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.
- Sebastian Gluth
- Nachshon Meiran
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
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.
- Leendert Van Maanen, University of Amsterdam, Netherlands
- Received: October 5, 2018
- Accepted: February 7, 2019
- Accepted Manuscript published: February 8, 2019 (version 1)
© 2019, Gluth & Meiran
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