Modeling nonlinear relationships in ERP data using mixed-effects regression with R examples

Psychophysiology

Volumn 52, Issue 1, 2015, Pages 124-139

  Tremblay, Antoine ^a   Newman, Aaron J ^a  

^a DALHOUSIE UNIVERSITY   (Canada)

Author keywords

EEG; ERP; GAMM; Nonlinear relationships; Regression splines

Indexed keywords

ADULT; ELECTROENCEPHALOGRAPHY; EVOKED RESPONSE; FEMALE; HUMAN; NONLINEAR SYSTEM; PHYSIOLOGY; RECALL; REGRESSION ANALYSIS; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; YOUNG ADULT;

ADULT; ELECTROENCEPHALOGRAPHY; EVOKED POTENTIALS; FEMALE; HUMANS; MENTAL RECALL; MODELS, STATISTICAL; NONLINEAR DYNAMICS; REGRESSION ANALYSIS; YOUNG ADULT;

EID: 84919844737     PISSN: 00485772     EISSN: 14698986     Source Type: Journal    
DOI: 10.1111/psyp.12299     Document Type: Article

References (56)

1. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. Petrov & F. Csaki (Eds.), 2nd International Symposium on Information Theory (pp. 267-281). Budapest, Hungary.
2. Assadollahi, R., & Pulvermüller, F. (2003). Early influences of word length and frequency: A group study using MEG. NeuroReport, 14, 1183-1187. doi: 10.1097/01.wnr.0000075305.76650.60
3. Baayen, R. H. (2008). Analyzing linguistic data: A practical introduction to statistics using R. Cambridge, UK: Cambridge University Press. doi: 10.1017/S0305000909990080
4. Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59, 390-412. doi: 10.1016/j.jml.2007.12.005
5. Baayen, R. H., Kuperman, V., & Bertram, R. (2010). Frequency effects in compound processing. In S. Scalise & I. Vogel (Eds.), Compounding. (pp. 257-270). Amsterdam, The Netherlands: John Benjamins.
6. Bagiella, E., Sloan, R., & Heitjan, D. (2000). Mixed-effects models in psychophysiology. Psychophysiology, 37, 13-20. doi: 10.1111/1469-8986.3710013
7. Boutheina, J., Coutya, J., Langer, C., & Roy, S. (2009). From upright to upside-down presentation: A spatio-temporal ERP study of the parametric effect of rotation on face and house processing. BMC Neuroscience, 10, 1-17. doi: 10.1186/1471-2202-10-100
8. Burnham, K., Anderson, D., & Huyvaert, K. (2011). AIC model selection and multimodel inference in behavioral ecology: Some background, observations, and comparisons. Behavioral Ecology and Sociobiology, 65, 25-35. doi: 10.1007/s00265-010-1029-6
9. Carbon, C.-C., Schweinberger, S. R., Kaufmann, J. M., & Leder, H. (2005). The Thatcher illusion seen by the brain: An event-related brain potentials study. Cognitive Brain Research, 24, 544-555. doi: 10.1016/j.cogbrainres.2005.03.008
10. Cohen, J. (1983). The cost of dichotomization. Applied Psychological Measurement, 7, 249-254. doi: 10.1177/014662168300700301
11. Daneman, M., & Carpenter, P. A. (1980). Individual differences in working memory and reading. Journal of Verbal Learning and Verbal Behavior, 19, 450-466. doi: 10.1037/0278-7393.9.4.561
12. Davies, M. (2004). BYU-BNC: The British national corpus. Available online at http://corpus.byu.edu/bnc
13. Faraway, J. J. (2005). Extending the linear model with R: Generalized linear, mixed effects and nonparametric regression models. New York, NY: Chapman & Hall. doi: 10.1007/s00180-009-0152-1
14. Fletcher, W. H. (2008). Phrases in English. Available online at http://pie.usna.edu/index.html
15. Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge, UK: Cambridge University Press. doi: 10.1017/CBO9780511790942
16. Gratton, G., Coles, M., & Donchin, E. (1983). A new method for off-line removal of ocular artifact. Electroencephalography and Clinical Neurophysiology, 55, 468-484. doi: 10.1016/0013-4694(83)90135-9
17. Gu, C. (2002). Smooth splines ANOVA models. New York, NY: Springer.
18. Gu, C., & Wahba, G. (1991). Minimizing GCV/GML scores with multiple smoothing parameters via the Newton method. SIAM Journal of Scientific and Statistical Computing, 12, 383-398.
19. Hastie, T., & Tibshirani, R. (1990). Generalized additive model regression. New York, NY: Chapman & Hall.
20. Hauk, O., Davis, M., Ford, M., Pulvermüller, F., & Marslen-Wilson, W. (2006). The time course of visual word recognition as revealed by linear regression analysis of ERP data. NeuroImage, 30, 1383-1400. doi: 10.1016/j.clinph.2003.12.020
21. Hauk, O., & Pulvermüller, F. (2004). Effects of word length and frequency on the human event-related potential. Clinical Neurophysiology, 115, 1090-1103. doi: 10.1016/j.clinph.2003.12.020
22. Hofman, D. A., & Gavin, M. B. (1998). Centering decisions in hierarchical linear models: Implications for research in organizations. Journal of Management, 24, 623-641. doi: 10.1177/014920639802400504
23. Inui, K., Urakawa, T., Yamashiro, K., Otsuru, N., Nishihara, M., Takeshima, Y., Kakigi, R. (2010). Non-linear laws of echoic memory and auditory change detection in humans. BMC Neuroscience, 11, 1-13. doi: 10.1186/1471-2202-11-80
24. Jones, T., & Oberauer, K. (2013). Serial-position effects for items and relations in short-term memory. Memory, 21, 347-365. doi: 10.1080/09658211.2012.726629
25. Keele, L. (2008). Semiparametric regression for the social sciences. New York, NY: Chapman & Hall/CRC. doi: 10.1002/9780470998137
26. Kreft, I., & De Leeuw, J. (1998). Introducing multilevel modeling. London, UK: Sage. doi: 10.4135/9781849209366
27. Kriegeskorte, N., Simmons, W., Bellgowan, P., & Baker, C. (2009). Circular analysis in systems neuroscience: The dangers of double dipping. Nature Neuroscience, 12, 535-540. doi: 10.1038/nn.2303
28. Kryuchkova, T., Tucker, B. V., Wurm, L. H., & Baayen, R. H. (2012). Danger and usefulness are detected early in auditory lexical processing: Evidence from electroencephalography. Brain and Language, 122, 81-91. doi: 10.1016/j.bandl.2012.05.005
29. Lin, X., & Zhang, D. (1999). Inference in generalized additive mixed models by using smoothing splines. Journal of the Royal Statistical Society, 61, 381-400. doi: 1369-7412/99/61381
30. Luck, S. J. (2005). An introduction to the event-related potential technique. Cambridge, MA: MIT Press.
31. Marra, G., & Wood, S. N. (2012). On p-values for smooth components of an extended generalized additive model. Scandinavian Journal of Statistics, 39, 53-74. doi: 10.1111/j.1467-9469.2011.00760.x
32. McCallum, R. C., Zhang, S., Preacher, K. J., & Rucker, D. D. (2002). On the practice of dichotomization of quantitative variables. Psychological Methods, 7, 19-40. doi: 10.1037/1082-989X.7.1.19
33. Murphy, K., Roodenrys, S., & Fox, A. (2006). Event-related potential correlates of the word length effect in working memory. Brain Research, 1112, 179-190. doi: 10.1016/j.brainres.2006.07.007
34. Newman, A., Tremblay, A., Nichols, E. S., Neville, H. J., & Ullman, M. (2012). The influence of language proficiency on lexical semantic processing in native and late learners of English. Journal of Cognitive Neuroscience, 25, 1205-1223. doi: 10.1162/jocn_a_00143
35. Oldfield, R. C. (1971). The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologica, 9, 97-113. doi: 10.1016/0028-3932(71)90067-4
36. Pablos Martin, X., Deltenre, P., Rossion, B., Hoonhorst, I., & Colin, C. (2007). Perceptual biases for rhythm: The mismatch negativity latency indexes the privileged status of binary vs non-binary interval ratios. Clinical Neurophysiology, 118, 2709-2715. doi: 10.1016/j.clinph.2007.08.019
37. Penolazzi, B., Hauk, O., & Pulvermüller, F. (2007). Early semantic context integration and lexical access as revealed by event-related brain potentials. Biological Psychology, 72, 373-388. doi: 10.1016/j.biopsycho.2006.09.008
38. Pinheiro, J. C., & Bates, D. M. (2000). Mixed-effects models in S and S-plus. New York, NY: Springer.
39. Quené, H., & van den Bergh, H. (2008). Examples of mixed-effects modeling with crossed random effects and with binomial data. Journal of Memory and Language, 59, 413-425. doi: 10.1016/j.jml.2008.02.002
40. R Development Core Team. (2013). R: A language and environment for statistical computing [Computer software manual]. Vienna, Austria. Retrieved from http://www.R-project.org
41. Ruppert, D., Wand, M., & Carroll, R. (2003). Semiparametric regression. Cambridge, UK: Cambridge University Press. doi: 10.1017/CBO9780511755453
42. Sahai, H., & Ageel, M. I. (1997). Analysis of variance: Fixed, random and mixed models. Boston, MA: Birkhauser.
43. Sereno, S., Rayner, K., & Posner, M. (1998). Establishing a time-line of word recognition: Evidence from eye movements and event-related potentials. NeuroReport, 9, 2195-2200. doi: 10.1097/00001756-199807130-00009
44. Symonds, M., & Moussalli, A. (2011). A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike's information criterion. Behavioral Ecology and Sociobiology, 65, 13-21. doi: 10.1007/s00265-010-1037-6
45. Tremblay, A. (2009). Processing advantages of lexical bundles: Evidence from self-paced reading, word and sentence recall, and free recall with event-related brain potential recordings. (Doctoral dissertation). University of Alberta, Edmonton, AB, Canada.
46. Tremblay, A., & Baayen, R. H. (2010). Holistic processing of regular four-word sequences: A behavioral and ERP study of the effects of structure, frequency, and probability on immediate free recall. In D. Wood (Ed.), Perspectives on formulaic language: Acquisition and communication (pp. 151-173). London, UK: Continuum.
47. Tremblay, A., & Tucker, B. V. (2011). The effects of n-gram probabilistic measures on the recognition and production of four-word sequences. Mental Lexicon, 6, 302-324. doi: 10.1075/ml.6.2.04tre
48. Venables, W. (1998). Exegeses on linear models. Paper presented at the S-PLUS User's Conference, Washington, DC. Retrieved from http://www.stats.ox.ac.uk/pub/MASS3/Exegeses.pdf
49. Vossen, H., van Breukelen, G., Hermens, H., van Os, J., & Lousberg, R. (2011). More potential in statistical analyses of event-related potentials: A mixed regression approach. International Journal of Methods in Psychiatric Research, 20, e56-e68. doi: 10.1002/mpr.348
50. Webb, S. J., Long, J. D., & Nelson, C. A. (2005). A longitudinal investigation of visual event-related potentials in the first year of life. Developmental Science, 8, 605-616. doi: 10.1111/j.1467-7687.2005.00452.x
51. Wood, S. N. (2006). Generalized additive models. New York, NY: Chapman & Hall/CRC.
52. Wood, S. N. (2012). mgcv: Mixed GAM computation vehicle with GCV/AIC/REML smoothness estimation (R package version 1.7-22) [Computer software manual]. Retrieved from http://CRAN.R-project.org/package=mgcv.
53. Wood, S. N. (2013). On p-values for smooth components of an extended generalized additive model. Biometrika, 100, 221-228. doi: 10.1093/biomet/ass048
54. Wu, H., & Zhang, J. (2006). Nonparametric regression methods for longitudinal data analysis. Hoboken, NJ: Wiley. doi: 10.1002/0470009675
55. Wu, L. (2010). Mixed effects models for complex data. New York, NY: Chapman & Hall.
56. Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A., & Smith, G. M. (2009). Mixed effects models and extensions in ecology with R. New York, NY: Springer.