Leave-One-Trial-Out, LOTO, a general approach to link single-trial parameters of cognitive models to neural data

  1. Sebastian Gluth  Is a corresponding author
  2. Nachshon Meiran
  1. University of Basel, Switzerland
  2. Ben-Gurion University of the Negev, Israel

Abstract

It has become a key goal of model-based cognitive neuroscience to estimate trial-by-trial fluctuations of cognitive model parameters for linking these fluctuations to brain signals. However, previously developed methods were limited by being difficulty to implement, time-consuming, or model-specific. Here, we propose an easy, efficient and general approach to estimating trial-wise changes in parameters: Leave-One-Trial-Out (LOTO). The rationale behind LOTO is that the difference between parameter estimates for the complete dataset and for the dataset with one omitted trial reflects the parameter value in the omitted trial. We show that LOTO is superior to estimating parameter values from single trials and compare it to previously proposed approaches. Furthermore, the method allows distinguishing true variability in a parameter from noise and from other sources of variability. In our view, the practicability and generality of LOTO will advance research on tracking fluctuations in latent cognitive variables and linking them to neural data.

Data availability

The relevant data and computer codes are uploaded on the Open Science Framework (https://osf.io/du85f/) and are freely available.

The following data sets were generated
    1. Gluth S
    2. Meiran N
    (2018) Leave-one-trial-out (LOTO)
    Open Science Framework, 10.17605/OSF.IO/DU85F.

Article and author information

Author details

  1. Sebastian Gluth

    Department of Psychology, University of Basel, Basel, Switzerland
    For correspondence
    sebastian.gluth@unibas.ch
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2241-5103
  2. Nachshon Meiran

    Department of Psychology, Ben-Gurion University of the Negev, Be'er Scheva, Israel
    Competing interests
    The authors declare that no competing interests exist.

Funding

Swiss National Science Foundation (100014_172761)

  • Sebastian Gluth

Israel Science Foundation (381-15)

  • Nachshon Meiran

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Leendert Van Maanen, University of Amsterdam, Netherlands

Ethics

Human subjects: All participants gave written informed consent, and the study was approved by the Aerztekammer Hamburg, Germany (case # PV4290). All experiments were performed in accordance with the relevant guidelines and regulations.

Version history

  1. Received: October 5, 2018
  2. Accepted: February 7, 2019
  3. Accepted Manuscript published: February 8, 2019 (version 1)
  4. Version of Record published: February 27, 2019 (version 2)
  5. Version of Record updated: February 28, 2019 (version 3)

Copyright

© 2019, Gluth & Meiran

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Sebastian Gluth
  2. Nachshon Meiran
(2019)
Leave-One-Trial-Out, LOTO, a general approach to link single-trial parameters of cognitive models to neural data
eLife 8:e42607.
https://doi.org/10.7554/eLife.42607

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https://doi.org/10.7554/eLife.42607

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