Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-17T23:19:39.010Z Has data issue: false hasContentIssue false
  • English
  • Français

Making Brains run Faster: are they Becoming Smarter?

Published online by Cambridge University Press:  05 December 2016

Anja Pahor  and
Norbert Jaušovec
Anja Pahor*
Affiliation:
University of Maribor (Slovenia)
Norbert Jaušovec
Affiliation:
University of Maribor (Slovenia)
*
*Correspondence concerning this article should be addressed to Anja Pahor. Univerza v Mariboru. Filozofska fakulteta. Koroška 160. 2000. Maribor (Slovenia). Fax: +38–62258180. E-mail: norbert.jausovec@um.si
Get access Rights & Permissions [Opens in a new window]

Abstract

A brief overview of structural and functional brain characteristics related to g is presented in the light of major neurobiological theories of intelligence: Neural Efficiency, P-FIT and Multiple-Demand system. These theories provide a framework to discuss the main objective of the paper: what is the relationship between individual alpha frequency (IAF) and g? Three studies were conducted in order to investigate this relationship: two correlational studies and a third study in which we experimentally induced changes in IAF by means of transcranial alternating current stimulation (tACS). (1) In a large scale study (n = 417), no significant correlations between IAF and IQ were observed. However, in males IAF positively correlated with mental rotation and shape manipulation and with an attentional focus on detail. (2) The second study showed sex-specific correlations between IAF (obtained during task performance) and scope of attention in males and between IAF and reaction time in females. (3) In the third study, individuals’ IAF was increased with tACS. The induced changes in IAF had a disrupting effect on male performance on Raven’s matrices, whereas a mild positive effect was observed for females. Neuro-electric activity after verum tACS showed increased desynchronization in the upper alpha band and dissociation between fronto-parietal and right temporal brain areas during performance on Raven’s matrices. The results are discussed in the light of gender differences in brain structure and activity.

Keywords

individual alpha frequencyscope of attentionsex differencestranscranial alternating current stimulationupper alphaworking memory
Type
Research Article
Information
The Spanish Journal of Psychology , Volume 19 , 2016 , E88
Copyright
Copyright © Universidad Complutense de Madrid and Colegio Oficial de Psicólogos de Madrid 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abad, F. J., Colom, R., Rebollo, I., & Escorial, S. (2004). Sex differential item functioning in the Raven’s advanced progressive matrices: Evidence for bias. Personality and Individual Differences, 36, 1459–1470. http://doi.org/10.1016/S0191-8869(03)00241-1 CrossRefGoogle Scholar
Amin, Z., Epperson, C. N., Constable, R. T., & Canli, T. (2006). Effects of estrogen variation on neural correlates of emotional response inhibition. NeuroImage, 32(1), 457–464. http://doi.org/10.1016/j.neuroimage.2006.03.013 Google Scholar
Andrev, C. (1999). Quantification of event-related coherence (ERCoh). In Pfurtscheller, G. & Lopes da Silva, F. H. (Eds.), Handbook of electroencephalography and clinical neuropsychology. Event-related desynchronization (pp. 119–137. Vol. 6). Amsterdam, the Netherlands: Elsevier.Google Scholar
Angelakis, E., Lubar, J. F., & Stathopoulou, S. (2004). Electroencephalographic peak alpha frequency correlates of cognitive traits. Neuroscience Letters, 371(1), 60–63. http://doi.org/10.1016/j.neulet.2004.08.041 Google Scholar
Angelakis, E., Lubar, J. F., Stathopoulou, S., & Kounios, J. (2004). Peak alpha frequency: An electroencephalographic measure of cognitive preparedness. Clinical Neurophysiology, 115, 887–897. http://doi.org/10.1016/j.clinph.2003.11.034 CrossRefGoogle ScholarPubMed
Anokhin, A. P., & Vogel, F. (1996). EEG alpha rhythm frequency and intelligence in normal adults. Intelligence, 23, 1–14. http://dx.doi.org/10.1016/S0160-2896(96)80002-X Google Scholar
Anokhin, A. P., Müller, V., Lindenberger, U., Heath, A. C., & Myers, E. (2006). Genetic influences on dynamic complexity of brain oscillations. Neuroscience Letters, 397, 93–98. http://dx.doi.org/10.1016/j.neulet.2005.12.025 Google Scholar
Aurlien, H., Gjerde, I. O., Aarseth, J. H., Eldoen, G., Karlsen, B., Skeidsvoll, H., & Gilhus, N. E. (2004). EEG background activity described by a large computerized database. Clinical Neurophysiology, 115, 665–673. http://dx.doi.org/10.1016/j.clinph.2003.10.019 CrossRefGoogle ScholarPubMed
Babiloni, F., Babiloni, C., Fattorini, L., Carducci, F., Onorati, P., & Urbano, A. (1995). Performances of surface Laplacian estimators: A study of simulated and realscalp potential distributions. Brain Topography, 8, 35–45. http://dx.doi.org/10.1007/BF01187668 Google Scholar
Baddeley, A. D. (1986). Working memory. New York, NY: Clarendon Press; Oxford University Press.Google Scholar
Baddeley, A. (2012). Working memory: Theories, models, and controversies. Annual Review of Psychology, 63(1), 1–29. http://doi.org/10.1146/annurev-psych-120710-100422 Google Scholar
Baddeley, A. D., Allen, R. J., & Hitch, G. J. (2011). Binding in visual working memory: The role of the episodic buffer. Neuropsychologia, 49, 1393–1400. http://doi.org/10.1016/j.neuropsychologia.2010.12.042 Google Scholar
Barry, R. J., Clarke, A. R., Johnstone, S. J., Magee, C. A., & Rushby, J. A. (2007). EEG differences between eyes-closed and eyes-open resting conditions. Clinical Neurophysiology, 118, 2765–2773. http://doi.org/10.1016/j.clinph.2007.07.028 Google Scholar
Basten, U., Hilger, K., & Fiebach, C. J. (2015). Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence, 51, 10–27. http://doi.org/10.1016/j.intell.2015.04.009 Google Scholar
Benjamini, Y., Krieger, A., & Yekutieli, D. (2006). Adaptive linear step-upprocedures that control the false discovery rate. Biometrika, 93, 491–507.Google Scholar
Benninger, C., Matthis, P., & Scheffner, D. (1984). EEG development of healthy boys and girls. Results of a longitudinal study. Electroencephalography and Clinical Neurophysiology, 57, 1–12. http://dx.doi.org/10.1016/0013-4694(84)90002-6 Google Scholar
Berman, K. F., Schmidt, P. J., Rubinow, D. R., Danaceau, M. A., van Horn, J. D., Esposito, G., … Weinberger, D. R. (1997). Modulation of cognition-specific cortical activity by gonadal steroids: A positron-emission tomography study in women. Proceedings of the National Academy of Sciences of the United States of America, 94, 8836–8841. http://dx.doi.org/10.1073/pnas.94.16.8836 Google Scholar
Boring, E. G. (1923). Intelligence as the tests test it. New Republic, 35, 35–37.Google Scholar
Boutros, N. N., Arfken, C., Galderisi, S., Warrick, J., Pratt, G., & Iacono, W. (2008). The status of spectral EEG abnormality as a diagnostic test for schizophrenia. Schizophrenia Research, 99, 225–237. http://dx.doi.org/10.1016/j.schres.2007.11.020 Google Scholar
Brancucci, A. (2012). Neural correlates of cognitive ability. Journal of Neuroscience Research, 90, 1299–1309. http://doi.org/10.1002/jnr.23045 Google Scholar
Bressler, S. L., & Menon, V. (2010). Large-scale brain networks in cognition: Emerging methods and principles. Trends in Cognitive Sciences, 14, 277–290. http://dx.doi.org/10.1016/j.tics.2010.04.004 Google Scholar
Brouwer, R. M., Hedman, A. M., van Haren, N. E. M., Schnack, H. G., Brans, R. G. H., Smit, D. J. A., … Hulshoff Pol, H. E. (2014). Heritability of brain volume change and its relation to intelligence. NeuroImage, 100, 676–683. http://doi.org/10.1016/j.neuroimage.2014.04.072 Google Scholar
Buehner, M., Krumm, S., & Pick, M. (2005). Reasoning working memory attention. Intelligence, 33, 251–272. http://doi.org/10.1016/j.intell.2005.01.002 Google Scholar
Burg, A. (1968). Lateral visual field as related to age and sex. Journal of Applied Psychology, 52, 10–15. http://dx.doi.org/10.1037/h0025270 Google Scholar
Burgaleta, M., Johnson, W., Waber, D. P., Colom, R., & Karama, S. (2014). Cognitive ability changes and dynamics of cortical thickness development in healthy children and adolescents. NeuroImage, 84, 810–819. http://dx.doi.org/10.1016/j.neuroimage.2013.09.038 Google Scholar
Burgaleta, M., MacDonald, P. A., Martínez, K., Román, F. J., Álvarez-Linera, J., González, A. R., … Colom, R. (2014). Subcortical regional morphology correlates with fluid and spatial intelligence: Basal Ganglia and Cognitive Abilities. Human Brain Mapping, 35, 1957–1968. http://doi.org/10.1002/hbm.22305 CrossRefGoogle Scholar
Buschkuehl, M., Hernandez-Garcia, L., Jaeggi, S. M., Bernard, J. A., & Jonides, J. (2014). Neural effects of short-term training on working memory. Cognitive, Affective, & Behavioral Neuroscience, 14(1), 147–160. http://doi.org/10.3758/s13415-013-0244-9 Google Scholar
Butler, T., Imperato-McGinley, J., Pan, H., Voyer, D., Cordero, J., Zhu, Y. -S., … Silbersweig, D. (2006). Sex differences in mental rotation: Top–down versus bottom–up processing. NeuroImage, 32(1), 445–456. http://dx.doi.org/10.1016/j.neuroimage.2006.03.030 Google Scholar
Cazzato, V., Basso, D., Cutini, S., & Bisiacchi, P. (2010). Gender differences in visuospatial planning: An eye movements study. Behavioural Brain Research, 206, 177–183. http://doi.org/10.1016/j.bbr.2009.09.010 Google Scholar
Chiang, M. -C., Barysheva, M., Shattuck, D. W., Lee, A. D., Madsen, S. K., Avedissian, C., … Thompson, P. M. (2009). Genetics of brain fiber architecture and intellectual performance. Journal of Neuroscience, 29, 2212–2224. http://doi.org/10.1523/JNEUROSCI.4184-08.2009 CrossRefGoogle ScholarPubMed
Chiang, M. -C., McMahon, K. L., de Zubicaray, G. I., Martin, N. G., Hickie, I., Toga, A. W., … Thompson, P. M. (2011). Genetics of white matter development: A DTI study of 705 twins and their siblings aged 12 to 29. NeuroImage, 54, 2308–2317. http://doi.org/10.1016/j.neuroimage.2010.10.015 Google Scholar
Chooi, W. -T., & Thompson, L. A. (2012). Working memory training does not improve intelligence in healthy young adults. Intelligence, 40, 531–542. http://dx.doi.org/10.1016/j.intell.2012.07.004 Google Scholar
Clark, C. R., Veltmeyer, M. D., Hamilton, R. J., Simms, E., Paul, R., Hermens, D., … Gordon, E. (2004). Spontaneous alpha peak frequency predicts working memory performance across the age span. International Journal of Psychophysiology, 53, 1–9.Google Scholar
Clarke, A. R., Barry, R. J., McCarthy, R., & Selikowitz, M. (2001). Age and sex effects in the EEG: Development of the normal child. Clinical Neurophysiology, 112, 806–814. http://dx.doi.org/10.1016/S1388-2457(01)00488-6 Google Scholar
Clayden, J. D., Jentschke, S., Munoz, M., Cooper, J. M., Chadwick, M. J., Banks, T., … Vargha-Khadem, F. (2012). Normative development of white matter tracts: Similarities and differences in relation to age, gender, and intelligence. Cerebral Cortex, 22, 1738–1747. http://dx.doi.org/10.1093/cercor/bhr243 Google Scholar
Cohn, N. B., Kircher, J., Emmerson, R. Y., & Dustman, R. E. (1985). Pattern reversal evoked potentials: Age, sex and hemispheric asymmetry. Electroencephalography and Clinical Neurophysiology, 62, 399–405. http://dx.doi.org/10.1016/0168-5597(85)90049-8 Google Scholar
Colom, R., Burgaleta, M., Román, F. J., Karama, S., Álvarez-Linera, J., Abad, F. J., … Haier, R. J. (2013). Neuroanatomic overlap between intelligence and cognitive factors: Morphometry methods provide support for the key role of the frontal lobes. NeuroImage, 72, 143–152. http://doi.org/10.1016/j.neuroimage.2013.01.032 Google Scholar
Colom, R., Jung, R. E., & Haier, R. J. (2006). Distributed brain sites for the g-factor of intelligence. NeuroImage, 31, 1359–1365. http://doi.org/10.1016/j.neuroimage.2006.01.006 Google Scholar
Corsi, P. M. (1972). Human memory and the medial temporal region of the brain. Dissertation Abstracts International, 34, 891B.Google Scholar
Court, J. H., & Raven, J. (1995). Manual for Raven’s progressive matrices and vocabulary scales. Section 7: Research and references: Summaries of normative, reliability, and validity studies and references to all sections. Oxford, UK: Oxford Psychologists Press; San Antonio, TX: The Psychological Corporation.Google Scholar
Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87–114. http://dx.doi.org/10.1017/S0140525X01003922 CrossRefGoogle ScholarPubMed
Cowan, N. (2011). The focus of attention as observed in visual working memory tasks: Making sense of competing claims. Neuropsychologia, 49, 1401–1406. http://doi.org/10.1016/j.neuropsychologia.2011.01.035 Google Scholar
Cowan, N., Elliott, E. M., Saults, J. S., Morey, C. C., Mattox, S., Hismjatullina, A., & Conway, A. R. A. (2005). On the capacity of attention: Its estimation and its role in working memory and cognitive aptitudes. Cognitive Psychology, 51(1), 42–100. http://doi.org/10.1016/j.cogpsych.2004.12.001 Google Scholar
Daffner, K. R. (2000). The central role of the prefrontal cortex in directing attention to novel events. Brain, 123, 927–939. http://doi.org/10.1093/brain/123.5.927 Google Scholar
Deary, I. J. (2000). Looking down on human intelligence: From psychometrics to the brain. Oxford, NY: Oxford University Press.Google Scholar
Deary, I. J. (2012). Intelligence. Annual Review of Psychology, 63(1), 453–482. http://doi.org/10.1146/annurev-psych-120710-100353 Google Scholar
D’Esposito, M., & Postle, B. R. (2015). The cognitive neuroscience of working memory. Annual Review of Psychology, 66(1), 115–142. http://doi.org/10.1146/annurev-psych-010814-015031 Google Scholar
Doppelmayr, M., Klimesch, W., Hödlmoser, K., Sauseng, P., & Gruber, W. (2005). Intelligence related upper alpha desynchronization in a semantic memory task. Brain Research Bulletin, 66, 171–177. http://doi.org/10.1016/j.brainresbull.2005.04.007 Google Scholar
Duncan, J. (2003). Intelligence tests predict brain response to demanding task events. Nature Neuroscience, 6, 207–208. http://dx.doi.org/10.1038/nn0303-207 Google Scholar
Duncan, J. (2005). Prefrontal cortex and Sperman’s g. In Duncan, J., McLeod, P. & Phillips, L. (Eds.), Measuring the mind: Speed, control, and age (pp. 249–271. 1st Ed.). Oxford, NY: Oxford University Press.Google Scholar
Duncan, J. (2010). The multiple-demand (MD) system of the primate brain: Mental programs for intelligent behaviour. Trends in Cognitive Sciences, 14, 172–179. http://doi.org/10.1016/j.tics.2010.01.004 Google Scholar
Duncan, J. (2013). The structure of cognition: Attentional episodes in mind and brain. Neuron, 80(1), 35–50. http://doi.org/10.1016/j.neuron.2013.09.015 Google Scholar
Duncan, J., & Owen, A. M. (2000). Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends in Neurosciences, 23, 475–483. http://doi.org/10.1016/S0166-2236(00)01633-7 Google Scholar
Duncan, J., McLeod, P., & Phillips, L. (2005). Measuring the mind: Speed, control, and age (1st Ed.). Oxford, NY: Oxford University Press.Google Scholar
Duncan, J., Seitz, R. J., Kolodny, J., Bor, D., Herzog, H., Ahmed, A., … Emslie, H. (2000). A neural basis for general intelligence. Science, 289, 457–460. http://doi.org/10.1126/science.289.5478.457 Google Scholar
Dunst, B., Benedek, M., Koschutnig, K., Jauk, E., & Neubauer, A. C. (2014). Sex differences in the IQ-white matter microstructure relationship: A DTI study. Brain and Cognition, 91, 71–78. http://doi.org/10.1016/j.bandc.2014.08.006 Google Scholar
Fernández, A., Hornero, R., Mayo, A., Poza, J., Gil-Gregorio, P., & Ortiz, T. (2006). EEG spectral profile in Alzheimer’s disease and mild cognitive impairment. Clinical Neurophysiology, 117, 306–314.Google Scholar
Galderisi, S., Mucci, A., Volpe, U., & Boutros, N. (2009). Evidence-based medicine and electrophysiology in schizophrenia. Clinical EEG and Neuroscience, 40, 62–77. http://dx.doi.org/10.1177/155005940904000206 Google Scholar
Ganjavi, H., Lewis, J. D., Bellec, P., MacDonald, P. A., Waber, D. P., Evans, A. C., … The Brain Development Cooperative Group. (2011). Negative associations between corpus callosum midsagittal area and IQ in a representative sample of healthy children and adolescents. PLoS ONE, 6, e19698. http://doi.org/10.1371/journal.pone.0019698 Google Scholar
Garai, J. E., & Scheinfeld, A. (1968). Sex differences in mental and behavioral traits. Genetic Psychology Monographs, 77, 169–299.Google Scholar
Garcés, P., Vicente, R., Wibral, M., Pineda-Pardo, J. Á., López, M. E., Aurtenetxe, S., … Fernández, A. (2013). Brain-wide slowing of spontaneous alpha rhythms in mild cognitive impairment. Frontiers in Aging Neuroscience, 5, 100. http://doi.org/10.3389/fnagi.2013.00100 Google Scholar
Gevins, A., & Smith, M. E. (2000). Neurophysiological measures of working memory and individual differences in cognitive ability and cognitive style. Cerebral Cortex, 10, 829–839. http://dx.doi.org/10.1093/cercor/10.9.829 Google Scholar
Gmehlin, D., Thomas, C., Weisbrod, M., Walther, S., Pfüller, U., Resch, F., & Oelkers-Ax, R. (2011). Individual analysis of EEG background-activity within school age: Impact of age and sex within a longitudinal data set. International Journal of Developmental Neuroscience, 29, 163–170. http://doi.org/10.1016/j.ijdevneu.2010.11.005 Google Scholar
Goljahani, A., Bisiacchi, P., & Sparacino, G. (2014). An EEGLAB plugin to analyze individual EEG alpha rhythms using the “channel reactivity-based method.” Computer Methods and Programs in Biomedicine, 113, 853–861.Google Scholar
Goljahani, A., D’Avanzo, C., Schiff, S., Amodio, P., Bisiacchi, P., & Sparacino, G. (2012). A novel method for the determination of the EEG individual alpha frequency. NeuroImage, 60(1), 774–786. http://doi.org/10.1016/j.neuroimage.2011.12.001 Google Scholar
Gootjes, L., Bruggeling, E. C., Magnée, T., & Van Strien, J. W. (2008a). Sex differences in functional connectivity during mental rotation: An EEG study. International Journal of Psychophysiology, 69, 228. http://doi.org/10.1016/j.ijpsycho.2008.05.081 CrossRefGoogle Scholar
Gootjes, L., Bruggeling, E. C., Magnée, T., & Van Strien, J. W. (2008b). Sex differences in the latency of the late event-related potential mental rotation effect. NeuroReport, 19, 349–353. http://doi.org/10.1097/WNR.0b013e3282f519b3 Google Scholar
Grandy, T. H., Werkle-Bergner, M., Chicherio, C., Schmiedek, F., Lövdén, M., & Lindenberger, U. (2013). Peak individual alpha frequency qualifies as a stable neurophysiological trait marker in healthy younger and older adults: Alpha stability. Psychophysiology, 50, 570–582. http://doi.org/10.1111/psyp.12043 CrossRefGoogle Scholar
Grazioplene, R. G., Ryman, S. G., Gray, J. R., Rustichini, A., Jung, R. E., & DeYoung, C. G. (2015). Subcortical intelligence: Caudate volume predicts IQ in healthy adults: Caudate volume and intelligence. Human Brain Mapping, 36, 1407–1416. http://doi.org/10.1002/hbm.22710 Google Scholar
Haász, J., Westlye, E. T., Fjær, S., Espeseth, T., Lundervold, A., & Lundervold, A. J. (2013). General fluid-type intelligence is related to indices of white matter structure in middle-aged and old adults. NeuroImage, 83, 372–383. http://doi.org/10.1016/j.neuroimage.2013.06.040 Google Scholar
Haier, R. J., Colom, R., Schroeder, D. H., Condon, C. A., Tang, C., Eaves, E., & Head, K. (2009). Gray matter and intelligence factors: Is there a neuro-g? Intelligence, 37, 136–144. http://doi.org/10.1016/j.intell.2008.10.011 Google Scholar
Haier, R. J., Siegel, B. V., Neuchterlein, K. H., Hazlett, E., Wu, J. C., Paek, J., … Buchsbaum, M. S. (1988). Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography. Intelligence, 12, 199–217. http://dx.doi.org/10.1016/0160-2896(88)90016-5 Google Scholar
Haier, R. J., Siegel, B., Tang, C., Abel, L., & Buchsbaum, M. S. (1992). Intelligence and changes in regional cerebral glucose mezabolic rate following learning. Intelligence, 16, 415–426.Google Scholar
Halpern, D. F. (2004). A cognitive-process taxonomy for sex differences in cognitive abilities. Current Directions in Psychological Science, 13, 135–139. http://doi.org/10.1111/j.0963-7214.2004.00292.x Google Scholar
Hampshire, A., Highfield, R. R., Parkin, B. L., & Owen, A. M. (2012). Fractionating human intelligence. Neuron, 76, 1225–1237. http://doi.org/10.1016/j.neuron.2012.06.022 Google Scholar
Hampshire, A., & Owen, A. M. (2014). RE: Comment about “Fractionating human intelligence”. Non-existent flaws in the original article and their relation to limitations of the P-FIT. Intelligence, 46, 333–340. http://dx.doi.org/10.1016/j.intell.2014.05.001 Google Scholar
Harmony, T., Marosi, E., Díaz de León, A. E., Becker, J., & Fernández, T. (1990). Effect of sex, psychosocial disadvantages and biological risk factors on EEG maturation. Electroencephalography and Clinical Neurophysiology, 75, 482–491. http://dx.doi.org/10.1016/0013-4694(90)90135-7 Google Scholar
Harrison, T. L., Shipstead, Z., Hicks, K. L., Hambrick, D. Z., Redick, T. S., & Engle, R. W. (2013). Working memory training may increase working memory capacity but not fluid intelligence. Psychological Science, 24, 2409–2419. http://doi.org/10.1177/0956797613492984 Google Scholar
Hayes, T. R., Petrov, A. A., & Sederberg, P. B. (2015). Do we really become smarter when our fluid-intelligence test scores improve? Intelligence, 48, 1–14. http://doi.org/10.1016/j.intell.2014.10.005 Google Scholar
Hedman, A. M., van Haren, N. E. M., Schnack, H. G., Kahn, R. S., & Hulshoff Pol, H. E. (2012). Human brain changes across the life span: A review of 56 longitudinal magnetic resonance imaging studies. Human Brain Mapping, 33, 1987–2002. http://doi.org/10.1002/hbm.21334 Google Scholar
Henderson, J. M., & Luke, S. G. (2014). Stable individual differences in saccadic eye movements during reading, pseudoreading, scene viewing, and scene search. Journal of Experimental Psychology: Human Perception and Performance, 40, 1390–1400. http://doi.org/10.1037/a0036330 Google Scholar
Hindriks, R., & van Putten, M. J. A. M. (2013). Thalamo-cortical mechanisms underlying changes in amplitude and frequency of human alpha oscillations. NeuroImage, 70, 150–163. http://doi.org/10.1016/j.neuroimage.2012.12.018 Google Scholar
Hooper, G. S. (2005). Comparison of the distributions of classical and adaptivelyaligned EEG power spectra. International Journal of Psychophysiology, 55, 179–189.Google Scholar
Huang, C., Wahlund, L. O., Dierks, T., Julin, P., Winblad, B., & Jelic, V. (2000). Discrimination of Alzheimer’s disease and mild cognitive impairment by equivalent EEG sources: Across-sectional and longitudinal study. Clinical Neurophysiology, 111, 1961–1967. http://doi.org/10.1016/S1388-2457(00)00454-5 Google Scholar
Hugdahl, K., Thomsen, T., & Ersland, L. (2006). Sex differences in visuo-spatial processing: An fMRI study of mental rotation. Neuropsychologia, 44, 1575–1583. http://doi.org/10.1016/j.neuropsychologia.2006.01.026 Google Scholar
Husain, M., & Nachev, P. (2007). Space and the parietal cortex. Trends in Cognitive Sciences, 11(1), 30–36. http://doi.org/10.1016/j.tics.2006.10.011 Google Scholar
Hutchinson, A. D., Mathias, J. L., Jacobson, B. L., Ruzic, L., Bond, A. N., & Banich, M. T. (2009). Relationship between intelligence and the size and composition of the corpus callosum. Experimental Brain Research, 192, 455–464. http://doi.org/10.1007/s00221-008-1604-5 Google Scholar
Ingalhalikar, M., Smith, A., Parker, D., Satterthwaite, T. D., Elliott, M. A., Ruparel, K., … Verma, R. (2014). Sex differences in the structural connectome of the human brain. Proceedings of the National Academy of Sciences, 111, 823–828. http://doi.org/10.1073/pnas.1316909110 Google Scholar
Irwing, P. (2012). Sex differences in g: An analysis of the US standardization sample of the WAIS-III. Personality and Individual Differences, 53, 126–131. http://doi.org/10.1016/j.paid.2011.05.001 Google Scholar
Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008). From the cover: Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences, 105, 6829–6833. http://doi.org/10.1073/pnas.0801268105 Google Scholar
Jann, K., Federspiel, A., Giezendanner, S., Andreotti, J., Kottlow, M., Dierks, T., & Koenig, T. (2012). Linking brain connectivity across different time scales with electroencephalogram, functional magnetic resonance imaging, and diffusion tensor imaging. Brain Connectivity, 2(1), 11–20. http://doi.org/10.1089/brain.2011.0063 Google Scholar
Jann, K., Koenig, T., Dierks, T., Boesch, C., & Federspiel, A. (2010). Association of individual resting state EEG alpha frequency and cerebral blood flow. NeuroImage, 51(1), 365–372. http://doi.org/10.1016/j.neuroimage.2010.02.024 Google Scholar
Jaušovec, N., & Jaušovec, K. (2000). Differences in resting EEG related to ability. Brain Topography, 12, 229–240.Google Scholar
Jaušovec, N., & Jaušovec, K. (2004). Intelligence related differences in induced brain activity during the performance of memory tasks. Personality and Individual Differences, 36, 597–612. http://doi.org/10.1016/S0191-8869(03)00120-X Google Scholar
Jaušovec, N., & Jaušovec, K. (2008). Spatial rotation and recognizing emotions: Gender related differences in brain activity. Intelligence, 36, 383–393. http://doi.org/10.1016/j.intell.2007.09.002 Google Scholar
Jaušovec, N., & Jaušovec, K. (2009a). Do women see things differently than men do? NeuroImage, 45(1), 198–207. http://doi.org/10.1016/j.neuroimage.2008.11.013 Google Scholar
Jaušovec, N., & Jaušovec, K. (2009b). Gender related differences in visual and auditory processing of verbal and figural tasks. Brain Research, 1300, 135–145. http://doi.org/10.1016/j.brainres.2009.08.093 Google Scholar
Jaušovec, N., & Jaušovec, K. (2010). Resting brain activity: Differences between genders. Neuropsychologia, 48, 3918–3925. http://doi.org/10.1016/j.neuropsychologia.2010.09.020 Google Scholar
Jaušovec, N., & Jaušovec, K. (2012). Working memory training: Improving intelligence – changing brain activity. Brain and Cognition, 79, 96–106. http://doi.org/10.1016/j.bandc.2012.02.007 Google Scholar
Jaušovec, N., & Pahor, A. (2016). The paradox of sex differences in intelligence. (Manuscript submitted for publication).Google Scholar
Jensen, A. R. (1998). The g factor. The science of mental ability. Westport, CT: Praeger.Google Scholar
Jensen, A. R. (2011). The theory of intelligence and its measurement. Intelligence, 39, 171–177. http://doi.org/10.1016/j.intell.2011.03.004 Google Scholar
Jolles, D. D., Grol, M. J., Van Buchem, M. A., Rombouts, S. A. R. B., & Crone, E. A. (2010). Practice effects in the brain: Changes in cerebral activation after working memory practice depend on task demands. NeuroImage, 52, 658–668. http://doi.org/10.1016/j.neuroimage.2010.04.028 Google Scholar
Johnson, W., & Bouchard, T. J. Jr. (2005). The structure of human intelligence: It is verbal, perceptual, and image rotation (VPR), not fluid and crystallized. Intelligence, 33, 393–416. http://doi.org/10.1016/j.intell.2004.12.002 Google Scholar
Johnson, W., & Bouchard, T. J. Jr. (2007a). Sex differences in mental abilities: g masks the dimensions on which they lie. Intelligence, 35(1), 23–39. http://doi.org/10.1016/j.intell.2006.03.012 CrossRefGoogle Scholar
Johnson, W., & Bouchard, T. J. Jr. (2007b). Sex differences in mental ability: A proposed means to link them to brain structure and function. Intelligence, 35, 197–209. http://doi.org/10.1016/j.intell.2006.07.003 Google Scholar
Johnson, W., Jung, R. E., Colom, R., & Haier, R. J. (2008). Cognitive abilities independent of IQ correlate with regional brain structure. Intelligence, 36(1), 18–28. http://doi.org/10.1016/j.intell.2007.01.005 Google Scholar
Jordan, K., Wüstenberg, T., Heinze, H. J., Peters, M., & Jäncke, L. (2002). Women and men exhibit different cortical activation patterns during mental rotation tasks. Neuropsychologia, 40, 2397–2408. http://dx.doi.org/10.1016/S0028-3932(02)00076-3 Google Scholar
Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences, 30, 135–154. http://doi.org/10.1017/S0140525X07001185 Google Scholar
Karama, S., Ad-Dab’bagh, Y., Haier, R. J., Deary, I. J., Lyttelton, O. C., Lepage, C., & Evans, A. C. (2009). Positive association between cognitive ability and cortical thickness in a representative US sample of healthy 6 to 18 year-olds. Intelligence, 37, 145–155. http://doi.org/10.1016/j.intell.2008.09.006 Google Scholar
Kimura, D. (1999). Sex and cognition. Cambridge, MA: TheMIT Press.Google Scholar
Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Research Reviews, 29, 169–195. http://dx.doi.org/10.1016/S0165-0173(98)00056-3 Google Scholar
Klimesch, W. (2012). Alpha-band oscillations, attention, and controlled access to stored information. Trends in Cognitive Sciences, 16, 606–617. http://doi.org/10.1016/j.tics.2012.10.007 Google Scholar
Klimesch, W., Sauseng, P., & Hanslmayr, S. (2007). EEG alpha oscillations: The inhibition–timing hypothesis. Brain Research Reviews, 53(1), 63–88. http://doi.org/10.1016/j.brainresrev.2006.06.003 Google Scholar
Klimesch, W., Schimke, H., & Pfurtscheller, G. (1993). Alpha frequency, cognitive load and memory performance. Brain Topography, 5, 241–251. http://dx.doi.org/10.1007/BF01128991 Google Scholar
Kondacs, A., & Szabó, M. (1999). Long-term intra-individual variability of the background EEG in normals. Clinical Neurophysiology, 110, 1708–1716. http://dx.doi.org/10.1016/S1388-2457(99)00122-4 Google Scholar
Kuo, M. -F., & Nitsche, M. A. (2012). Effects of transcranial electrical stimulation on cognition. Clinical EEG and Neuroscience, 43, 192–199. http://doi.org/10.1177/1550059412444975 Google Scholar
Langer, N., von Bastian, C. C., Wirz, H., Oberauer, K., & Jäncke, L. (2013). The effects of working memory training on functional brain network efficiency. Cortex, 49, 2424–2438. http://doi.org/10.1016/j.cortex.2013.01.008 Google Scholar
Lansbergen, M. M., Arns, M., van Dongen-Boomsma, M., Spronk, D., & Buitelaar, J. K. (2011). The increase in theta/beta ratio on resting-state EEG in boys with attention-deficit/hyperactivity disorder is mediated by slow alpha peak frequency. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 35(1), 47–52. http://doi.org/10.1016/j.pnpbp.2010.08.004 Google Scholar
Lebedev, A. N. (1994). The neurophysiological parameters of human memory. Neuroscience and Behavioral Physiology, 24, 254–259. http://dx.doi.org/10.1007/BF02362031 Google Scholar
Li, J., Yu, C., Li, Y., Liu, B., Liu, Y., Shu, N., … Jiang, T. (2009). COMT val158met modulates association between brain white matter architecture and IQ. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 150B, 375–380. http://doi.org/10.1002/ajmg.b.30825 Google Scholar
Lilienthal, L., Tamez, E., Shelton, J. T., Myerson, J., & Hale, S. (2013). Dual n-back training increases the capacity of the focus of attention. Psychonomic Bulletin & Review, 20, 135–141. http://dx.doi.org/10.3758/s13423-012-0335-6 Google Scholar
Lippa, R. A., Collaer, M. L., & Peters, M. (2010). Sex differences in mental rotation and line angle judgments are positively associated with gender equality and economic development across 53 nations. Archives of Sexual Behavior, 39, 990–997. http://doi.org/10.1007/s10508-008-9460-8 Google Scholar
Lipp, I., Benedek, M., Fink, A., Koschutnig, K., Reishofer, G., Bergner, S., … Neubauer, A. (2012). Investigating neural efficiency in the visuo-spatial domain: An fmri Study. PLoS ONE, 7, e51316. http://doi.org/10.1371/journal.pone.0051316 Google Scholar
Lopes da Silva, F. H., Vos, J. E., Mooibroek, J., & van Rotterdam, A. (1980). Relative contributions of intracortical and thalamo-cortical processes in the generation of alpha rhythms, revealed by partial coherence analysis. Electroencephalography Clinical Neurophysiology, 50, 449–456. http://dx.doi.org/10.1016/0013-4694(80)90011-5 Google Scholar
Luck, S. J., & Vogel, E. K. (1997). The capacity of visual working memory for features and conjunctions. Nature, 390, 279–281.Google Scholar
Luders, E., Narr, K. L., Bilder, R. M., Szeszko, P. R., Gurbani, M. N., Hamilton, L., … Gaser, C. (2008). Mapping the relationship between cortical convolution and intelligence: Effects of gender. Cerebral Cortex, 18, 2019–2026. http://doi.org/10.1093/cercor/bhm227 Google Scholar
Luders, E., Narr, K. L., Bilder, R. M., Thompson, P. M., Szeszko, P. R., Hamilton, L., & Toga, A. W. (2007). Positive correlations between corpus callosum thickness and intelligence. NeuroImage, 37, 1457–1464. http://doi.org/10.1016/j.neuroimage.2007.06.028 Google Scholar
Luders, E., Narr, K. L., Thompson, P. M., & Toga, A. W. (2009). Neuroanatomical correlates of intelligence. Intelligence, 37, 156–163. http://doi.org/10.1016/j.intell.2008.07.002 Google Scholar
Luders, E., Thompson, P. M., Narr, K. L., Zamanyan, A., Chou, Y. -Y., Gutman, B., … Toga, A. W. (2011). The link between callosal thickness and intelligence in healthy children and adolescents. NeuroImage, 54, 1823–1830. http://doi.org/10.1016/j.neuroimage.2010.09.083 Google Scholar
Mackintosh, N. J., & Bennett, E. S. (2005). What do Raven’s matrices measure? An analysis in terms of sex differences. Intelligence, 33, 663–674. http://doi.org/10.1016/j.intell.2005.03.004 Google Scholar
Marshall, P. J., Bar-Haim, Y., & Fox, N. A. (2002). Development of the EEG from 5 months to 4 years of age. Clinical Neurophysiology, 113, 1199–1208. http://dx.doi.org/10.1016/S1388-2457(02)00163-3 CrossRefGoogle ScholarPubMed
Martín-Loeches, M., Bruner, E., de la Cuétara, J. M., & Colom, R. (2013). Correlation between corpus callosum shape and cognitive performance in healthy young adults. Brain Structure and Function, 218, 721–731. http://doi.org/10.1007/s00429-012-0424-3 Google Scholar
Martínez, K., Burgaleta, M., Román, F. J., Escorial, S., Shih, P. C., Quiroga, M. A., & Colom, R. (2011). Can fluid intelligence be reduced to “simple” short-term storage? Intelligence, 39, 473–480. http://doi.org/10.1016/j.intell.2011.09.001 Google Scholar
Masters, M. S., & Sanders, B. (1993). Is the gender difference in mental rotation disappearing? Behavior Genetics, 23, 337–341. http://doi.org/10.1007/BF01067434 Google Scholar
Mazaheri, A., & Jensen, O. (2010). Rhythmic pulsing: Linking ongoing brain activity with evoked responses. Frontiers in Human Neuroscience, 4, 177. http://dx.doi.org/10.3389/fnhum.2010.00177 Google Scholar
McKendrick, R., Ayaz, H., Olmstead, R., & Parasuraman, R. (2014). Enhancing dual-task performance with verbal and spatial working memory training: Continuous monitoring of cerebral hemodynamics with NIRS. NeuroImage, 85, 1014–1026. http://doi.org/10.1016/j.neuroimage.2013.05.103 Google Scholar
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81–97. http://dx.doi.org/10.1037/h0043158 Google Scholar
Miller, E. K., & Buschman, T. J. (2013). Cortical circuits for the control of attention. Current Opinion in Neurobiology, 23, 216–222. http://doi.org/10.1016/j.conb.2012.11.011 Google Scholar
Miyahira, A., Morita, K., Yamaguchi, H., Morita, Y., & Maeda, H. (2000). Gender differences and reproducibility in exploratory eye movements of normal subjects. Psychiatry and Clinical Neurosciences, 54(1), 31–36. http://dx.doi.org/10.1046/j.1440-1819.2000.00632.x Google Scholar
Moliadze, V., Antal, A., & Paulus, W. (2010). Boosting brain excitability by transcranial high frequency stimulation in the ripple range. The Journal of Physiology, 588, 4891–4904. https://doi.org/10.1113/jphysiol.2010.196998 Google Scholar
Moreno, M. B., Concha, L., González-Santos, L., Ortiz, J. J., & Barrios, F. A. (2014). Correlation between corpus callosum sub-segmental area and cognitive processes in school-age children. PLoS ONE, 9, e104549. http://doi.org/10.1371/journal.pone.0104549 Google Scholar
Moretti, D. V., Babiloni, C., Binetti, G., Cassetta, E., Dal Forno, G., Ferreric, F., … Rossini, P. M. (2004). Individual analysis of EEG frequency and band power in mild Alzheimer’s disease. Clinical Neurophysiology, 115, 299–308. http://doi.org/10.1016/S1388-2457(03)00345-6 Google Scholar
Morrison, A. B., & Chein, J. M. (2011). Does working-memory training work? The promise and challenges of enhancing cognition by training working memory. Psychonomic Bulletin & Review, 18, 46–60. http://doi.org//10.3758/s13423-010-0034-0 Google Scholar
Mundy-Castle, A. C., & Nelson, G. K. (1960). Intelligence, personality and brain rhythms in a socially isolated community. Nature, 185, 484–485. http://dx.doi.org/10.1038/185484a0 Google Scholar
Narr, K. L., Woods, R. P., Thompson, P. M., Szeszko, P., Robinson, D., Dimtcheva, T., … Bilder, R. M. (2007). Relationships between IQ and regional cortical gray matter thickness in healthy adults. Cerebral Cortex, 17, 2163–2171. http://doi.org/10.1093/cercor/bhl125 Google Scholar
Navas-Sánchez, F. J., Alemán-Gómez, Y., Sánchez-Gonzalez, J., Guzmán-De-Villoria, J. A., Franco, C., Robles, O., … Desco, M. (2014). White matter microstructure correlates of mathematical giftedness and intelligence quotient: White matter microstructure. Human Brain Mapping, 35, 2619–2631. http://doi.org/10.1002/hbm.22355 Google Scholar
Neubauer, A. C., & Fink, A. (2003). Fluid intelligence and neural efficiency: Effects of task complexity and sex. Personality and Individual Differences, 35, 811–827. http://doi.org/10.1016/S0191-8869(02)00285-4 Google Scholar
Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency: Measures of brain activation versus measures of functional connectivity in the brain. Intelligence, 37, 223–229. http://doi.org/10.1016/j.intell.2008.10.008 Google Scholar
Neubauer, A. C., Fink, A., & Schrausser, D. G. (2002). Intelligence and neural efficiency: The influence of task content and sex on the brain–IQ relationship. Intelligence, 30, 515–536. http://dx.doi.org/10.1016/S0160-2896(02)00091-0 Google Scholar
Neubauer, A. C., Grabner, R. H., Fink, A., & Neuper, C. (2005). Intelligence and neural efficiency: Further evidence of the influence of task content and sex on the brain–IQ relationship. Cognitive Brain Research, 25(1), 217–225. http://doi.org/10.1016/j.cogbrainres.2005.05.011 Google Scholar
Neuling, T., Rach, S., & Herrmann, C. S. (2013). Orchestrating neuronal networks: Sustained after-effects of transcranial alternating current stimulation depend upon brain states. Frontiers in Human Neuroscence, 7, 161. http://dx.doi.org/10.3389/fnhum.2013.00161 Google Scholar
Nikulin, V. V., & Brismar, T. (2005). Long-range temporal correlations in electroencephalographic oscillations: Relation to topography, frequency band, age and gender. Neuroscience, 130, 549–558. http://dx.doi.org/10.1016/j.neuroscience.2004.10.007 Google Scholar
Nitsche, M. A., Cohen, L. G., Wassermann, E. M., Priori, A., Lang, N., Antal, A., … Pascual-Leone, A. (2008). Transcranial direct current stimulation: State of the art 2008. Brain Stimulation, 1, 206–223. http://dx.doi.org/10.1016/j.brs.2008.06.004 Google Scholar
Nunez, P. L. (1995). Mind, brain, and electroencephalography. In Nunez, P. L. (Ed.), Neocortical dynamics and human EEG rhythms (pp. 133–194). Oxford, NY: Oxford University Press.Google Scholar
Nunez, P. L., Wingeier, B. M., & Silberstein, R. B. (2001). Spatial– temporal structures of human alpha rhythms: Theory, microcurrent sources, multiscale measurements, and global binding of local networks. Human Brain Mapping, 13, 125–164.Google Scholar
Nussbaumer, D., Grabner, R. H., & Stern, E. (2015). Neural efficiency in working memory tasks: The impact of task demand. Intelligence, 50, 196–208. http://doi.org/10.1016/j.intell.2015.04.004 Google Scholar
Owen, A. M., Hampshire, A., Grahn, J. A., Stenton, R., Dajani, S., Burns, A. S., … Ballard, C. G. (2010). Putting brain training to the test. Nature, 465, 775–778. http://dx.doi.org/10.1038/nature09042 Google Scholar
Pahor, A., & Jaušovec, N. (2014). The effects of theta transcranial alternating current stimulation (tACS) on fluid intelligence. International Journal of Psychophysiology, 93, 322–331. http://doi.org/10.1016/j.ijpsycho.2014.06.015 Google Scholar
Pashler, H. (1988). Familiarity and visual change detection. Perception & Psychophysics, 44, 369–378. http://dx.doi.org/10.3758/BF03210419 Google Scholar
Petrides, M. (2000). Mapping prefrontal cortical systems for the control of cognition. In Toga, A. W. & Mazziotta, J. C. (Eds.), Brain mapping: The systems. (pp. 159–176). San Diego, CA: Academic Press.Google Scholar
Pfurtscheller, G. (1999). Quantification of ERD and ERS in the time domain. In Pfurtscheller, G. & Lopes da Silva, F. H. (Eds.), Handbook of electroencephalography and clinical neuropsychology (pp. 89–105. Vol. 6). Amsterdam, the Netherlands: Elsevier.Google Scholar
Pietschnig, J., Penke, L., Wicherts, J. M., Zeiler, M., & Voracek, M. (2015). Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean? Neuroscience & Biobehavioral Reviews, 57, 411–432. http://doi.org/10.1016/j.neubiorev.2015.09.017 Google Scholar
Pineda-Pardo, J. A., Martínez, K., Román, F. J., & Colom, R. (2016). Structural efficiency within a parieto-frontal network and cognitive differences. Intelligence, 54, 105–116. http://doi.org/10.1016/j.intell.2015.12.002 Google Scholar
Posthuma, D., Neale, M. C., Boomsma, D. I., & De Geus, E. J. C. (2001). Are smarter brains running faster? Heritability of alpha peak frequency, IQ, and their interrelation. Behavior Genetics, 31, 567–579.Google Scholar
Raichle, M. E. (2006). The brain’s dark energy. Science-New York Then Washington, 314, 1249.Google Scholar
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceeding of the National Academic of Science, 98, 676–682. http://dx.doi.org/10.1073/pnas.98.2.676 Google Scholar
Raven, J. C. (1990). Advanced progressive matrices: Sets I, II. Oxford, UK: Oxford University Press.Google Scholar
Redick, T. S., Shipstead, Z., Harrison, T. L., Hicks, K. L., Fried, D. E., Hambrick, D. Z., … Engle, R. W. (2013). No evidence of intelligence improvement after working-memory training: A randomized, placebocontrolled study. Journal of Experimental Psychology: General, 142, 359–379. http://dx.doi.org/10.1037/a0029082 Google Scholar
Rhein, C., Mühle, C., Richter-Schmidinger, T., Alexopoulos, P., Doerfler, A., & Kornhuber, J. (2014). Neuroanatomical correlates of intelligence in healthy young adults: The role of basal ganglia volume. PLoS ONE, 9, e93623. http://doi.org/10.1371/journal.pone.0093623 Google Scholar
Ritchie, S. J., Booth, T., Valdés Hernández, M. del C., Corley, J., Muñoz Maniega, S., Gow, A. J., … Deary, I. J. (2015). Beyond a bigger brain: Multivariable structural brain imaging and intelligence. Intelligence, 51, 47–56. http://doi.org/10.1016/j.intell.2015.05.001 Google Scholar
Román, F. J., Abad, F. J., Escorial, S., Burgaleta, M., Martínez, K., Álvarez-Linera, J., … Colom, R. (2014). Reversed hierarchy in the brain for general and specific cognitive abilities: A morphometric analysis. Human Brain Mapping, 35, 3805–3818. http://doi.org/10.1002/hbm.22438 Google Scholar
Román, F. J., Lewis, L. B., Chen, C. -H., Karama, S., Burgaleta, M., Martínez, K., … Colom, R. (2015). Gray matter responsiveness to adaptive working memory training: A surface-based morphometry study. Brain Structure and Function, 1–14. http://doi.org/10.1007/s00429-015-1168-7 Google Scholar
Rushton, J. P., & Ankney, C. D. (2009). Whole brain size and general mental ability: A review. International Journal of Neuroscience, 119, 692–732. http://doi.org/10.1080/00207450802325843 Google Scholar
Salinsky, M. C., Oken, B. S., & Morehead, L. (1991). Test-retest reliability in EEG frequency analysis. Electroencephalography & Clinical Neurophysiology, 79, 382–392. http://dx.doi.org/10.1016/0013-4694(91)90203-G Google Scholar
Samson-Dollfus, D., Delapierre, G., Do Marcolino, C., & Blondeau, C. (1997). Normal and pathological changes in alpha rhythms. International Journal of Psychophysiology, 26, 395–409. http://dx.doi.org/10.1016/S0167-8760(97)00778-2 Google Scholar
Sandman, C. A., Head, K., Muftuler, L. T., Su, L., Buss, C., & Davis, E. P. (2014). Shape of the basal ganglia in preadolescent children is associated with cognitive performance. NeuroImage, 99, 93–102. http://doi.org/10.1016/j.neuroimage.2014.05.020 Google Scholar
Santarnecchi, E., Tatti, E., Rossi, S., Serino, V., & Rossi, A. (2015). Intelligence-related differences in the asymmetry of spontaneous cerebral activity: Intelligence and brain functional asymmetry. Human Brain Mapping, 36, 3586–3602. http://doi.org/10.1002/hbm.22864 Google Scholar
Sarnthein, J., Stern, J., Aufenberg, C., Rousson, V., & Jeanmonod, D. (2005). Increased EEG power and slowed dominant frequency in patients with neurogenic pain. Brain, 129(1), 55–64. http://doi.org/10.1093/brain/awh631 CrossRefGoogle ScholarPubMed
Schmiedek, F., Lövdén, M., & Lindenberger, U. (2010). Hundred days of cognitive training enhance broad cognitive abilities in adulthood: Findings from the COGITO study. Frontiers in Aging Neuroscience, 2, 27. http://doi.org/10.3389/fnagi.2010.00027 Google Scholar
Schnack, H. G., van Haren, N. E. M., Brouwer, R. M., Evans, A., Durston, S., Boomsma, D. I., … Hulshoff Pol, H. E. (2015). Changes in thickness and surface area of the human cortex and their relationship with intelligence. Cerebral Cortex, 25, 1608–1617. http://doi.org/10.1093/cercor/bht357 Google Scholar
Schwaighofer, M., Fischer, F., & Bühner, M. (2015). Does working memory training transfer? A meta-analysis including training conditions as moderators. Educational Psychologist, 50, 138–166. http://doi.org/10.1080/00461520.2015.1036274 Google Scholar
Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., … Greicius, M. D. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. The Journal of Neuroscience, 27, 2349–2356. http://doi.org/10.1523/JNEUROSCI.5587-06.2007 Google Scholar
Sellers, K. K., Mellin, J. M., Lustenberger, C. M., Boyle, M. R., Lee, W. H., Peterchev, A. V., & Fröhlich, F. (2015). Transcranial direct current stimulation (tDCS) of frontal cortex decreases performance on the WAIS-IV intelligence test. Behavioural Brain Research, 290, 32–44. http://doi.org/10.1016/j.bbr.2015.04.031 Google Scholar
Shaw, J. C. (2004). The brain’s alpha rhythms and the mind. Amsterdam, the Netherlands: Elsevier Science.Google Scholar
Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., … Giedd, J. (2006). Intellectual ability and cortical development in children and adolescents. Nature, 440, 676–679. http://doi.org/10.1038/nature04513 Google Scholar
Shelton, J. T., Elliott, E. M., Matthews, R. A., Hill, B. D., & Gouvier, W. D. (2010). The relationships of working memory, secondary memory, and general fluid intelligence: Working memory is special. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36, 813–820. http://doi.org/10.1037/a0019046 Google Scholar
Smit, C. M., Wright, M. J., Hansell, N. K., Geffen, G. M., & Martin, N. G. (2006). Genetic variation of individual alpha frequency (IAF) and alpha power in a large adolescent twin sample. International Journal of Psychophysiology, 61, 235–243. http://doi.org/10.1016/j.ijpsycho.2005.10.004 Google Scholar
Smith, S. M., Vidaurre, D., Beckmann, C. F., Glasser, M. F., Jenkinson, M., Miller, K. L., … Van Essen, D. C. (2013). Functional connectomics from resting-state fMRI. Trends in Cognitive Sciences, 17, 666–682. http://doi.org/10.1016/j.tics.2013.09.016 Google Scholar
Spearman, C. (1927). The abilities of man. London, UK: Macmillan Google Scholar
Sreenivasan, K. K., Curtis, C. E., & D’Esposito, M. (2014). Revisiting the role of persistent neural activity during working memory. Trends in Cognitive Sciences, 18, 82–89. http://doi.org/10.1016/j.tics.2013.12.001 Google Scholar
Sridharan, D., Levitin, D. J., & Menon, V. (2008). A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proceedings of the National Academy of Sciences, 105, 12569–12574. http://dx.doi.org/10.1073/pnas.0800005105 Google Scholar
Stephenson, C. L., & Halpern, D. F. (2013). Improved matrix reasoning is limited to training on tasks with a visuospatial component. Intelligence, 41, 341–357. http://doi.org/10.1016/j.intell.2013.05.006 Google Scholar
Sternberg, R. J. (2008). Increasing fluid intelligence is possible after all. Proceedings of the National Academy of Sciences, 105, 6791–6792. http://dx.doi.org/10.1073/pnas.0803396105 Google Scholar
Sternberg, R. J., Ferrari, M., Clinkenbeard, P., & Grigorenko, E. L. (1996). Identification, instruction, and assessment of gifted children: A construct validation of a triarchic model. Gifted Child Quarterly, 40, 129–137. http://doi.org/10.1177/001698629604000303 Google Scholar
Takeuchi, H., Sekiguchi, A., Taki, Y., Yokoyama, S., Yomogida, Y., Komuro, N., … Kawashima, R. (2010). Training of working memory impacts structural connectivity. Journal of Neuroscience, 30, 3297–3303. http://doi.org/10.1523/JNEUROSCI.4611-09.2010 Google Scholar
Takeuchi, H., Taki, Y., Nouchi, R., Hashizume, H., Sekiguchi, A., Kotozaki, Y., … Kawashima, R. (2013). Effects of working memory training on functional connectivity and cerebral blood flow during rest. Cortex, 49, 2106–2125. http://doi.org/10.1016/j.cortex.2012.09.007 Google Scholar
Tamnes, C. K., Østby, Y., Walhovd, K. B., Westlye, L. T., Due-Tønnessen, P., & Fjell, A. M. (2010). Intellectual abilities and white matter microstructure in development: A diffusion tensor imaging study. Human Brain Mapping, 31, 1609–1625. http://doi.org/10.1002/hbm.20962 Google Scholar
Tement, S., Pahor, A., & Jaušovec, N. (2016). EEG alpha frequency correlates of burnout and depression: The role of gender. Biological Psychology, 114, 1–12. http://doi.org/10.1016/j.biopsycho.2015.11.005 Google Scholar
Thompson, T. W., Waskom, M. L., Garel, K. -L. A., Cardenas-Iniguez, C., Reynolds, G. O., Winter, R., … Gabrieli, J. D. E. (2013). Failure of working memory training to enhance cognition or intelligence. PLoS ONE, 8, e63614. http://doi.org/10.1371/journal.pone.0063614 Google Scholar
Tsunoda, M., Kawasaki, Y., Matsui, M., Tonoya, Y., Hagino, H., Suzuki, M., … Kurachi, M. (2005). Relationship between exploratory eye movements and brain morphology in schizophrenia spectrum patients. European Archives of Psychiatry and Clinical Neuroscience, 255, 104–110. http://doi.org/10.1007/s00406-004-0540-z Google Scholar
Tulbure, B. T., & Siberescu, I. (2013). Cognitive training enhances working memory capacity in healthy adults. A pilot study. Procedia - Social and Behavioral Sciences, 78, 175–179. http://doi.org/10.1016/j.sbspro.2013.04.274 Google Scholar
Thut, G., & Miniussi, C. (2009). New insights into rhythmic brain activity fromTMS-EEG studies. Trends in Cognitive Sciences, 13, 182–189.Google Scholar
Thut, G., Veniero, D., Romei, V., Miniussi, C., Schyns, P., & Gross, J. (2011). Rhythmic TMS causes local entrainment of natural oscillatory signatures. Current Biology, 21, 1176–1185. http://dx.doi.org/10.1016/j.cub.2011.05.049 Google Scholar
Valdés-Hernández, P. A., Ojeda-González, A., Martínez-Montes, E., Lage-Castellanos, A., Virués-Alba, T., Valdés-Urrutia, L., & Valdes-Sosa, P. A. (2010). White matter architecture rather than cortical surface area correlates with the EEG alpha rhythm. NeuroImage, 49, 2328–2339. http://doi.org/10.1016/j.neuroimage.2009.10.030 Google Scholar
van den Heuvel, M. P., & Sporns, O. (2013). Network hubs in the human brain. Trends in Cognitive Sciences, 17, 683–696. http://doi.org/10.1016/j.tics.2013.09.012 Google Scholar
Vernon, P. A., Wickett, J. C., Bazana, P. C., & Stelmack, R. M. (2000). The neuropsychology and psychophysiology of human intelligence. In Sternberg, R. J. (Ed.), Handbook of intelligence (pp. 245–264). Cambridge, UK: Cambridge University Press.Google Scholar
Vogel, W., & Broverman, D. M. (1964). Relationship between EEG and test intelligence: A critical review. Psychological Bulletin, 62, 132–144. http://dx.doi.org/10.1037/h0049067 Google Scholar
von Bastian, C. C., & Oberauer, K. (2013). Distinct transfer effects of training different facets of working memory capacity. Journal of Memory and Language, 69(1), 36–58. http://doi.org/10.1016/j.jml.2013.02.002 Google Scholar
Vossen, A., Gross, J., & Thut, G. (2015). Alpha power increase after transcranial alternating current stimulation at alpha frequency (α-tACS) reflects plastic changes rather than entrainment. Brain Stimulation, 8, 499–508. http://doi.org/10.1016/j.brs.2014.12.004 Google Scholar
Voyer, D. (2011). Time limits and gender differences on paper-and-pencil tests of mental rotation: A meta-analysis. Psychonomic Bulletin & Review, 18, 267–277. http://doi.org/10.3758/s13423-010-0042-0 Google Scholar
Voyer, D., Voyer, S., & Bryden, M. P. (1995). Magnitude of sex differences in spatial abilities: A meta-analysis and consideration of critical variables. Psychological Bulletin, 117, 250–270. http://dx.doi.org/10.1037/0033-2909.117.2.250 Google Scholar
Wammerl, M., Benedek, M., Jauk, E., & Jaušovec, N. (2015). The influence of transcranial alternating current on fluid intelligence. A fMRI study. Brain Stimulation, 8, 311. http://dx.doi.org/10.1016/j.brs.2015.01.010 Google Scholar
Waris, O., Soveri, A., & Laine, M. (2015). Transfer after working memory updating training. PLOS ONE, 10, e0138734. http://doi.org/10.1371/journal.pone.0138734 Google Scholar
Wechsler, D. (1981). Manual for the Wechsler Adult Intelligence Scale — Revised. New York, NY: Psychological Corporation.Google Scholar
Wolohan, F. D. A., Bennett, S. J. V., & Crawford, T. J. (2013). Females and attention to eye gaze: Effects of the menstrual cycle. Experimental Brain Research, 227, 379–386. http://doi.org/10.1007/s00221-013-3515-3 Google Scholar
Zaehle, T., Rach, S., & Herrmann, C. S. (2010). Transcranial alternating current stimulation enhances individual alpha activity in human EEG. PLoS ONE, 5, e13766. http://doi.org/10.1371/journal.pone.0013766 Google Scholar
Zappasodi, F., Pasqualetti, P., Tombini, M., Ercolani, M., Pizzella, V., Rossini, P. M., & Tecchio, F. (2006). Hand cortical representation at rest and during activation: Gender and age effects in the two hemispheres. Clinical Neurophysiology, 117, 1518–1528. http://doi.org/10.1016/j.clinph.2006.03.016 Google Scholar