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

Sebastian Gluth, Nachshon Meiran
2019 eLife  
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
more » ... r 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.
doi:10.7554/elife.42607 pmid:30735125 pmcid:PMC6392499 fatcat:gbuym2scbvgwxfyqoyrqtdy6ua