Detecting when timeseries differ: Using the Bootstrapped Differences of Timeseries (BDOTS) to analyze Visual World Paradigm data (and more)

Michael Seedorff; Jacob Oleson; Bob McMurray

doi:10.1016/j.jml.2018.05.004

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Detecting when timeseries differ: Using the Bootstrapped Differences of Timeseries (BDOTS) to analyze Visual World Paradigm data (and more)

Journal article

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Detecting when timeseries differ: Using the Bootstrapped Differences of Timeseries (BDOTS) to analyze Visual World Paradigm data (and more)

Michael Seedorff, Jacob Oleson and Bob McMurray

Journal of memory and language, Vol.102, pp.55-67

10/2018

DOI: 10.1016/j.jml.2018.05.004

PMCID: PMC7450631

PMID: 32863563

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https://www.ncbi.nlm.nih.gov/pmc/articles/7450631View

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Abstract

•A new R package for analyzing Visual World Paradigm eyetracking data is presented.•Familywise error rate is maintained through use of a timeseries correction.•Time windows of significance are automatically detected, minimizing p-hacking. In the last decades, major advances in the language sciences have been built on real-time measures of language and cognitive processing, measures like mouse-tracking, event related potentials and eye-tracking in the visual world paradigm. These measures yield densely sampled timeseries that can be highly revealing of the dynamics of cognitive processing. However, despite these methodological advances, existing statistical approaches for timeseries analyses have often lagged behind. Here, we present a new statistical approach, the Bootstrapped Differences of Timeseries (BDOTS), that can estimate the precise timewindow at which two timeseries differ. BDOTS makes minimal assumptions about the error distribution, uses a custom family-wise error correction, and can flexibly be adapted to a variety of applications. This manuscript presents the theoretical basis of this approach, describes implementational issues (in the associated R package), and illustrates this technique with an analysis of an existing dataset. Pitfalls and hazards are also discussed, along with suggestions for reporting in the literature.

Visual world paradigm

Statistical methods

Timeseries analysis

Family-wise error

Details

Title: Subtitle: Detecting when timeseries differ: Using the Bootstrapped Differences of Timeseries (BDOTS) to analyze Visual World Paradigm data (and more)
Creators: Michael Seedorff - Dept. of Biostatistics, University of Iowa, United States
Jacob Oleson - Dept. of Biostatistics, University of Iowa, United States
Bob McMurray - Dept. of Psychological and Brain Sciences, University of Iowa, United States
Resource Type: Journal article
Publication Details: Journal of memory and language, Vol.102, pp.55-67
DOI: 10.1016/j.jml.2018.05.004
PMID: 32863563
PMCID: PMC7450631
NLM abbreviation: J Mem Lang
ISSN: 0749-596X
eISSN: 1096-0821
Publisher: Elsevier Inc
Grant note: DOI: 10.13039/100000002, name: National Institutes of Health, award: DC000242, DC008089
Language: English
Date published: 10/2018
Academic Unit: Communication Sciences and Disorders; Linguistics; Psychological and Brain Sciences; Iowa Neuroscience Institute; Biostatistics; Otolaryngology
Record Identifier: 9984070611402771

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