Leave-One-Trial-Out, LOTO, a general approach to link single-trial parameters of cognitive models to neural data
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.
Article and author information
Author details
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
- 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
- Received: October 5, 2018
- Accepted: February 7, 2019
- Accepted Manuscript published: February 8, 2019 (version 1)
- Version of Record published: February 27, 2019 (version 2)
- 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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Further reading
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- Neuroscience
Probing memory of a complex visual image within a few hundred milliseconds after its disappearance reveals significantly greater fidelity of recall than if the probe is delayed by as little as a second. Classically interpreted, the former taps into a detailed but rapidly decaying visual sensory or ‘iconic’ memory (IM), while the latter relies on capacity-limited but comparatively stable visual working memory (VWM). While iconic decay and VWM capacity have been extensively studied independently, currently no single framework quantitatively accounts for the dynamics of memory fidelity over these time scales. Here, we extend a stationary neural population model of VWM with a temporal dimension, incorporating rapid sensory-driven accumulation of activity encoding each visual feature in memory, and a slower accumulation of internal error that causes memorized features to randomly drift over time. Instead of facilitating read-out from an independent sensory store, an early cue benefits recall by lifting the effective limit on VWM signal strength imposed when multiple items compete for representation, allowing memory for the cued item to be supplemented with information from the decaying sensory trace. Empirical measurements of human recall dynamics validate these predictions while excluding alternative model architectures. A key conclusion is that differences in capacity classically thought to distinguish IM and VWM are in fact contingent upon a single resource-limited WM store.
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- Neuroscience
Our ability to recall details from a remembered image depends on a single mechanism that is engaged from the very moment the image disappears from view.