Genetic variation, brain, and intelligence differences

Frames of Mind: The Theory of Multiple Intelligences

Klitgaard, R. E., & Gardner, H. (1984) · Journal of Policy Analysis and Management · doi:10.2307/3324560 · Cited by 13,639

Executive functions

Diamond, A. (2013) · Annual Review of Psychology · doi:10.1146/annurev-psych-113011-143750 · Cited by 13,268

"General Intelligence," Objectively Determined and Measured

Spearman, C. (1904) · The American Journal of Psychology · doi:10.2307/1412107 · Cited by 6,207

Human Cognitive Abilities: A Survey of Factor-Analytic Studies

Carroll, J. B. (1993) · doi:10.1017/cbo9780511571312 · Cited by 4,121 · cited 3×

The Bell Curve: Intelligence and Class Structure in American Life.

Preston, S. H., Herrnstein, R. J., & Murray, C. A. (1995) · Population and Development Review · doi:10.2307/2137757 · Cited by 4,027

The mismeasure of man

(1996) · Choice Reviews Online · doi:10.5860/choice.34-2458 · Cited by 3,746

Human cognitive abilities a survey of factor-analytic studies

Carroll, J. B. (2004) · Cited by 3,395

Intelligence: Knowns and unknowns.

Neisser, U., Boodoo, G., Bouchard, T. J., Boykin, A. W., Brody, N., Ceci, S. J., Halpern, D. F., Loehlin, J. C., Perloff, R., Sternberg, R. J., et al. (1996) · American Psychologist · doi:10.1037/0003-066x.51.2.77 · Cited by 2,913

The g factor: the science of mental ability

(1998) · Choice Reviews Online · doi:10.5860/choice.36-2443 · Cited by 2,763

Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals

23andMe Research Team, Lee, J. J., COGENT (Cognitive Genomics Consortium), Social Science Genetic Association Consortium, Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., Nguyen-Viet, T. A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0147-3 · Cited by 2,470

Timing of lifespan influences on brain and cognition

Walhovd, K. B., Lövden, M., & Fjell, A. M. (2023) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.07.001 · Cited by 112

The impact of digital media on children’s intelligence while controlling for genetic differences in cognition and socioeconomic background

Sauce, B., Liebherr, M., Judd, N., & Klingberg, T. (2022) · Scientific Reports · doi:10.1038/s41598-022-11341-2 · Cited by 74

Personality Neuroscience: An Emerging Field With Bright Prospects

DeYoung, C. G., Beaty, R. E., Genç, E., Latzman, R. D., Passamonti, L., Servaas, M. N., Shackman, A. J., Smillie, L. D., Spreng, R. N., Viding, E. M., et al. (2022) · Personality Science · doi:10.5964/ps.7269 · Cited by 69

Fluid intelligence and the locus coeruleus–norepinephrine system

Tsukahara, J. S., & Engle, R. W. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2110630118 · Cited by 56

Associations between Socioeconomic Status, Cognition, and Brain Structure: Evaluating Potential Causal Pathways Through Mechanistic Models of Development

Thomas, M. S. C., & Dündar‐Coecke, S. (2023) · Cognitive Science · doi:10.1111/cogs.13217 · Cited by 45

Multivariate genetic analysis of personality and cognitive traits reveals abundant pleiotropy

Hindley, G., Shadrin, A. A., van der Meer, D., Parker, N., Cheng, W., O’Connell, K. S., Bahrami, S., Lin, A., Karadag, N., Holen, B., et al. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01630-9 · Cited by 37

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

Yeung, H. W., Stolicyn, A., Buchanan, C. R., Tucker–Drob, E. M., Bastin, M. E., Luz, S. F., McIntosh, A. M., Whalley, H. C., Cox, S. R., & Smith, K. (2022) · Human Brain Mapping · doi:10.1002/hbm.26182 · Cited by 27

Interindividual Differences in Cognitive Variability Are Ubiquitous and Distinct From Mean Performance in a Battery of Eleven Tasks

Judd, N., Aristodemou, M., Klingberg, T., & Kievit, R. (2024) · Journal of Cognition · doi:10.5334/joc.371 · Cited by 27

Using DNA to predict intelligence

von Stumm, S., & Plomin, R. J. (2021) · Intelligence · doi:10.1016/j.intell.2021.101530 · Cited by 33

Brain and cognitive ageing: The present, and some predictions (…about the future)

Cox, S. R., & Deary, I. J. (2022) · Aging Brain · doi:10.1016/j.nbas.2022.100032 · Cited by 29

The biological basis of intelligence: Benchmark findings

Hilger, K., Spinath, F. M., Troche, S. J., & Schubert, A. (2022) · Intelligence · doi:10.1016/j.intell.2022.101665 · Cited by 24

Brain aging differs with cognitive ability regardless of education

Walhovd, K., Nyberg, L., Lindenberger, U., Amlien, I., Sørensen, Ø., Wang, Y.-P., Mowinckel, A., Kievit, R., Ebmeier, K. P., Bartrés-Faz, D., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-17727-6 · Cited by 24

Schooling substantially improves intelligence, but neither lessens nor widens the impacts of socioeconomics and genetics

Judd, N., Sauce, B., & Klingberg, T. (2022) · npj Science of Learning · doi:10.1038/s41539-022-00148-5 · Cited by 27

Genetically proxied lean mass and risk of Alzheimer’s disease: mendelian randomisation study

Daghlas, I., Nassan, M. M., & Gill, D. (2023) · BMJ Medicine · doi:10.1136/bmjmed-2022-000354 · Cited by 23

Genetic evidence that the causal association of educational attainment with reduced risk of Alzheimer's disease is driven by intelligence

Thorp, J. G., Mitchell, B. L., Gerring, Z. F., Ong, J., Gharahkhani, P., Derks, E. M., & Lupton, M. K. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.07.011 · Cited by 19

Effect of strategic memory advanced reasoning training (SMART) therapy for enhancing final-year high school students career choices

Mahmood, A., Rehman, N., Huang, X., & Zamani, N. (2025) · BMC Psychology · doi:10.1186/s40359-025-02767-0 · Cited by 17

Mutation or not, what directly establishes a neoplastic state, namely cellular immortality and autonomy, still remains unknown and should be prioritized in our research

Zhu, S., Wang, J., Zellmer, L., Xu, N., Liu, M., Hu, Y., Ma, H., Deng, F., Yang, W., & Liao, D. J. (2022) · Journal of Cancer · doi:10.7150/jca.72628 · Cited by 22

Innate ability, health, motivation, and social capital as predictors of students’ cognitive, affective and psychomotor learning outcomes in secondary schools

Valentine Joseph Owan, P. D., Ekpenyong, J. A., Chuktu, O., Asuquo, M. E., Ogar, J. O., Owan, M. V., & Okon, S. A. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1024017 · Cited by 43

A General Cognitive Ability Factor for the UK Biobank

Williams, C. M., Labouret, G., Wolfram, T., Peyre, H., & Ramus, F. (2023) · Behavior Genetics · doi:10.1007/s10519-022-10127-6 · Cited by 18

Educational attainment polygenic scores, socioeconomic factors, and cortical structure in children and adolescents

Merz, E. C., Strack, J., Hurtado, H., Vainik, U., Thomas, M. L., Evans, A. C., & Khundrakpam, B. S. (2022) · Human Brain Mapping · doi:10.1002/hbm.26034 · Cited by 21

A multi-faceted role of dual-state dopamine signaling in working memory, attentional control, and intelligence

Matzel, L. D., & Sauce, B. (2023) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2023.1060786 · Cited by 17

Life-course neighbourhood deprivation and brain structure in older adults: the Lothian Birth Cohort 1936

Baranyi, G., Buchanan, C. R., Conole, E. L. S., Backhouse, E. V., Maniega, S. M., Hernández, M. D. C. V., Bastin, M. E., Wardlaw, J. M., Deary, I. J., Cox, S. R., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02591-9 · Cited by 18

Robust associations between white matter microstructure and general intelligence

Stammen, C., Fraenz, C., Grazioplene, R. G., Schlüter, C., Merhof, V., Johnson, W., Güntürkün, O., DeYoung, C. G., & Genç, E. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhac538 · Cited by 17

Multivariate prediction of cognitive performance from the sleep electroencephalogram

Ujma, P. P., Bódizs, R., Dresler, M., Simor, P., Purcell, S., Stone, K. L., Yaffe, K. S., & Redline, S. S. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120319 · Cited by 18

Combined repetitive transcranial magnetic stimulation and gut microbiota modulation through the gut–brain axis for prevention and treatment of autism spectrum disorder

Feng, P., Zhang, Y., Zhao, Y., Zhao, P., & Li, E. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1341404 · Cited by 14

Anticholinergic burden in middle and older age is associated with lower cognitive function, but not with brain atrophy

Mur, J., Marioni, R. E., Russ, T. C., Terrera, G. M., & Cox, S. R. (2023) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.15698 · Cited by 11

Structural architecture and brain network efficiency link polygenic scores to intelligence

Genç, E., Metzen, D., Fraenz, C., Schlüter, C., Voelkle, M. C., Arning, L., Streit, F., Nguyen, H. P., Güntürkün, O., Ocklenburg, S., et al. (2023) · Human Brain Mapping · doi:10.1002/hbm.26286 · Cited by 15

The Causal Relationships Between Gut Microbiota, Brain Volume, and Intelligence: A Two-Step Mendelian Randomization Analysis

Yao, S., Han, J.-Z., Guo, J., Wang, X., Qian, L., Wu, H., Shi, W., Zhu, R., Wang, J.-H., Dong, S., et al. (2024) · Biological Psychiatry · doi:10.1016/j.biopsych.2024.02.1012 · Cited by 13

Ian Deary and Robert Sternberg answer five self-inflicted questions about human intelligence

Deary, I. J., & Sternberg, R. J. (2021) · Intelligence · doi:10.1016/j.intell.2021.101539 · Cited by 16

Evolving networks of human intelligence

Savi, A. O., Marsman, M., & van der Maas, H. L. J. (2021) · Intelligence · doi:10.1016/j.intell.2021.101567 · Cited by 11

Brain volumes, thicknesses, and surface areas as mediators of genetic factors and childhood adversity on intelligence

Williams, C. M., Peyre, H., & Ramus, F. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac468 · Cited by 10

Cognitive predictors of mental health trajectories are mediated by inferior frontal and occipital development during adolescence

Li, Q., Cao, M., Stein, D. J., Sahakian, B. J., Jia, T., Langley, C., Gu, Z., Hou, W., Han, L., Cao, L., et al. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-02912-6 · Cited by 11

The Contribution of Mental Capacity to Personality Formulation. An Overview

Antonopoulou, H. (2024) · Technium Education and Humanities · doi:10.47577/teh.v7i.9720 · Cited by 10

Interrelationships between polygenic risk scores, cognition, symptoms, and functioning in first-episode psychosis: A network analysis approach.

Gil-Berrozpe, G., Segura, À., Sánchez-Torres, A., Amoretti, S., Giné-Servén, E., Vieta, E., Mezquida, G., Lobo, A., González-Pinto, A., Andreu-Bernabeu, Á., et al. (2024) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2024.12.002 · Cited by 11

Effects of gene dosage on cognitive ability: A function-based association study across brain and non-brain processes

Huguet, G., Renne, T., Poulain, C., Dubuc, A., Kumar, K., Kazem, S., Engchuan, W., Shanta, O., Douard, E. A., Proulx, C., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100721 · Cited by 8

Assessment of primary schoolchildren’s fluid intelligence using Raven's Colored progressive matrices: A pilot cross-sectional study in Lebanon

Manana, M., Ghanem, D., Tarhini, S., Ismaiil, L. A., Sabra, R., & Hatem, G. (2023) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2023.19.3.1801 · Cited by 10

A new beginning of intelligence research. Designing the playground

Protzko, J., & Colom, R. (2021) · Intelligence · doi:10.1016/j.intell.2021.101559 · Cited by 8

Polygenic indices for cognition in healthy aging; the role of brain measures

Tsapanou, A., Mourtzi, N., Gu, Y., Habeck, C. G., Belsky, D. W., & Stern, Y. (2022) · Neuroimage Reports · doi:10.1016/j.ynirp.2022.100153 · Cited by 8

Education and Social Science

Paterson, L. (2022) · Scottish Affairs · doi:10.3366/scot.2022.0430 · Cited by 8

Neurocognitive Aging

Cox, S. R. (2024) · Annual Review of Developmental Psychology · doi:10.1146/annurev-devpsych-010923-102441 · Cited by 12

Rethinking personhood and agency: how AI challenges human-centered concepts

Gao, L., & Mvondo, G. F. N. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1717828 · Cited by 7

The impact of early adversity on the cerebral cortex - a Mendelian randomization study

Wang, Z., Zou, J., Zhang, L., Ning, J., Zhang, X., Jiang, B., Liang, Y., & Zhang, Y. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1283159 · Cited by 6

A new look at the relations between attachment and intelligence

Giudice, M. D., & Haltigan, J. D. (2022) · Developmental Review · doi:10.1016/j.dr.2022.101054 · Cited by 8

Cognitive variability is ubiquitous and distinct from mean performance across eleven tasks with over 7 million trialsPreprint

Judd, N., Aristodemou, M., Klingberg, T., & Kievit, R. (2023) · doi:10.31234/osf.io/b29rn · Cited by 7

Intersectional Inequalities in the Transition to Grandparenthood and Cognitive Functioning: A Longitudinal Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy

Alonso-Pérez, E., Heisig, J. P., Kreyenfeld, M. R., Gellert, P. R., & O’Sullivan, J. L. (2024) · The Journals of Gerontology Series B · doi:10.1093/geronb/gbae088 · Cited by 6

Toward a Molecular Biology of Human Intelligence: Psychometrics Meets Gene Expressions and Brain Metabolism

Gargus, J. J., & Haier, R. J. (2025) · Intelligence & Cognitive Abilities · doi:10.65550/001c.146520 · Cited by 6

Shared genetic architecture between ADHD and intelligence varies across ADHD subtypes

Zhao, Q., Yang, K., Liu, M., Shi, Z., Zhao, J., Wu, Y. H., Wu, Q., Zhai, Y., Xu, J., Zhang, Z., et al. (2026) · BMC Medicine · doi:10.1186/s12916-026-04998-3 · Cited by 6

Cognition and beyond: Intersections of personality traits and language

Li, B., & Huang, H.-W. (2024) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/bs.plm.2024.03.003 · Cited by 6

Brain volumes and cortical thickness and associations with cognition in children born extremely preterm

Kvanta, H., Padilla, N., Nosko, D., Mårtensson, G., Broström, L., de Gamarra‐Oca, L. F., Bolk, J., & Ådén, U. (2024) · Pediatric Research · doi:10.1038/s41390-024-03480-1 · Cited by 6

Biomarkers of preschool children with autism spectrum disorder: quantitative analysis of whole-brain tissue component volumes, intelligence scores, ADOS-CSS, and ages of first-word production and walking onset

Zhou, X., Lin, W.-S., Zou, F., Zhong, S., Deng, Y., Luo, X., Shen, L., Wang, S.-H., & Guo, R. (2024) · World Journal of Pediatrics · doi:10.1007/s12519-024-00800-7 · Cited by 5

Aging Brain from a Lifespan Perspective

Fjell, A. M. (2024) · Current topics in behavioral neurosciences · doi:10.1007/7854_2024_476 · Cited by 7

Examining the neurostructural architecture of intelligence: The Lothian Birth Cohort 1936 study

Page, D., Buchanan, C. R., Moodie, J. E., Harris, M. A., Taylor, A. M., Hernández, M. V., Maniega, S. M., Corley, J., Bastin, M. E., Wardlaw, J. M., et al. (2024) · Cortex · doi:10.1016/j.cortex.2024.06.007 · Cited by 4

Openness/Intellect: A Unique Trait Requires Unique Considerations

Schwaba, T., & Thalmayer, A. G. (2025) · Personality and Social Psychology Review · doi:10.1177/10888683251377227 · Cited by 15

Integration of multiomic and multi-phenotypic data identifies biological pathways associated with physical fitness

Alizadeh, A., Graf, J. F., Misner, M. J., Burns, A., Ginty, F., O’Donovan, K. J., Wickiser, J. K., Barringer, N., Freisinger, G. M., Hermansen, N., et al. (2026) · Communications Biology · doi:10.1038/s42003-026-09663-2 · Cited by 4

Interactive roles of preterm-birth and socioeconomic status in cortical thickness of language-related brain structures: Findings from the Adolescent Brain Cognitive Development (ABCD) study

Nolte, C., Michalska, K. J., Nelson, P. M., & Demir‐Lira, Ö. E. (2024) · Cortex · doi:10.1016/j.cortex.2024.05.024 · Cited by 4

Why Some Mice Are Smarter than Others: The Impact of Bone Morphogenetic Protein Signaling on Cognition

Bonds, J. A., Tunc‐Ozcan, E., Dunlop, S. R., Rawat, R., Peng, C., & Kessler, J. A. (2023) · eNeuro · doi:10.1523/eneuro.0213-22.2022 · Cited by 4

A partially functional linear regression framework for integrating genetic, imaging, and clinical data

Li, T., Yu, Y., Marron, J. S., & Hongtu Zhu, B. (2024) · The Annals of Applied Statistics · doi:10.1214/23-aoas1808 · Cited by 4

Intrauterine chromium exposure and cognitive developmental delay: The modifying effect of genetic predisposition

Jia, Z., Zhang, H., Lv, Y., Yu, L., Yuan, C., Zhang, L., Yang, C., Liu, H., Zheng, T., Xia, W., et al. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.174350 · Cited by 5

The Role of Bioengineering in Healthcare

Dhivya, S., Areche, F. O., Kumar, B., Hariprabhu, M., & Mutha, S. (2023) · Advances in chemical and materials engineering book series · doi:10.4018/978-1-6684-7412-9.ch016 · Cited by 5

SynaptopathyDB: a resource for studying the genetic and synaptic basis of nervous system disordersPreprint

Sorokina, O., Dominic, D., Bayés, À., Armstrong, J. D., & Grant, S. G. N. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.27.661932 · Cited by 4

Brain maps of general cognitive functioning: neuroimaging and neurobiological signatures

Moodie, J. E., Buchanan, C. R., Fürtjes, A. E., Conole, E. L. S., Stolicyn, A., Corley, J., Ferguson, K. J., Hernández, M. V., Maniega, S. M., Russ, T. C., et al. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03617-8 · Cited by 5

Race, intelligence and genetics: colonialism in the era of neurotechnology

Pyrrho, M. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1737069 · Cited by 4

Intelligence in Multiple Dimensions: Evolutionary Framework for the Study of Human Cognition

Geary, D. C. (2026) · Intelligence & Cognitive Abilities · doi:10.65550/001c.154815 · Cited by 3

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

Corley, E., Fahey, L., Fitzgerald, J. C., Holleran, L., Walton, E., Morris, D. W., & Donohoe, G. J. (2023) · Genes Brain & Behavior · doi:10.1111/gbb.12850 · Cited by 2

Two Cheers for the Cognitive Irregulars: Intelligence’s Contributions to Ageing Well and Staying Alive

Deary, I. J. (2021) · Journal of Intelligence · doi:10.3390/jintelligence9030041 · Cited by 4

Transcriptome-wide and Stratified Genomic Structural Equation Modeling Identify Neurobiological Pathways Underlying General and Specific Cognitive FunctionsPreprint

Grotzinger, A. D., de la Fuente, J., Davies, G., Nivard, M. G., & Tucker–Drob, E. M. (2021) · medRxiv · doi:10.1101/2021.04.30.21256409 · Cited by 3

Brain maps of general cognitive function and spatial correlations with neurobiological cortical profilesPreprint

Moodie, J. E., Buchanan, C. R., Fürtjes, A. E., Conole, E. L. S., Stolicyn, A., Corley, J., Ferguson, K. J., Hernández, M. D. C. V., Maniega, S. M., Russ, T. C., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.17.628670 · Cited by 3

Functional Tests Guide Complex “Fidelity“ Tradeoffs in Whole-Brain Emulation

Linssen, C., & Koene, R. A. (2025) · Journal of Ethics and Emerging Technologies · doi:10.55613/jeet.v35i1.152 · Cited by 2

“Those Evenings of the Brain – When Not a Moon Disclose a Sign”

Deary, I. J., Harris, S. E., Fürtjes, A. E., & Cox, S. R. (2026) · Intelligence & Cognitive Abilities · doi:10.65550/001c.154856 · Cited by 3

Common and rare genetic variant associations with cognitive performance across development in British birth cohorts

Malawsky, D. S., Koko, M., Danecek, P., Huang, W., Wootton, O., Huang, Q. Q., Wade, E. E., Lindsay, S., Arden, R., Hurles, M. E., et al. (2026) · Nature Human Behaviour · doi:10.1038/s41562-026-02491-8 · Cited by 6

Intelligence, empathy, and memory: Exploring moral enhancement through gene editing, training, and computer–brain interfaces

Molhoek, B. (2021) · HTS Teologiese Studies / Theological Studies · doi:10.4102/hts.v77i3.6728 · Cited by 2

Islamic Resilience of Marginal School Communities Management in An Effort to Educational Transformation

Suud, F. M., & Armansyah, A. D. (2023) · Afkaruna Indonesian Interdisciplinary Journal of Islamic Studies · doi:10.18196/afkaruna.v19i2.17580 · Cited by 2

Age-related changes of node degree in the multiple-demand network predict fluid intelligence

Yu, L., Zhang, Q., Li, X., Zhang, M., Chen, X., Lu, M., & Ouyang, Z. (2024) · IBRO Neuroscience Reports · doi:10.1016/j.ibneur.2024.06.005 · Cited by 3

The impact of metabolic syndrome on the cerebral cortex: a Mendelian randomization study

Zhang, P., Zhang, X., Gao, B., Gao, Y., & Pan, Y. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae342 · Cited by 2

How Mild Is the Mild Long COVID? A Comprehensive Neuropsychological Assessment of Patients with Cognitive Complaints

Santos, L. E. L. E., Ferreira, D. D. L., de Angelis, G., Foss, M. P., Trevisan, A. C., de Lacerda, K. J. C. C., Tumas, V., Bellissimo‐Rodrigues, F., & Wichert‐Ana, L. (2024) · Archives of Clinical Neuropsychology · doi:10.1093/arclin/acae071 · Cited by 2

Neurobiological Definition of Intelligence: A Neuroscience Review

Aghanouri, R. (2024) · Biomedical and Biotechnology Research Journal (BBRJ) · doi:10.4103/bbrj.bbrj_229_24 · Cited by 7

What is intelligence, really? The futile search for a Holy Grail

Sternberg, R. J. (2024) · Learning and Individual Differences · doi:10.1016/j.lindif.2024.102568 · Cited by 2

SynaptopathyDB integrates synaptic proteomes, genetic and phenotypic data to advance research on nervous system disorders

Sorokina, O., Dominic, D., Bayés, À., Armstrong, J. D., & Grant, S. G. N. (2025) · Scientific Reports · doi:10.1038/s41598-025-26969-z · Cited by 6

Causal effects of cingulate morphology on executive functions in healthy young adults

Razzaq, F. A., Bringas-Vega, M. L., Ontivero-Ortega, M., Riaz, U., & Valdes-Sosa, P. A. (2022) · Human Brain Mapping · doi:10.1002/hbm.25960 · Cited by 1

Intelligence predicts better cognitive performance after normal sleep but larger vulnerability to sleep deprivation

Balter, L. J. T., Sundelin, T., Holding, B. C., Petrović, P., & Axelsson, J. (2022) · Journal of Sleep Research · doi:10.1111/jsr.13815 · Cited by 3

Effectiveness of Early Childhood Care and Education on Intelligence of Children 2 to 24 Months, and their Mother’s Affective Capital and Parenting Stress

Abdoli, F., Golparvar, M., & Aghaei, A. (2021) · Pajouhan Scientific Journal · doi:10.61186/psj.19.4.33 · Cited by 2

The differential effects of common and rare genetic variants on cognitive performance across developmentPreprint

Malawsky, D. S., Koko, M., Danecek, P., Huang, W., Wootton, O., Huang, Q. Q., Wade, E. E., Lindsay, S., Arden, R., Hurles, M. E., et al. (2024) · medRxiv · doi:10.1101/2024.09.04.24313061 · Cited by 2

Multiple methods for assessing learning and memory in Drosophila melanogaster demonstrates the highly complex, context-dependent genetic underpinnings of cognitive traitsPreprint

Hamlin, V., Ansaf, H., Heffern, R., Williams‐Simon, P. A., & King, E. G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.26.640179 · Cited by 2

Lessons we learned from the Lothian Birth Cohorts of 1921 and 1936

Deary, I. J., & Cox, S. R. (2024) · Genomic psychiatry : · doi:10.61373/gp024i.0076 · Cited by 1

El talento académico: una revisión conceptual

Gómez-Pedraza, M. A., & Báez, A. L. M. (2025) · Educación y Educadores · doi:10.5294/edu.2023.26.3.4 · Cited by 1

Modifiable traits and genetic associations with grey matter volume in mid-to-late adulthood: a population-based study in the UK biobank

Pan, G., Zhang, Y., Kang, J., Jiang, Y., Zhang, W., Ren, P., You, J., Gong, W., Yu, J.-T., Feng, J., et al. (2025) · npj Aging · doi:10.1038/s41514-025-00255-8 · Cited by 2

Neurite density but not myelination of specific fiber tracts links polygenic scores to general intelligence

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Esping, A., & Plucker, J. A. (2013) · Oxford University Press eBooks · doi:10.1093/obo/9780199828340-0092 · Cited by 1

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DiLalla, L. F. (2011) · Oxford University Press eBooks · doi:10.1093/obo/9780199828340-0010 · Cited by 1

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Fahey, L., Morris, D. W., & Broin, P. Ó. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.12.495467 · Cited by 1

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Deary, I. J., Penke, L., & Johnson, W. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2793 · Cited by 1,038

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Shaw, P. (2007) · BioEssays · doi:10.1002/bies.20641 · Cited by 61

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Haier, R. J. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108770422.020 · Cited by 11

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