Individual Differences In Cognition Research Articles

Making the rich richer: Frontoparietal tDCS enhances transfer effects of a single-session distractor inhibition training on working memory in high capacity individuals but reduces them in low capacity individuals

Working memory (WM) performance depends on the ability to extract relevant while inhibiting irrelevant information from entering the WM storage. This distractor inhibition ability can be trained and is known to induce transfer effects on WM performance. Here we asked whether transfer on WM can be boosted by transcranial direct current stimulation (tDCS) during a single-session distractor inhibition training. As WM performance is ascribed to the frontoparietal network, in which prefrontal areas are associated with inhibiting distractors and posterior parietal areas with storing information, we placed the anode over the prefrontal and the cathode over the posterior parietal cortex during a single-session distractor inhibition training. This network-oriented stimulation protocol should enhance inhibition processes by shifting the neural activity from posterior to prefrontal regions. WM improved after a single-session distractor inhibition training under verum stimulation but only in subjects with a high WM capacity. In subjects with a low WM capacity, verum tDCS reduced the transfer effects on WM. We assume tDCS to strengthen the frontostriatal pathway in individuals with a high WM capacity leading to efficient inhibition of distractors. In contrast, the cathodal stimulation of the posterior parietal cortex might have hindered usual compensational mechanism in low capacity subjects, i.e. maintaining also irrelevant information in memory. Our results thus stress the need to adjust tDCS protocols to well-founded knowledge about neural networks and individual cognitive differences.

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.

Exploring predictors of translation performance

The study reported on examined the question of whether translators’ cognitive faculties might be able to predict their performance on a translation task. Research on individual cognitive differences in translation has been a new issue in the realm of Translation Studies. However, the majority of previous studies have targeted single cognitive faculties for the purpose of investigation and we therefore know little about how sets of cognitive faculties can affect the process of translation from one language into another. In this study, three individual cognitive differences were targeted: emotional intelligence (EI), tolerance of ambiguity (TA), and working memory (WM). For the purposes of the study, 54 Iranian MA students of Translation Studies were sampled as participants. The participants completed measures of their EI, TA, and WM. In addition, they were requested to translate an excerpt from English (Source Language) into Persian (Target Language). The results of multiple regression analysis indicated that the regression model, incorporating EI, TA, and WM as its predictors, was able to predict a significant amount of variance in participants’ translation performance. Of the three predictors, the contributions of TA and WM to the total variance in the participants’ translation scores were statistically significant, while the contribution of EI to the total variance was not. The findings of the study have some implications for research and practice in the field of Translation Studies.

Personality and reasoning ability during retirement age: Report from a Swedish population-based longitudinal study

We examined the association between personality and level and change in reasoning ability in a population-based sample of older adults (62–68 years) using a three-year annual follow-up longitudinal study design (HEARTS; N = 3851). Personality traits were measured using the Mini-IPIP scale and reasoning using a short form of Raven’s Matrices. Findings from a structural equation model, controlling for age, education, and sex, revealed that higher levels on extraversion, conscientiousness, and neuroticism were associated with lower reasoning ability (βs: −0.17 to −0.09). Higher levels of openness were associated with better reasoning (β: 0.16). We found no association with rate of change. This evidence replicates previous findings demonstrating that personality traits are associated with individual differences in cognition among older adults.

A generative network model of neurodevelopmental diversity in structural brain organization

The formation of large-scale brain networks, and their continual refinement, represent crucial developmental processes that can drive individual differences in cognition and which are associated with multiple neurodevelopmental conditions. But how does this organization arise, and what mechanisms drive diversity in organization? We use generative network modeling to provide a computational framework for understanding neurodevelopmental diversity. Within this framework macroscopic brain organization, complete with spatial embedding of its organization, is an emergent property of a generative wiring equation that optimizes its connectivity by renegotiating its biological costs and topological values continuously over time. The rules that govern these iterative wiring properties are controlled by a set of tightly framed parameters, with subtle differences in these parameters steering network growth towards different neurodiverse outcomes. Regional expression of genes associated with the simulations converge on biological processes and cellular components predominantly involved in synaptic signaling, neuronal projection, catabolic intracellular processes and protein transport. Together, this provides a unifying computational framework for conceptualizing the mechanisms and diversity in neurodevelopment, capable of integrating different levels of analysis—from genes to cognition.

Who benefits from computerized cognitive training? Lower processing speed predicts greater cognitive improvement

• How does baseline cognition influence gains from computerized cognitive training? • Depressed young adults with lower processing speed show greater cognitive gains. • Gains were significantly greater in processing speed tasks. • Training type did not affect the relationship between baseline cognition and gains. Computerized cognitive training (CCT) is an intervention used to target cognitive deficits associated with depression, and has been found to improve mood, cognition, and everyday functioning. The aim of the present study is to determine if baseline cognitive presentation predicts improvement in cognition from CCT, and if this improvement varies based on the type of training received. Forty-six young adults with depressive symptoms were recruited as part of a randomized controlled trial studying the effects of CCT on mood, cognition, and everyday functioning. Participants completed training on their mobile devices for 15 min/day, 5 days/week, for 8 weeks. Clinical and neuropsychological assessments were completed at baseline and 8 weeks. Individuals with below average processing speed (PS) scores showed greater improvement in 2 out of 4 PS tasks (Cohen's d range > 1) compared to above average performers. The high and low performers did not differ in change in performance in non-PS tasks. Type of training had no effect. Small college-aged sample. CCT has differential effects on PS depending on baseline cognitive presentation. Individuals with an initial relative PS weakness improve more from CCT than those with higher baseline functioning. This study highlights the importance of identifying individual differences in cognition that may factor into CCT response.

Cognition in context: Pathways and compound risk in a sample of US non-Hispanic whites

The population of individuals with cognitive impairment and dementia is growing rapidly, necessitating etiological investigation. It is clear that individual differences in cognition later in life have both genetic and multi-level environmental correlates. Despite significant recent progress in cellular and molecular research, the exact mechanisms linking genes, brains, and cognition remain elusive. In relation to cognition, it is unlikely that genetic and environmental risk factors function in a vacuum, but rather interact and cluster together. The purpose of the present study was to examine whether aspects of individual socioeconomic status (SES) explain the cognitive genotype-phenotype association, and whether neighborhood SES modifies the effects of genes and individual SES on cognitive ability. Using data from non-Hispanic White participants in the 2016 wave of the Health and Retirement Study, a national sample of United States adults, we examined links between a polygenic score for general cognition and performance-based cognitive functioning. In a series of weighted linear regressions and formal tests of mediation, we observed a significant genotype-phenotype association that was partially attenuated after including individual education to the baseline model, although little reductions were observed for household wealth or census tract-level percent poverty. These findings suggest that genetic risk for poor cognition is partially explained by education, and this pathway is not modified by poverty-level of the neighborhood.

Accelerated Brain Aging in Amnestic Mild Cognitive Impairment: Relationships with Individual Cognitive Decline, Risk Factors for Alzheimer Disease, and Clinical Progression.

To determine whether a brain age prediction model could quantify individual deviations from a healthy brain-aging trajectory (predicted age difference [PAD]) in patients with amnestic mild cognitive impairment (aMCI) and to determine if PAD was associated with individual cognitive impairment. In this retrospective study, a machine learning approach was trained to determine brain age based on T1-weighted MRI scans. Two datasets were used for model training and testing-the Beijing Aging Brain Rejuvenation Initiative (BABRI) (616 healthy controls and 80 patients with aMCI, 2010-2018) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) (589 healthy controls and 144 patients with aMCI, 2010-2018). A total of 974 healthy controls were used for model training (490 from BABRI and 484 from ADNI; age range, 49-95 years). The trained model was then tested on both healthy controls (126 from BABRI and 105 from ADNI) and patients with aMCI (80 from BABRI and 144 from ADNI) to estimate PAD (predicted age - actual age). Furthermore, the associations between PAD with cognitive impairment, genetic risk factors and pathologic markers of Alzheimer disease (AD), and clinical progression in patients with aMCI were examined using a partial correlation analysis, a two-way analysis of covariance, and a general linear model, respectively. Based on the prediction model, patients with aMCI were found to have higher PADs than those of healthy controls (BABRI: 2.65 ± 4.91 [standard deviation] vs 0.18 ± 4.79 [P < .001]; ADNI: 1.68 ± 5.28 vs 0.05 ± 4.41 [P < .001]). Moreover, the PAD was significantly associated with individual cognitive impairment in several cognitive domains in patients with aMCI (P < .05, corrected). When considering different AD-related risk factors, apolipoprotein E ε4 allele carriers were observed to have higher PADs than noncarriers (3.76 ± 4.82 vs 0.10 ± 5.05; P = .017), and patients with amyloid-positive aMCI were observed to have higher PADs than patients with amyloid-negative status (2.40 ± 5.25 vs 0.93 ± 5.20; P = .003). Finally, PAD combined with other markers of AD at baseline for differentiating between progressive and stable aMCI resulted in an area under the curve value of 0.87. The PAD is a sensitive imaging marker related to individual cognitive differences in patients with aMCI.Keywords: MR Imaging, Brain/Brain Stem, Brain Age, Machine Learning, Mild Cognitive Impairment, Structural MRI Supplemental material is available for this article. © RSNA, 2021.

Thematic and other semantic relations central to abstract (and concrete) concepts.

In this article, we discuss multiple types of meaningful (semantic) relations underlying abstract (as compared to concrete) concepts. We adopt the viewpoint that words act as cues to meaning (Elman in Ment Lexicon 6(1):1-34, 2011; Lupyan and Lewis in Lang Cogn Neurosci 34(10):1319-1337, 2019), which is dependent on the dynamic contents of a comprehender's mental model of the situation. This view foregrounds the importance of both linguistic and real-world context as individuals make sense of words, flexibly access relevant knowledge, and understand described events and situations. We discuss theories of, and experimental work on, abstract concepts through the lens of the importance of thematic and other semantic relations. We then tie these findings to the sentence processing literature in which such meaningful relations within sentential contexts are often experimentally manipulated. In this literature, some specific classes/types of abstract words have been studied, although not comprehensively, and with limited connection to the literature on knowledge underlying abstract concepts reviewed herein. We conclude by arguing that the ways in which humans understand relatively more abstract concepts, in particular, can be informed by the careful study of words presented not in isolation, but rather in situational and linguistic contexts, and as a function of individual differences in knowledge, goals, and beliefs.

Racism in Pain Medicine: We Can and Should Do More

Racism in Pain Medicine: We Can and Should Do More

Human Population Variability in Cortical Anatomy and its Impact on Neuroimaging-Based Prediction of Mental Traits

In building models to predict individual differences in cognition and mental health using neuroimaging data, a critical analytic stage is extracting measures from a common brain region of interest. However, the impacts that these analytic choices may have on results are less known.

Life History Is a Major Source of Adaptive Individual and Species Differences: a Critical Commentary on Zietsch and Sidari (2020)

Zietsch and Sidari (2020) argue that life history theory does not offer an appropriate model for understanding inter-individual trait covariation. We examine in great detail four major claims of Zietsch and Sidari’s critique of life history theory as an explanation of inter-individual covariation among phenotypic, especially psychometric, traits. We review relevant theoretical and empirical research overlooked by Zietsch and Sidari and conduct reanalyses of comparative cross-species “pace-of-life” and human behaviour-genetic data. Our results challenge their arguments concerning (a) the implications for life history theory of the absence of individual-differences-level covariation in a variety of species and (b) the genetic aetiology of psychometric life history covariance. None of the four major claims made by Zietsch and Sidari withstand close scrutiny. Critiques of life history theory, such as the one developed by Zietsch and Sidari, should (if they are to be more persuasive) show what alternative theory might be at least as successful as life history theory in consiliently and parsimoniously explaining patterns of covariation among various traits within and between species, including the predictable absence of such covariation among certain traits. Evolutionary life history theory—as applied to non-human animals and humans—offers one of the broadest and empirically best-supported frameworks for understanding individual differences in physiology, cognition, behaviour, reproduction, and other traits and life outcomes.

Distinct Regionalization Patterns of Cortical Morphology are Associated with Cognitive Performance Across Different Domains.

Cognitive performance in children is predictive of academic and social outcomes; therefore, understanding neurobiological mechanisms underlying individual differences in cognition during development may be important for improving quality of life. The belief that a single, psychological construct underlies many cognitive processes is pervasive throughout society. However, it is unclear if there is a consistent neural substrate underlying many cognitive processes. Here, we show that a distributed configuration of cortical surface area and apparent thickness, when controlling for global imaging measures, is differentially associated with cognitive performance on different types of tasks in a large sample (N = 10 145) of 9-11-year-old children from the Adolescent Brain and Cognitive DevelopmentSM (ABCD) study. The minimal overlap in these regionalization patterns of association has implications for competing theories about developing intellectual functions. Surprisingly, not controlling for sociodemographic factors increased the similarity between these regionalization patterns. This highlights the importance of understanding the shared variance between sociodemographic factors, cognition and brain structure, particularly with a population-based sample such as ABCD.

Individual Uniqueness in the Neonatal Functional Connectome.

The functional connectome is highly distinctive in adults and adolescents, underlying individual differences in cognition and behavior. However, it remains unknown whether the individual uniqueness of the functional connectome is present in neonates, who are far from mature. Here, we utilized the multiband resting-state functional magnetic resonance imaging data of 40 healthy neonates from the Developing Human Connectome Project and a split-half analysis approach to characterize the uniqueness of the functional connectome in the neonatal brain. Through functional connectome-based individual identification analysis, we found that all the neonates were correctly identified, with the most discriminative regions predominantly confined to the higher-order cortices (e.g., prefrontal and parietal regions). The connectivities with the highest contributions to individual uniqueness were primarily located between different functional systems, and the short- (0-30mm) and middle-range (30-60mm) connectivities were more distinctive than the long-range (>60mm) connectivities. Interestingly, we found that functional data with a scanning length longer than 3.5min were able to capture the individual uniqueness in the functional connectome. Our results highlight that individual uniqueness is present in the functional connectome of neonates and provide insights into the brain mechanisms underlying individual differences in cognition and behavior later in life.

PRIME: A Personalized Recommender System for Information Visualization Methods via Extended Matrix Completion

Adapting user interface designs for specific tasks performed by different users is a challenging yet important problem. Automatically adapting visualization designs to users and contexts (e.g., tasks, display devices, environments, etc.) can theoretically improve human–computer interaction to acquire insights from complex datasets. However, effectiveness of any specific visualization is moderated by individual differences in knowledge, skills, and abilities for different contexts. A modeling framework called P ersonalized R ecommender System for I nformation visualization M ethods via E xtended matrix completion (PRIME) is proposed for recommending the optimal visualization designs for individual users in different contexts. PRIME quantitatively models covariates (e.g., psychological and behavioral measurements) to predict recommendation scores (e.g., perceived complexity, mental workload, etc.) for users to adapt the visualization specific to the context. An evaluation study was conducted and showed that PRIME can achieve satisfactory recommendation accuracy for adapting visualization, even when there are limited historical data. PRIME can make accurate recommendations even for new users or new tasks based on historical wearable sensor signals and recommendation scores. This capability contributes to designing a new generation of visualization systems that will adapt to users’ states. PRIME can support researchers in reducing the sample size requirements to quantify individual differences, and practitioners in adapting visualizations according to user states and contexts.

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.

From Genome-Wide to Environment-Wide: Capturing the Environome

Genome-wide association (GWA) studies have shown that genetic influences on individual differences in affect, behavior, and cognition are driven by thousands of DNA variants, each with very small effect sizes. Here, we propose taking inspiration from GWA studies for understanding and modeling the influence of the environment on complex phenotypes. We argue that the availability of DNA microarrays in genetic research is comparable with the advent of digital technologies in psychological science that enable collecting rich, naturalistic observations in real time of the environome, akin to the genome. These data can capture many thousand environmental elements, which we speculate each influence individual differences in affect, behavior, and cognition with very small effect sizes, akin to findings from GWA studies about DNA variants. We outline how the principles and mechanisms of genetic influences on psychological traits can be applied to improve the understanding and models of the environome.

A Review of the Effects of Valenced Odors on Face Perception and Evaluation.

How do valenced odors affect the perception and evaluation of facial expressions? We reviewed 25 studies published from 1989 to 2020 on cross-modal behavioral effects of odors on the perception of faces. The results indicate that odors may influence facial evaluations and classifications in several ways. Faces are rated as more arousing during simultaneous odor exposure, and the rated valence of faces is affected in the direction of the odor valence. For facial classification tasks, in general, valenced odors, whether pleasant or unpleasant, decrease facial emotion classification speed. The evidence for valence congruency effects was inconsistent. Some studies found that exposure to a valenced odor facilitates the processing of a similarly valenced facial expression. The results for facial evaluation were mirrored in classical conditioning studies, as faces conditioned with valenced odors were rated in the direction of the odor valence. However, the evidence of odor effects was inconsistent when the task was to classify faces. Furthermore, using a z-curve analysis, we found clear evidence for publication bias. Our recommendations for future research include greater consideration of individual differences in sensation and cognition, individual differences (e.g., differences in odor sensitivity related to age, gender, or culture), establishing standardized experimental assessments and stimuli, larger study samples, and embracing open research practices.

Developmental Coupling of Cerebral Blood Flow and fMRI Fluctuations in Youth

To support brain development during youth, the brain must balance energy delivery and consumption. Previous studies in adults have demonstrated high coupling between cerebral blood flow and brain function as measured using functional neuroimaging, but how this relationship evolves over adolescence is unknown. To address this gap, we studied a sample of 831 children and adolescents (478 females, ages 8-22) from the Philadelphia Neurodevelopmental Cohort who were scanned at 3T with both arterial spin-labeled (ASL) MRI and resting-state functional MRI (fMRI). Local coupling between cerebral blood flow (CBF, from ASL) and the amplitude of low frequency fluctuations (ALFF, from fMRI) was first quantified using locally weighted regressions on the cortical surface. We then used generalized additive models to evaluate how CBF-ALFF coupling was associated with age, sex, and executive function. Enrichment of effects within canonical functional networks was evaluated using spin-based permutation tests. Our analyses revealed tight CBF-ALFF coupling across the brain. Whole-brain CBF-ALFF coupling decreased with age, largely driven by coupling decreases in the inferior frontal cortex, precuneus, visual cortex, and temporoparietal cortex (pfdr<0.05). Furthermore, we found that females had stronger coupling in the frontoparietal network than males (pfdr<0.05) and that better executive function was associated with decreased coupling in the somatomotor network (pfdr<0.05). These results emphasize that CBF-ALFF coupling undergoes development in youth and is linked to individual differences in cognition. As such, this relationship may be a valuable marker of both normal brain development and abnormalities associated with neuropsychiatric illness in youth.

К ВОПРОСУ ОБ ОРГАНИЗАЦИИ ПСИХОЛЕКСИЧЕСКОГО ИССЛЕДОВАНИЯ РУССКОЙ ТАКСОНОМИИ ЧЕРТ

According the psycho-lexical hypothesis, any substantial individual differences in human behavior, emo-tions, cognition and motivation are supposed to be reflected in the language, which means that by analyzing the language one will be able to identify these differences. Further, using the factor analysis of self-reported individual differences, it is possible to identify the structure of personality traits. For example, based on a psycho-lexical study of English descriptors, the Big Five has been articulated. Meanwhile, the Russian taxonomy of personality traits has remained understudied. In the present article, we discuss some aspects of the organization of a psycho-lexical study. First, we analyze possible sources for the initial analysis of a lan-guage: free self-descriptions, everyday talks, dictionaries, etc. A conclusion is made about the advantage of using lists, normally dictionaries, compiled by professional linguists. In our future study, we also plan to use a similar list, namely, a corpus of the Russian language. Second, possible parts of speech taken as a ba-sis for the analysis of words are discussed. Usually researchers focus on adjectives, less often they add nouns to the list. However, the analysis of scientific literature shows the need to use all possible parts of speech for such a study. In our research, we plan to use adjectives, nouns, adverbs, and verbs. Third, fea-tures of German and Dutch word selection methodologies are analyzed. We conclude that the German methodology may be preferable to the Dutch methodology in some aspects; therefore, we plan to employ the former in our study. Finally, the need for a psycho-lexical study in Russian and its blueprint are dis-cussed.