Parallel or sequential? Decoding conceptual and phonological/phonetic information from MEG signals during language production

References

• Abdel Rahman, R., & Sommer, W. (2008). Seeing what we know and understand: How knowledge shapes perception. Psychonomic Bulletin & Review, 15(6), 1055–1063. https://doi.org/10.3758/PBR.15.6.1055
   PubMed Web of Science ®Google Scholar
• Akbari, H., Khalighinejad, B., Herrero, J. L., Mehta, A. D., & Mesgarani, N. (2019). Towards reconstructing intelligible speech from the human auditory cortex. Scientific Reports, 9(1), 1–12. https://doi.org/10.1038/s41598-018-37359-z
   PubMedGoogle Scholar
• Ala-Salomäki, H., Kujala, J., Liljeström, M., & Salmelin, R. (2021). Picture naming yields highly consistent cortical activation patterns: Test–retest reliability of magnetoencephalography recordings. Neuroimage, 227, 117651. https://doi.org/10.1016/j.neuroimage.2020.117651
   PubMed Web of Science ®Google Scholar
• Bastiaansen, M. C., & Knösche, T. R. (2000). Tangential derivative mapping of axial MEG applied to event-related desynchronization research. Clinical Neurophysiology, 111(7), 1300–1305. https://doi.org/10.1016/S1388-2457(00)00272-8
   PubMed Web of Science ®Google Scholar
• Bell, A. J., & Sejnowski, T. J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129–1159. https://doi.org/10.1162/neco.1995.7.6.1129
   PubMed Web of Science ®Google Scholar
• Boersma, P., & Weenink, D. (2019). Praat: doing phonetics by computer [Computer program]. Version 6.0.37 from http://www.praat.org/
   Google Scholar
• Bouchard, K. E., Mesgarani, N., Johnson, K., & Chang, E. F. (2013). Functional organization of human sensorimotor cortex for speech articulation. Nature, 495(7441), 327–332. https://doi.org/10.1038/nature11911
   PubMed Web of Science ®Google Scholar
• Brodeur, M. B., Guérard, K., & Bouras, M. (2014). Bank of standardized stimuli (BOSS) phase II: 930 new normative photos. PLoS One, 9(9), e106953. https://doi.org/10.1371/journal.pone.0106953
   PubMed Web of Science ®Google Scholar
• Carlson, T. A., Simmons, R. A., Kriegeskorte, N., & Slevc, L. R. (2014). The emergence of semantic meaning in the ventral temporal pathway. Journal of Cognitive Neuroscience, 26(1), 120–131. https://doi.org/10.1162/jocn_a_00458
   PubMed Web of Science ®Google Scholar
• Carota, F., Kriegeskorte, N., Nili, H., & Pulvermüller, F. (2017). Representational mapping of distributional semantics in left inferior frontal, middle temporal and motor cortex. Cerebral Cortex. https://doi.org/10.1093/cercor/bhw379
   PubMed Web of Science ®Google Scholar
• Carota, F., Nili, H., Pulvermüller, F., & Kriegeskorte, N. (2021). Distinct fronto-temporal substrates of distributional and taxonomic similarity among words: evidence from RSA of BOLD signals. NeuroImage, 224, 117408. https://doi.org/10.1016/j.neuroimage.2020.117408
   PubMed Web of Science ®Google Scholar
• Carota, F., Schoffelen, J.-M., Oostenveld, R., & Indefrey, P. (2022). The time course of language production as revealed by pattern classification of meg sensor data. Journal of Neuroscience, 42(29), 5745–5754. https://doi.org/10.1523/JNEUROSCI.1923-21
   PubMed Web of Science ®Google Scholar
• Cichy, R. M., & Pantazis, D. (2017). Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space. NeuroImage, 158, 441–454. https://doi.org/10.1016/j.neuroimage.2017.07.023
   PubMed Web of Science ®Google Scholar
• Cichy, R. M., Pantazis, D., & Oliva, A. (2014). Resolving human object recognition in space and time. Nature Neuroscience, 17(3), 455–462. https://doi.org/10.1038/nn.3635
   PubMed Web of Science ®Google Scholar
• Clarke, A., Taylor, K. I., Devereux, B., Randall, B., & Tyler, L. K. (2013). From perception to conception: how meaningful objects are processed over time. Cerebral Cortex, 23(1), 187–197. https://doi.org/10.1093/cercor/bhs002
   PubMed Web of Science ®Google Scholar
• Contini, E. W., Goddard, E., Grootswagers, T., Williams, M., & Carlson, T. (2020). A humanness dimension to visual object coding in the brain. NeuroImage, 221, 117139. https://doi.org/10.1016/j.neuroimage.2020.117139
   PubMed Web of Science ®Google Scholar
• Corchs, S. E., Ciocca, G., Bricolo, E., & Gasparini, F. (2016). Predicting complexity perception of real world images. PLoS One, 11, e0157986.
   PubMed Web of Science ®Google Scholar
• Dell, G. S., & Gordon, J. K. (2003). Phonetics and phonology in language comprehension and production. Phonetics and Phonology in Language Comprehension and Production: Differences and Similarities, 6, 9–38. https://doi.org/10.1515/9783110895094.9
   Google Scholar
• Dell, G. S., Schwartz, M. F., Martin, N., Saffran, E. M., & Gagnon, D. A. (1997). Lexical access in aphasic and nonaphasic speakers. Psychological Review, 104(4), 801. https://doi.org/10.1037/0033-295X.104.4.801
   PubMed Web of Science ®Google Scholar
• Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009
   PubMed Web of Science ®Google Scholar
• Fairs, A., Michelas, A., Dufour, S., & Strijkers, K. (2021). The same ultra-rapid parallel brain dynamics underpin the production and perception of speech. Cerebral Cortex Communications, 2(3), tgab040. https://doi.org/10.1093/texcom/tgab040
   PubMedGoogle Scholar
• Forsythe, A., Mulhern, G., & Sawey, M. (2008). Confounds in pictorial sets: The role of complexity and familiarity in basic-level picture processing. Behavior Research Methods, 40, 116–129.
   PubMed Web of Science ®Google Scholar
• Fuster, J. M. (2009). Cortex and memory: Emergence of a new paradigm. Journal of Cognitive Neuroscience, 21(11), 2047–2072. https://doi.org/10.1162/jocn.2009.21280
   PubMed Web of Science ®Google Scholar
• Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C., Parkkonen, L., & Hämäläinen, M. S. (2014). MNE software for processing MEG and EEG data. Neuroimage, 1(86), 446–460. https://doi.org/10.1016/j.neuroimage.2013.10.027
   Google Scholar
• Guo, T. M., Peng, D. L., Lu, C. M., & Liu, H. Y. (2005). The temporal course of semantic and phonological activation in Chinese word production: An ERP study. Acta Psychologica Sinica, 37, 569–574.
   Google Scholar
• Hagoort, P. (2020). The meaning-making mechanism(s) behind the eyes and between the ears. Philosophical Transactions of the Royal Society B: Biological Sciences, 375(1791), 20190301. https://doi.org/10.1098/rstb.2019.0301
   PubMed Web of Science ®Google Scholar
• Hagoort, P., & Levelt, W. J. (2009). The speaking brain. Science, 326(5951), 372–373. https://doi.org/10.1126/science.1181675
   PubMed Web of Science ®Google Scholar
• Hämäläinen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J., & Lounasmaa, O. V. (1993). Magnetoencephalography – theory, instrumentation, and applications to noninvasive studies of the working human brain. Reviews of Modern Physics, 65(2), 413–497. https://doi.org/10.1103/RevModPhys.65.413
   Web of Science ®Google Scholar
• Hanulová, J., Davidson, D. J., & Indefrey, P. (2011). Where does the delay in L2 picture naming come from? Psycholinguistic and neurocognitive evidence on second language word production. Language and Cognitive Processes, 26(7), 902–934. https://doi.org/10.1080/01690965.2010.509946
   Web of Science ®Google Scholar
• Harley, T. A., & Bown, H. E. (1998). What causes a tip-of-the-tongue state? Evidence for lexical neighbourhood effects in speech production. British Journal of Psychology, 89(1), 151–174. https://doi.org/10.1111/j.2044-8295.1998.tb02677.x
   Web of Science ®Google Scholar
• Hauk, O., Davis, M. H., Ford, M., Pulvermüller, F., & Marslen-Wilson, W. D. (2006). The time course of visual word recognition as revealed by linear regression analysis of ERP data. Neuroimage, 30(4), 1383–1400. https://doi.org/10.1016/j.neuroimage.2005.11.048
   PubMed Web of Science ®Google Scholar
• Hebb, D. O. (1949). The organization of behavior; a neuropsychological theory. Wiley.
   Google Scholar
• Hickok, G. (2022). Handbook of clinical neurology. Handbook of Clinical Neurology, 185, 57–69. https://doi.org/10.1016/B978-0-12-823384-9.00003-7
   PubMedGoogle Scholar
• Hickok, G., Houde, J., & Rong, F. (2011). Sensorimotor integration in speech processing: Computational basis and neural organization. Neuron, 69(3), 407–422.
   PubMed Web of Science ®Google Scholar
• Hultén, A., Vihla, M., Laine, M., & Salmelin, R. (2009). Accessing newly learned names and meanings in the native language. Human Brain Mapping, 30(3), 976–989. https://doi.org/10.1002/hbm.20561
   PubMed Web of Science ®Google Scholar
• Indefrey, P. (2011). The spatial and temporal signatures of word production components: A critical update. Frontiers in Psychology, 2, 255. https://doi.org/10.3389/fpsyg.2011.00255
   PubMed Web of Science ®Google Scholar
• Indefrey, P. (2016). On putative shortcomings and dangerous future avenues: response to Strijkers & Costa. Language, Cognition and Neuroscience, 31(4), 517–520. https://doi.org/10.1080/23273798.2015.1128554
   Web of Science ®Google Scholar
• Indefrey, P., & Levelt, W. J. (2004). The spatial and temporal signatures of word production components. Cognition, 92(1-2), 101–144. https://doi.org/10.1016/j.cognition.2002.06.001
   PubMed Web of Science ®Google Scholar
• Isik, L., Meyers, E. M., Leibo, J. Z., & Poggio, T. (2014). The dynamics of invariant object recognition in the human visual system. Journal of Neurophysiology, 111(1), 91–102. https://doi.org/10.1152/jn.00394.2013
   PubMed Web of Science ®Google Scholar
• Jürgens, U. (2009). The neural control of vocalization in mammals: A review. Journal of Voice, 23(1), 1–10. https://doi.org/10.1016/j.jvoice.2007.07.005
   PubMed Web of Science ®Google Scholar
• Kaiser, D., Oosterhof, N. N., & Peelen, M. V. (2016). The neural dynamics of attentional selection in natural scenes. The Journal of Neuroscience, 36(41), 10522–10528. https://doi.org/10.1523/JNEUROSCI.1385-16.2016
   PubMed Web of Science ®Google Scholar
• Keuleers, E., Brysbaert, M., & New, B. (2010). SUBTLEX-NL: A new measure for Dutch word frequency based on film subtitles. Behavior Research Methods, 42(3), 643–650. https://doi.org/10.3758/BRM.42.3.643
   PubMed Web of Science ®Google Scholar
• Kietzmann, T. C., Spoerer, C. J., Sörensen, L. K., Cichy, R. M., Hauk, O., & Kriegeskorte, N. (2019). Recurrence is required to capture the representational dynamics of the human visual system. Proceedings of the National Academy of Sciences, 116(43), 21854–21863. https://doi.org/10.1073/pnas.1905544116
   PubMed Web of Science ®Google Scholar
• Kriegeskorte, N., Mur, M., & Bandettini, P. A. (2008). Representational similarity analysis – connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2(1), 4–5. https://doi.org/10.3389/neuro.01.016.2008
   PubMedGoogle Scholar
• Laaksonen, H., Kujala, J., Hultén, A., Liljeström, M., & Salmelin, R. (2012). MEG evoked responses and rhythmic activity provide spatiotemporally complementary measures of neural activity in language production. NeuroImage, 60(1), 29–36. https://doi.org/10.1016/j.neuroimage.2011.11.087
   PubMed Web of Science ®Google Scholar
• Laganaro, M. (2017). Inter-study and inter-Individual consistency and variability of EEG/ERP microstate sequences in referential word production. Brain Topography, 30(6), 785–796. https://doi.org/10.1007/s10548-017-0580-0
   PubMed Web of Science ®Google Scholar
• Levelt, W. J. (1989). Speaking: From intention to articulation. The MIT Press.
   Google Scholar
• Levelt, W. J. (1999). Models of word production. Trends in Cognitive Sciences, 3(6), 223–232. https://doi.org/10.1016/S1364-6613(99)01319-4
   PubMed Web of Science ®Google Scholar
• Levelt, W. J., Praamstra, P., Meyer, A. S., Helenius, P., & Salmelin, R. (1998). An MEG study of picture naming. Journal of Cognitive Neuroscience, 10(5), 553–567. https://doi.org/10.1162/089892998562960
   PubMed Web of Science ®Google Scholar
• Levelt, W. J., Roelofs, A., & Meyer, A. S. (1999). A theory of lexical access in speech production. Behavioral and Brain Sciences, 22(1), 1–38.
   PubMed Web of Science ®Google Scholar
• Liljeström, M., Hultén, A., Parkkonen, L., & Salmelin, R. (2009). Comparing MEG and fMRI views to naming actions and objects. Human Brain Mapping, 30(6), 1845–1856. https://doi.org/10.1002/hbm.20785
   PubMed Web of Science ®Google Scholar
• Liljeström, M., Stevenson, C., Kujala, J., & Salmelin, R. (2015). Task- and stimulus-related cortical networks in language production: Exploring similarity of MEG- and fMRI-derived functional connectivity. NeuroImage, 120, 75–87. https://doi.org/10.1016/j.neuroimage.2015.07.017
   PubMed Web of Science ®Google Scholar
• Maess, B., Friederici, A. D., Damian, M., Meyer, A. S., & Levelt, W. J. (2002). Semantic category interference in overt picture naming: Sharpening current density localization by PCA. Journal of Cognitive Neuroscience, 14(3), 455–462. https://doi.org/10.1162/089892902317361967
   PubMed Web of Science ®Google Scholar
• Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG- and MEG-data. Journal of Neuroscience Methods, 164(1), 177–190. https://doi.org/10.1016/j.jneumeth.2007.03.024
   PubMed Web of Science ®Google Scholar
• Mesgarani, N., Cheung, C., Johnson, K., & Chang, E. F. (2014). Phonetic feature encoding in human superior temporal gyrus. Science, 343(6174), 1006–1010. https://doi.org/10.1126/science.1245994
   PubMed Web of Science ®Google Scholar
• Miozzo, M., Pulvermüller, F., & Hauk, O. (2015). Early parallel activation of semantics and phonology in picture naming: Evidence from a multiple linear regression MEG study. Cerebral Cortex, 25(10), 3343–3355. https://doi.org/10.1093/cercor/bhu137
   PubMed Web of Science ®Google Scholar
• Mitchell, T. M. (1997). Machine learning. McGraw-Hill.
   Google Scholar
• Munding, D., Dubarry, A. S., & Alario, F. X. (2016). On the cortical dynamics of word production: A review of the MEG evidence. Language, Cognition and Neuroscience, 31(4), 441–462. https://doi.org/10.1080/23273798.2015.1071857
   Web of Science ®Google Scholar
• Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J. M. (2011). FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Computational Intelligence and Neuroscience, 2011. https://doi.org/10.1155/2011/156869
   Web of Science ®Google Scholar
• Papoutsi, M., de Zwart, J. A., Jansma, J. M., Pickering, M. J., Bednar, J. A., & Horwitz, B. (2009). From phonemes to articulatory codes: An fMRI study of the role of Broca’s area in speech production. Cerebral Cortex, 19(9), 2156–2165. https://doi.org/10.1093/cercor/bhn239
   PubMed Web of Science ®Google Scholar
• Price, C. J., Devlin, J. T., Moore, C. J., Morton, C., & Laird, A. R. (2005). Meta-analyses of object naming: Effect of baseline. Human Brain Mapping, 25(1), 70–82. https://doi.org/10.1002/hbm.20132
   PubMed Web of Science ®Google Scholar
• Proklova, D., Kaiser, D., & Peelen, M. V. (2019). MEG sensor patterns reflect perceptual but not categorical similarity of animate and inanimate objects. NeuroImage, 193, 167–177. https://doi.org/10.1016/j.neuroimage.2019.03.028
   PubMed Web of Science ®Google Scholar
• Rahman, R. A., & Sommer, W. (2003). Does phonological encoding in speech production always follow the retrieval of semantic knowledge? Electrophysiological evidence for parallel processing Cognitive Brain Research, 16(3), 372–382. https://doi.org/10.1016/S0926-6410(02)00305-1
   PubMedGoogle Scholar
• Rapp, B., & Goldrick, M. (2000). Discreteness and interactivity in spoken word production. Psychological Review, 107(3), 460. https://doi.org/10.1037/0033-295X.107.3.460
   PubMed Web of Science ®Google Scholar
• Rogalsky, C., Basilakos, A., Rorden, C., Pillay, S., LaCroix, A. N., Keator, L., Mickelsen, S., Anderson, S. W., Love, T., Fridriksson, J., Binder, J., & Hickok, G. (2022). The neuroanatomy of speech processing: A large-scale lesion study. Journal of Cognitive Neuroscience, 34(8), 1355–1375.
   PubMedGoogle Scholar
• Roos, N. M., & Piai, V. (2020). Across-session consistency of context-driven language processing: A magnetoencephalography study. European Journal of Neuroscience, 52(5), 3457–3469. https://doi.org/10.1111/ejn.14785
   PubMed Web of Science ®Google Scholar
• Rosenholtz, R., Li, Y., & Nakano, L. (2007). Measuring visual clutter. Journal of Vision, 7, 17.
   PubMed Web of Science ®Google Scholar
• Salmelin, R., Hari, R., Lounasmaa, O. V., & Sams, M. (1994). Dynamics of brain activation during picture naming. Nature, 368(6470), 463–465. https://doi.org/10.1038/368463a0
   PubMed Web of Science ®Google Scholar
• Schoffelen, J.-M., Hultén, A., Lam, N., Marquand, A. F., Uddén, J., & Hagoort, P. (2017). Frequency-specific directed interactions in the human brain network for language. Proceedings of the National Academy of Sciences of the United States of America, 114, 8083–8088. https://doi.org/10.1073/pnas.1703155114
   PubMed Web of Science ®Google Scholar
• Simanova, I., Hagoort, P., Oostenveld, R., & Van Gerven, M. A. (2014). Modality-independent decoding of semantic information from the human brain. Cerebral Cortex, 24(2), 426–434. https://doi.org/10.1093/cercor/bhs324
   PubMed Web of Science ®Google Scholar
• Simanova, I., Van Gerven, M., Oostenveld, R., & Hagoort, P. (2010). Identifying object categories from event-related EEG: toward decoding of conceptual representations. PLoS One, 5(12), e14465. https://doi.org/10.1371/journal.pone.0014465
   PubMed Web of Science ®Google Scholar
• Sörös, P., Cornelissen, K., Laine, M., & Salmelin, R. (2003). Naming actions and objects: Cortical dynamics in healthy adults and in an anomic patient with a dissociation in action/object naming. Neuroimage, 19(4), 1787–1801. https://doi.org/10.1016/S1053-8119(03)00217-9
   PubMed Web of Science ®Google Scholar
• Stokes, M. G., Wolff, M. J., & Spaak, E. (2015). Decoding rich spatial information with high temporal resolution. Trends in Cognitive Sciences, 19(11), 636–638. https://doi.org/10.1016/j.tics.2015.08.016
   PubMed Web of Science ®Google Scholar
• Stolk, A., Todorovic, A., Schoffelen, J. M., & Oostenveld, R. (2013). Online and offline tools for head movement compensation in MEG. Neuroimage, 68, 39–48. https://doi.org/10.1016/j.neuroimage.2012.11.047
   PubMed Web of Science ®Google Scholar
• Strijkers, K., & Costa, A. (2016). The cortical dynamics of speaking: Present shortcomings and future avenues. Language, Cognition and Neuroscience, 31(4), 484–503. https://doi.org/10.1080/23273798.2015.1120878
   Web of Science ®Google Scholar
• Strijkers, K., Costa, A., & Pulvermüller, F. (2017). The cortical dynamics of speaking: Lexical and phonological knowledge simultaneously recruit the frontal and temporal cortex within 200 ms. NeuroImage, 163, 206–219. https://doi.org/10.1016/j.neuroimage.2017.09.041
   PubMed Web of Science ®Google Scholar
• Teghipco, A., Okada, K., Murphy, E., & Hickok, G. (2022). Predictive coding and internal error correction in speech production. Neurobiology of Language, 1–77.
   Google Scholar
• Treder, M. S. (2020). MVPA-light: A classification and regression toolbox for multi-dimensional data. Frontiers in Neuroscience, 14, 289. https://doi.org/10.3389/fnins.2020.00289
   PubMed Web of Science ®Google Scholar
• Tremblay, P., & Gracco, V. L. (2010). On the selection of words and oral motor responses: Evidence of a response-independent fronto-parietal network. Cortex, 46(1), 15–28. https://doi.org/10.1016/j.cortex.2009.03.003
   PubMed Web of Science ®Google Scholar
• Tremblay, P., & Small, S. (2011). Motor response selection in overt sentence production: A functional MRI study. Frontiers in Psychology, 2. https://doi.org/10.3389/fpsyg.2011.00253
   PubMed Web of Science ®Google Scholar
• Turken, A. U., & Dronkers, N. F. (2011). The neural architecture of the language comprehension network: Converging evidence from lesion and connectivity analyses. Frontiers in System Neuroscience, 5, 1. https://doi.org/10.3389/fnsys.2011.00001
   PubMedGoogle Scholar
• Van Essen, D. C., Drury, H. A., Dickson, J., Harwell, J., Hanlon, D., & Anderson, C. H. (2001). An integrated software suite for surface-based analyses of cerebral cortex. Journal of the American Medical Informatics Association, 8(5), 443–459. https://doi.org/10.1136/jamia.2001.0080443
   PubMed Web of Science ®Google Scholar
• Van Veen, B. D., Van Drongelen, W., Yuchtman, M., & Suzuki, A. (1997). Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Transactions on Biomedical Engineering, 44(9), 867–880. https://doi.org/10.1109/10.623056
   PubMed Web of Science ®Google Scholar
• Vihla, M., Laine, M., & Salmelin, R. (2006). Cortical dynamics of visual/semantic vs. phonological analysis in picture confrontation. Neuroimage, 33(2), 732–738. https://doi.org/10.1016/j.neuroimage.2006.06.040
   PubMed Web of Science ®Google Scholar
• Vindiola, M., & Wolmetz, M. (2011). Mental encoding and neural decoding of abstract cognitive categories: A commentary and simulation. Neuroimage, 54(4), 2822–2827. https://doi.org/10.1016/j.neuroimage.2010.09.091
   PubMed Web of Science ®Google Scholar
• Vitevitch, M. S. (2002). The influence of phonological similarity neighborhoods on speech production. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28(4), 735. https://doi.org/10.1037/0278-7393.28.4.735
   PubMed Web of Science ®Google Scholar
• Vitevitch, M. S., Luce, P. A., Pisoni, D. B., & Auer, E. T. (1999). Phonotactics, neighborhood activation, and lexical access for spoken words. Brain Lang, 68, 306–311. https://doi.org/10.1006/brln.1999.2116 pmid:10433774
   PubMed Web of Science ®Google Scholar
• Wilson, S. M., Henry, M. L., Besbris, M., Ogar, J. M., Dronkers, N. F., & Jarrold, W., Miller B. L., Gorno-Tempini M.L. (2010) Connected speech production in three variants of primary progressive aphasia. Brain, 133(7), 2069–2088.
   PubMed Web of Science ®Google Scholar