![[PDF]](/pb-assets/Icons/adobe-pdf-icon-1498649896243.png){kind=icon}
Abstract
In multiple fields of study, time series measured at high frequencies are used to estimate population curves that describe the temporal evolution of some characteristic of interest. These curves are typically nonlinear, and the deviations of each series from the corresponding curve are highly autocorrelated. In this scenario, we propose a procedure to compare the response curves for different groups at specific points in time. The method involves fitting the curves, performing potentially hundreds of serially correlated tests, and appropriately adjusting the overall alpha level of the tests. Our motivating application comes from psycholinguistics and the visual world paradigm. We describe how the proposed technique can be adapted to compare fixation curves within subjects as well as between groups. Our results lead to conclusions beyond the scope of previous analyses.
References
| 1. | McClellend, JL, Elman, J. The TRACE model of speech perception. Cogn Psychol 1986; 18: 1–86. Google Scholar | Crossref | Medline | ISI |
| 2. | Shortlist, ND . A connectionist model of continuous speech recognition. Cognition 1994; 52: 189–234. Google Scholar | Crossref | ISI |
| 3. | McMurray, B, Samelson, VS, Lee, SH Individual differences in online spoken word recognition: implications for SLI. Cogn Psychol 2010; 60: 1–39. Google Scholar | Crossref | Medline | ISI |
| 4. | Dahan, D, Magnuson, JS, Tanenhaus, MK. Time course of frequency effects in spoken-word recognition: evidence from eye movements. Cogn Psychol 2001; 42: 317–367. Google Scholar | Crossref | Medline | ISI |
| 5. | Tanenhaus, MK, Spivey-Knowlton, MJ, Eberhard, KM Integration of visual and linguistic information in spoken language comprehension. Science 1995; 268: 1632–1634. Google Scholar | Crossref | Medline | ISI |
| 6. | Farris-Trimble, A, McMurray, B. Test/retest reliability for the visual world paradigm as a measure of real-time lexical processes. J Speech Lang Hear Res 2013; 56: 1328–1345. Google Scholar | Crossref | Medline | ISI |
| 7. | Salverda, AP, Brown, M, Tanenhaus, MK. A goal-based perspective on eye movements in visual world studies. Acta Psychol 2011; 137: 172–180. Google Scholar | Crossref | Medline | ISI |
| 8. | Allopenna, P, Magnuson, JS, Tanenhaus, MK. Tracking the time course of spoken word recognition using eye-movements: evidence for continuous mapping models. J Memory Lang 1998; 38: 419–439. Google Scholar | Crossref | ISI |
| 9. | Magnuson, JS, Dixon, J, Tanenhaus, MK The dynamics of lexical competition during spoken word recognition. Cogn Sci 2007; 31: 1–24. Google Scholar | Crossref | Medline | ISI |
| 10. | Toscano, JC, Anderson, ND, McMurray, B. Reconsidering the role of temporal order in spoken word recognition. Psychon Bull Rev 2013; 20: 1–7. Google Scholar | Crossref | Medline | ISI |
| 11. | McMurray, B, Tanenhaus, MK, Aslin, RN. Gradient effects of within-category phonetic variation on lexical access. Cognition 2002; 86: B33–B42. Google Scholar | Crossref | Medline | ISI |
| 12. | Apfelbaum, KS, Blumstein, SE, McMurray, B. Semantic priming is affected by real-time phonological competition: evidence for continuous cascading systems. Psychon Bull Rev 2011; 18: 141–149. Google Scholar | Crossref | Medline | ISI |
| 13. | Farris-Trimble, A, McMurray, B, Cigrand, N The process of spoken word recognition in the face of signal degradation: cochlear implant users and normal-hearing listeners. J Exp Psychol Hum Percept Perfor 2014; 40: 308–327. Google Scholar | Crossref | Medline | ISI |
| 14. | Mirman, D, Yee, E, Blumstein, SE Theories of spoken word recognition deficits in aphasia: evidence from eye-tracking and computational modeling. Brain Lang 2011; 117: 53–68. Google Scholar | Crossref | Medline | ISI |
| 15. | Revill, KP, Spieler, DH. The effect of lexical frequency on spoken word recognition in young and older listeners. Psychol Aging 2012; 27: 80–80. Google Scholar | Crossref | Medline | ISI |
| 16. | Niparko, J . Cochlear implants: principles and practices, 2nd ed. Philadelphia: Lippincott, Williams, and Wilkins, 2009. Google Scholar |
| 17. | Wilson, BS Cochlear implant technology. In: Niparko, J, Kirk, K, Mellon, N(eds). Cochlear implants: principles and practices, New York: Lippincott, Williams, and Wilkins, 2000, pp. 109–118. Google Scholar |
| 18. | McMurray, B, Aslin, RN, Tanenhaus, MK Gradient sensitivity to within-category variation in words and syllables. J Exp Psychol Hum Percep Perform 2008; 34: 1609–1631. Google Scholar | Crossref | Medline | ISI |
| 19. | McMurray, B, Tanenhaus, MK, Aslin, RN. Within-category VOT affects recovery from “lexical” garden paths: evidence against phoneme-level inhibition. J Memory Lang 2009; 60: 65–91. Google Scholar | Crossref | Medline | ISI |
| 20. | Blumenfeld, HK, Marian, V. Constraints on parallel activation in bilingual spoken language processing: examining proficiency and lexical status using eye-tracking. Lang Cogn Process 2007; 22: 633–660. Google Scholar | Crossref |
| 21. | Ju, M, Luce, PA. Falling on sensitive ears: constraints on bilingual lexical activation. Psychol Sci 2004; 15: 314–318. Google Scholar | SAGE Journals | ISI |
| 22. | Mirman, D, Dixon, J, Magnuson, JS. Statistical and computational models of the visual world paradigm: growth curves and individual differences. J Memory Lang 2008; 59: 475–494. Google Scholar | Crossref | Medline | ISI |
| 23. | Oleson, JJ, Cavanaugh, JE, Tomblin, JB Combining growth curves when a longitudinal study switches measurement tools. Stat Methods Med Res 2016; 25: 2925–2938. Google Scholar | SAGE Journals | ISI |
| 24. | Guo, W . Functional mixed effects models. Biometrics 2002; 58: 121–128. Google Scholar | Crossref | Medline | ISI |
| 25. | Kliethermes, SA, Oleson, JJ. A Bayesian approach to functional mixed-effects modeling for longitudinal data with binomial outcomes. Stat Med 2014; 33: 3130–3146. Google Scholar | Crossref | Medline | ISI |
| 26. | Pinheiro J, Bates D, DebRoy S, et al. nlme: linear and nonlinear mixed effects models (2015, accessed 5 June 2015). R package version 3.1-119, http://CRAN.R-project.org/package=nlme. Google Scholar |
| 27. | R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (2008, accessed 5 June 2015). Google Scholar |
| 28. | Benjamini, Y, Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 1995; 57: 289–300. Google Scholar |
| 29. | Hastie, T, Tibshirani, R, Friedman, J. The elements of statistical learning: data mining, inference, and prediction, New York: Springer, 2009. Google Scholar | Crossref |
| 30. | Benjamini, Y, Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann Stat 2001; 29: 1165–1188. Google Scholar | ISI |
| 31. | Baayen, RH, Davidson, DJ, Bates, DM. Mixed-effects modeling with crossed random effects for subjects and items. J Memory Lang 2008; 59: 390–412. Google Scholar | Crossref | ISI |
| 32. | Clark, HH . The language-as-fixed-effect fallacy: a critique of language statistics in psychological research. J Verb Learn Verb Behav 1973; 12: 335–359. Google Scholar | Crossref |
