ArticleeLife2019
Leave-One-Trial-Out, LOTO, a general approach to link single-trial parameters of cognitive models to neural data.
Article in eLife, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- The Trembling Hand Unraveled: Motor Dynamics and Choice Inconsistency.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Dynamic patterns of functional connectivity in the human brain underlie individual memory formation.Nature communications · 2024Article
- Detecting Prolonged Stress in Real Life Using Wearable Biosensors and Ecological Momentary Assessments: Naturalistic Experimental Study.Journal of medical Internet research · 2023Article
- Measuring utility with diffusion models.Science advances · 2023Article
- Disentangling task-selection failures from task-execution failures in task switching: an assessment of different paradigms.Psychological research · 2023Article
- Mapping individual differences across brain network structure to function and behavior with connectome embedding.NeuroImage · 2021Article
- Response time models separate single- and dual-process accounts of memory-based decisions.Psychonomic bulletin & review · 2021Article
- An Overcomplete Approach to Fitting Drift-Diffusion Decision Models to Trial-By-Trial Data.Frontiers in artificial intelligence · 2021Article
- Neural entrainment to music is sensitive to melodic spectral complexity.Journal of neurophysiology · 2020Article
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Authors and funding
2 authors.
Funding
Abstract
A key goal of model-based cognitive neuroscience is to estimate the trial-by-trial fluctuations of cognitive model parameters in order to link these fluctuations to brain signals. However, previously developed methods are limited by being difficult 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 makes it possible to distinguish 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.
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Registered trials
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