Detecting time-specific differences between temporal nonlinear curves: Analyzing data from the visual world paradigm

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.

[1]  Bob McMurray,et al.  Semantic priming is affected by real-time phonological competition: Evidence for continuous cascading systems , 2011, Psychonomic bulletin & review.

[2]  Y. Benjamini,et al.  THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .

[3]  Wensheng Guo.  Functional Mixed Effects Models , 2002 .

[4]  M. Tanenhaus,et al.  Gradient effects of within-category phonetic variation on lexical access , 2002, Cognition.

[5]  Jacob Oleson,et al.  A Bayesian approach to functional mixed‐effects modeling for longitudinal data with binomial outcomes , 2014, Statistics in medicine.

[6]  Sung Hee Lee,et al.  Individual differences in online spoken word recognition: Implications for SLI , 2010, Cognitive Psychology.

[7]  James L. McClelland,et al.  The TRACE model of speech perception , 1986, Cognitive Psychology.

[8]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[9]  D. Norris.  Shortlist: a connectionist model of continuous speech recognition , 1994, Cognition.

[10]  Paul D. Allopenna,et al.  Tracking the Time Course of Spoken Word Recognition Using Eye Movements: Evidence for Continuous Mapping Models , 1998 .

[11]  Anne Pier Salverda,et al.  A goal-based perspective on eye movements in visual world studies. , 2011, Acta psychologica.

[12]  V. Marian,et al.  Constraints on parallel activation in bilingual spoken language processing: Examining proficiency and lexical status using eye-tracking , 2007 .

[13]  Bob McMurray,et al.  Journal of Experimental Psychology : Human Perception and Performance The Process of Spoken Word Recognition in the Face of Signal Degradation , 2013 .

[14]  H. H. Clark.  The language-as-fixed-effect fallacy: A critique of language statistics in psychological research. , 1973 .

[15]  Peter Dalgaard,et al.  R Development Core Team (2010): R: A language and environment for statistical computing , 2010 .

[16]  Elizabeth Walker,et al.  Combining growth curves when a longitudinal study switches measurement tools , 2016, Statistical methods in medical research.

[17]  B. McMurray,et al.  Test-retest reliability of eye tracking in the visual world paradigm for the study of real-time spoken word recognition. , 2013, Journal of speech, language, and hearing research : JSLHR.

[18]  D. Spieler,et al.  The effect of lexical frequency on spoken word recognition in young and older listeners. , 2012, Psychology and aging.

[19]  M. Tanenhaus,et al.  Within-category VOT affects recovery from "lexical" garden paths: Evidence against phoneme-level inhibition. , 2009, Journal of memory and language.

[20]  Joseph C. Toscano,et al.  Reconsidering the role of temporal order in spoken word recognition , 2013, Psychonomic bulletin & review.

[21]  D. Mirman,et al.  Statistical and computational models of the visual world paradigm: Growth curves and individual differences. , 2008, Journal of memory and language.

[22]  Julie C. Sedivy,et al.  Subject Terms: Linguistics Language Eyes & eyesight Cognition & reasoning , 1995 .

[23]  James S. Magnuson,et al.  The Dynamics of Lexical Competition During Spoken Word Recognition , 2007, Cogn. Sci..

[24]  R. Baayen,et al.  Mixed-effects modeling with crossed random effects for subjects and items , 2008 .

[25]  R. Stone.  Cochlear Implant Technology: The Future is Now , 1995 .

[26]  Michael J. Spivey,et al.  Gradient sensitivity to within-category variation in words and syllables. , 2008, Journal of experimental psychology. Human perception and performance.

[27]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[28]  B. Wilson,et al.  Cochlear Implants: Principles & Practices , 2000 .

[29]  S. Blumstein,et al.  Theories of spoken word recognition deficits in Aphasia: Evidence from eye-tracking and computational modeling , 2011, Brain and Language.

[30]  M. Tanenhaus,et al.  Time Course of Frequency Effects in Spoken-Word Recognition: Evidence from Eye Movements , 2001, Cognitive Psychology.

[31]  P. Luce,et al.  Falling on Sensitive Ears , 2004, Psychological science.