2015
DOI: 10.1177/0962280215607411
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Detecting time-specific differences between temporal nonlinear curves: Analyzing data from the visual world paradigm

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 … Show more

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Cited by 39 publications
(49 citation statements)
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“…In our case, since we did not find a main effect of place, both /t/ and /p/, as well as /d/ and /b/ are collapsed into one analysis per voicing group. Through bootstrapping 2 Although Oleson et al (2015) present a by-subject analysis, we chose here to conduct a by-item analysis for reasons of statistical power. Given that each subject contributes few data points to each experimental condition, the bdots package fails to adequately model the by-subject data.…”
Section: Voicingmentioning
confidence: 99%
See 1 more Smart Citation
“…In our case, since we did not find a main effect of place, both /t/ and /p/, as well as /d/ and /b/ are collapsed into one analysis per voicing group. Through bootstrapping 2 Although Oleson et al (2015) present a by-subject analysis, we chose here to conduct a by-item analysis for reasons of statistical power. Given that each subject contributes few data points to each experimental condition, the bdots package fails to adequately model the by-subject data.…”
Section: Voicingmentioning
confidence: 99%
“…We used the bdots package (Seedorff et al, 2017) in R to ascertain the time window in which fixations to the target word differed as a function of glottalization. Following Oleson et al (2015), a nonlinear curve is fit to the fixation proportions for each target word 2 in the glottalized and non-glottalized conditions. In our case, since we did not find a main effect of place, both /t/ and /p/, as well as /d/ and /b/ are collapsed into one analysis per voicing group.…”
Section: Voicingmentioning
confidence: 99%
“…To determine this, we computed looks to the target over 4 msec intervals for each participant. We then used BDOTs (Oleson et al, 2017) to identify time regions at which these curves differed significantly as a function of language group (data and R scripts are publicly available at https://osf.io/kfwxu/). Details of BDOTs analyses are shown in Table 1.…”
Section: Resultsmentioning
confidence: 99%
“…Data were analyzed with the bootstrapped difference of timeseries (Oleson, Cavanaugh, McMurray, & Brown, 2017) implemented as an R package (BDOTS). Target fixations in each condition were fit with the logistic function using a constrained least-squares minimization (https://osf.io/4atgv/), not the standard BDOTs fitter.…”
Section: Discussionmentioning
confidence: 99%
“…Fortunately, new methods are being developed that can take the exact shapes of the curves into account (e.g., Baayen, et al, to appear;Mirman, Dixon, & Magnuson, 2008;Oleson, Cavanaugh, McMurray, & Brown, 2015).…”
Section: Experimental Paradigmsmentioning
confidence: 99%