first maze, consistent with spatial learning, but not in the second. Individuals varied in the trajectory of mean performance with trial number in both mazes, suggesting they differ in ‘learning rate’. Acute stress response behaviour was repeatable but predicted neither average time to solve the maze nor learning rate. We found no support for between-individual correlation between acute stress response and cognitive performance. However, we highlight the possibility that cumulative, chronic stress effects may nevertheless cause declines in performance for some individuals (leading to lack of improvement in mean time to solve the second maze). If so, this may represent a pervasive but difficult challenge for our ability to robustly estimate learning rates in studies of animal cognition.

Probability Weighting and Cognitive Ability

Probability weighting is a major concept for accommodating systematic departures from expected utility theory. We examine the relation between probability weighting and cognitive ability with two experiments: one recruiting subjects with a large variation in cognitive ability and the other using the within-subject manipulation of time constraints in lottery choices and cognitive tests. We find a significant association between likelihood insensitivity—the cognitive component of probability weighting—and cognitive limitation such that subjects with a lower cognitive score or more interrupted cognition due to time pressure respond less discriminately to intermediate probabilities and more over-sensitively to extreme probabilities. Our findings shed light on the sources of anomalous choices against expected utility theory. This paper was accepted by Yan Chen, behavioral economics and decision analysis.

Bold and bright: shy and supple? The effect of habitat type on personality-cognition covariance in the Aegean wall lizard (Podarcis erhardii).

Animals exhibit considerable and consistent among-individual variation in cognitive abilities, even within a population. Recent studies have attempted to address this variation using insights from the field of animal personality. Generally, it is predicted that animals with "faster" personalities (bolder, explorative, and neophilic) should exhibit faster but less flexible learning. However, the empirical evidence for a link between cognitive style and personality is mixed. One possible reason for such conflicting results may be that personality-cognition covariance changes along ecological conditions, a hypothesis that has rarely been investigated so far. In this study, we tested the effect of habitat complexity on multiple aspects of animal personality and cognition, and how this influenced their relationship, in five populations of the Aegean wall lizard (Podarcis erhardii). Overall, lizards from both habitat types did not differ in average levels of personality or cognition, with the exception that lizards from more complex habitats performed better on a spatial learning task. Nevertheless, we found an intricate interplay between ecology, cognition, and personality, as behavioral associations were often habitat- but also year-dependent. In general, behavioral covariance was either independent of habitat, or found exclusively in the simple, open environments. Our results highlight that valuable insights may be gained by taking ecological variation into account while studying the link between personality and cognition.

The Heritability of Cognitive Aging: A Systematic Review of Longitudinal Twin Studies

Heritability of cognitive ability changes across late adulthood, although whether genetic variance increases or decreases in importance is not understood well. We performed a systematic review of the heritability of cognitive ability derived from longitudinal twin studies of middle-aged and older adult twins. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, articles were identified in APA PsycINFO and Clarivate Web of Science electronic databases. Identified articles were screened by title and abstract; remaining full-text articles were then fully evaluated. Reference sections served as an additional method for identification of relevant articles. In total, 3,106 articles were identified and screened, 28 of which were included and were based on data from 10 longitudinal twin studies published from 1994-2021. There are large genetic influences on an initial level of cognitive performance across domains whereas there are small to moderate genetic influences on change in performance with age. Evidence was less definitive about whether the same or different genetic factors contribute to both level and change. Non-shared environmental influences appeared to drive individual changes in cognitive performance. Heritability tended to either be stable or decline after 65 years, possibly because of the increasing importance of non-shared environmental influences on cognitive ability. Recent studies report increases in heritability across specific subtests and domains. Shared environmental variance accounted for little variance in cognitive ability. Emerging research questions and future directions for understanding genetic and environment influences in the context of gene-environment interplay are highlighted in this review.

Decisional capacity in young people with first episode psychosis, major depressive disorder and no mental disorder

We aimed to (1) examine decisional capacity for treatment in young people (aged 15 to 25 years) with first-episode psychosis (FEP), Major Depressive Disorder (MDD) and no mental disorder, and (2) determine which theoretically relevant factors are associated with, and predict decisional capacity. We assessed decisional capacity (using MacArthur Competence Assessment Tool-Treatment; MacCAT-T), cognitive abilities, insight and symptom severity in young people with no mental disorder (n = 38), MDD (n = 38) and FEP (n = 18) from inpatient and outpatient services. Most young people with MDD (84.2%) or no mental disorder (86.8%) had adequate decisional capacity to consent to treatment based on recommended cut-off scores, compared with fewer than half of the those with FEP (44.4%). Levels of capacity were not significantly different between young people with MDD and those with no mental disorder (p = .861). However, young people with FEP demonstrated significantly poorer decisional capacity than those with no mental disorder (p = .006) and MDD (p = .009). A hierarchical regression analysis suggested that differences may be better explained by variation in cognitive ability, especially thematic verbal recall. Greater symptom severity and poorer insight were associated with poorer decisional capacity for FEP (p = .008 and p < .001, respectively), but not MDD (p = .050 and p = .805, respectively). Cognitive performance (i.e., predicted IQ, processing speed, mental flexibility and thematic verbal memory) collectively explained 36.6% of the variance in decisional capacity (p < .001). Thematic verbal memory was the strongest predictor of decisional capacity (p < .001). Supports for memory should be implemented to facilitate involvement in treatment decisions during the early course of illness.

Multimodal neuroimaging markers of variation in cognitive ability in older HIV+ men.

This study used converging methods to examine the neural substrates of cognitive ability in middle-aged and older men with well-controlled HIV infection. Seventy-six HIV+ men on antiretroviral treatment completed an auditory oddball task and an inhibitory control (Simon) task while time-locked high-density EEG was acquired; 66 had usable EEG data from one or both tasks; structural MRI was available for 43. We investigated relationships between task-evoked EEG responses, cognitive ability and immunocompromise. We also explored the structural correlates of these EEG markers in the sub-sample with complete EEG and MRI data (N = 27). EEG activity was associated with cognitive ability at later (P300) but not earlier stages of both tasks. Only the oddball task P300 was reliably associated with HIV severity (nadir CD4). Source localization confirmed that the tasks engaged partially distinct circuits. Thalamus volume correlated with oddball task P300 amplitude, while globus pallidus volume was related to the P300 in both tasks. This is the first study to use task-evoked EEG to identify neural correlates of individual differences in cognition in men living with well-controlled HIV infection, and to explore the structural basis of the EEG markers. We found that EEG responses evoked by the oddball task are more reliably related to cognitive performance than those evoked by the Simon task. We also provide preliminary evidence for a subcortical contribution to the effects of HIV infection severity on P300 amplitudes. These results suggest brain mechanisms and candidate biomarkers for individual differences in cognition in HIV.

Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability.

Variation in cognitive ability arises from subtle differences in underlying neural architecture. Understanding and predicting individual variability in cognition from the differences in brain networks requires harnessing the unique variance captured by different neuroimaging modalities. Here we adopted a multi-level machine learning approach that combines diffusion, functional, and structural MRI data from the Human Connectome Project (N = 1050) to provide unitary prediction models of various cognitive abilities: global cognitive function, fluid intelligence, crystallized intelligence, impulsivity, spatial orientation, verbal episodic memory and sustained attention. Out-of-sample predictions of each cognitive score were first generated using a sparsity-constrained principal component regression on individual neuroimaging modalities. These individual predictions were then aggregated and submitted to a LASSO estimator that removed redundant variability across channels. This stacked prediction led to a significant improvement in accuracy, relative to the best single modality predictions (approximately 1% to more than 3% boost in variance explained), across a majority of the cognitive abilities tested. Further analysis found that diffusion and brain surface properties contribute the most to the predictive power. Our findings establish a lower bound to predict individual differences in cognition using multiple neuroimaging measures of brain architecture, both structural and functional, quantify the relative predictive power of the different imaging modalities, and reveal how each modality provides unique and complementary information about individual differences in cognitive function.

Analysis of the Association of Haplotypes with Alzheimer’s Disease and Mild Cognitive Impairment in the Russian Population

Haplotype associations of 894 DNA samples of elderly people from the Russian population of Tomsk were analyzed using 49 SNPs (single nucleotide polymorphisms). As the analyzed markers, SNPs were selected that showed an association with Alzheimer’s disease or variability of cognitive abilities in genome-wide association studies. As a result of the analysis, blocks on chromosomes 1, 2, 6, 8, 11, 19, and 20 were identified. Significant associations were shown, taking into account 50 000 permutations, for haplotypes localized on chromosomes 1 and 19 in the genes CR1 and APOE, APOC1, respectively. Our results confirm the role of the APOE and CR1 genes in the pathogenesis of Alzheimer’s disease in the Russian population.

Differential ageing trajectories in motivation, inhibitory control and cognitive flexibility in Barbary macaques (Macaca sylvanus).

Across the lifespan, the performance in problem-solving tasks varies strongly, owing to age-related variation in cognitive abilities as well as the motivation to engage in a task. Non-human primates provide an evolutionary perspective on human cognitive and motivational ageing, as they lack an insight into their own limited lifetime, and ageing trajectories are not affected by customs and societal norms. To test age-related variation in inhibitory control, cognitive flexibility and persistence, we presented Barbary macaques (Macaca sylvanus), living at La Forêt des Singes in Rocamadour (France), with three problem-solving tasks. We conducted 297 trials with 143 subjects aged 2–30 years. We found no effect of age on success and latency to succeed in the inhibitory control task. In the cognitive flexibility task, 21 out of 99 monkeys were able to switch their strategy, but there was no evidence for an effect of age. Yet, the persistence in the motivation task as well as the overall likelihood to participate in any of the tasks declined with increasing age. These results suggest that motivation declines earlier than the cognitive abilities assessed in this study, corroborating the notion that non-human primates and humans show similar changes in motivation in old age.This article is part of the theme issue ‘Evolution of the primate ageing process'.

Validity of Cognitive Tests for Non-human Animals: Pitfalls and Prospects.

Comparative psychology assesses cognitive abilities and capacities of non-human animals and humans. Based on performance differences and similarities in various species in cognitive tests, it is inferred how their minds work and reconstructed how cognition might have evolved. Critically, such species comparisons are only valid and meaningful if the tasks truly capture individual and inter-specific variation in cognitive abilities rather than contextual variables that might affect task performance. Unlike in human test psychology, however, cognitive tasks for non-human primates (and most other animals) have been rarely evaluated regarding their measurement validity. We review recent studies that address how non-cognitive factors affect performance in a set of commonly used cognitive tasks, and if cognitive tests truly measure individual variation in cognitive abilities. We find that individual differences in emotional and motivational factors primarily affect performance via attention. Hence, it is crucial to systematically control for attention during cognitive tasks to obtain valid and reliable results. Aspects of test design, however, can also have a substantial effect on cognitive performance. We conclude that non-cognitive factors are a minor source of measurement error if acknowledged and properly controlled for. It is essential, however, to validate and eventually re-design several primate cognition tasks in order to ascertain that they capture the cognitive abilities they were designed to measure. This will provide a more solid base for future cognitive comparisons within primates but also across a wider range of non-human animal species.

Cognitive phenotypes and their functional differences in the honey bee, Apis mellifera

Interindividual variation in cognitive ability has been widely reported across a diverse array of taxa and has recently been modelled as a broad consequence of a speed–accuracy trade-off. This trade-off can result in alternative cognitive phenotypes, which in turn could be associated with differences in behavioural and life history traits. While a growing body of literature suggests that performance in certain cognitive and behavioural traits do covary, multiple traits must be measured at these different levels to understand how differences in cognition determine variation in other traits. We tested for the presence of distinct cognitive phenotypes in the honey bee, Apis mellifera, by measuring multiple cognitive traits and determining whether these traits covary in a manner that can be used to define a cognitive axis. Our results indicate the presence of two distinct cognitive phenotypes that meet the predictions of the speed–accuracy trade-off such that fast bees are described by high associative learning, high preference for novelty and high preference for variance, compared to slow bees, and that these differences are consistent across colonies. We then determined whether there were functional differences between these two cognitive phenotypes by measuring their performance in several behavioural and life history traits. We found that fast bees also engaged in more nursing behaviour and transitioned to becoming foragers at an earlier age but we detected no difference in their social behaviour or survival. We discuss our results in terms of their possible implications for social behaviour and the pace-of-life hypothesis.

Early learning in the common marmoset (Callithrix jacchus): Behavior in the family group is related to preadolescent cognitive performance.

Early environment can have a major impact on development, with family life known to play an important role. Longitudinal studies can therefore help increase our understanding of variance in cognitive abilities in young animals, as well as over time. We followed 22 marmosets (11 male and 11 female) from infancy through to early adolescence. At 3 months old, the marmosets were trained to reliably touch a rewarded stimulus. At 5 months, behavior was observed within the natal group. At 9 months, the marmosets were given a visual discrimination task to assess learning ability. Mann-Whitney U tests found no sex or family size differences in number of errors at 3 or 9 months. While no significant relationships were found between behavior in the family and learning at 3 months, significant negative correlations were found between duration spent in locomotion and learning errors (p = .05), as well as between frequency of calm vocalizations and learning errors (p = .001) at 9 months. A U-shape curve was found between amount of social play and learning at 9 months. Positive family interactions, including moderate amounts of play, as well as calm individual behavior, may therefore be important in learning. This study sheds light on cognitive development in much younger marmosets than previously studied, and helps increase understanding of how individual differences in learning may arise.