Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-24T00:55:21.200Z Has data issue: false hasContentIssue false

Fluid Intelligence and the Cross-Frequency Coupling of Neuronal Oscillations

Published online by Cambridge University Press:  06 December 2016

Adam Chuderski*
Affiliation:
Jagiellonian University (Poland)
*
*Correspondence concerning this article should be addressed to Adam Chuderski. Institute of Philosophy. Jagiellonian University. Grodzka, 52. 31–044. Krakow (Poland). E-mail: [email protected]

Abstract

Several existing theoretical models predict that the individual capacity of working memory and abstract reasoning (fluid intelligence) strongly depends on certain features of neuronal oscillations, especially their cross-frequency coupling. Empirical evidence supporting these predictions is still scarce, but it makes the future studies on oscillatory coupling a promising line of research that can uncover the physiological underpinnings of fluid intelligence. Cross-frequency coupling may serve as the optimal level of description of neurocognitive processes, integrating their genetic, structural, neurochemical, and bioelectrical underlying factors with explanations in terms of cognitive operations driven by neuronal oscillations.

Type
Research Article
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

Arroyo, S., Lesser, R. P., Gordon, B., Uematsu, S., Jackson, D., & Webber, R. (1993). Functional significance of the mu rhythm of human cortex: An electrophysiologic study with subdural electrodes. Electroencephalography and Clinical Neurophysiology, 87, 7687. http://dx.doi.org/10.1016/0013-4694(93)90114-B Google Scholar
Axmacher, N., Henseler, M. M., Jensen, O., Weinreich, I., Elger, C. E., & Fell, J. (2010). Cross-frequency coupling supports multi-item working memory in the human hippocampus. PNAS, 107, 32283233. http://dx.doi.org/10.1073/pnas.0911531107 CrossRefGoogle ScholarPubMed
Başar, E. (2012). A review of alpha activity in integrative brain function: Fundamental physiology, sensory coding, cognition, and pathology. International Journal of Psychophysiology, 86, 124.Google Scholar
Begleiter, H., & Porjesz, B. (2006). Genetics of human brain oscillations. International Journal of Psychophysiology, 60, 162171. http://dx.doi.org/10.1016/j.ijpsycho.2005.12.013 Google Scholar
Bonnefond, M., & Jensen, O. (2012). Alpha oscillations serve to protect working memory maintenance against anticipated distracters. Current Biology, 22, 19691974. http://dx.doi.org/10.1016/j.cub.2012.08.029 Google Scholar
Buschman, T. J., & Miller, E. K. (2007). Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science, 315, 18601862. http://dx.doi.org/10.1126/science.1138071 Google Scholar
Buzsáki, G. (2006). Rhythms of the brain. Oxford, UK: Oxford University Press.CrossRefGoogle Scholar
Buzsáki, G. (2010). Neural syntax: Cell assemblies, synapsembles, and readers. Neuron, 68, 362385.CrossRefGoogle ScholarPubMed
Canolty, R. T., Edwards, E., Dalal, S. S., Soltani, M., Nagarajan, S. S., … Knight, R. T. (2006). High gamma power is phase-locked to theta oscillations in human neocortex. Science, 313, 16261628. http://dx.doi.org/10.1126/science.1128115 Google Scholar
Canolty, R. T., & Knight, R. T. (2010). The functional role of cross-frequency coupling. Trends in Cognitive Sciences, 14, 506515. http://dx.doi.org/10.1016/j.tics.2010.09.001 Google Scholar
Carpenter, P. A., Just, M. A., & Shell, P. (1990). What one intelligence test measures: A theoretical account of the processing in the Raven Progressive Matrices Test. Psychological Review, 97, 404431. http://dx.doi.org/10.1037/0033-295X.97.3.404 Google Scholar
Chuderski, A. (2013). When are fluid intelligence and working memory isomorphic and when are they not? Intelligence, 41, 244262. http://dx.doi.org/10.1016/j.intell.2013.04.003 CrossRefGoogle Scholar
Chuderski, A., & Andrelczyk, K. (2015). From neural oscillations to complex cognition: Simulating the effect of the theta-to-gamma cycle length ratio on analogical reasoning. Cognitive Psychology, 76, 78102.CrossRefGoogle Scholar
Chuderski, A., Andrelczyk, K., & Smoleń, T. (2013). An oscillatory model of individual differences in working memory capacity and relational integration. Cognitive Systems Research, 24, 8795. http://dx.doi.org/10.1016/j.cogsys.2012.12.005 Google Scholar
Cohen, M. X. (2011). It’s about time. Frontiers in Human Neuroscience, 5, 2.Google Scholar
Cohen, M. X. (2014). A neural microcircuit for cognitive conflict detection and signaling. Trends in Neurosciences, 37, 480490. http://dx.doi.org/10.1016/j.tins.2014.06.004 Google Scholar
Cole, M. W., Yarkoni, T., Repovs, G., Anticevic, A., & Braver, T. S. (2012). Global connectivity of prefrontal cortex predicts cognitive control and intelligence. Journal of Neuroscience, 32, 89888999. http://dx.doi.org/10.1523/JNEUROSCI.0536-12.2012 Google Scholar
Colgin, L. L. (2013). Mechanisms and functions of theta rhythms. Annual Review of Neuroscience, 36, 295312. http://dx.doi.org/10.1146/annurev-neuro-062012-170330 Google Scholar
Colom, R., Haier, R. J., Head, K., Alvarez-Linera, J., Quiroga, M. A., Shih, P. C., & Jung, R. E. (2009). Gray matter correlates of fluid, crystallized, and spatial intelligence: Testing the P-FIT model. Intelligence, 37, 124135. http://dx.doi.org/10.1016/j.intell.2008.07.007 Google Scholar
Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, 87114. http://dx.doi.org/10.1017/S0140525X01003922 Google Scholar
Cowan, N., Blume, C. L., & Saults, J. S. (2013). Attention to attributes and objects in working memory. Journal of Experimental Psychology: Learning, Memory, & Cognition, 39, 731747. http://dx.doi.org/10.1037/a0029687 Google Scholar
Coyle, T. R. (2003). A review of the worst performance rule: Evidence, theory, and alternative hypotheses. Intelligence, 31, 567587. http://dx.doi.org/10.1016/S0160-2896(03)00054-0 Google Scholar
Doumas, L. A. A., & Hummel, J. E. (2005). Approaches to modeling human mental representations: What works, what doesn’t and why. In Holyoak, K. J. & Morrison, R. (Eds.), The Cambridge handbook of thinking and reasoning (pp. 7391). Cambridge, UK: Cambridge University Press.Google Scholar
Doumas, L. A. A., Hummel, J. E., & Sandhofer, C. M. (2008). A theory of the discovery and predication of relational concepts. Psychological Review, 115, 143. http://dx.doi.org/10.1037/0033-295X.115.1.1 Google Scholar
Egner, T., & Gruzelier, J. H. (2004). EEG biofeedback of low beta band components: Frequency-specific effects on variables of attention and event-related brain potentials. Clinical Neurophysiology, 115, 131139. http://dx.doi.org/10.1016/S1388-2457(03)00353-5 Google Scholar
Eliasmith, C. (2013). How to build a brain: A neural architecture for biological cognition. Oxford, UK: Oxford University Press.Google Scholar
Fries, P., Nikolić, D., & Singer, W. (2007). The gamma cycle. Trends in Neurosciences, 30, 309316. http://dx.doi.org/10.1016/j.tins.2007.05.005 Google Scholar
Galton, F. (1883). Inquiries into human faculty and its development. New York, NY: MacMillan Co Inquiries into human faculty and its development. http://dx.doi.org/10.1037/14178-000 Google Scholar
Gignac, G. E. (2015). The magical numbers 7 and 4 are resistant to the Flynn effect: No evidence for increases in forward or backward recall across 85 years of data. Intelligence, 48, 8595. http://dx.doi.org/10.1016/j.intell.2014.11.001 Google Scholar
Gu, B.-M., van Rijn, H., & Meck, W. H. (2015). Oscillatory multiplexing of neural population codes for interval timing and working memory. Neuroscience and Biobehavioral Reviews, 48, 160185.Google Scholar
Halford, G. S., Andrews, G., & Wilson, W. H. (2014). Relational processing in reasoning: The role of working memory. In Feeney, A. & Thompson, V. (Eds.), Reasoning as Memory (pp. 3452). Hove, UK: Psychology Press.Google Scholar
Herrmann, C. S., Strüber, D., Helfrich, R. F., & Engel, A. K. (2016). EEG oscillations: From correlation to causality. International Journal of Psychophysiology, 103, 1221. http://dx.doi.org/10.1016/j.ijpsycho.2015.02.003 Google Scholar
Hiltunen, T., Kantola, J., Elseoud, A. A., Lepola, P., Suominen, K., Starck, T., … Palva, J. M. (2014). Infra-slow EEG fluctuations are correlated with resting-state network dynamics in fMRI. Journal of Neuroscience, 34, 356362. http://dx.doi.org/10.1523/JNEUROSCI.0276-13.2014 Google Scholar
Holyoak, K. J. (2012). Analogy and relational reasoning. In Holyoak, K. J. & Morrison, R. G. (Eds.), The Oxford handbook of thinking and reasoning (pp. 234259). New York, NY: Oxford University Press.Google Scholar
Horn, D., & Usher, M. (1991). Parallel activation of memories in an oscillatory neural network. Neural Computation, 3, 3143.Google Scholar
Horn, D., & Usher, M. (1992). Oscillatory model of short term memory. In Moody, J. E., Hanson, S. J., & Lippmann, R. P. (Eds.), Advances in neural processing and information systems (Vol. 4., pp. 125132). Waltham, MA: Morgan and Kaufmann.Google Scholar
Hsieh, L.-T., Ekstrom, A. D., & Ranganath, C. (2011). Neural oscillations associated with item and temporal order maintenance in working memory. Journal of Neuroscience, 31, 1080310810. http://dx.doi.org/10.1523/JNEUROSCI.0828-11.2011 Google Scholar
Hummel, J. E., & Holyoak, K. J. (1997). Distributed representations of structure: A theory of analogical access and mapping. Psychological Review, 104, 427466. http://dx.doi.org/10.1037/0033-295X.104.3.427 Google Scholar
Hummel, J. E., & Holyoak, K. J. (2003). A symbolic-connectionist theory of relational inference and generalization. Psychological Review, 110, 220264. http://dx.doi.org/10.1037/0033-295X.110.2.220 Google Scholar
Jaušovec, N., & Jaušovec, K. (2014). Increasing working memory capacity with theta transcranial alternating current stimulation (tACS). Biological Psychology, 96, 4247.Google Scholar
Jaušovec, N., Jaušovec, K., & Pahor, A. (2014). The influence of theta transcranial alternating current stimulation (tACS) on working memory storage and processing functions. Acta Psychologica, 146, 16.Google Scholar
Jensen, A. R. (1982). Reaction time and psychometric g . In Eysenck, H. J. (Ed.), A model for intelligence (pp. 93132). Berlin, Germany: Springer Verlag.Google Scholar
Jensen, A. R. (1998). The g factor: The science of mental ability. Westport, CT: Praeger.Google Scholar
Jensen, O., Bonnefond, M., & VanRullen, R. (2012). An oscillatory mechanisms for prioritizing salient unattended stimuli. Trends in Cognitive Sciences, 16, 200206. http://dx.doi.org/10.1016/j.tics.2012.03.002 Google Scholar
Jensen, O., & Colgin, L. L. (2007). Cross-frequency coupling between neuronal oscillations. Trends in Cognitive Sciences, 11, 267269. http://dx.doi.org/10.1016/j.tics.2007.05.003 Google Scholar
Jensen, O., Kaiser, J., & Lachaux, J. -P. (2007). Human gamma-frequency oscillations associated with attention and memory. Trends in Neurosciences, 30, 317324. http://dx.doi.org/10.1016/j.tins.2007.05.001 CrossRefGoogle ScholarPubMed
Jensen, O., & Lisman, J. E. (1998). An oscillatory short-term memory buffer model can account for data on the Sternberg task. Journal of Neuroscience, 18, 1068810699.Google Scholar
Johnson-Laird, P. N. (2006). How we reason? Oxford, UK: Oxford University Press.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, 135154. http://dx.doi.org/10.1017/S0140525X07001185 Google Scholar
Kamiński, J., Brzezicka, A., & Wróbel, A. (2011). Short-term memory capacity (7 ± 2) predicted by theta to gamma cycle length ratio. Neurobiology of Learning & Memory, 95, 1923.Google Scholar
Keizer, A. W., Verschoor, M., Verment, R. S., & Hommel, B. (2010). The effect of gamma enhancing neurofeedback on the control of feature bindings and intelligence measures. International Journal of Psychophysiology, 75, 2532. http://dx.doi.org/10.1016/j.ijpsycho.2009.10.011 Google Scholar
Knowlton, B. J., Morrison, R. G., Hummel, J. E., & Holyoak, K. J. (2012). A neurocomputational system for relational reasoning. Trends in Cognitive Sciences, 16, 373381. http://dx.doi.org/10.1016/j.tics.2012.06.002 CrossRefGoogle ScholarPubMed
Koene, R. A., & Hasselmo, M. E. (2007). First-in-first-out item replacement in a model of short-term memory based on persistent spiking. Cerebral Cortex, 17, 17661781. http://dx.doi.org/10.1093/cercor/bhl088 Google Scholar
Leszczyński, M., Fell, J., & Axmacher, N. (2015). Rhythmic working memory activation in the human hippocampus. Cell Reports, 13, 111.CrossRefGoogle ScholarPubMed
Lisman, J. E., & Idiart, M. A. P. (1995). Storage of 7 ± 2 short-term memories in oscillatory subcycles. Science, 267, 15121514. http://dx.doi.org/10.1126/science.7878473 Google Scholar
Lisman, J. E., & Jensen, O. (2013). The theta-gamma neural code. Neuron, 77, 10021016. http://dx.doi.org/10.1016/j.neuron.2013.03.007 Google Scholar
Luders, E., Narr, K. L., Thompson, P. M., & Toga, A. W. (2009). Neuroanatomical correlates of intelligence. Intelligence, 37, 156163. http://dx.doi.org/10.1016/j.intell.2008.07.002 Google Scholar
Nettlebeck, T., & Lally, M. (1976). Inspection time and measured intelligence. British Journal of Psychology, 67, 1722. http://dx.doi.org/10.1111/j.2044-8295.1976.tb01493.x Google Scholar
Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience and Biobehavioral Reviews, 33, 10041023. http://dx.doi.org/10.1016/j.neubiorev.2009.04.001 Google Scholar
Oberauer, K., Schultze, R., Wilhelm, O., & Süß, H.-M. (2005). Working memory and intelligence – their correlation and their relation: Comment on Ackerman, Beier, and Boyle (2005). Psychological Bulletin, 131, 6165. http://dx.doi.org/10.1037/0033-2909.131.1.61 Google Scholar
O’Keefe, J., & Recce, M. L. (1993). Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus, 3, 317330.Google Scholar
Onslow, A. C. E., Bogacz, R., & Jones, M. W. (2011). Quantifying phase-amplitude coupling in neuronal network oscillations. Progress in Biophysics and Molecular Biology, 105, 4957. http://dx.doi.org/10.1016/j.pbiomolbio.2010.09.007 Google Scholar
Othani, T., Nestor, P. G., Bouix, S., Saito, Y., Hosokawa, T., & Kubicki, M. (2014). Medial frontal white and gray matter contributions to general intelligence. PLoS One, 9, e112691. http://dx.doi.org/10.1371/journal.pone.0112691 Google Scholar
Pahor, A., & Jaušovec, N. (2014a). Theta-gamma cross-frequency coupling relates to the level of human intelligence. Intelligence, 46, 283290.Google Scholar
Pahor, A., & Jaušovec, N. (2014b). The effects of theta transcranial alternating current stimulation (tACS) on fluid intelligence. International Journal of Psychophysiology, 93, 322331. http://dx.doi.org/10.1016/j.ijpsycho.2014.06.015 Google Scholar
Palva, J. M., Monto, S., Kulashekhar, S., & Palva, S. (2010). Neuronal synchrony reveals working memory networks, predicts individual memory capacity. PNAS, 107, 75807585. http://dx.doi.org/10.1073/pnas.0913113107 Google Scholar
Palva, S., & Palva, J. M. (2012). Discovering oscillatory interaction networks with M/EEG: Challenges and breakthroughs. Trends in Cognitive Sciences, 16, 219230. http://dx.doi.org/10.1016/j.tics.2012.02.004 Google Scholar
Park, J. Y., Jhung, K., Lee, J., & An, S. K. (2013). Theta-gamma coupling during a working memory task as compared to a simple vigilance task. Neuroscience Letters, 532, 3943. http://dx.doi.org/10.1016/j.neulet.2012.10.061 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 and Biobehavioral Reviews, 57, 411432. http://dx.doi.org/10.1016/j.neubiorev.2015.09.017 Google Scholar
Polanía, R., Nitsche, M. A., Korman, C., Batsikadze, G., & Paulus, W. (2012). The importance of timing in segregated theta phase-coupling for cognitive performance. Current Biology, 22, 13141318.Google Scholar
Preacher, K. J., Rucker, D. D., MacCallum, R. C., & Nicewander, W. A. (2005). Use of the extreme groups approach: A critical reexamination and new recommendations. Psychological Methods, 10, 178192. http://dx.doi.org/10.1037/1082-989X.10.2.178 Google Scholar
Raffone, A., & Wolters, G. (2001). A cortical mechanism for binding in visual memory. Journal of Cognitive Neuroscience, 13, 766785. http://dx.doi.org/10.1162/08989290152541430 Google Scholar
Raghavachari, S., Kahana, M. J., Rizzuto, D. S., Caplan, J. B., Kirschen, M. P., Bourgeois, B., … Lisman, J. E. (2001). Gating of human theta oscillations by a working memory task. Journal of Neuroscience, 21, 31753183.CrossRefGoogle ScholarPubMed
Rasmussen, D., & Eliasmith, C. (2014). A spiking neural model applied to the study of human performance and cognitive decline on Raven’s advanced progressive matrices. Intelligence, 42, 5382.Google Scholar
Rizzuto, D. S., Madsen, J. R., Bromfield, E. B., Schulze-Bonhage, A., & Kahana, M. J. (2006). Human cortical oscillations exhibit theta phase differences between encoding and retrieval. NeuroImage, 31, 13521358.Google Scholar
Roberts, B. M., Hsieh, L.-T., & Ranganath, C. (2013). Oscillatory activity during maintenance of spatial and temporal information in working memory. Neuropsychologia, 51, 349357. http://dx.doi.org/10.1016/j.neuropsychologia.2012.10.009 Google Scholar
Roux, F., & Uhlhaas, P. J. (2014). Working memory and neural oscillations: Alpha-gamma versus theta-gamma codes for distinct WM information? Trends in Cognitive Sciences, 18, 1625. http://dx.doi.org/10.1016/j.tics.2013.10.010 Google Scholar
Salvucci, D. D., & Anderson, J. R. (2001). Integrating analogical mapping and general problem solving: The path-mapping theory. Cognitive Science, 25, 67110. http://dx.doi.org/10.1207/s15516709cog2501_4 Google Scholar
Santarnecchi, E., Galli, G., Polizzotto, N. R., Rossi, A., & Rossi, S. (2014). Efficiency of weak brain connections support general cognitive functioning. Human Brain Mapping, 35, 45664582. http://dx.doi.org/10.1002/hbm.22495 Google Scholar
Santarnecchi, E., Muller, T., Rossi, S., Sarkar, A., Polizzotto, N. R., Rossi, A., & Cohen Kadosh, R. (2016). Individual differences and specificity of prefrontal gamma frequency-tACS on fluid intelligence capabilities. Cortex, 75, 3343. http://dx.doi.org/10.1016/j.cortex.2015.11.003 Google Scholar
Santarnecchi, E., Polizzotto, N. R., Godone, M., Giovannelli, F., Feurra, M., Matzen, L., … Rossi, S. (2013). Frequency-dependent enhancement of fluid intelligence induced by transcranial oscillatory potentials. Current Biology, 23, 14491453. http://dx.doi.org/10.1016/j.cub.2013.06.022 Google Scholar
Sauseng, P., Griesmayr, B., Freunberger, R., & Klimesch, W. (2010). Control mechanisms in working memory. Neuroscience and Biobehavioral Reviews, 34, 10151022.Google Scholar
Sauseng, P., Klimesch, W., Heise, K. F., Gruber, W. R., Holz, E., Karim, A. A., … Hummel, F. C. (2009). Brain oscillatory substrates of visual short-term memory capacity. Current Biology, 19, 18461852. http://dx.doi.org/10.1016/j.cub.2009.08.062 Google Scholar
Schroeder, C. E., Wilson, D. A., Radman, T., Scharfman, H., & Lakatos, P. (2010). Dynamics of active sensing and perceptual selection. Current Opinion in Neurobiology, 20, 172176. http://dx.doi.org/10.1016/j.conb.2010.02.010 Google Scholar
Sederberg, P. B., Kahana, M. J., Howard, M. W., Donner, E. J., & Madsen, J. R. (2003). Theta and gamma oscillations during encoding predict subsequent recall. Journal of Neuroscience, 23, 1080910814.Google Scholar
Shastri, L., & Ajjanagadde, V. (1993). From simple associations to systematic reasoning. Behavioral and Brain Sciences, 16, 417494.Google Scholar
Sheppard, L. D., & Vernon, P. A. (2008). Intelligence and speed of information-processing: A review of 50 years of research. Personality and Individual Differences, 44, 535551. http://dx.doi.org/10.1016/j.paid.2007.09.015 Google Scholar
Siegel, M., Warden, M. R., & Miller, E. K. (2009). Phase-dependent neuronal coding of objects in short-term memory. PNAS, 106, 2134121346. http://dx.doi.org/10.1073/pnas.0908193106 Google Scholar
Stankov, L., Danthiir, V., Williams, L. M., Pallier, G., Roberts, R. D., & Gordon, E. (2006). Intelligence and the tunning-in of brain networks. Learning and Individual Differences, 16, 217233.Google Scholar
Tort, A. B. L., Komorowski, R. W., Manns, J. R., Kopell, N. J., & Eichenbaum, H. (2009). Theta-gamma coupling increases during the learning of item-context associations. PNAS, 106, 2094220947. http://dx.doi.org/10.1073/pnas.0911331106 Google Scholar
Troche, S. J., & Rammsayer, T. H. (2009). The influence of temporal resolution power and working memory capacity on psychometric intelligence. Intelligence, 37, 479486. http://dx.doi.org/10.1016/j.intell.2009.06.001 Google Scholar
Usher, M., Cohen, J. D., Haarmann, H., & Horn, D. (2001). Neural mechanism for the magical number 4: Competitive interactions and nonlinear oscillation. Behavioral and Brain Sciences, 24, 151152. http://dx.doi.org/10.1017/S0140525X01583922 Google Scholar
Vogel, E. K., Woodman, G. F., & Luck, S. J. (2001). Storage of features, conjunctions, and objects in visual working memory. Journal of Experimental Psychology: Human Perception & Performance, 27, 92114. http://dx.doi.org/10.1037/0096-1523.27.1.92 Google Scholar
Voloh, B., & Womelsdorf, T. (2016). A role of phase-resetting in coordinating large scale neural networks during attention and goal-directed behavior. Frontiers in Systems Neuroscience, 10, 18. http://dx.doi.org/10.3389/fnsys.2016.00018 Google Scholar
von der Malsburg, C. (1981). The correlation theory of brain function. Internal report 81–2. Göttingen, Germany: Max Planck Institute for Biophysical Chemistry.Google Scholar
Voytek, B., Canolty, R. T., Shestyuk, A., Crone, N. E., Parvizi, J., & Knoght, R. T. (2010). Shifts in gamma phase–amplitude coupling frequency from theta to alpha over posterior cortex during visual tasks. Frontiers in Human Neuroscience, 4, 191.Google Scholar