Forget binning and get SMART: Getting more out of the time-course of response data

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Many experiments aim to investigate the time-course of cognitive processes while measuring a single response per trial. A common first step in the analysis of such data is to divide them into a limited number of bins. As we demonstrate here, the way one chooses these bins can considerably influence the resulting time-course. As a solution to this problem, we here present the smoothing method for analysis of response time-course (SMART)—a complete package for reconstructing the time-course from one-sample-per-trial data and performing statistical analysis. After smoothing the data, the SMART weights the data based on the effective number of data points per participant. A cluster-based permutation test then determines at which moments the responses differ from a baseline or between two conditions. We show here that, in contrast to contemporary binning methods, the chosen temporal resolution has a negligible effect on the SMART reconstructed time-course. To facilitate its use, the SMART method, accompanied by a tutorial, is available as an open-source package.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalAttention, Perception, and Psychophysics
DOIs
Publication statusPublished - 18 Jun 2019

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Reaction Time
Statistical Data Interpretation
Response Time
Smoothing Methods
Time Course
time
statistical analysis
Weights and Measures
experiment

Keywords

  • Binning
  • Perception and action
  • Reaction time methods
  • Statistics

Cite this

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title = "Forget binning and get SMART: Getting more out of the time-course of response data",
abstract = "Many experiments aim to investigate the time-course of cognitive processes while measuring a single response per trial. A common first step in the analysis of such data is to divide them into a limited number of bins. As we demonstrate here, the way one chooses these bins can considerably influence the resulting time-course. As a solution to this problem, we here present the smoothing method for analysis of response time-course (SMART)—a complete package for reconstructing the time-course from one-sample-per-trial data and performing statistical analysis. After smoothing the data, the SMART weights the data based on the effective number of data points per participant. A cluster-based permutation test then determines at which moments the responses differ from a baseline or between two conditions. We show here that, in contrast to contemporary binning methods, the chosen temporal resolution has a negligible effect on the SMART reconstructed time-course. To facilitate its use, the SMART method, accompanied by a tutorial, is available as an open-source package.",
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N2 - Many experiments aim to investigate the time-course of cognitive processes while measuring a single response per trial. A common first step in the analysis of such data is to divide them into a limited number of bins. As we demonstrate here, the way one chooses these bins can considerably influence the resulting time-course. As a solution to this problem, we here present the smoothing method for analysis of response time-course (SMART)—a complete package for reconstructing the time-course from one-sample-per-trial data and performing statistical analysis. After smoothing the data, the SMART weights the data based on the effective number of data points per participant. A cluster-based permutation test then determines at which moments the responses differ from a baseline or between two conditions. We show here that, in contrast to contemporary binning methods, the chosen temporal resolution has a negligible effect on the SMART reconstructed time-course. To facilitate its use, the SMART method, accompanied by a tutorial, is available as an open-source package.

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