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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 betweendoi:10.7554/elife.42607 pmid:30735125 pmcid:PMC6392499 fatcat:gbuym2scbvgwxfyqoyrqtdy6ua